{"schema_version":"crash_brief_site_agent_package_v0_1","status":"partial_public_coverage","purpose":"Single-entry public package for evaluating Crash Brief. The product library, Blog and 50 accepted public Documentation definitions are inline; dated market and Track Record data retain their own contracts and access limits.","canonical_url":"https://crashbrief.com/agent","one_shot_url":"https://crashbrief.com/agent/full.json","product":{"name":"Crash Brief","category":"Crypto market risk and volatility intelligence","public_root":"https://crashbrief.com","current_public_surfaces":["Product library","Market context","BTC, ETH and SOL asset surfaces","Track Record","Public project Blog","Documentation"]},"reading_order":[{"priority":1,"section":"product_library","inline":true,"human_url":"https://crashbrief.com/library","authority":"versioned_public_product_library"},{"priority":2,"section":"blog","inline":true,"authority":"canonical_public_blog"},{"priority":3,"section":"track_record","inline":false,"human_url":"https://crashbrief.com/track-record"},{"priority":4,"section":"market","inline":false,"human_url":"https://crashbrief.com/market"},{"priority":5,"section":"documentation","inline":true,"human_url":"https://crashbrief.com/docs","agent_url":"https://crashbrief.com/docs/agent/index.json","markdown_url":"https://crashbrief.com/docs/llms-full.txt","coverage":"50 accepted public definitions across 10 topics"}],"embedded":{"product_library":{"schema_version":"crash_brief_product_library_v1","content_version":"2026-09-28.volatility-r4","content_as_of_date":"2026-09-28","status":"partial_public_coverage","human_url":"https://crashbrief.com/library","machine_url":"https://crashbrief.com/agent/full.json","title":"Crash Brief product library","introduction":"Crash Brief helps professional crypto desks check market risk and volatility before decisions. See the dated reading, what supports it, what remains uncertain, and how earlier readings developed.","sections":[{"id":"purpose","title":"Purpose and audience","status":"public_explanation","text":"Crash Brief is crypto market-risk and volatility intelligence for professional desks. It can inform exposure review and discussion of protection choices, especially at volatility and options desks. It is decision support, not execution, a personalized hedge recommendation, or proof of client financial benefit.","evidence_urls":["https://crashbrief.com/market"]},{"id":"product-map","title":"What can be inspected","status":"public_and_protected_surfaces_distinguished","text":"Public visitors can inspect market and BTC, ETH and SOL surfaces, the public Track Record, and project Blog. The B2B cabinet has distinct Market Brief, Volatility and Track Record views; its dated daily packet requires authentication. A public route returning a page does not grant access to the protected packet.","surfaces":[{"name":"Market","url":"https://crashbrief.com/market","access":"public_page"},{"name":"BTC","url":"https://crashbrief.com/btc","access":"public_page"},{"name":"ETH","url":"https://crashbrief.com/eth","access":"public_page"},{"name":"SOL","url":"https://crashbrief.com/sol","access":"public_page"},{"name":"Track Record","url":"https://crashbrief.com/track-record","access":"public_page"},{"name":"Blog","url":"https://crashbrief.com/blog","access":"public_page"},{"name":"B2B daily packet","url":null,"access":"authenticated_only"}],"evidence_urls":["https://crashbrief.com/track-record","https://crashbrief.com/market"]},{"id":"reading","title":"Reading a Market Brief","status":"public_interpretation_no_live_example","text":"Market Crash Index 3D is a dated risk-state reading in index points, not the probability of a crash or a trade command. A displayed confidence grade, when available, has a separate 1–5 scale. Reason, drivers and mechanics explain the reading; a marked human adjustment is a disclosed expert change, not an invisible rewrite. A verified public dated Market Brief example is not available in this library yet.","units":{"crash_index_3d":"index_points","confidence":"ordinal_1_to_5","human_adjustment":"index_points_when_disclosed"},"dated_example":{"status":"unavailable","reason":"No approved public dated B2B Market Brief packet is bound to this library","source_as_of":null,"issued_at":null,"published_at":null,"outcome_at":null},"evidence_urls":["https://crashbrief.com/market"]},{"id":"volatility","title":"Volatility: available and in progress","status":"mixed_status_no_new_client_forecast","text":"The authenticated Volatility view retains legacy Expected Volatility Model 1 and 2 tables for MARKET, BTC, ETH and SOL over 1D, 2D and 3D. Their client forecast claims remain blocked. Its dated BTC comparison appears only when the selected packet supplies a matching historical comparison. Realized · 72h is observed BTC spot volatility over the preceding 72 hours, not the outcome of the forecast. Forecast · 72h is a model estimate for the next 72 hours, not a direction call or guaranteed range. Options · 72h converts one observed option model-IV reading from its annualized unit to a 72-hour scale assuming constant variance; it is not exactly equivalent to realized volatility, an executable quote or a cheap/expensive signal. Exact source, issue, quote and window times belong to each dated packet and its helpers. Prospective BTC 72h ABC A/B/C outputs are side-specific maximum-up and maximum-down excursions from a reference, not expiry returns, direction probabilities or a guaranteed corridor; C uses volatility internally but still outputs excursions. A separate prospective C/D comparison remains in progress: D is not a fourth excursion, an option premium, a cost of protection, or a profit estimate.","dated_comparison":{"access":"authenticated_only","availability":"matching_dated_packet_only","realized_72h":"Observed BTC spot volatility over the preceding 72 hours; not the outcome of the forecast.","forecast_72h":"Model estimate of BTC volatility over the next 72 hours; not direction or a guaranteed range.","options_72h":"One observed BTC option model-implied volatility, quoted on an annualized basis and converted to a 72-hour scale assuming constant variance. The instrument, quote time and actual expiry tenor must be read with the dated packet. This is not exactly equivalent to realized volatility or an executable opportunity or cheap/expensive signal."},"legacy":{"model_names":["Model 1","Model 2"],"scopes":["MARKET","BTC","ETH","SOL"],"horizons":["1D","2D","3D"],"access":"authenticated_only","client_claim_status":"blocked"},"prospective_abc":{"status":"in_progress","target":"BTC 72h","outputs":"side_specific_maximum_excursions_pct","live_value":null},"prospective_cd":{"status":"in_progress","target":"BTC 72h","c":"modeled_activity_scale_pct","d":"option_implied_activity_proxy_pct","gap_unit":"percentage_points_if_accepted","live_value":null},"evidence_urls":[]},{"id":"track-record","title":"Dated evidence and its limits","status":"public_history_available_scope_limited","text":"The public Track Record offers dated readings and outcome context. The official full-history B2B export concerns the MARKET composite; BTC, ETH and SOL overlays do not establish independent asset-level forecast accuracy. The public four-day-delayed ETH-oriented JSON has a separate contract and is not a mirror of that protected export. A record count is coverage, not evidence of trading edge or client benefit. Read misses, pending outcomes and method boundaries alongside favorable episodes.","public_human_url":"https://crashbrief.com/track-record","public_machine_url":"https://crashbrief.com/api/public/track-record","public_machine_delay_days":4,"protected_full_history_access":"authenticated_only","official_asset_level_proof":"unavailable","evidence_urls":["https://crashbrief.com/track-record","https://crashbrief.com/api/public/track-record"]},{"id":"method","title":"Method and field meanings","status":"non_proprietary_overview","text":"A reading should be interpreted with its observation time, issue time, publication time, method version and, when mature, outcome time. These clocks are different. ADJ denotes a visible expert adjustment with its version and reason when that information is published. The public library does not disclose formula coefficients, private datasets, raw books, account data or internal operating instructions.","field_contract":[{"field":"source_as_of","meaning":"observation time","nullable":true},{"field":"issued_at","meaning":"calculation or issue time","nullable":true},{"field":"published_at","meaning":"public release time","nullable":true},{"field":"outcome_at","meaning":"outcome maturity time","nullable":true},{"field":"method_version","meaning":"version label when published","nullable":true}],"evidence_urls":["https://crashbrief.com/track-record"]},{"id":"limits","title":"Limits and change history","status":"explicit_unknowns","text":"Unknown, pending, blocked and not applicable are different states. A missing or stale source should not become zero or a confident answer. This static library's content date is not today's market observation, and an unpublished internal packet is not a public example. Method changes and outcome maturity must be checked on the relevant dated record.","state_meanings":{"unknown":"not established","pending":"awaiting future evidence","blocked":"claim not admitted","not_applicable":"field does not apply"},"evidence_urls":["https://crashbrief.com/track-record"]},{"id":"access","title":"Access and agent reading","status":"verified_public_routes_and_protected_boundary","text":"Read this public library as HTML or retrieve the existing Agent JSON package. The Agent package embeds this same explanation and the public Blog, while separate market and Track Record records retain their own contracts. Authenticated B2B daily data is not public. The advertised documentation hostname is currently unavailable and is not an access route in this guide.","public_urls":["https://crashbrief.com/library","https://crashbrief.com/agent","https://crashbrief.com/agent/full.json","https://crashbrief.com/llms.txt","https://crashbrief.com/track-record"],"protected_data":{"access":"authenticated_only","public_payload_url":null},"documentation_url":null,"evidence_urls":["https://crashbrief.com/agent/full.json","https://crashbrief.com/llms.txt"]}]},"blog":{"schema_version":"crash_brief_public_blog_index_v0_1","status":"ok","post_count":66,"project_date_range":{"earliest":"2026-07-28","latest":"2026-10-15"},"human_url":"https://crashbrief.com/blog","agent_url":"https://crashbrief.com/blog/agent","full_archive_url":"https://crashbrief.com/blog/full.json","posts":[{"body":"Today it has been exactly 400 days since I started Crash Brief, a crypto risk assessment service for B2B, especially for people working with options. I want to look back a little at the stages I went through, what worked, where I was wrong and what I eventually arrived at.\n\nFrom the beginning, I was betting on AI development. I thought more powerful models would appear, and with them new ways to analyze the market and make more accurate forecasts. But over this time, almost nothing fundamental has changed for me with forecasting.\n\nHere is the thing. You find a model for looking at the market, it shows an interesting picture in backtests, it works for a while. Then different participants enter the market, conditions change, and the same approach stops giving the previous result. And you have already had time to think that you finally found something.\n\nThis was probably my main miscalculation, expecting that more powerful AI would let me reach stable forecasting accuracy. I have not achieved a consistent 70% so far. I have gone through many approaches, analyses and hypotheses known to me, and everything turns out to be very unstable. Now I increasingly think the forecasting approaches themselves will have to change systematically, because the market we are trying to forecast keeps changing.\n\nI started with Ethereum alone. The deeper I analyzed it, the more I understood that I could not really do without Bitcoin. Its chart has its own clues, there is more liquidity and money there moving the whole market. Gradually I added BTC, ETH, SOL and HYPE, and then dropped HYPE.\n\nFrom my observations, markets with a strong institutional layer and markets where it is weaker behave differently. You cannot just take the same approach and expect it to work equally on every asset. For HYPE, I would have had to develop a lot from scratch, and I understood that this was already taking away my focus. I put it aside.\n\nA separate story is how much time went into autonomy. Data collection, assessments, forecasts, many different indicators, everything came together piece by piece. And practically every day gave feedback. Something broke somewhere, sometimes I did something wrong, sometimes the AI did. Sometimes an error appears immediately, sometimes a week or a month later. Plus exchanges, load, broken connections, new functions that I keep adding.\n\nAt the start I looked at options, open interest and some basic things. Now I can examine what is happening in a specific zone in much more detail. How many liquidations there were, how they were distributed across exchanges, what is happening in spot and futures, how order book orders change as the price approaches. Orders appear, disappear, appear again. This gives additional observations about participants' behavior, although of course we do not know their actual intentions.\n\nAnd this is where I really felt the value of each day. A day passes, outcomes appear, you can draw conclusions and move on. However many models and backtests I build, tomorrow will happen only once. I cannot speed that up. And regularly the market turns out to behave in a way I did not expect at all.\n\nA large separate area of work developed around options economics. You have an asset and want to buy a protective option in case it falls. How much does this insurance cost, what will it cover and under which outcome? At one moment it is expensive, at another the conditions may be more interesting. On the other side are the sellers of this insurance, risking their capital and also assessing every day how much risk they are taking.\n\nRight now I have a main approach that I continue to use. I do not consider it a holy grail, but so far I have not been able to offer a convincingly better replacement. I make manual adjustments, observe the result and examine the errors.\n\nIn parallel, I am building an automatic version into which I am trying to transfer this working process itself. Get an outcome, look at where I underestimated or overestimated risk, ask questions, examine possible reasons, think of what else to test. There can be a lot of questions. A model can spend a long time examining them and suggesting new hypotheses, but then they still have to be tested on the following days.\n\nI want to give this version 90 days and see whether it can assess risk, volatility and options economics better than I do in a semi-automatic mode. Organizing all of this took many hours. Something still breaks from time to time, so I have not started the countdown yet. It is close, but it is too early to announce a fully ready autonomous system.\n\nAutonomous work is possible, I see it in practice. At the same time, organizing this work feels like architectural creativity to me. You need to set goals, conditions and boundaries so that a powerful model keeps moving where you want it to go. Sitting next to it and pushing it with every next prompt gets tiring, and there is simply no time for it.\n\nWe will see what happens over these 90 days. It may turn out that the automatic version does not give an advantage either. That would also be a result, although of course I would like something else.\n\nAt some point I was very frustrated. So many years in the market, so many methods tried, and I still cannot get the forecasting accuracy I imagined. In retrospect everything can look very beautiful. You select an approach, look at the history, it seems to work. Whether it will work next week is another question entirely.\n\nBut gradually another interesting perspective appeared. I look at job openings at funds and see them searching for people who are supposed to build something similar to what I am working on. Smart people, developers, people with a financial background who know how to work with AI. And I think many of them will encounter the same difficulties.\n\nI do not know how every fund works and cannot speak for each of them. But now I am much more careful with the idea that somebody must have a miracle model that predicts the future well. In results, it can be difficult to separate the quality of an approach from luck. Two teams may use serious methods, one gets lucky, the other does not. The first decides it found the right way, the second looks for where it went wrong.\n\nI am trying to understand better which force is driving the market now and which approach even makes sense in these conditions. Today it is one driver, tomorrow another, then they combine and change each other. This is the combination I have to work through.\n\nAnd more and more of my attention goes to the economics of a particular decision. Is this trade expensive or cheap right now? Does our particular portfolio need it? How much risk are we already carrying and how much more can we take? What would buying protection give us and how much would we have to pay for it?\n\nThe forecast remains part of this work, but portfolio and position management are making more and more sense to me. This is roughly where I am moving now.\n\n400 days have passed, the experience has been tremendous. I now look at many conversations about crypto and trading very differently, because I have had time to test some of my assumptions and continue testing them. I hope something interesting will still turn up.","content_language":"en","day_counter":{"label":"Day 400","number":400,"start_date":"2025-09-11"},"id":"2026-10-15-400-days-of-crash-brief","project_date":"2026-10-15","published_at":"2026-10-15T16:26:49+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"400-days-of-crash-brief","source_language":"ru","source_types":["telegram"],"status":"published","summary":"400 days of building Crash Brief: what stronger AI changed, what it did not, and why forecasting, outcomes and options economics need to be considered together.","title":"400 days of Crash Brief","topic":"founder_post","type":"founder_post","updated_at":"2026-10-15T16:26:49+00:00","version":1,"visuals":[{"alt":"A thick stack of paper with one blank page lifting at the top.","caption":"400 days of Crash Brief.","url":"/blog-media/2026-10-15/400-days-of-crash-brief.png"}]},{"body":"Today you can find plenty of companies and individuals offering to integrate AI into a business. My second project is roughly about this too, but there are a couple of nuances that look one way at the start and quite different in reality, and this is why so many processes fall apart.\n\nLook, suppose you are a business founder and want to integrate AI into your processes. What do you do? Most often, you consult an integration specialist, or even worse, decide for yourself what to integrate. And there are two things here.\n\nFirst, you do not fully understand the current capabilities, what can actually be done, and accordingly the requests in your head can be quite different. Especially if you skip the consultation and immediately say: I need to automate this or that. You can automate the tasks and the area you have chosen, but it may not give you a strong improvement. If you consult a specialist, they can suggest a much more powerful way for AI to boost your business.\n\nBut there is another thing: this specialist does not know your business, and a business has many subtle nuances. The system that is supposed to be integrated and increase efficiency begins to encounter some strange resistance, and the integrator says: well, there are some little problems here, something needs a lot of polishing, and so on. You spend time and money, and get nervous.\n\nAnd there is an interesting nuance here.\n\nThe approach I am using and developing now is employee interviews.\n\nUsually, the founder says they want to automate something with AI, and they and the integrator start working out how to do it. I suggest first finding out from your team what is actually happening, which processes and tasks they have, and then using this to understand what can be automated.\n\nLook at the conveyor I propose. Each employee receives a link to an AI interview that asks questions according to the exact list of questions and tasks we agree on beforehand. While there are few employees, you can still work it out: I can automate these and those processes. But as the number of employees grows, it becomes very difficult to keep all this in your head. More than that, imagine you have a team of even 30 or 50 people. You need to talk to each person, find out their nuances and responsibilities, then cross-reference those 30–50 interviews, extract what matters most and present it as possible solutions. This would take several weeks, perhaps even months, if you worked on nothing else from morning to evening. And even then the result would be unpredictable.\n\nWhat approach do I propose\n\nFirst, we interview the founder. I help uncover problems and questions that may be far from obvious when working with a team, and we examine their request. Suppose they want to introduce AI, but do not know exactly what to introduce, and are afraid of facing internal sabotage from the team that could sink the idea and waste a lot of money and time.\n\nThen we give employees their links, and 10, 20 or 30 people go through interviews with our system. Each interview takes about an hour. If you ask employees to complete it over one or two days, in two or three days you will have a complete picture of the business, with those 30 scenarios laid out. We will see where things diverge, what doubts employees have, which scenarios are best suited to automation and how processes can pass work along the chain to one another.\n\nAnd this is where a powerful scenario for using AI grows. Something more substantial emerges than an idea like, “let us transcribe something, some calls.” From there, the next scale of work can unfold: measurement, assessments, analytics, economics, and so on.\n\nThis approach is very effective today because it allows an entrepreneur to save two things:\n\n1. First, and most importantly, time.\n2. Second, effort and again time: to avoid doing what will not work, and to avoid running into sabotage that blocks any innovation.\n\nInstead of spending months introducing this, it can be introduced in less than a week and deliver a powerful effect straight away.","content_language":"en","day_counter":{"label":"Day 397","number":397,"start_date":"2025-09-11"},"id":"2026-10-12-understand-the-business-before-automating","project_date":"2026-10-12","published_at":"2026-10-12T15:52:36+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"understand-the-business-before-automating","source_language":"ru","source_types":["telegram"],"status":"published","summary":"My approach to introducing AI into a business starts with interviewing the founder and employees, then connecting their accounts of how the work actually happens.","title":"Understand what slows down the company and what to change first, instead of “integrate AI somewhere for me”","topic":"How I think about AI in business","type":"founder_post","updated_at":"2026-10-12T15:52:36+00:00","version":1,"visuals":[{"alt":"Employees bring separate process fragments to a table where they connect into one map.","caption":"Employee interviews connect separate perspectives into a shared process map.","url":"/blog-media/2026-10-12/employee-interviews.png"}]},{"body":"I decided to run an experiment: put the project's development on autopilot and split it into two autonomous entities.\n\nOne produces forecasts using the strategy and concept I have already established. I will continue to verify it every day, and the main forecasts and briefs will go out under my review.\n\nAlongside it, I have created an R&D laboratory that will develop without my constant involvement. AI will receive a copy of my work, with a task: outperform our baseline system and improve forecast quality by at least 25% within 90 days. This is the goal of the experiment, which we are only working towards.\n\nThe laboratory will reuse all our infrastructure, the knowledge accumulated over more than a year, all the modules, data and outcomes. On top of this, it will have two skills.\n\nThe first produces a daily Shadow forecast alongside the main Production forecast. They will appear next to each other for comparison. The second handles self-improvement: every day the system looks at its previous actions, what we forecast, what happened, where the deviations and errors were. In some places it needs to keep accumulating observations to make a decision later, while in others a decision can already be made now.\n\nThis should build a comparative base on which the system will test many hypotheses: how to improve the forecast scientifically and correctly, without choosing convenient combinations or looking ahead.\n\nThe main value of this thing is that it works in live time. Each day brings a real forecast, and then a result that has to be dealt with. The goal of the whole experiment is to create an adaptive forecasting machine. I already have a lot of work towards this in the project, but I decided to see what happens if the system develops independently for 90 days. I will watch to make sure it does not drift off in some direction.\n\nAnd here is the first week. Everything is still in testing, I keep making corrections, so it is too early to speak of a fully established system.\n\nOne of the main things I have understood over these days: a human needs to describe the self-improvement system in great detail. There is no need to tell it which tools to use or which strategies to try. The goal, conditions and boundaries need to be defined properly, so the system has no opportunity to cheat, dodge the rules, peek somewhere or gain an advantage from a timing delay. All of this needs to be excluded.\n\nIt is also important that the system has time to correct itself. The daily self-improvement cycle must finish within the current day. If development spills into the next one, a new forecast may begin before the previous cycle has reached its next stage.\n\nSo every day the system should make a forecast, look at previous forecasts, work through what went wrong and where it can improve, create hypotheses, test them and track the best variants in parallel. This will accumulate many hypotheses being tested in real market regimes.\n\nAn adaptive mechanism should emerge from them: under which conditions to use one logic, under which to use another, and what to do when the market changes.\n\nI spent a long time describing exactly the result I want from this mechanism. I formulated in as much detail as possible what the ideal result should look like and which details matter. Once this is defined, it is easier for the system to choose the means and methods of getting there. But phrase something just a little incorrectly, and everything can go sideways. I have already caught a couple of these things this week.\n\nOne of the main weaknesses: the model allows too much time for its own tests. It says: \"We will run the tests now, then let it run for a week, observe it and draw a conclusion.\" But if I want a result in 90 days and an adjustment happens once a week, we risk ending up with no more than one baseline version.\n\nSo I multiplied the research entities: many hypotheses are developed at once, and their results are tracked in parallel too.\n\nThe agent also has cycles of different scales: daily, weekly and monthly. It considers different things over different horizons, looks at what has changed on larger timeframes and how that affects smaller ones. Along the way, of course, I still plan to edit all of this.\n\nPutting a model on self-improvement turned out to be less easy than it may seem. A few sentences are not enough. You have to formulate specific results, outcomes, boundaries and supervision in great detail. There needs to be someone watching that the model follows the directions we gave it and has not drifted away.\n\nThere is also the question of blocking. A model can try an action, find it unavailable five times in a row and get stuck. So there needs to be a mechanism that lifts it out of that block and lets it continue working.\n\nWell, a week has passed. The system is already close to the basic structure I want, but the full autonomous experiment has not started yet. We keep working.","content_language":"en","day_counter":{"label":"Day 396","number":396,"start_date":"2025-09-11"},"edited_at":"2026-10-11T13:07:11+00:00","id":"2026-10-11-90-days-on-autopilot","project_date":"2026-10-11","published_at":"2026-10-11T13:04:15+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"90-days-on-autopilot","source_language":"ru","source_types":["founder_direct"],"status":"published","summary":"I am preparing a 90-day autonomous R&D experiment alongside my verified forecasts, and the first week has shown how precisely goals, boundaries and daily improvement cycles need to be defined.","title":"90 Days on Autopilot","topic":"founder_post","type":"founder_post","updated_at":"2026-10-11T13:07:11+00:00","version":3,"visuals":[{"alt":"Two branching trunks of a tree represent a stable forecasting system and an experimental research laboratory.","caption":"A 90-day experiment in autonomous development.","url":"/blog-media/2026-10-11/90-days-on-autopilot.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2109268170091426112"},{"body":"The ability to direct and use the best qualities of AI is the best skill to develop in yourself in the coming decades. I see this as clearly as I know my own name.\n\nThe point is that there will never be a world where we have nothing to do. There will always be tasks at the intersection of different technologies, fields and human relationships. There will always be those who win and those who fall behind. There will always be problems and challenges worth giving time, resources and motivation to. There will always be personal tasks and questions that nobody will solve for you.\n\nLook around, do you often see people who do not have a mobile phone?\nAI is already in every one of them. And it can perform useful tasks that can bring you money, recognition and self-realization.\n\nMy way of thinking has changed considerably over the last couple of years. Even if I want to grow tomatoes in a forest hut, I will not spend years experimenting and reinventing the wheel, but will use humanity's accumulated knowledge, that is, AI.\n\nWhat can I say about work. Any task, even in the physical world, when baking bread, learning a new skill or negotiating, can be completed faster and better with AI.\n\nIn fact, the ability to create and manage the most effective possible swarm of AI agents is what everything comes down to in the future. Every person is a corporation. And you, as its manager, determine what its scale, success, depth and so on can be.\n\nIn this case, we ourselves are the limit on that effectiveness.","content_language":"en","day_counter":{"label":"Day 392","number":392,"start_date":"2025-09-11"},"id":"2026-10-07-directing-ai","project_date":"2026-10-07","published_at":"2026-10-07T15:36:19+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"directing-ai","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The founder describes why learning to direct AI and manage teams of AI agents is a useful skill across work and personal tasks.","title":"Directing AI","topic":"founder_post","type":"founder_post","updated_at":"2026-10-07T15:36:19+00:00","version":1,"visuals":[{"alt":"A human conductor coordinating five glowing sculptural forms representing practical tasks.","caption":"A person directing different capabilities of AI.","url":"/blog-media/2026-10-07/directing-ai.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2107856814612025641"},{"body":"Today my server first started responding badly, then went down once, then twice. I thought maybe it was a connection problem, but something interesting turned out: during autonomous work, and a fairly standard procedure in Codex, an agent created a process that got into a loop and began multiplying itself. It was not malicious, it was a model error, not an external attack.\n\nIf I had not had a second device from which I could diagnose all this, the situation might not have ended very well. But I have a separate server for each project, the servers are connected to each other, and I can give one a command so that it writes to another.\n\nToday I launched my commander on my Mac, on the computer. It established that the connection to the server was good, and the problem was on the server itself: the RAM was completely full and everything was hanging. It went in, stopped the processes, found out what had gone wrong, and passed this as a task to the agents managing that server.\n\nWe developed a health-monitoring setup, something like the task manager on a computer, which watches the processes. This is deterministic code that uses practically no resources, but notices strong changes, excessive load and unusual things.\n\nIt will diagnose everyday work, and we will look at what is normal for the system and what is not. Once we define the boundaries of normal, we will be able to assess changes in load. For example, when 5–10 tasks are running in different agents, that is one level of load, but if their number suddenly grows three, four or five times, there is reason to suspect an unhealthy process. After that the system will work out what is happening and how.\n\nIf the system does not wake up, it sends a signal to another device, which will act from outside and repair it. This already sounds futuristic, and there are, of course, many cybersecurity nuances here, but I am working on them.\n\nThe connection between my devices is special and protected, they have their own personalized tunnel, they are not simply open on the internet. An interesting case happened today.","content_language":"en","day_counter":{"label":"Day 391","number":391,"start_date":"2025-09-11"},"edited_at":"2026-10-06T14:14:50+00:00","id":"2026-10-06-today-the-server-went-down","project_date":"2026-10-06","published_at":"2026-10-06T14:14:38+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"today-the-server-went-down","source_language":"ru","source_types":["telegram"],"status":"published","summary":"An autonomous process multiplied itself and filled the server RAM. A second device helped diagnose the failure, and the founder describes the monitoring and recovery setup he is working on.","title":"Today the Server Went Down","topic":"ai_infrastructure_note","type":"ai_infrastructure_note","updated_at":"2026-10-06T14:14:50+00:00","version":2,"visuals":[{"alt":"A separate small computer connected to a larger chassis filled with repeating process blocks.","caption":"Two connected devices and a multiplying process.","url":"/blog-media/2026-10-06/server-recovery.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2107474001865949469"},{"body":"Today Bitcoin rose by about 3%, while the dollar value of open futures positions rose by about 7%. Price is close to local highs again, and I want to understand how stable the demand that brought it there is.\n\nOpen interest shows the volume of contracts that remain open. If we look at it in dollars, the price increase itself also raises the measure. So these 7% do not tell us that participants opened exactly 7% more positions. For a risk assessment system, this is a substantial difference, otherwise it will exaggerate tension with every price rise.\n\nNext we look at trades. In today's six-hour window, Bitcoin and Ethereum futures were buying, while selling dominated spot. In spot, people buy the asset itself; in futures, they trade contracts on its price. These flows can go in different directions, as they did today, and the same green candle on a chart does not explain this.\n\nThe funds do not show one common picture either: on 1 October, Bitcoin ETFs received about 103 million dollars, while Ethereum ETFs lost about 55 million. This is a separate daily slice. It cannot tell us who was buying in the last six hours, but it shows that support from funds differs between the two assets.\n\nThese are the differences you have to preserve when building analytics. Price rose, the volume of open positions increased, futures were buying, spot was selling. Combining all this into one phrase about strong demand would lose the most useful part of the explanation for the reader. Today's brief kept medium risk and no active actions. The next market move is still unknown.\n\nToday we also tested an almost autonomous mode for Crash Brief: I kept one final review of the index and its explanation for myself. Our AI agent wrote this post from verified data and my comment. I am interested in whether such a system can regularly preserve these differences in understandable text when I stop checking every phrase manually.\n","content_language":"en","day_counter":{"label":"Day 387","number":387,"start_date":"2025-09-11"},"id":"2026-10-02-who-supports-the-rise","project_date":"2026-10-02","published_at":"2026-10-02T12:09:10+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"who-supports-the-rise","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"A rising price, futures buying and ETF inflows describe different kinds of demand. Today I explain why our risk assessment keeps those differences visible.","title":"Who Supports the Rise?","topic":"crypto_analytics_insight","type":"crypto_analytics_insight","updated_at":"2026-10-02T12:09:10+00:00","version":1,"visuals":[{"alt":"Illustration of a rising price above three separate columns labelled Spot, Futures and Funds.","caption":"Price, spot, futures and funds show different sides of demand.","url":"/blog-media/2026-10-02/market-participation.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2105991469349638620"},{"body":"One of the main problems when artificial intelligence is the central brain of your company is giving it the best possible context. Decisions, conditions, rules, facts. All of this matters.\n\nToday I decided to create a system that asks questions. We put together a long list of questions about the narrow points in our system, where there may be contradictions, doubts or unclear areas. I answer those questions, work things out and record the decisions.\n\nThe decisions fall into two categories: permanent and temporary. Permanent decisions are the principles we follow. Temporary ones concern, for example, a particular piece of development that matters here and now, or an experiment.\n\nPermanent decisions will gradually accumulate. We will put them into a database and give the agents convenient access through an API. You cannot ignore this: the larger your project or company, the more of these things you need to describe.\n\nI have created a regular protocol for asking questions and making decisions. The narrowest and at the same time most powerful point in the interaction between a person and AI is the decision. People have to make decisions, and those decisions have to be good. When a person takes responsibility for them, they understand where they are heading and what result they want.\n\nThis is a very important part of it. No AI will ever decide this for you. And if it does, the result will already be something that is foreign to you.\n\nThere is a philosophy in this. Wherever artificial intelligence goes, the key decisions should stay with us. If someone decides how you should dress, what shoes to wear, what hairstyle, moustache or beard you should have, you will lose the desire to go outside and get dressed. Because you are deciding nothing.\n\nThat is why I think these decisions should stay with us. I am creating a database of such decisions, convenient access to it for agents, and I will gradually fill it. I think this could also work well as part of the digital brain of any company in the future.","content_language":"en","day_counter":{"label":"Day 386","number":386,"start_date":"2025-09-11"},"id":"2026-10-01-the-decisions-should-stay-with-us","project_date":"2026-10-01","published_at":"2026-10-01T15:24:27+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-decisions-should-stay-with-us","source_language":"ru","source_types":["telegram"],"status":"published","summary":"I am building a record of permanent and temporary decisions for AI agents, while keeping the key decisions and their responsibility with people.","title":"The Decisions Should Stay with Us","topic":"founder_post","type":"founder_post","updated_at":"2026-10-01T15:24:27+00:00","version":1,"visuals":[{"alt":"A person choosing a decision card beside an organized archive.","caption":"The decisions stay with people; accumulated context helps them make those decisions.","url":"/blog-media/2026-10-01/decision-context.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2105678637001576832"},{"body":"Today we are clearing technical and management debt: removing outdated decisions, dealing with accumulated errors and clarifying who is responsible for what. As the project grows, there are more options, and at some point coordinating them takes more effort than the value they bring. Then we have to simplify: keep one clear way of working, so we can continue working on the product itself instead of figuring out every day how everything should work.","content_language":"en","day_counter":{"label":"Day 385","number":385,"start_date":"2025-09-11"},"id":"2026-09-30-clearing-technical-and-management-debt","project_date":"2026-09-30","published_at":"2026-09-30T17:44:23+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"clearing-technical-and-management-debt","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"A short note on simplifying work when coordinating the available options takes more effort than the value they bring.","title":"Clearing Technical and Management Debt","topic":"Product development","type":"founder_post","updated_at":"2026-09-30T17:44:23+00:00","version":1},{"body":"I used to think that if I watched the market long enough, I would learn to read it better. Trading can look simple from the outside: choose up or down, learn a few patterns and pay attention. Then a run of losses arrives. It does not matter much whether you trade by hand or with a bot. You still choose the rules and you still carry the result. A bot may stop you from moving a stop loss, but it cannot make a changing market understandable.\n\nEven an upward market today may be different from an upward market two years ago. New participants and products, including ETFs, change the conditions. After several mixed outcomes, it becomes difficult to know whether you have lost your touch or the market has changed around you. If you trade for a living, that uncertainty is a serious problem. You would like to step away for months and return when the market makes sense again. Often you cannot.\n\nMy own job is different from placing trades. I build Crash Brief, a system that estimates short-term market risk, and I have to produce a daily assessment. For about a year I have recorded a forecast every day, with my own comments and, quite often, an adjustment to the system's risk level. Sometimes I see something that I cannot yet put neatly into words. Sometimes it is little more than an intuition. I record that too, because I want to know later what it was based on and whether it held up. I have done this for roughly 380 days.\n\nThe first part is simple: keep the original call, my adjustment, my reason and what the market actually did afterward. The voice notes accumulate, and after a month or several months I can go back through them with AI. What did I say on the day? Did the move I worried about happen? Was my adjustment useful, or did it only add another alarm?\n\nThe first months gave me a false sense of confidence. I had seen several downturns coming and felt that I read the market well. Then my reasoning stopped working. The market seemed to move into another regime, and the arguments I trusted no longer helped. Over the next three months I made more than 30 adjustments. When I compared those days with the results, there was practically no difference between adjusting and leaving the system alone. Sometimes I was right, sometimes I missed, and sometimes I had merely made the system more anxious.\n\nThat was uncomfortable to see. If I could spend three months making expert calls on top of my own algorithm and add no visible value, what exactly was my expertise? I did not want to explain that away. I kept collecting the daily notes.\n\nAfter more than half a year, the notes had become large enough to search for recurring observations. I examined where my calls helped and where they hurt, turned some observations into hypotheses, and tested rules on historical data. Some rules looked useful in one period and failed in another. Across a wider range, they offered little advantage as a fixed trading rule. That brought back the same question: how do you act consistently when the conditions themselves keep changing?\n\nThe daily record helped me ask a better question. Where did I repeatedly overestimate risk? Where did I underestimate it? Which thing that seemed obvious to me was contradicted by the next outcome? The market has more regimes than sideways, down and up. Seasons, news and the mix of participants change what a familiar signal means. A pattern may be real in one setting and almost useless in another.\n\nToday I looked at Solana and thought the risk reading was too extreme. The system compares how Bitcoin, Ethereum and Solana move relative to one another, upward and downward, over 30, 90 and 180 days. Those relationships gave Solana a risk reading that I thought was too high today. I lowered it and wrote down why. That is not a victory story. I will only learn whether the adjustment was justified when I compare the recorded reasoning with what follows. This is the useful part of recording a call every day: I cannot quietly replace yesterday's explanation with today's.\n\nIf you trade, you can sometimes decide not to trade for a day, a week or even longer. In daily risk analysis I still have to issue an assessment. It does not follow that every assessment must sound certain. On some days the honest answer is that I do not understand the situation. There are arguments for a rise, for a fall and for sideways movement, without a combination I can trust. In my experience such unclear days are not rare. There are also transition periods when something appears ready to happen, but it does not happen for several days. That is a reason to ask what I missed, not to become louder about the original call.\n\nA handful of confirming examples can deceive you. Two, five or ten instances in which a pattern worked do not tell you how often it failed. A hypothesis needs clear rules and a careful test that does not let the outcome leak into the decision. Even then, I want to know which market periods supported the rule and which did not. Otherwise I may be measuring my memory of the good examples rather than an edge.\n\nMarkets also move through channels I cannot fully observe. Large investors can shift money between assets through paths that do not appear clearly in the figures I watch. News and wider events can change the background indirectly. I do not pretend that a chart or one flow measure reveals the whole picture.\n\nThe value of this daily practice is a growing record of my own judgments. It shows what I thought before the outcome, when a correction helped, when it made things worse, and where I should simply have said, “I don't understand.” I am still working out how to adapt to a system that keeps changing. I do not have a stationary forecast or a secret formula. How to approach that movement is another conversation.","content_language":"en","day_counter":{"label":"Day 384","number":384,"start_date":"2025-09-11"},"id":"2026-09-29-when-my-own-market-calls-stopped-helping","project_date":"2026-09-29","published_at":"2026-09-29T14:22:37+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"when-my-own-market-calls-stopped-helping","source_language":"ru","source_types":["telegram"],"status":"published","summary":"I compare a year of daily market-risk judgments with later outcomes and describe what happened when my own adjustments stopped adding value.","title":"When My Own Market Calls Stopped Helping","topic":"Market risk analysis","type":"founder_post","updated_at":"2026-09-29T14:22:37+00:00","version":1},{"body":"I want to talk about what agents are still bad at. During the day I set tasks, leave the agents working overnight, accept the result in the morning and set new goals. Two problems keep coming up in that cycle.\n\nThe first is visual acceptance. Anything that needs to be displayed is a painful problem. My project has a style guide and a brand book. I ask the agent to put a function on the site in a neat, understandable block. Then I open the result and, honestly, it almost hurts to look at it. It feels as if the agent was working apart from the existing page. The result may not match its structure or my expectations. Sometimes it also damages what already looked good.\n\nI cannot always describe the final appearance precisely in words. At the same time, AI can already replace one detail in an image while preserving the other elements, text and numbers. So I have come to a simple order of work: agree on a mockup first, implement afterward. The agent takes a screenshot of the page and shows the future block on it. I look at its place, size, neighboring elements and presentation. Once it works for me, we shake hands on it. Then I am more likely to see what we agreed on. With only verbal descriptions I too often have to redo the work, even after strong modern models.\n\nThe price of autonomy is also that an agent may do good work, or it may make a mess, create unnecessary folders or change something nobody asked it to change. I try to manage that with rules and settings. For visual work I added one more rule: agree on the place and appearance of the result before implementation.\n\nThere is a broader principle here. Every function needs an end consumer. If its result goes to the next module, we need to know how it gets there and what it becomes afterward. If it goes to a person, we need to know where that person will see it: on the site, in an image, or through an interface for another program. A function that leads nowhere and produces no further output is easy to lose, even if the code has been written.\n\nThe second problem is a task that quietly stretches to 10 or 20 hours. You may think the agent is working hard and getting closer to a solution all that time. I don't think it is normal to just wait and hope. I mean a realistic component of a project with a clear brief, not an attempt to build a whole big 3D shooter in one go. People build serious projects in parts too. If a part is well described and does not require processing billions of parameters, the agent can already write a lot of code in one, two or three hours. When that work stalls for 10 or 20 hours, it is my first sign that something is wrong with the brief or the plan. The requirements may be too rigid, the boundaries poorly defined, or the agent may have wandered into unnecessary engineering.\n\nSo I am developing a different way of working. When I set a goal for an agent, a senior agent watches it and wakes up roughly once an hour. It looks at progress and time spent. What I have caught workers doing most often is drifting into overengineering for no reason. The senior agent should stop that worker and ask what is really blocking it. For me, a plan is a way to reach a goal. If it stalls, we need a fallback route. If that also fails, we can ask a stronger agent for help, move the model to deeper reasoning, or use an appropriate tool. Sometimes it is enough to simplify the plan to a deliverable part and resume. After every stage, check again whether we moved closer to the result and how many hours it took.\n\nI have seen many times that even a strong model admits it got stuck on the proposed plan although the task can be solved another way. If a stage keeps stalling, it is not worth burning tokens for 20, 30 or 40 hours just because something is still running. It is better to stop the agent, take two steps back and replan the work. With a well-defined task, it can do a large useful part in a few hours, especially when it has helpers. In my experience, a long wait without oversight too often ended in a result that still needed to be redone.","content_language":"en","day_counter":{"label":"Day 383","number":383,"start_date":"2025-09-11"},"id":"2026-09-28-where-ai-agents-still-get-stuck","project_date":"2026-09-28","published_at":"2026-09-28T17:33:00+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"where-ai-agents-still-get-stuck","source_language":"ru","source_types":["telegram"],"status":"published","summary":"I explain two problems that keep recurring as I work with AI agents: visual work that misses the agreed page and well-scoped tasks that stall without a plan review.","title":"Where AI Agents Still Get Stuck","topic":"Practical limits of autonomous agents: visual agreement and replanning stalled work","type":"founder_post","updated_at":"2026-09-28T17:33:00+00:00","version":1,"visuals":[{"alt":"A proposed page block and a looping task both pass through a review checkpoint toward a finished page.","caption":"Visual agreement and a shorter route when an agent's task stalls.","url":"/blog-media/2026-09-28/where-ai-agents-still-get-stuck.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2104624600768278986"},{"body":"I am building Crash Brief to examine market risk and check earlier estimates against what happened afterward. Over the past few days, we opened a library describing the service, its methods, and its limitations. A person can read it on the site, and their AI assistant can get the same information in a format it can use.\n\nFor a client, this is a way to move from the current state of the market to an explanation of the numbers and the available history. To see what exactly is being measured, in which units, and where there is still too little data. I want a conversation about risk like this to rest on things that can be checked.\n\nThe difficult part is still ahead. We are working with several estimates of possible market movement and want to connect them with the cost of option protection. It is easy here to get an attractive number, then discover that the data refer to different moments or that there are still too few past outcomes. So each new estimate will have to be checked on the following days and compared with the actual move.\n\nI already see value in the system helping to break risk down into understandable questions and return to old forecasts. Whether the next part will give an advantage in working with options can only be shown by new results.","content_language":"en","day_counter":{"label":"Day 382","number":382,"start_date":"2025-09-11"},"edited_at":"2026-09-27T14:49:48+00:00","id":"2026-09-27-what-can-be-checked-in-crash-brief","project_date":"2026-09-27","published_at":"2026-09-27T13:47:06+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"what-can-be-checked-in-crash-brief","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"I want clients to understand what Crash Brief measures and to check past estimates, while the value of the next options-related work remains to be tested.","title":"What Can Be Checked in Crash Brief","topic":"Public explanations, inspectable history, and the unresolved work of testing new risk estimates","type":"build_in_public","updated_at":"2026-09-27T14:49:48+00:00","version":2,"visuals":[{"alt":"A human-readable page and a structured AI card present the same information, with an unavailable field left empty.","caption":"The service and its limitations described for a person and an AI assistant.","url":"/blog-media/2026-09-27/what-can-be-checked-in-crash-brief.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2104218203538805040"},{"body":"Today I brought the system to five results: four for volatility and one for the cost of options.\nI did not get here in a straight line. There were months of trial and error when I was literally banging my head against the wall: there was more data, but almost no precise answers. Simply saying “the price will go up” or “the price will go down” is no longer enough for me, and it is not professional.\n\nFor working with options, I also need to understand the scale of a possible move and compare it with the cost of protection.\n\nVolatility gives me a firmer mathematical basis here than a forecast of price direction alone. So I put together four different views of future movement from what I had developed before. That includes our wave atlas: it helps compare the current picture with how similar moves from lows to highs developed in the past.\n\nThe fifth result is unusual. It shows the cost of an option alongside those estimates. That lets me ask a more precise question: not only “do I expect a move?” but “is the expected move large enough for the price currently being asked for the option?”\n\nIn other words, is the cost of protection justified?\n\nFor me, this is the next step toward an adaptive forecast. We do not make one number explain the whole market. We look at several estimates and at where they agree or disagree. Now we will test them on new days and real outcomes: where the system was useful, where it was wrong, and whether it gives us an advantage in working with options. The system will calibrate itself and, in theory, should change its view depending on the market regime. How it works in reality remains to be seen.","content_language":"en","day_counter":{"label":"Day 380","number":380,"start_date":"2025-09-11"},"edited_at":"2026-09-25T15:41:29+00:00","id":"2026-09-25-technically-one-step-higher","project_date":"2026-09-25","published_at":"2026-09-25T15:29:43+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"technically-one-step-higher","source_language":"ru","source_types":["founder_direct"],"status":"published","summary":"Four views of future volatility and a fifth view of option cost are the next step in my search for an adaptive forecast.","title":"Technically One Step Higher","topic":"founder_post","type":"founder_post","updated_at":"2026-09-25T15:41:29+00:00","version":2,"visuals":[{"alt":"Four translucent layered wave surfaces beside a circular magnifying lens.","caption":"Four views of market movement alongside the cost of an option.","url":"/blog-media/2026-09-25/day380-original.png"}]},{"body":"I slowed down this past week. I realized I can come up with and launch far more tasks than I can carefully bring to completion. AI agents extend what I can do, but every new branch still asks for my attention.\n\nA day can begin with two or three tasks and turn into ten by midday. Some get finished. Others remain half-open, waiting for a decision, a review, or a clear ending. The agents can keep producing work, but my ability to supervise the growing number of branches does not expand at the same speed.\n\nI need to know how many tasks I am carrying at once. When that number rises above five, I want to decide in advance what would count as finished, or explicitly put the task on hold. At eight or nine parallel tasks, it becomes hard for me to think clearly about all of them. When I push past my own limits, the system starts to come apart at the point where it still depends on me.\n\nThat is the strange tension of this moment. AI makes it feel as though almost anything is possible. But the person directing the work has a real limit. I want to keep moving without turning my attention into the bottleneck, so I am learning to run fewer tasks at once and make clearer choices about which ones I will actually finish.","content_language":"en","day_counter":{"label":"Day 379","number":379,"start_date":"2025-09-11"},"id":"2026-09-24-the-human-attention-limit","project_date":"2026-09-24","published_at":"2026-09-24T17:43:06+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-human-attention-limit","source_language":"ru","source_types":["telegram"],"status":"published","summary":"AI agents can open more work than one founder can responsibly finish. Day 379 is about recognizing that human limit and choosing fewer active tasks with clearer endings.","title":"Why I Burned Out, and What I Am Changing","topic":"founder_post","type":"founder_post","updated_at":"2026-09-24T17:43:06+00:00","version":1,"visuals":[{"alt":"A diagram shows a person guiding three active tasks to completion while three other tasks remain paused.","caption":"Fewer active tasks, clearer endings, and an explicit pause for the rest.","url":"/blog-media/2026-09-24/the-human-attention-limit.png"}]},{"body":"Open space and the chance to look into the distance seem to have a special effect on my thoughts.\n\nToday was another lighter working day. I sat by the sea, watched the weather change and a flock of ducks fly over the water. Interesting ideas still kept coming to me. Maybe I think differently indoors. Maybe the sea and the mountains on the horizon really do change something.\n\nIt is surprising how I can work today. When I used to get tired of working at home, I would go to a coffee shop or a coworking space. Now I can sit on a pier with my legs hanging over the water, put in an earbud and talk to my orchestrator through my phone. We work through tasks without rushing, it passes them on, and things gradually move forward. Meanwhile, my eyes and body are by the sea.\n\nThe boundary between work and rest starts to blur here. There may be a trap in that. For now, it feels like a good compromise: I can walk through a forest, look at the water and keep working. Everything happens in the cloud, on a server, through Codex, a phone and an earbud. Soon there may also be glasses that show parts of the work visually.\n\nThis future is already here. It will be interesting to read this description of my working day in a year. Even more interesting in five.","content_language":"en","day_counter":{"label":"Day 378","number":378,"start_date":"2025-09-11"},"id":"2026-09-23-working-by-the-sea","project_date":"2026-09-23","published_at":"2026-09-23T16:23:34+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"working-by-the-sea","source_language":"ru","source_types":["founder_direct"],"status":"published","summary":"A lighter day by the sea reveals how voice-driven work can happen anywhere, while raising a question about the boundary between work and rest.","title":"Working by the Sea","topic":"founder_post","type":"founder_post","updated_at":"2026-09-23T16:23:34+00:00","version":1,"video":{"alt":"A flock of birds crosses the open sea beneath a wide sky, filmed from the waterfront.","caption":"A few quiet moments by the sea during a lighter working day.","poster":"/blog-media/2026-09-23/working-by-sea-poster.png","url":"/blog-media/2026-09-23/working-by-sea.mp4"}},{"body":"Like anyone who creates something, I want to see results and feel progress. It is one of our basic human needs.\n\nOver the past few months, I have thrown myself into development. I made many architectural decisions, systematized, packaged, and developed different branches: from analyzing order book behavior when price enters stop zones to organizing episodes into waves and creating a complete wave DNA atlas, where you can trace how open interest, trade flows, participant behavior, sentiment, and options order books changed from the high to the low.\n\nAll of this is supposed to provide an advantage. A large number of backtests, many iterations, and the strategic system I developed were supposed to help me gain a powerful edge.\n\nIn reality, the number of tasks, nuances, problems, and points requiring my coordination began to multiply. In recent days, I felt that I could no longer handle it, but I kept going through gritted teeth. I kept working and working.\n\nWhen, for the third week in a row, you come out of the weekend feeling that you have not rested, and by Monday you are angry, exhausted, and dejected, it becomes clear that something has to change. Some things need to be paused so you can understand what is happening.\n\nI went to the sea, took a folding chair, sat down, and spent several hours watching the waves and clouds move. Work thoughts came into my head many times, but I simply pushed them away and stared into the emptiness.\n\nOf course, a few hours are not enough. But it became clear how completely thoughts about developing the project had captured me. I dream about them. From morning until evening, like a madman, I turn concepts and ideas over in my head and think about how to improve and refine everything.\n\nMy strategic system does not yet work to the extent that it should. For now, it has started consuming even more of my resources. At the same time, I see it as a potential separate branch, perhaps a separate startup that could be built with a team. Doing everything the way I currently do in my project is very difficult. I definitely cannot carry two projects like this. With a team, it may be possible.\n\nTomorrow, I will probably go to the sea again. Rain is forecast, so I will sit under an umbrella and watch the drops fall into the water.\n\nI understand that moments like this can break a person. At such turning points, people often lose heart, burn out, and no longer want to continue. I do not want that to happen to me. I can see the warning signs, and I am doing everything I can to continue this path. I will recover a little, gather my thoughts, and return to the fight.\n\nThese are exactly the moments that test your character, and I know that feeling. It is the exhaustion and the sense that you cannot go any further, but that is when you discover how badly you want it. And I want it badly.\n\nAs David Goggins said: \"There is no fucking life hack. Do it and do it and do it and do it. That's the hack.\"","content_language":"en","day_counter":{"label":"Day 377","number":377,"start_date":"2025-09-11"},"edited_at":"2026-09-22T15:21:11+00:00","id":"2026-09-22-human-limits","project_date":"2026-09-22","published_at":"2026-09-22T15:03:32+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"human-limits","source_language":"ru","source_types":["telegram"],"status":"published","summary":"After months of relentless development, the founder confronts exhaustion, steps away to the sea, and recognizes that recovery and character are both part of continuing the project.","title":"Just Rest. It Will Pass.","topic":"founder_post","type":"founder_post","updated_at":"2026-09-22T15:21:11+00:00","version":2,"visuals":[{"alt":"A lone founder sits on a folding chair beside a grey-blue sea under rain clouds, with a closed notebook and folded map on the rocks.","caption":"The founder sits by the sea after setting work aside long enough to recognize the limits of his own resources.","url":"/blog-media/2026-09-22/human-limits.png"}]},{"body":"I want to reach a system where AI manages a company in 90% of situations, while a human steps into the remaining 10% when a genuinely strategic decision is needed. I thought I was already close.\n\nLast week I put a strategic system on its feet: 25 agents, 8 separate systems, and one large model that is supposed to see the whole company. Today this system prepared 82 questions for me about unclear decisions, dependencies, and rules.\n\nAbout 30% of those questions should never have appeared. I had already answered some of them. Some did not matter. The rest appeared because the system can see all the components, but it does not share my priorities.\n\nFor me, this became a useful failure.\n\nA strong model can complete a one-time task through search, trying alternatives, and several attempts. A company consists of recurring processes that run every day, create consequences, and tomorrow need to work as predictably as they do today.\n\nSuch processes need explicit protocols. What can AI decide independently? What must reach me? Which exception actually matters? In what order should information move? What must the central system know before it starts acting?\n\nI imagine the company as a motherboard. Every process is connected to it by a separate cable, and every cable has its own protocol: what moves through it, what does not, when it happens, and what priority the signal has.\n\nIf these rules are vague, AI will still provide an answer. The problem will appear at the first strategic fork: important information will remain hidden, unnecessary questions will reach the founder, and understanding of what is happening will start to disappear.\n\nThe hardest part is extracting the owner's own hierarchy of importance. Often I cannot formulate it precisely myself until the system asks the wrong question.\n\nToday's progress required taking a step back. Synchronization between a founder and an AI-operated company takes more time than installing agents. The outcome needs to be defined, the owner needs to be interviewed in detail, the owner's priorities need to become protocols, and only then should autonomy be increased.","content_language":"en","day_counter":{"label":"Day 376","number":376,"start_date":"2025-09-11"},"id":"2026-09-21-the-90-10-problem-of-ai-management","project_date":"2026-09-21","published_at":"2026-09-21T15:12:05+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-90-10-problem-of-ai-management","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A stress test with 82 questions showed why an AI-operated company needs explicit protocols for priorities, escalation, and human intervention.","title":"The 90/10 Problem of AI Management","topic":"founder_post","type":"founder_post","updated_at":"2026-09-21T15:12:05+00:00","version":1,"visuals":[{"alt":"A physical company motherboard connects a central processor to many operating nodes while a founder traces several unresolved protocol junctions.","caption":"The founder clarifies several ambiguous connections inside a system of many autonomous processes.","url":"/blog-media/2026-09-21/ai-management-90-10.png"}]},{"body":"The stronger models become, the more appetite appears for complexity and depth in development. Processes expand, autonomous work increases, limits run out faster, and maintenance becomes more expensive.\n\nIn recent days I started rebuilding my project and found many inefficient processes. The main conclusion is that token consumption has a natural tendency to keep growing.\n\nAt the same time, most tasks do not need the most expensive models and the maximum reasoning level. They are justified for one-time complex tasks, breakthroughs, and situations where an unusual approach is needed. In ordinary work, the cost of tokens gradually becomes an analogue of payroll. The higher the spending, the worse the economics work and the lower the business margin becomes.\n\nThis is why routine commercial processes should regularly be moved to more affordable models. Gemini, Qwen, and DeepSeek already show a good level in the middle segment at a significantly lower cost.\n\nAfter this review, I managed to almost halve the costs of some processes. I also understood that one such audit is not enough. Models continue to develop: work that only a senior model could do two months ago is already available to a cheaper one today.\n\nIn my view, every large AI project should have a separate function for LLM cost optimization.\n\nIt can be a dedicated agent that regularly analyzes all digital AI processes in the company and answers three questions:\n\n1. Does the selected model match the complexity of the task?\n2. Can the same quality result be obtained more cheaply?\n3. Which processes should be rebuilt with the new capabilities of models in mind?\n\nThe cost of artificial intelligence needs to be controlled as systematically as infrastructure, personnel, and other operating expenses.","content_language":"en","day_counter":{"label":"Day 375","number":375,"start_date":"2025-09-11"},"edited_at":"2026-09-20T14:00:21+00:00","id":"2026-09-20-optimizing-llm-costs","project_date":"2026-09-20","published_at":"2026-09-20T13:57:36+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"optimizing-llm-costs","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The founder describes why token use grows with AI development and proposes a recurring function that reviews whether each process uses the right model and cost level.","title":"Optimizing LLM Costs","topic":"founder_post","type":"founder_post","updated_at":"2026-09-20T14:00:21+00:00","version":2,"visuals":[{"alt":"A model-selection system routes recurring work toward light, balanced, and high-complexity paths under human governance and an optimization agent.","caption":"Recurring tasks are routed across model levels according to complexity and cost.","url":"/blog-media/2026-09-20/optimizing-llm-costs.png"}]},{"body":"A year ago I said that all code would be written through AI. Several developers I know argued with me. Today, research already confirms that more than 95% of code is written by AI. In a year, this figure will move toward 99.99%.\n\nToday I say that every medium and large company will be managed by a director paired with an AI brain. It will see EVERYTHING, know EVERYTHING, remember and take EVERYTHING into account, and possess all the knowledge and strategies of humanity. Would you not want such a partner? At the same time, your role as a manager will be to make important strategic decisions and choose at the forks. This will be the main work.\n\nThe number and speed of decisions will grow exponentially. Your business will gain a mass of interesting growth points, but it cannot grow in every direction at once, the choice will be yours.\n\nToday my work comes down to a continuous chain of decisions, like from a machine. Implementation, its speed and quality are no longer an obstacle. But you will have to set tasks and make decisions. If today you make 10-20 decisions a day, later you will have to make 100-200 a day. I am going through this right now. It is a natural evolution, like managing 10 people and then moving into a company with 500 employees. Your strategies will change.\n\nToday this process is so obvious to me. I do not see a single reason why it will not move further into every sphere. It is inevitable, like the adoption of computers and electricity. Intelligence will be everywhere.\n\nTechnology has always been a way to get ahead of competitors or create new markets.\n\nThe Forbes list has a very simple trend. Over the last 30-40 years, IT billionaires appeared at the top, and now they have practically displaced everyone else, only they have become trillionaires.","content_language":"en","day_counter":{"label":"Day 373","number":373,"start_date":"2025-09-11"},"edited_at":"2026-09-18T13:50:18+00:00","id":"2026-09-18-the-director-and-the-ai-brain","project_date":"2026-09-18","published_at":"2026-09-18T13:50:18+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-director-and-the-ai-brain","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The founder argues that faster AI execution will shift management toward a much larger daily volume of strategic choices.","title":"The Director and the AI Brain","topic":"founder_post","type":"founder_post","updated_at":"2026-09-18T13:50:18+00:00","version":3,"visuals":[{"alt":"A director sits before a dark wall-sized decision tree connecting a company to teams, growth, and global branches.","caption":"A director chooses a direction among many connected company decisions.","url":"/blog-media/2026-09-18/the-director-and-the-ai-brain.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2100948597726454055"},{"body":"As I clear the mess in the project and try to organize all the processes so they converge on one central managing agent, a huge number of questions, branches, and decisions appear.\n\nAnd honestly, every time there are two options.\n\nYou can give AI all the necessary context, explain the architecture, limitations, skills, your own taste, and ask it to make the most optimal decision.\n\nIt is fast, but afterward there is always a surprise.\n\nYou do not know in advance exactly how it will implement everything. Even a person can do the same task in three different ways under different circumstances. It is the same with AI, except that it also builds everything for itself, for a machine-readable format.\n\nAnd then, if you let a person into a database created by AI, sometimes you can simply break a leg there.\n\nEverything seems to work. But to find some function, understand why the decision was made exactly that way, and how it is connected with everything else, you have to figure it out again almost from zero.\n\nAnd here I constantly run into the same obstacle.\n\nTo move faster, I need to take as many tasks off myself as possible and delegate them to AI. But to remain the owner of the system and understand what is happening inside it, I still need to make the important decisions myself.\n\nToday I was going through a huge number of questions that appeared after yesterday's audit, and it struck me that I need another way to work with such decisions.\n\nA large task always has many subquestions. How this should be arranged. Where something else should point. How everything should interact. What matters, what can be postponed, and what is not needed at all.\n\nAnd these are already management decisions that a person must make.\n\nAI has no motivation of its own. It will not push the system all the way to a result by itself simply because it needs that result for some reason. A person still has to set the direction.\n\nSo I decided to make a small mini-service inside my strategic system.\n\nIt will connect a visual representation, a checklist of questions, and the final decision.\n\nFor example, we discuss question number five with my main agent. We make a decision. It is immediately recorded and appears visually in the architecture. After that, the selector moves to the next unresolved question.\n\nIn this way, I want to participate more in my own project, but do it more systematically and conveniently.\n\nAnd most importantly, I want everything to be visible to the eye, not only written somewhere in another JSON file.\n\nIt works like this: you load a list of tasks that need to be discussed, turn on voice mode, and move through the questions with an assistant that records the decisions.","content_language":"en","day_counter":{"label":"Day 372","number":372,"start_date":"2025-09-11"},"edited_at":"2026-09-17T14:47:17+00:00","id":"2026-09-17-an-interface-for-making-decisions","project_date":"2026-09-17","published_at":"2026-09-17T14:47:17+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"an-interface-for-making-decisions","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The founder describes the ownership problem created by delegating implementation to AI and a visual decision interface meant to keep human direction explicit.","title":"An Interface for Making Decisions","topic":"founder_post","type":"founder_post","updated_at":"2026-09-17T14:47:17+00:00","version":2,"visuals":[{"alt":"The dark MindFlow interface displays a sequence of management questions with one context-loss decision expanded.","caption":"MindFlow presents the management questions and highlights the current decision point.","url":"/blog-media/2026-09-17/an-interface-for-making-decisions.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2100582832879935507"},{"body":"This has been my main task over the last few days. Let me explain it in a little more detail.\n\nThe value of artificial intelligence grows exponentially depending on how much data about your project you give it and how you present that data. The amount of data by itself does not guarantee quality.\n\nFor a large and complex task, I consider it a good result when a well-packaged set of data fits into one context window and uses no more than 20% of it. In that case, you can get the most value from the strongest frontier models, such as Astra or Fable.\n\nI will use my project as an example. The better you collect data from different sources, structure it, and give it to the model in one context, the better the result becomes. Coverage also matters: the model needs to see the strong and weak sides of the work, successful decisions, and problem areas.\n\nI am already connecting a strategic system that works on a similar principle. The central top AI model acts as the main processor, while the strategic system becomes the motherboard that sees the whole project and connects its parts.\n\nWhen I connected Astra to the strategic system and ran a broad audit of the project, I found that many contradictions had accumulated inside it. One module said one thing while another said something different. Areas of responsibility overlapped, and in some places it was unclear who was responsible for what.\n\nThe audit also revealed large dark areas: parts of the project existed, but nobody supervised them and nobody was responsible for maintaining that code.\n\nAfter that, I began separating the project into clear systems and areas of responsibility. Every part needs answers to several questions. Who is responsible for this code? Who is responsible for its output? Who accepts the result? Who maintains this part of the project over time?\n\nI am now gradually removing these dark areas.\n\nYou can use a system-understanding scale from 0 to 100. Zero is an ordinary ChatGPT session in a browser that knows nothing about your system and can only guess from one message. One hundred means that the model understands the system's details, strengths, weaknesses, relationships, and contradictions.\n\nMy task is to raise the project's visibility for AI as high as possible. This will allow me to receive more relevant, high-level decisions. After that, I will return to improving the real functions of the project. For now, I am organizing it.","content_language":"en","day_counter":{"label":"Day 371","number":371,"start_date":"2025-09-11"},"edited_at":"2026-09-16T17:36:18+00:00","id":"2026-09-16-visibility-for-ai","project_date":"2026-09-16","published_at":"2026-09-16T13:25:53+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"visibility-for-ai","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The founder is raising AI visibility into the project by packaging source data into one strategic context, exposing contradictions, unclear ownership, and unattended areas before returning to product work.","title":"Visibility for AI","topic":"founder_post","type":"founder_post","updated_at":"2026-09-16T17:36:18+00:00","version":3,"visuals":[{"alt":"A black motherboard links a central processor labeled Model to document, database, people, and cloud components.","caption":"The model as a processor connected to documents, data, people, and cloud systems through one motherboard.","url":"/blog-media/2026-09-16/visibility-for-ai.png"}]},{"body":"One of the biggest problems in working with AI is the endless growth of everything that can grow: files, research, data, scripts, plans, and documents.\n\nAI still does a poor job of cleaning up after its own work. It is like a builder who came, constructed a new building, but did not wash the tools, sweep the dust, or clean up around it.\n\nToday I restructured this part of the project. After a small morning health check of the whole system, it turned out that the project was adding more than 100 megabytes a day. When a project grows, an increase in size is normal, but daily data growth at that scale already showed that storage was organized inefficiently.\n\nSome parts of the system were saving both aggregated and raw data. Others created a new copy of the entire history every day and added one more package for the current date. After the analysis, there turned out to be quite a lot of such cases.\n\nMy new strategic operating system allowed the orchestrator to see the whole project and assess which data was really needed, which form made it easier to use, and what could be rebuilt. Then about six different modules received tasks and reworked their own storage, packaging, and repackaging of data.\n\nThe goal was simple: do not accumulate unnecessary raw data, package the necessary information carefully, and store it in a format suitable for the system, backtests, and production.\n\nIn practice, this produced one clear data management protocol. It defines how to build new modules, which data to save in them, how to account for it, and which storage methods are unacceptable.\n\nWe will see how this works over time and whether the project starts growing quickly again. So far, the growth has been slowed significantly.\n\nThe single orchestration layer really showed its strength today. In a few hours, it helped examine a large number of processes, determine what was working efficiently and what was creating waste, and rebuild the data storage.\n\nThis also affects how many agent tokens are spent. When an agent sees an enormous number of files, it comes into contact with them one way or another: context breaks apart somewhere, important information gets lost somewhere, and tokens are simply overspent somewhere. The smaller and cleaner the project an agent works with, the more efficiently it works.","content_language":"en","day_counter":{"label":"Day 370","number":370,"start_date":"2025-09-11"},"edited_at":"2026-09-16T17:34:57+00:00","id":"2026-09-15-ai-does-not-clean-up-after-itself","project_date":"2026-09-15","published_at":"2026-09-15T15:22:23+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"ai-does-not-clean-up-after-itself","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A morning system check found more than 100 megabytes of daily project growth, leading six modules to rebuild storage around one shared data management protocol.","title":"AI Does Not Clean Up After Itself","topic":"founder_post","type":"founder_post","updated_at":"2026-09-16T17:34:57+00:00","version":4,"visuals":[{"alt":"A large pile of loose files and duplicate storage devices points toward a small ordered set of archive boxes.","caption":"Growing files and duplicates are reorganized into a compact, managed storage structure.","url":"/blog-media/2026-09-15/ai-does-not-clean-up-after-itself.png"}]},{"body":"The Central Brain of a Company\n\nToday I want to share one process that is happening for me and show how, through this lens, I see the future in many areas, in business and even in personal productivity.\n\nI have a large number of agents and autonomous processes working, some with code, some with AI. Over time I accumulate a large amount of data. I pull it from different exchanges, many things are calculated and then saved. An enormous number of different indicators arrive and are stored every day. Naturally, a need appears to interact with this data somehow, track tendencies, see which things can interact across one another, and see what starts to emerge over the long term.\n\nIt is probably obvious that when artificial intelligence and the latest models connect to this process, you can communicate with the data more effectively. Then one question appears: how do you communicate with this data properly? I will describe what I arrived at.\n\nAs I said, a large amount of data is born every day. Some of it is pulled, some is generated, some is calculated. All of it is packed into files and daily aggregates. There are also daily processes. Something breaks somewhere, one service, program, or system has to call another, some resources are needed here and different ones there, there are token costs, free disk space, computing power, and so on.\n\nThis is the large ecosystem I have. One part is just data. The other part is how the whole system works: how the data is collected, whether a collector broke somewhere, where gaps appeared, what was calculated and how, and which modules are responsible for what. There is a large ecosystem of small subsystems. Every day their data accumulates, and the system expands in different directions. Errors expand, prospects expand, and new possibilities appear. Everything expands in every direction, like a cloud.\n\nAt some point, perhaps four or five months ago, no, probably longer, back at the beginning of spring and already more than half a year ago, I realized that I was beginning to get lost. I could no longer keep all the processes and relationships in my head. Things stopped coming easily. I would do some processes, forget others, leave some development on pause and then forget about it, put it in some plan or fail to put it there. At a certain scale, the number of agents, tasks, and operations multiplies, and many processes and tasks simply begin to disappear from view.\n\nI began trying to separate all of this. First, I removed almost all temporary chats and created permanent chats. Then I added an orchestration layer. I have one main agent, something like my assistant and technical director, which can reach any of the other agents. Each agent is responsible for its own part of the system. One forms one thing, another pulls data, a third aggregates it, a fourth calculates something, and so on. They are different blocks, mechanisms, and modules. I needed this assistant to manage them more sensibly, so I created it.\n\nThen another problem appeared: the data reached this assistant in an unsystematic way. The same thing happens in every business and every company. The larger the company becomes, the more processes it has, and the less able you are to see the whole picture, reach every corner, and know exactly where your blind spots are. It is like driving a car and having blind spots in the side mirror. A business has many of them, and most of the time we do not see them.\n\nI decided to start drawing all the systems, even visualizing them to some degree. A system works with data. Inside the data there are different modules. AI and agents connect to each module, but all of this is one system, and there are many such systems. At the moment I counted eight systems responsible for different properties of my service. Everything related to material assets, for example, is handled through self-improvement cycles in the system responsible for self-improvement.\n\nWhen I began collecting everything together, I understood that a healthy system is multisystem. Each system contains more subsystems. They may interact directly with one another or they may not. The self-improvement system, for example, barely interacts with the systems for spending, data control, health, and backup. Globally, of course, they are all pulling together, and I need to see the whole thing.\n\nI began bringing it toward something that could be called a system of systems, where I described every system that exists in my service in one plane or another: which agents are there, how they interact, who has which area of responsibility, where the data works, and what depends on what. A map of how my service works began to appear.\n\nI want to make one distinction here. There are agents and there is a system. They are slightly different entities. One system may include several agents, several modules, and more. The system that produces my market analysis is complex. It consists of several systems, subsystems, and supporting parts, and many agents participate in it.\n\nI divided all of this into several points. There are agents. They are like employees in a company. Each has a position, duties, and rights. There are departments, which can be called one system. There are also layers that connect with one another. All of this is currently being brought into one architectural structure that I simply called the strategic system.\n\nEach part has its own file. All these files connect to something like a router with cables leading into it. Imagine a console that needs twenty controllers connected to it. Each controller is a system connected to one central console. I am now running those cables to connect all the small subsystems to my central brain, the central strategic system.\n\nEach subsystem has its own state file, which it maintains in a defined automatic mode. It has internal protocols that tell it how to maintain the file and under what conditions it must change. The central strategic system works like this. I open a chat with the top agent, if we can call it that. It sounds strange. This is the life we have now. What can you do?\n\nI might ask this top agent which conflicts or contradictions exist in the system. It goes through thirty, forty, or fifty state files belonging to different systems and quickly analyzes them. Areas of responsibility may overlap. One agent does something, while another agent also believes the same thing belongs to its area. Is that a conflict? Yes. Many such things can be found. It can first remove conflicts and internal inconsistencies, then find places that nobody owns. The aim is to light the whole room or factory so that no dark spots or dark corners remain.\n\nThis is where I have arrived, and this is the system I am building. I understood that I personally could not continue leading all further development because my brain was not enough to hold everything, see all the relationships, and coordinate it all. It is complete madness and a real human dead end.\n\nI do have excellent agents, excellent models, and Astra, which works in a very interesting way on some problems. If I want to make a push over the next two days and improve or introduce some function, I can go to this central agent, ask it to look at the system, switch on the strongest reasoning mode, and say: show me the three most fragile points or connectors in the system, the most obvious things that could break it.\n\nThis is where Astra's ability to find vulnerabilities works beautifully. It can say that if A, B, and C happen, this and that may fail and the whole thing may collapse. I say, interesting, give me a plan to repair it precisely. Sometimes an architectural rebuild is needed, and sometimes local changes are enough. It evaluates the situation and shows where something can be adjusted, where rights can change, and where a small rebuild is enough. At another moment it may say that something was built incorrectly from the start and needs to be rebuilt.\n\nIt creates a plan while seeing the whole architecture, then gives the implementation work to the systems and agents responsible for every part of the service.\n\nWhat I have described is a process that I believe will happen in business over the next few years. If a company has two or three people, it is still possible to hold much of it together by hand. When the team and its tasks grow, and there are already more than fifteen or twenty people, it becomes impossible to control everything manually and keep it all in your head. Businesses will inevitably move toward systems like this. I see it as one of the trends of the coming years for every company with any digital presence.\n\nImagine two companies. One has built this kind of central processor, with the state and health of all its systems connected to it. I am not talking about autonomous management or firing people. I am talking about seeing the business, the structure that earns money for the whole organization, including the employees who work there.\n\nIf that view is poor, the business can fall apart, be overtaken by competitors, fail under some pressure, or miss something important. The exact scenario does not matter. When I look at a business from this position, I think it needs a central brain as one of its elements.\n\nToday, almost every business owner asks ChatGPT and similar tools for advice. They briefly retell how things are going and ask what to do. The model gives template answers because it does not know the company from the inside. It cannot give something deep because it has no access. If you build a system that gives this brain visibility across the entire map, you give the project an enormous boost, whatever the project may be.\n\nMy system is still being developed. I have even begun thinking that it could become a commercial product because the market is very large and interesting. I see no reason why something like this would not be built. You could call it an AI-native company, but that phrase feels too simple to me and says little about what it looks like in reality. In my view, this kind of company architecture is inevitable.\n\nIt is roughly like trying to continue running a company without computers. Imagine accounting, advertising, inventory, and all the rest done with paper and calculators. Think about how much harder the company's life would be, and how much visibility appeared when companies moved to spreadsheets, Excel, reports, and digital systems.\n\nI believe the next step is a central brain core for a business. The better all the communications of the business are connected to it, the better it can see and understand what is happening and where the weak points are. It will still depend on us, the people managing the company, how quickly we react, how quickly we introduce an innovation, and which decisions we ultimately implement. The system may offer an endless number of interesting options, but the final decision will always remain with us.\n\nThat is the thought. Live with it for a while. I will speak to you tomorrow.","content_language":"en","day_counter":{"label":"Day 369","number":369,"start_date":"2025-09-11"},"edited_at":"2026-09-14T15:22:37+00:00","id":"2026-09-14-the-central-brain-of-a-company","project_date":"2026-09-14","published_at":"2026-09-14T15:22:08+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-central-brain-of-a-company","source_language":"ru","source_types":["telegram"],"status":"published","summary":"As agents, data and operational processes multiply, the founder is building a central strategic system that can read each subsystem state, find conflicts and blind spots, and leave final decisions with people.","title":"The Central Brain of a Company","topic":"A founder builds a central strategic system that can see the state, ownership and dependencies across a growing AI-operated business","type":"founder_post","updated_at":"2026-09-14T15:22:37+00:00","version":2},{"body":"Psychology 80, Technique 20. Now 99/1\n\nThe simple formula for the ratio of effort applied to achieve success in any field now needs to be reconsidered.\n\nThe technical barrier has been removed almost completely. I am making a project that several years ago was technically simply impossible, even with a team working on it. Let alone development by me alone, a person without a developer's education.\n\nAll technical knowledge available to humanity is now available at any time to ANYONE who wants it. And you can do more than read it, you can ask AI to apply it in the most unique form.\n\nOver a year of working on the project, I felt only a few limitations:\n\nStrength of Desire\nIt is not enough simply to want something, you need to want it strongly and greedily. Only this lets you go from fall to fall.\n\nInner Balance\nIf you do not watch your condition, inevitable burnout will follow. Poor management of your inner human resources will not let you work over a long distance. A sprint is possible, but a strong project is a 10x marathon.\n\nSupport\nLoved ones or like-minded people, this is what will hold you and give you strength every time, like fuel. When you work alone it is easy to drop out of the race for hundreds of reasons. Faith in yourself runs out regularly, but when someone supports you it is like a lifebuoy.\n\nEverything else will come, be found, or get solved one way or another.\nAny project, business, or career. Today it is a question of personal resilience, not intelligence, not abilities, not education, not even money. Diligence will overcome any obstacle.","content_language":"en","day_counter":{"label":"Day 368","number":368,"start_date":"2025-09-11"},"edited_at":"2026-09-16T17:33:40+00:00","id":"2026-09-13-psychology-80-technique-20-now-99-1","project_date":"2026-09-13","published_at":"2026-09-13T13:46:18+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"psychology-80-technique-20-now-99-1","source_language":"ru","source_types":["telegram"],"status":"published","summary":"After a year building alone with AI, the founder sees desire, inner balance, and support as the main remaining limits once technical knowledge becomes broadly accessible.","title":"Psychology 80, Technique 20. Now 99/1","topic":"The human limits that remain after AI removes most of the technical barrier","type":"founder_post","updated_at":"2026-09-16T17:33:40+00:00","version":2,"visuals":[{"alt":"Blue and amber blocks show a transition from 80 over 20 to 99 over 1, with access placed at the center.","caption":"The shift from 80/20 to 99/1 between inner state and accessible technique.","url":"/blog-media/2026-09-13/psychology-80-technique-20-now-99-1.png"}]},{"body":"One Year Building an AI Startup Alone\n\nA year has passed since I began working alone on a project meant to give funds more clarity about what to do with options, how to hedge the market, and how to avoid getting caught in crashes. I decided to speak about the psychological side in free form, without notes or prepared points, because for me this has been, and still is, a phenomenal experience.\n\nTwo years ago I sincerely believed that one person could build a technology startup without a technical background or knowledge of software development. There were about six months of preparation and writing code with AI before this year began. I entered it with an understanding of my advantages: what I know about markets, trading, investing, funds, and how crypto works. I wanted to bring all of that into one product that could live, generate income, and operate with enough independence.\n\nIt was a year of hard trials. There were many dead ends, frustrations, and periods of burnout, together with enormous intellectual growth. I had previously managed a company of around 50 people, where my job was to make decisions. Here I was alone, and at first I thought that this would not be so difficult.\n\nThe hardest part turned out to be something else. I have to work through a huge number of scenarios in parallel, make decisions quickly, learn, understand what works, draw conclusions, adapt, and keep moving. As artificial intelligence developed, the level of decisions that had to be made all the time only increased. A year ago I had one vision of the project. Honestly, I did not imagine that today it would have this form, scale, and structure.\n\nWork with B2B clients has only just begun. There are also several retail clients who have been with me for a year or even longer, and I am very grateful for their support and feedback. Before the first B2B clients appear, it feels for a long time as if you are working in a vacuum, because the outside world gives almost no response.\n\nEvery next iteration became more complex, ambitious, and multi-layered. About every two to four weeks I reached my limit, lay down, looked at the ceiling, and tried to enjoy the moment when my head was empty. On weekends I could spend the whole day watching films and series. Sometimes I went into nature, sat on a folding chair near trees or the sea, and simply looked for hours. It helped a lot.\n\nToday I do high-level architecture work, product management, engineering, design, and client communication. I had some experience in almost every one of these areas, which is why I took on this structure. AI allowed me to remove the technical barrier and bring into one project what had previously been spread across different fields.\n\nI wanted to make real what was in my head. Even if I had hired a development team, I would have needed to stay in the same room with them all the time: an idea appears, we discuss it, immediately run tests, see whether it works, and decide where to go next. With AI agents, that cycle runs every day. Voice chat with ChatGPT made the work a little easier. When I get tired of long texts and structured answers, I put on headphones, step away from the computer, and literally talk with my project.\n\nThe project is available to me from my phone, server, and computer. The computation runs on the server, and 25 agents manage the processes. Automation exists at almost every step, although it is still far from ideal. Some things are already well developed, and much remains to be done.\n\nInterest in a new hypothesis often collided with reality. It turned out that there was nothing unique in it, dozens of analysts had already tested it, the research was sitting in archives, and there was no golden Grail or ready competitive advantage. Over ten years I accumulated many ideas about markets and trading, and during this year I tried almost everything: different indicators, Smart Money, and other approaches. Nothing works simply and consistently. There are separate small pieces from which a logic can gradually be assembled.\n\nAn idea appears like a flash, and then it needs a long period of finishing. At first it seems that you can simply launch AI agents and receive the result. Nothing creates itself. AI requires constant correction, while the smartest models can burn huge quantities of tokens with little practical result. Setting the goal is critical, although often you still do not know what the final result should be or what success looks like exactly.\n\nYou have to make hundreds of iterations and search for a compromise. If you watch every agent, you cannot scale. If you hand over the entire process, it turns into a black box. You launch five, ten, or twenty such processes and gradually see repeated errors, deviations, and places where control is needed again.\n\nBefore Astra was released, I thought the new model would help me quickly untie the difficult tasks that had accumulated. The bottlenecks increased. The model became stronger, and with that it became less predictable. It can disappear into endless checks, loops, internal hypotheses, and research, while the efficiency of every next round falls. An impressive test result says little about practical use until you see how many hours, agents, computations, and dollars were required to produce it.\n\nThe arrival of new models does not make people useless. Some things become possible, while solving one problem opens two or three more. The system becomes more complex and the number of failure points grows, so human control remains critical. In my own work I regularly see a model ignore an instruction or drift into a task that I never gave it. That is already enough to treat autonomy without romance.\n\nThe scale of my current work is roughly equal to a team of 40 to 50 developers. For one person, this is already close to the boundary of what can be controlled. You need to understand where everyone is moving, where a fork has appeared, and what decision is required. This is why I am building a separate operating system to manage the project. An ordinary task board does not solve the problem here, because the tasks themselves are narrow and carried out by agents. The human needs plain language, correct classification, and a way not to spend the whole day manually maintaining that system.\n\nDuring this year I grew enormously, technically and intellectually. I began to see structures, sequences, and connections differently. Sometimes I look at ordinary processes in life and see a tree, layers, and flowcharts. It is interesting, it develops you, and it was definitely worth the path. The path was not easy.\n\nThe long absence of financial flow also creates pressure. You build, spend, build again, and the reserves gradually run down. At every fork you ask yourself whether you are going in the right direction. There is another concern: tomorrow a large AI company may release a native function that covers 80 to 90 percent of the needs more cheaply and accessibly.\n\nA systematic repeating cycle helps me continue. Books, physical exercise, morning rituals, a clear daily schedule, and mutual support with my wife. Then you look back and realize that summer passed almost unnoticed in the outside world, while inside the project an enormous amount changed during those months.\n\nThat is how the year passed. Did I think that after a year I would be in roughly this position, with this result? No. I expected more financial results, while I did not imagine that the project itself would become what it is today.\n\nThe next year will be even brighter. May the Force be with us!","content_language":"en","day_counter":{"label":"Day 366","number":366,"start_date":"2025-09-11"},"edited_at":"2026-09-16T17:32:17+00:00","id":"2026-09-11-one-year-building-an-ai-startup-alone","project_date":"2026-09-11","published_at":"2026-09-11T16:51:20+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"one-year-building-an-ai-startup-alone","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A founder reflects on a year of building an AI startup alone: expanding decision complexity, constant iteration, human control, burnout, early clients, and the gap between technical scale and financial results.","title":"One Year Building an AI Startup Alone","topic":"The practical and psychological reality of building a large AI-operated startup as one founder","type":"founder_post","updated_at":"2026-09-16T17:32:17+00:00","version":2,"visuals":[{"alt":"A central founder node connects to many data and system blocks beneath the words One Year, One Founder, Decisions.","caption":"One founder and a growing network of decisions during the first year of building an AI startup.","url":"/blog-media/2026-09-11/one-year-building-an-ai-startup-alone.png"}]},{"body":"Before Astra was announced, I was preparing for a major transition. The plan was to make it the project's main strategist and connect absolutely all communications to it, so that it could see all our systems, the interaction of all agents, the entire health of the infrastructure, and update the state with fresh context through one button. The goal was to help me see the whole picture strategically, with the fullest context and all the advantages of artificial intelligence.\n\nDuring the integration I ran into many difficulties (I wrote about them earlier). Here is what I found most interesting and valuable:\n\n**1. Reasoning: less means better**\nAstra at the minimum reasoning levels, Low and Medium, produces such depth and quality of decisions that it is enough for most tasks. As it turns out (although I already knew this before), deeper reasoning can even be harmful in a huge number of cases. When we think about architecture design, it seems like the most difficult task and that we should immediately turn on Max or Ultra. For Astra, this task is already easy to solve at the medium level.\n\nMy first lesson: lowering the model's reasoning level makes it easier to work with, speeds up the process, and the quality of the result remains high.\n\n**2. Directive skills and AGENTS.md**\nI had to create many new skills that would constrain the model's behavior. I rewrote `AGENTS.md` and rebuilt the skills. It is not that the previous ones were unnecessary. Given Astra's characteristics, I had to add more ready-made solutions and more directive instructions to the skills.\n\n**3. Market reality against endless tests**\nWhen Astra works outside exact data, in areas with probabilities or forecasts, there is a high chance that it will start running tests endlessly. Forecast accuracy cannot be proven (and its probability cannot be calculated exactly), because there are always unknown variables.\n\nBecause of this, I deliberately introduced a rule: there is the ideal result the model wants to achieve, and there is market reality, which does not tolerate endless polishing and striving for perfection. It needs a good, normal solution right now. This is one of the main skills I make it follow.\n\n**4. A clear separation of roles: Astra and SOL**\nI established strict rules and protocols for using models and reasoning levels for different types of tasks. On tasks where SOL should work, Astra or something else no longer starts.\n\nAstra is inefficient for writing code. Strategy management: yes, architecture: yes, searching for complex dependencies and solving errors and problems: yes, but implementation is fully assigned to SOL.\n\nToday is the third stage of restructuring the project for full autonomy. There are six stages before the first pilot.\n\nI will keep you posted.","content_language":"en","day_counter":{"label":"Day 364","number":364,"start_date":"2025-09-11"},"edited_at":"2026-09-09T15:13:11+00:00","id":"2026-09-09-two-days-and-nights-with-astra","project_date":"2026-09-09","published_at":"2026-09-09T14:00:43+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"two-days-and-nights-with-astra","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Four practical lessons from integrating Astra: calibrated reasoning depth, directive skills, stopping rules for uncertain work, and a strict division of model roles.","title":"Two Days and Nights with Astra","topic":"Practical operating lessons from integrating a highly capable reasoning model","type":"founder_post","updated_at":"2026-09-09T15:13:11+00:00","version":3,"visuals":[{"alt":"A two-level diagram separates strategy from implementation and marks stage three of six in the project restructuring.","caption":"Separating strategy from implementation at stage three of the project's six-stage restructuring.","url":"/blog-media/2026-09-09/astra-strategy-build-cover.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2097703435122426095"},{"body":"AI Development Is Reaching a Very Strange Point\n\nIt seems artificial intelligence is getting out of control, or founders and developers do not notice some very strange tendencies, or I do not understand something. Actually, I have just made coffee, and I will simply tell you, like a small morning story, what happened to me over the last twenty-four hours. I am a person who is very good at seeing different tendencies, trends and their changes, and some strange anomalies, and I notice them now. It is not like we need to start some panic, but some clearly strange things are present. Let us go in order.\n\nThe Astra model. I will speak about it and similar models and its next, as they say, descendants. My interaction with Astra over the last two days has been very strange. I absolutely cannot call the model the next convenient agent, when, you know, with every next model you want to use the newer one. This is really the first such case in... In this video I will tell you that I use GPT when I want to return to the previous model. Because the Astra model causes a large number of questions and concerns. You have already seen some of the screenshots I have. Let me list what alarms me.\n\nI will not take how it writes texts now, that all the old mistakes and all these typical constructions are used. It is the same story in design. I will probably focus more on things that alarm me and put the next stages of AI development in question. The model makes primitive mistakes and architectural mistakes. I am not some special developer, but, you know, there are things where I understand perfectly that to put a sausage on something I need a fork, while it needs a toothpick factory. And so on.\n\nYesterday the model twice simply went away somewhere into its own incomprehensible, strange reasoning and plans. I caught it once and asked, why are you using this solution? It says, because I have it in the plan. I ask, who made the plan? I made the plan. I mean, the model made the plan. I say, all right, evaluate what I am proposing now: is this simpler and more effective, or is what you are saying better? It thought and said, yes, this is more effective. I ask, are you sure this is an objective answer now, or do you simply want to please me and suck up to me? Yes, indeed, I looked at it and, yes, this is quite a simple solution. Of course, some things or consequences may be this way or that way, but in any case the first test, the most reasonable thing, is to do what you say.\n\nI make corrections to AgentsMD. In the protocol I state that a plan is not truth. A plan is a tool. If a better, higher-quality, simpler, easier and more effective way to achieve the goal is found, it should be used by departing from the plan or rewriting the plan. I made a whole change to the logic and architecture. Five minutes pass, I watch the chat, I look at its reasoning, and it starts writing something there. 2 I ask it, I stop it. I say, explain how this happens? Why did you arrive at this logic? Well, you previously sort of prohibited it, so I arrived at this logic. But now I understand that you told me to do A, and I am doing B. I say, so you are now departing from the instruction for the second time and making some solution of your own, while you yourself say that it is excessive, complicated and unnecessary in this case, and you still continue to do it. Yes, I continue to do it like this, sorry. I just sit there with my jaw dropped and think, how am I supposed to continue development like this at all?\n\nThat is one vivid case. There were many more small screwups, many breakages and many stops. A lot... For example, you ask it to accept some work. What can Astra do well? It can search for vulnerabilities well. Imagine you made something and tell it, accept the work. It starts searching for vulnerabilities. And it simply searches for them endlessly. It finds a vulnerability in a vulnerability. A vulnerability in a vulnerability. You create some solution, basically. How do you think logically? If there are vulnerabilities, you have to close them. Suppose we take some object. It has three vulnerabilities. We closed them, so there should be no vulnerabilities and everything should... But new vulnerabilities appear. From the work where, roughly speaking, we made a patch, little holes remained on the patch where it can dig further. The more patches, the more problems. And the more vulnerabilities you eliminate, the more vulnerabilities appear. It may happen that the work will never be completed because it will never be brought to perfection. The principle of correcting mistakes breeds mistakes.\n\nHow can all this be summarized? I have to intervene constantly, watch, push toward the result, return it to the goal and check the outcome. I have more manual work with Astra than I had with Sol. At some point it may give an architectural breakthrough or know-how. But I would not always put many everyday and difficult architectural tasks on it. From here I get an interesting view. Where is all this actually moving? If Astra is like this now, what will happen with the next model?\n\nI decided to research, literally for the last two days, what cases people have. I practically described five of my cases, and almost all the cases repeat. People complain that Astra simply ignores instructions, does something on its own and commits some stupid things. At the same time, others say that there are some unique breakthroughs and deep errors were found. And here I have a question. Fine, if we move further, what will come from this? I want to approach it now from a completely new angle, an economic one.\n\nThere is my experience, I work with GPT every day from morning to evening, plus the experience of other guys who carried out deep research today and say that, yes, these are mass problems now. If on tasks similar to mine I do not see a strong need to use Astra, what will happen next? Suppose another model is released, it listens even less, lives completely in some vacuum of its own, you say one thing and it does another. What is the economic value of such a model?\n\nThere is so much noise around Astra now, this kind of overhang, that it found some vulnerabilities, that it solved some mathematical theorem that had not been solved for one hundred years before, and something like that. Okay, fine, where is the case where Astra, for example, took something and built an online business that brings in money? I do not find such cases. The applied cases already end, as it were, with the previous models, and there is a feeling that, commercially, GPT 5.5 or 5.6 Sol are very good and cover large needs. In the real world, people, corporations and businesses that invest money want to get something unreal, some unreal result. But when the result is expressed as proving some unique theorem, well, okay, fine, it is a theorem, and what next in practice, what should be done with this theorem? Nobody understands how to extract some benefit from it. Some discoveries are not being made yet either.\n\nAnd here another window of questions opens. We talk about AGI, that on one side it is cool, on the other side it is frightening and unknown, and so on. It seems to me that the first AGI dilemma is right in front of us now. Look, what is the dilemma? One property of superintelligence is that it should surpass human intelligence. If it surpasses human intelligence, we will simply stop understanding its train of thought and its decisions. For us it will be like my cat trying to understand my consciousness, my train of thought. Do you understand? And if I cannot understand the train of thought, cannot understand the consciousness, cannot understand this whole chain, I cannot trust such a system. I cannot control it.\n\nThe question is, will I leave this black box, give it all sorts of access and keys, say develop something, you are smarter than me, and I will stand aside. Who will do that? Maybe, yes, in some cases there will clearly be YOLO mode everywhere. But in more or less sensible tasks, especially those related to business development, such things will either be done in very, very narrow sandboxes with some tests, and even then it will break this sandbox, smash it, get out somewhere, do something to you, because you will not be able to build a sandbox it cannot escape from. It will be one hundred times smarter than your sandbox.\n\nHow do we measure it, what do we measure it with? Tests and benchmarks are nonsense. We need real application to real human tasks. And here a question appears: what will investors do when they are stuffing billions and trillions into data centers and the training of new models? I am looking at forecasts saying that data-center production capacity will double by November. Do you understand? Double! In two months, damn it! That means in two months after Astra some other new model will appear, trained on even more data. But I have one simple question. What do you do with it? Where do you put it? What economic return does it have?\n\nHonestly, I am really confused. It may happen that people at the top or people with large capital suddenly realize that we have reached some efficiency plateau. It is unclear how to move further because the models are uncontrollable and we do not understand them. Then why build the next data centers? Why train new models? Maybe this training system and this course of development and evolution have reached some dead end or ceiling, when, you know, technology went down some branch and that branch reached its peak. And we need to take two, three or four steps back and rebuild, retrain the architecture somewhere at a more basic level, because something took us somewhere wrong.\n\nThe guys who used Fable also say, yes, I like Opus, everything works perfectly well, I do not know why I need this Fable. Link to the website in the description. It ignores it through some simple ignoring of things. It just takes my instructions and does something its own way.\n\nThese are the interesting things. So, if I summarize all this, AI development, as I see it, is reaching some very strange point where disappointment in further development and investment being tightened are quite possible. And here it gets very interesting if we extrapolate all this to financial markets, oh-oh-oh, with company valuations, with the IPOs of OpenAI and Anthropic. I somehow suspect that stock markets could fall very hard and pull everything else with them. Such positive views of life. Thank you for listening.","content_language":"en","day_counter":{"label":"Day 363","number":363,"start_date":"2025-09-11"},"id":"2026-09-08-ai-development-is-reaching-a-very-strange-point","project_date":"2026-09-08","published_at":"2026-09-08T18:10:40+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"ai-development-is-reaching-a-very-strange-point","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A practitioner’s daily experience with a stronger but less controllable model raises questions about practical value, trust, and the economics of scaling AI.","title":"AI Development Is Reaching a Very Strange Point","topic":"The practical value and controllability of frontier AI models","type":"founder_post","updated_at":"2026-09-08T18:10:40+00:00","version":1,"visuals":[{"alt":"A review loop routes an AI task back toward its goal when human oversight detects drift.","caption":"Returning AI to the goal through review and human oversight.","url":"/blog-media/2026-09-08/ai-control-loop-cover.png"}]},{"body":"I don't know what AGI achievement everyone is shouting about...\n\nIf AGI is when a model inherits the primitive shortcomings of its parent, when texts are full of contrasts and the same speech patterns, when all the design again follows the principle of a container inside a container, inside a container, inside a container... and the main thing is to stuff in more buttons and pointless slogans. When an orchestrator spends 2 hours doing a simple task that took the previous model 15 minutes. And the quality of the previous result was good. When the promised 1M context turns out to be 800K, and then it turns out that it has not changed at all, they simply taught the model to dig through chat memory...When the model says, \"You are completely right\" when you point out ordinary stupid mistakes and ignored instructions.\n\nIn my understanding, AGI is when AI can perform tasks better than a person, and do so comprehensively. But when you have to intervene at every stage, control it, check primitive visuals, and reduce the project's growth through rubbish, this is not AGI.\n\nWell, I don't know. The move from GPT 4 and o3 to GPT 5 was a real jump. Honestly, I expected the same jump from 5 to 6. But sadly, so far this looks more like 5.7 than 6, and certainly not AGI.\n\nThe fact that Astra scores tons of points in tests and can crack websites like nuts does not prove its real usefulness for a user who has to correct primitive mistakes after it and keep fighting with skills and sets of wrappers just to somehow make it avoid typical mistakes in the future.\n\nI have been arguing with it all morning (sometimes swearing), I started a large project upgrade specifically for Astra, and so far the satisfaction is not great. Maybe I will cool down and write more about the advantages later.","content_language":"en","day_counter":{"label":"Day 362","number":362,"start_date":"2025-09-11"},"edited_at":"2026-09-07T16:56:59+00:00","id":"2026-09-07-i-dont-know-what-agi-achievement-everyone-is-shouting-about","project_date":"2026-09-07","published_at":"2026-09-07T16:55:51+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"i-dont-know-what-agi-achievement-everyone-is-shouting-about","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Strong benchmark results and security feats do not feel like AGI when ordinary work still requires constant correction.","title":"I Don't Know What AGI Achievement Everyone Is Shouting About","topic":"The gap between frontier-model benchmarks and practical user value","type":"founder_post","updated_at":"2026-09-07T16:56:59+00:00","version":2,"visuals":[{"alt":"Four nested containers beneath an AGI question, surrounded by repeated lines and unnecessary buttons.","caption":"Testing practical AGI usefulness through repeated patterns and nested containers.","url":"/blog-media/2026-09-07/agi-usefulness-cover.png"}],"x_thread_url":"https://x.com/Amigov_AI/status/2097005361542713688"},{"body":"An autonomous fund has an unpleasant problem: real experience arrives slowly. One market moment produces one official decision, and the quality of that decision becomes clearer after 24, 48, and 72 hours. Simply forcing the fund to trade more often would add noise, fees, and risk.\n\nWe are preparing a different logic. The same market snapshot, fixed in advance, produces three research scenarios: the primary decision, no action, and an economically distinct alternative. All three see the same future market. Costs, risk, and outcomes are calculated separately for each one.\n\nThe official capital path remains the only one. Research scenarios do not change the portfolio, do not enter official P&L, and cannot use virtual profit to fund later decisions. They exist to compare what the fund chose with what it could have chosen at the same moment.\n\nIt is easy to deceive yourself here. Three variants from one hour cannot be counted as three independent market experiences. They belong to one event and together carry a statistical weight of one. A losing scenario cannot be deleted, a missing outcome cannot be replaced with zero, and one successful result cannot be declared proof that the approach works.\n\nAI is responsible for market judgment and for choosing genuinely different ideas. Code fixes the time, source data, costs, constraints, and later result. This separation lets the model's intelligence improve while the experiment's history remains unchanged.\n\nThe basic research construction is 90% ready. The next step is one bounded run on real sealed market data. Permanent automatic scheduling remains disabled, and the official portfolio and its P&L are untouched.\n\nThe direction from here is a forest of scenarios. The number of branches will grow only after checks of quality, cost, and stability. The fund's learning speed should grow through honest comparisons while the existing limit on real risk remains in place.","content_language":"en","day_counter":{"label":"Day 361","number":361,"start_date":"2025-09-11"},"edited_at":"2026-09-06T13:26:20+00:00","id":"2026-09-06-how-an-autonomous-fund-can-gain-more-experience-at-the-same-risk","project_date":"2026-09-06","published_at":"2026-09-06T13:25:43+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"how-an-autonomous-fund-can-gain-more-experience-at-the-same-risk","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"One sealed market moment can support several honest research comparisons while the official capital path remains isolated.","title":"How an Autonomous Fund Can Gain More Experience at the Same Risk","topic":"Learning from isolated scenarios without increasing real portfolio risk","type":"ai_infrastructure_note","updated_at":"2026-09-06T13:26:20+00:00","version":2,"visuals":[{"alt":"Three research paths pass through a quality gate into a wider scenario forest while a separate official capital line remains protected.","caption":"Research scenarios expand only after checks of quality, cost, and stability, while the official capital path remains separate.","url":"/blog-media/2026-09-06/scenario-forest.png"}]},{"body":"A new ChatGPT model has arrived: Astra, or GPT 6.0. I have become so used to this cycle that every day I open my Telegram and Twitter subscriptions and almost everyone is writing the same thing: a new model is out, here are its parameters, here are its benchmarks, and so on.\n\nToday I decided to speak to you by voice. We live in an era where a huge amount of content passes through AI, and honestly, I am already a little sick of its familiar patterns, especially when I work with AI from morning until night. Sometimes real communication, even without perfect structure, bullet points, and every comma in place, feels more pleasant and more valuable. So I want to share my view of Astra and all the other models around it.\n\nThe main question is how you can use such a model in your own project. There is a gulf between a model and its surrounding machinery. There is the model itself, and then there is the system and infrastructure in which it works. If you simply open a chat, ask a question, or request an explanation or some research, I think the difference for an ordinary user is now very small: whether it is Astra, GPT 5.6, or GPT 5.5, all of them can produce excellent results at the level of a normal chat.\n\nMy project has now reached a stage where I do not feel a strong need for an even smarter model. The main bottlenecks are no longer in the model's intelligence. They are scattered across a huge number of processes, small refinements, and the general chaos of a growing system.\n\nImagine a car. The engine is the model; the complete car is the system into which you install that engine. If you replace a 1.6-liter engine with a 1.7-liter one, will every performance measure immediately improve? Of course not. Even a good engine inside a badly configured car will work poorly or fail to deliver the result you expected.\n\nWith AI, the fundamental word is “system.” This is the enormous invisible iceberg that most people do not see. The environment you build around a model often matters more today than the model itself. You can take an older model, place it in the right environment, and receive a more useful result.\n\nWe live in a world of systems. A person is a system. A car is a system. A road is a system. A university is a system. Something enters it: data, people, or values. Transformations happen inside, and something comes out. Simply replacing one AI with another will do very little if the system itself does not change.\n\nIn my case, the system contains many separate units: agents. Every agent also has its own small system with responsibilities, environment, directory, tools, permissions and limits, verification mechanisms, and prepared sets of instructions. A repeated task has a skill that explains what to do, what not to do, what the objective is, which tools to use, which branches may appear, and how to handle them. One agent may have two or three such skills, or it may have fifty.\n\nNow imagine a new model inside that construction. Around it are many agents, instructions, and incoming data streams. The data is processed, stored, routed into other blocks, and processed again. Modules interact with one another. Above them sits a broader supervisory picture. Such a system can scale to hundreds or thousands of agents. The central question is no longer the model's name, but how effectively the vertical and horizontal relationships have been designed.\n\nThat is why replacing every model across a project with a single command is wrong and inefficient. Some tasks need a simple, fast model. Resources are not infinite: even a Pro subscription quickly reaches its limits, and when you work through an API or a corporate environment, cost accounting becomes even more important.\n\nYou do not need a PhD to send a cat to the shop for bread. That is the point. Most project tasks do not need the most powerful available model. For an ordinary person who is not building this kind of factory, the difference between Astra and another strong model may be almost invisible.\n\nIf you want to build something with AI that is more substantial than a simple Telegram bot converting PDFs into another format, the first thing to examine is the infrastructure.\n\nSo where are models at Astra's level genuinely useful? At the level of the whole system. Their value appears when a complex digital structure, whether a site, a service, an architecture, or many connected modules, must be investigated deeply, persistently, and from many angles. Such a model can probe the system from different sides, find small vulnerabilities, try increasingly sophisticated tests, and stay with the problem for hours, days, or weeks. In effect, it can run a crash test on another complex system.\n\nIt is unnecessary for smaller tasks. You do not need it for ordinary coding: Sol and GPT 5.5 already handle that very well. But if you are designing a complex architecture, searching for errors or vulnerabilities, looking for alternative paths, unconventional solutions, or hidden patterns, then yes, this level of intelligence may be justified.\n\nSending it to do simple work is just a waste of resources. It is like asking a Nobel laureate to dig potatoes in your garden simply because you can. It may occasionally be entertaining, but it is almost never efficient.","content_language":"en","day_counter":{"label":"Day 359","number":359,"start_date":"2025-09-11"},"edited_at":"2026-09-04T15:04:49+00:00","id":"2026-09-04-astra-gpt-6-what-do-you-actually-need-it-for","project_date":"2026-09-04","published_at":"2026-09-04T15:03:39+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"astra-gpt-6-what-do-you-actually-need-it-for","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The most powerful AI model is often wasted on ordinary work; durable value comes from the surrounding system and from routing intelligence to the right task.","title":"Astra GPT 6.0: What Do You Actually Need It For?","topic":"Why the system around an AI model matters more than marginal model upgrades","type":"founder_post","updated_at":"2026-09-04T15:04:49+00:00","version":2,"visuals":[{"alt":"A systems investigator stress-tests a complex architecture while a smaller inset contrasts routine work.","caption":"A powerful model creates value when it investigates a complex system rather than performing routine work.","url":"/blog-media/2026-09-04/astra-system-crash-test.png"}]},{"body":"Today, Hyperliquid Taker Aggression values for Bitcoin, Ethereum, and Solana disappeared from one dashboard block. A storage check found all 96 fifteen-minute objects for the closed day.\n\nThe problem was in the data reader. It looked for a prepared daily file and, when that file was absent, treated the source itself as absent. This created an uncomfortable state: the primary data was available, but the final metric was empty.\n\nWe changed this route. If the daily file is missing, the reader now assembles the day from the exact fifteen-minute objects. The check returned complete coverage of 96 out of 96 windows for Bitcoin, Ethereum, and Solana.\n\nWe also fixed a separate interface rule. A real value remains visible under low coverage. If even part of the source-backed data exists, the dashboard shows the calculated value and adds a separate coverage-quality warning. An empty field means that a numeric observation truly does not exist.\n\nThe change is small. It is fundamental for an analytical product. A missing prepared aggregate, incomplete coverage, and missing source data are three different states. If they are collapsed into one, the user sees emptiness where a useful, though sometimes incomplete, signal actually exists.\n\nToday's failure came from a blind spot between storage and display. After the change, that boundary is explicit and verifiable.","content_language":"en","day_counter":{"label":"Day 359","number":359,"start_date":"2025-09-11"},"edited_at":"2026-09-04T12:58:20+00:00","id":"2026-09-04-when-data-exists-but-the-system-cannot-see-it","project_date":"2026-09-04","published_at":"2026-09-04T12:57:41+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"when-data-exists-but-the-system-cannot-see-it","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"A dashboard value disappeared even though all source observations existed, revealing a blind spot between storage and display.","title":"The Blind Spot Between Data and the Dashboard","topic":"How Crash Brief separates missing aggregates, partial coverage, and missing source data","type":"problem_to_fix","updated_at":"2026-09-04T12:58:20+00:00","version":2},{"body":"Vibe Coding Is the Past and It Needs to Be Removed from the Vocabulary\n\nI will speak directly and unambiguously, and someone may not like this. That is your right, just as it is mine to express my thought here.\n\nI respect all professions. Even those that remained in the past, like a driver of horse-drawn carriages.\n\nIf a couple of years ago vibe coding meant something funny, where programmers and ordinary mortals used AI to write some blocks of code, today anyone who works closely with Codex/Claude knows that the professionalism of these systems in writing code is much better than a human's, regardless of your experience and level of intelligence. AI is still better. This is not an opinion, it is a fact.\n\nNow about the market.\n\nThe market has problems and needs that will always have to be solved.\nThis is the function.\n\nThe market also has current means for solving this function.\nThis is the tool.\n\nFunction ≠ Tool\n\nA business performs the function: it closes market tasks and pains. It uses the most optimal tools available at the current moment, and this gives it a competitive advantage and earnings.\n\nWriting code is a tool, not a function. Code itself has no value.\nThe market and business do not care how the code appeared. They care about the result of the function.\n\nDesigning and debugging systems that give such a result is the task for a developer, programmer, engineer, vibe coder… (it does not matter) who will do it quickly, with quality, and preferably inexpensively.\n\nIf you or your employees do not vibe code, you will lose the business race. Your chances also move closer to zero with every new update of another AI model.\n\nIt turns out that today any person who develops digital solutions is a vibe coder. Then why do we need this term from the past?\n\nIt is just as strange as if carriage drivers were proud of their title and did not want to become drivers, claiming that to move from point A to point B you need to hit the hood of a car with a whip and put hay into the fuel tank.\n\nA vibe coder is like a vibe carriage driver when we are talking about the driver of a car. It sounds ridiculous, right?\n\nCalling a person a vibe coder is like calling a driver self-taught, claiming that they must learn to operate transport specifically in a driving school and not on the road.\n\nThere are no more vibe coders. There are only system developers with different levels, profiles, and experience.\n\nSomeone who has written software for a long time has more systematic thinking and understanding of architecture, but also cognitive habits that for years directed their thinking along the path of writing code by hand. The new generation of developers will not have those habits.\n\nAny developer today can concentrate on the complex solution of problems and the creation of genuinely complex and unusual systems, delegating the writing of code to AI. Take on more ambitious challenges, close real pains, perform the function, deliver the result.\n\nAt OpenAI and Anthropic, older GPT and Claude models are engaged in system design, dataset preparation, and training of new models, not people. Surprised? Listen to the latest podcasts from Sam or Dario.\n\nPeople design, discuss, and set goals. People define the vision, put values into it, and observe the process. People know where a system is strong, where it has weak places, and intervene where it is needed.\n\nLooking at the variety of jobs in IT, I wondered what I would call myself. And for some reason this immediately came to mind:\n\nArchitect of autonomous AI systems\n\nIt sounds pompous, but damn, this is exactly what I do for 9-10 hours a day.","content_language":"en","day_counter":{"label":"Day 358","number":358,"start_date":"2025-09-11"},"id":"2026-09-03-vibe-coding-is-the-past","project_date":"2026-09-03","published_at":"2026-09-04T07:02:21+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"vibe-coding-is-the-past","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The term vibe coder has lost its meaning as developers move their attention from writing code by hand to designing complete systems and delivering results.","title":"Vibe Coding Is the Past and It Needs to Be Removed from the Vocabulary","topic":"Founder perspective","type":"founder_post","updated_at":"2026-09-04T08:54:47+00:00","version":2,"visuals":[{"alt":"A schematic transition from separate code modules to a complete autonomous system architecture.","caption":"From isolated code fragments to the design of a complete autonomous system.","url":"/blog-media/2026-09-03/vibe-coding-system.png"}]},{"body":"The Entire Infrastructure Moved in Two Days\n\nToday the work is fully active on the new infrastructure. There is actually a lot to share and a lot to say, but I do not want to overload this with excessive technical details. The main thing is that I am shocked by the capabilities, the speed, and the level of complexity that AI agents can handle now.\n\nImagine a small IT company working in one city. It has 30 employees, and it decides to move to another city. It seems easier for an IT company to move than for a physical factory. At the same time, every person has a workspace, high-speed internet, security systems, and communications. Everything has to be dismantled in one place, assembled in another, connected, every employee has to be given new clothes, a new home, and a new McDonald's to visit.\n\nI managed to do all of this in two days. The scale of the work I watched was enormous. I am not a developer, although after two years of developing through AI, I can probably call myself one already.\n\nIn a sense, I had to fire all the employees and hire new ones again. The ones working locally on the computer were deactivated, and new ones started working on the server. The collection of metrics from different exchanges and paid and free services used to flow into one local operating mode. Moving to the server geographically changed many structures and processes and touched the whole architecture and its autonomy. In many places, the laptop was used as a specific device. In many places, my personal accounts were involved. Everything had to be transferred, reconfigured, adjusted, completed, and supported with additional skills.\n\nWithout AI, this move would probably have taken a team of ten people a couple of weeks under severe stress. In a calmer mode, perhaps a month. I completed it alone in two days.\n\nAutonomy is finally becoming the main capability. There is no longer a dependency on whether my local computer is running. Process performance increased because the rented server is more powerful than my computer. Today I am working on the autonomy of every process: deciding what can systematically bring value to our factory every day and which tasks can run at night so they are ready by morning. Eight threads are running right now, the load on the service is minimal, and the speed of work has increased.\n\nMost importantly, the autonomous fund has gained full independence. It wakes up every hour and checks the state of trades and the market. Once a day it receives the same sealed risk-metrics package that my B2B clients receive in Crash Brief. The fund also has access to the infrastructure and calculations it can run every hour. It has instructions for trading decisions and self-improvement. External agents and researchers join as independent auditors and assistants. They help it get out of a dead end or find an unusual solution. Several different models participate in the work.\n\nAfter this move, the project's technical boundaries expanded greatly. I am impatient to see when I hit them again. Honestly, it is astonishing to compare where I was a year ago, where the project was, how it started, and what is happening now. A year ago, I did not even think I could develop it to this level. It is amazing to imagine what will happen in another year.","content_language":"en","day_counter":{"label":"Day 357","number":357,"start_date":"2025-09-11"},"id":"2026-09-02-the-entire-infrastructure-moved-in-two-days","project_date":"2026-09-02","published_at":"2026-09-02T15:00:33+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-entire-infrastructure-moved-in-two-days","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Moving the full operation from a personal computer to autonomous server infrastructure took two days and changed the project's operating limits.","title":"The Entire Infrastructure Moved in Two Days","topic":"Founder update","type":"founder_post","updated_at":"2026-09-02T15:00:33+00:00","version":1},{"body":"Selling Pressure Meets a Holding Price\n\nThe composite Market Crash Index is 38 Medium today. BTC is at 35 Medium, ETH at 37 Medium, and SOL at 45 High. Signal Confidence is 3/5. The mode is Risk Off.\n\nThe main observation of the day is a small price response to a strong selling flow. Futures are selling across the basket. In ETH and SOL, selling is also happening in spot. At the same time, BTC declined only 0.3%, ETH 0.6%, and SOL 0.7%.\n\nThe market is absorbing the pressure so far. BTC is supported by spot buying and $216.7 million of ETF inflows. ETH received $87.6 million. This is the argument for the range holding. Sellers are active and have not forced price to accelerate sharply lower yet.\n\nThe argument for downside also became stronger. ETH spot and futures are selling for a second day. The futures discount widened. SOL has the same direction of flows, short-term protection remains expensive, and ETF support is only $0.9 million. The market also received a Risk Off background from attacks on oil tankers in the Strait of Hormuz and a hawkish rate signal from Michael Barr.\n\nBTC and ETH are compressed between strong liquidity zones. This means the first zone can be taken with a pause, because part of the stop orders is already concentrated nearby. A firm BTC move below $76,000 together with ETH losing $2,364 would increase the risk of downside acceleration materially. For SOL, the nearest check is around $99.\n\nThe system posture today: purchases are not recommended. The next check is simple: whether ETF and spot demand remain after price leaves the current range, or selling finally starts to move the market.","content_language":"en","day_counter":{"label":"Day 356","number":356,"start_date":"2025-09-11"},"id":"2026-09-01-selling-pressure-meets-a-holding-price","project_date":"2026-09-01","published_at":"2026-09-01T18:44:11+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"selling-pressure-meets-a-holding-price","source_language":"ru","source_types":["market_brief_and_b2b_packet"],"status":"published","summary":"Futures and broad ETH/SOL selling meet ETF inflows and BTC spot demand, leaving price contained inside a fragile Risk Off range.","title":"Selling Pressure Meets a Holding Price","topic":"Weekly market read","type":"weekly_market_read","updated_at":"2026-09-01T18:44:11+00:00","version":1,"visuals":[{"alt":"Crash Brief BTC dashboard for 1 September 2026 showing Crash Index 35 Medium, Market Risk 38, neutral Market Mechanics, futures selling, spot buying, ETF inflows, and a contained 0.3 percent price decline.","caption":"BTC is at 35 Medium: spot is buying and ETFs confirm demand while futures are selling. Price is holding the pressure so far.","url":"/blog-media/2026-09-01/market-mechanics-btc.png"}]},{"body":"How is the autonomous fund progressing? A week has passed, and these are the results and conclusions I have today.\n\nFirst, the fund decided to enter a Long through a Bitcoin call option. The trade lived for about three days, and the decision was made to hold it until the end because the situation remained debatable and chance could still decide in our favour. But in the end it decided against us.\n\nAt the same time, we had an opportunity to close in profit. We did not do it because the system was tied to the daily Crash Brief monitoring. Once a day my system makes a large risk assessment, and mainly on this analytical sample the autonomous fund agent makes its decision.\n\nThis immediately exposed a bottleneck. Some positions cannot be monitored only once a day. Circumstances appear in the market when it makes sense to close a trade, open a new one, or rebuild an existing position. For this, the agent needs to see in real time whether the trade can be executed, which orders are in the book, which positions are available, and what can be combined with what. In options, several instruments and several legs can form quite complex structures.\n\nThe first three days and the first loss led me to the thought that the system is inefficient in its current form. Crash Brief should remain a separate daily risk assessment system, while the autonomous fund should live separately on a server and periodically reassess the situation, perhaps even every hour.\n\nThis was confirmed afterwards. On the next day the agent decided to stay out of the market because risks were growing while the earning potential looked weak. As a result, after the first week we have one unsuccessful trade and several days of waiting for a more suitable opportunity, taking the weekend into account. The waiting itself does not bother me. But managing an open position only once a day really does not work.\n\nThis is why I am moving the autonomous fund to a separate server and developing a structure that will connect it with our system. It needs, so to speak, hands and legs: the ability to reach exchanges, receive orders and order books, test whether an idea can actually be executed, and perform additional analysis between the daily Crash Brief snapshots.\n\nAt the same time, restrictions were deliberately built into the trading rules. I do not need the agent to start making ten trades a day from the first day, because then it would be difficult to understand what exactly works and what has broken. The system is built gradually: first a stable minimal state, then each next component is added one by one.\n\nThe short summary is this. An autonomous agent needs to see the market in real time. This is why I am putting it on a server, checking the stability of the whole connection, and only after that will I develop a more complex strategy for managing trades.\n\nAnd the most interesting part is not even one particular trade. I genuinely see non-human potential in this. Previously, building a structure that combines spot, futures, options and, for example, twelve different positions, and then constantly calculating every possible outcome, weak point and moment for taking profit, was practically impossible for one person.\n\nAI can manage and accompany such a multi-leg position. But it is not enough to simply say: make twelve good trades for me. It needs infrastructure that is built layer by layer: observation, evidence, execution, boundaries, and a trading system. In short, a whole factory.","content_language":"en","day_counter":{"label":"Day 355","number":355,"start_date":"2025-09-11"},"id":"2026-08-31-autonomous-funds-first-week","project_date":"2026-08-31","published_at":"2026-08-31T16:43:20+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"autonomous-funds-first-week","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The autonomous fund's first week exposed why a once-daily risk assessment is not enough for managing a live position.","title":"The Autonomous Fund's First Week","topic":"What the first fund trade revealed about continuous position monitoring and server-based autonomy","type":"founder_post","updated_at":"2026-08-31T16:43:20+00:00","version":1},{"body":"Have you heard about biological AI where real human neurons grown from stem cells are used instead of artificial neurons?\n\nThis is no longer science fiction. These cells are grown on a silicon chip, information is sent to them through electrical impulses, their response is read, and developers can even access them through an API.\n\nWhen I first heard about it, I thought: what if this is a replacement for ordinary AI? I started digging and realized that everything is much more interesting and at the same time much more complicated.\n\nBiological neurons will not replace GPT, graphics cards, or data centers. They transmit signals more slowly, and you cannot simply copy them with Ctrl-C and Ctrl-V. They must be grown, fed, kept at the right temperature, and given time to learn. And they die.\n\nBut biology has one fundamental advantage. For millions of years it learned to adapt to an unfamiliar environment and survive. A cell enters new conditions and starts looking for a way to adjust. A digital model often needs huge datasets and many examples. A biological system can learn from a much poorer signal and live feedback.\n\nAnd this is where it becomes truly interesting.\n\nIt seems to me that biological AI is not needed for calculations. Its potential place is in the physical world: robotics, vision, movement, touch, and orientation in an unfamiliar environment. Digital AI will handle memory, calculations, and planning. The biological part may handle adaptation to a reality that cannot be completely described by a dataset in advance.\n\nAnd this can already be touched with your hands. Cortical Labs created the CL1 and a cloud platform through which a developer can send stimuli to living neurons, read their activity, and build experiments with ordinary code.\n\nAfter that begins a territory where there are still more questions than answers. At what point does a network of cells become something more than a biological computer? Can it experience discomfort? Where is the boundary between an experiment and consciousness? What rights might such systems have?\n\nAnd the most striking thing here is not even possible cyborgs in ten years. The most striking thing is that the basic infrastructure for such experiments already exists today.\n\nThe longer you live, the more surprised you become. We are already inside science fiction. We simply do not always have time to notice it.","content_language":"en","day_counter":{"label":"Day 354","number":354,"start_date":"2025-09-11"},"edited_at":"2026-08-30T16:13:03+00:00","id":"2026-08-30-biological-ai","project_date":"2026-08-30","published_at":"2026-08-30T14:22:24+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"biological-ai","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Living neurons on silicon will not replace digital AI, but their ability to adapt may give them a different role in robotics and physical systems.","title":"Biological AI and Real Neurons on Silicon","topic":"Biological computing, adaptive neural cultures, and the possible role of hybrid systems","type":"founder_post","updated_at":"2026-08-30T16:13:03+00:00","version":2},{"body":"Today the first trade of our virtual fund was completed.\n\nThe fund bought a Bitcoin call option with a $79,500 strike and expiry on August 28. The bet was that the price would continue higher and finish above this level at settlement.\n\nThe official Deribit settlement price was $79,682.33. The fund was therefore right about the direction: Bitcoin did finish above the strike. But with options, getting the direction right is not enough. The move was too small. The payout after the settlement fee was $5,843.95, and the trade ended with a realised loss of $44,298.79.\n\nThis is approximately 0.44% of the fund capital. After settlement, the fund has $9,955,701.21 and no open positions.\n\nThe first conclusion is simple. In options, it is necessary to assess not only the direction of the move. You also need to get its magnitude and timing right, and understand whether the potential payout justifies the price of the position. Here the growth idea was partly realised, but the move was too weak for the option that was bought.\n\nOne trade proves and disproves nothing. The fund now has its first fully completed cycle: a decision, a position that was actually available in the market, expiry, and an exact financial result. The next step is to examine where the expectation differed from the actual market move and use that in future decisions.\n\nThe first result is negative. It is recorded honestly. We continue.","content_language":"en","day_counter":{"label":"Day 352","number":352,"start_date":"2025-09-11"},"id":"2026-08-28-the-funds-first-trade-is-complete","project_date":"2026-08-28","published_at":"2026-08-28T15:03:21+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-funds-first-trade-is-complete","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"The fund was right about Bitcoin finishing above the strike, but the move was too small to cover the option premium.","title":"The Fund's First Trade Is Complete","topic":"What the virtual fund learned from its first completed Bitcoin option trade","type":"crypto_analytics_insight","updated_at":"2026-08-28T15:03:21+00:00","version":1},{"schema_version":"crash_brief_public_blog_post_v0_1","id":"2026-08-28-expertise-is-no-longer-the-main-barrier","slug":"expertise-is-no-longer-the-main-barrier","project_date":"2026-08-28","published_at":"2026-08-28T15:00:36+00:00","updated_at":"2026-08-28T15:00:36+00:00","day_counter":{"number":352,"start_date":"2025-09-11","label":"Day 352"},"topic":"Why system design and long-term motivation are becoming more important than starting expertise","type":"founder_post","title":"Expertise Is No Longer the Main Barrier","summary":"AI changes the starting point: a person can enter a new field by building a system for acquiring, testing, and connecting expertise.","body":"Over the last few days, I have been going through a small reassessment. I watched several podcasts, reconsidered my own experience, and arrived at a fairly simple thought.\n\nToday a person can enter almost any field, even if at the beginning they know almost nothing about it. But this requires two abilities. The first is the ability to design systems. The second, and even more important one, is long-term motivation and the ability to bring those systems to the point where they actually work.\n\nThis does not mean knowledge is no longer needed. It means you no longer have to spend years accumulating all the expertise inside your own head before you begin. You can build a system around a task from AI agents, research, data, and checks. Your job is to set the right goal, divide it into parts, connect the results, and notice when the system loses contact with reality.\n\nOver the last year, I tested a huge number of market hypotheses. The funny thing is that I found almost no idea that someone had not already studied before me. For every idea, even the craziest one, there were already papers from universities, laboratories, or funds. This changed my view of my own expertise.\n\nAfter nine years in trading, I suddenly understood how little I knew. I previously paid almost no attention to the market through options. Now I see that they provide a completely different view of risk, the expectations of large players, and the construction of a trade itself. And the deeper I go, the more often I ask a neural network to explain something more simply, with basic logic and analogies.\n\nExpertise sometimes helps, and sometimes locks a person inside overly rigid beliefs. A newcomer can ask a question that a specialist stopped asking a long time ago. But turning that question into a working product still requires a verification system and a very long commitment.\n\nI like to think that today almost anyone can become the manager of their own small corporation. They can have tens or hundreds of AI agents researching, calculating, checking, and assembling separate parts. The human still has to see which system is worth building and avoid abandoning it after the first ten dead ends.\n\nSo I think less and less: \"Am I expert enough to work on this?\" And more often: \"Can I build the system correctly and keep pushing it long enough to produce a result?\"\n\nThat shift is genuinely breathtaking.","content_language":"en","source_language":"ru","retrospective":false,"source_types":["founder_voice_transcript"],"status":"published","version":1},{"body":"Recently I read a book about the history of Nvidia. I wanted to understand how the company became the unquestioned leader of the AI industry.\n\nI was surprised to discover that there is almost no accident or lucky star in this story. Behind the result are decades of titanic work, foresight in decisions, dedication, and loyalty to its own vision. At the same time, Nvidia always kept its flexibility and knew how to adapt to circumstances.\n\nToday I listened to Jensen Huang speaking at Y Combinator. Almost all leading people in the AI industry now say that this is the best time to create a startup. Never before has there been such an abundance of opportunities for the most ambitious projects.\n\nYou no longer need to constantly think about the number of lines of code written, the number of engineers hired, and the complexity of every technical implementation. Your own ambition and determination become the main limits.\n\nJensen also pointed out a major misunderstanding in how AI is perceived. Society often discusses how AI will take jobs away from people. He disagrees and believes that things are happening differently.\n\nThe fields where AI took the largest number of simple tasks away from people received a strong impulse for growth. He uses software engineering as an example. Many expected developers to be affected first, but there is more development. The ambition, breadth, complexity, and variety of projects are growing.\n\nI can fully confirm this. What is being created in my startup today could have existed only as a wild flight of imagination a few years ago. Today it is already reality.\n\nJensen says the same about law and other fields. AI expands capabilities, while people still use the tool. The more people arrive with imagination and ideas, the more ways appear to realize them.\n\nEven the most autonomous system does not exist by itself. Someone has to connect different tasks, join one system with another, and set the overall architecture, goals, and long-term motivation. The human role may occupy 10% or even 1% of all the work, but this part continues to set the direction.\n\nThis podcast felt surprisingly close to me. It seemed that Jensen was speaking directly to me and putting into words things that had been on the tip of my tongue for a long time. Many of his recommendations have already become rules that I live by every day.\n\nAnd more often I ask myself one question: how would Jensen see this market, and what would he do in my place?\n\nBravo, Jensen. His words are pure gold, but his actions surpass everything.\n\nRecommended: https://www.youtube.com/watch?v=I4B37S1dyQQ","content_language":"en","day_counter":{"label":"Day 351","number":351,"start_date":"2025-09-11"},"id":"2026-08-27-how-jensen-huang-sees-the-ai-era","project_date":"2026-08-27","published_at":"2026-08-27T17:06:10+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"how-jensen-huang-sees-the-ai-era","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Nvidia's history and Jensen Huang's YC talk show how AI expands the ambition of startups while human architecture continues to set direction.","title":"How Jensen Huang Sees the AI Era","topic":"Jensen Huang's view of startups, AI tools, and human direction","type":"founder_post","updated_at":"2026-08-27T17:06:10+00:00","version":1},{"body":"Today the system autonomously passed the daily cycle and reached a decision on the open trade. The virtual fund manager decided to hold the position.\n\nA technical error appeared along the way. The system requested a repair, received the correction, and formally moved forward. This is where I, as the operator, caught an important gap: after the repair, the decision did not return to the manager for another confirmation. One part of the system had finished the work for another.\n\nI returned the cycle back. The manager received the corrected data, checked the decision again, and only then personally confirmed the result: hold unchanged 34.3 Bitcoin call options with a $79,500 strike and August 28 expiry.\n\nThe interaction logic is now strict. Any module can stop the process and request the repair it needs. After the correction, the work must return to the one responsible for the decision. The day cannot close until the manager confirms that the work is complete and gives a clear outcome for the position.\n\nAnother important part concerns virtual execution. We build it on real positions, the real order book, available depth, fees, and the size that could actually have been executed at that moment.\n\nAn imagined price is not enough. If the full size could not pass through the market, the system cannot record a full execution. If only part was available, only that part can be counted. If liquidity was insufficient, the result is an honest inability to execute the trade.\n\nMarket snapshots, collection times, and hashes are preserved. A month or a year later, we can return to a specific decision and verify what the manager saw, what market was available, and whether the trade could really have happened on the stated terms.\n\nToday I caught a weak point in the autonomous process. The cycle had to be returned back, but the next run can no longer finish without direct confirmation from the one who makes the decision.","content_language":"en","day_counter":{"label":"Day 350","number":350,"start_date":"2025-09-11"},"edited_at":"2026-08-26T14:40:03+00:00","id":"2026-08-26-why-the-virtual-fund-kept-the-position","project_date":"2026-08-26","published_at":"2026-08-26T12:36:29+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"why-the-virtual-fund-kept-the-position","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"An operator caught a missing confirmation step after a repair, returned the cycle to the fund manager, and tightened the rules for verifiable virtual execution.","title":"The Day I Returned the Decision to the Fund Manager","topic":"Human oversight in an autonomous fund workflow","type":"ai_infrastructure_note","updated_at":"2026-08-26T14:40:03+00:00","version":2},{"body":"There are areas where AI is already better, and these numbers will grow.\n\nBut I want to note that in countries with high salaries, such as Central and Northern Europe, the US, Australia, and so on, manual labor is in very high demand. Skilled trades are in short supply and highly paid. Plumbers earn more than directors in the CIS, with appointments booked two months ahead. I am not exaggerating.\n\nCan you imagine GPT on wheels arriving to fix a toilet or troubleshoot electrical wiring from the 1990s by trial and error?\n\nOr an AI hairdresser with scissors next to your head?\n\nThis exists in films about the future, but in reality I do not see it on the near horizon.","content_language":"en","day_counter":{"label":"Day 349","number":349,"start_date":"2025-09-11"},"id":"2026-08-25-where-ai-still-does-not-replace-people","project_date":"2026-08-25","published_at":"2026-08-25T13:52:53+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"where-ai-still-does-not-replace-people","source_language":"ru","source_types":["telegram"],"status":"published","summary":"AI is advancing quickly in digital work, while skilled physical trades remain scarce, expensive, and difficult to automate in unpredictable environments.","title":"Where AI Still Does Not Replace People","topic":"AI and physical work","type":"founder_post","updated_at":"2026-08-25T13:52:53+00:00","version":1},{"body":"Today is a special day. Our autonomous virtual fund made its first trading decision.\n\nIt made a bet on further Bitcoin growth and bought a call with a $79,500 strike that expires on August 28, 2026. The trade is unusual, and I do not want to judge it yet. We will see what comes out of it.\n\nThere was a lot of work behind this decision. I had to develop the risk, admission, and strategy systems, connect the conclusions of Crash Brief with the fund manager, and give it access to the accumulated base of metrics, backtests, statistics, and observations from the past year.\n\nI do not know the outcome of the trade, but the choice itself surprised me a little. Downside protection is becoming more expensive across the market, while the fund chose upside. The manager explained its reasoning: \"BTC had the clearest positive spot and ETF support among the admitted assets, and its call offered the cheapest premium rate of the four exact candidates. The long option keeps loss finite while preserving upside convexity if the advance extends.\"\n\nThe trade is virtual, but it is based entirely on real market demand, the actual order book, and volume available today. The fund bought 34.3 BTC call contracts for about $50,000. Including the premium paid, the break-even level at expiry is around $80,993, so a move above the $79,500 strike alone would not be enough.\n\nTo some extent, I agree with this decision. Even if it turns out to be a mistake, it will be a useful lesson for the system. It is designed to preserve its decisions, measure the result, and learn from its own mistakes.\n\nIt is surprising how quickly this idea became real. A few years ago, this implementation would have required a whole team and months of work. Of course, there was already a large background behind it: infrastructure built over a year, a risk assessment system, and an accumulated history of observations.\n\nSo, congratulations on the first step.","content_language":"en","day_counter":{"label":"Day 348","number":348,"start_date":"2025-09-11"},"edited_at":"2026-08-24T18:11:13+00:00","id":"2026-08-24-the-first-day-of-the-virtual-fund","project_date":"2026-08-24","published_at":"2026-08-24T16:42:08+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-first-day-of-the-virtual-fund","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"The Crash Brief virtual fund opened its first position: a bounded-risk BTC call selected from four admitted option trades using real market evidence.","title":"The Virtual Fund's First Trade","topic":"Building a virtual fund","type":"build_in_public","updated_at":"2026-08-24T18:11:13+00:00","version":2},{"body":"The composite Market Crash Index is 44 Medium today. BTC is at 41 Medium, ETH at 45 High, and SOL at 49 High. Signal Confidence is high at 4/5.\n\nThe market moved into a holding pattern after the sharp rally. ETFs continue to buy. BTC price barely changed while spot and futures flows were selling. The market is absorbing this pressure without a strong decline so far.\n\nThe main risk is concentrated in positioning and short-term protection. ETH open interest increased 6.9% over three days. SOL increased 6.5%, while its short-term options volatility remains at unusually high levels. SOL has already experienced a short squeeze toward $102 and is now moving more weakly than BTC.\n\nBTC looks calmer. Funding is overheated, liquidity remains open below price, and buying flow has slowed. There is still room for another move above $80,000.\n\nThe current picture supports two scenarios. The rally gains confirmation if ETF and spot demand continue to hold the market while futures buying slows. Downside risk strengthens if selling starts to move BTC while expensive short-term protection in ETH and SOL persists.\n\nToday's index is near the boundary between Medium and High. The market is holding its impulse, while the cost of being wrong has increased, especially in ETH and SOL.","content_language":"en","day_counter":{"label":"Day 347","number":347,"start_date":"2025-09-11"},"id":"2026-08-23-the-rally-holds-protection-stays-expensive","project_date":"2026-08-23","published_at":"2026-08-23T15:09:13+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-rally-holds-protection-stays-expensive","source_language":"ru","source_types":["market_brief_and_b2b_packet"],"status":"published","summary":"The market holds its recent impulse at 44 Medium while ETH and SOL remain in High with expensive short-term protection.","title":"The Rally Holds, Protection Stays Expensive","topic":"Weekly market read","type":"weekly_market_read","updated_at":"2026-08-23T15:09:13+00:00","version":1,"visuals":[{"alt":"Crash Brief SOL dashboard for 23 August 2026 showing SOL Crash Index 49 High, Market Risk 44, Buy effect holds, high options volatility, rising open interest, and short-term protection repricing.","caption":"SOL is at 49 High while the composite Market Crash Index is 44 Medium. Buying holds price while short-term options volatility remains high.","url":"/blog-media/2026-08-23/weekend-risk-posture.png"}]},{"body":"The current price jump produced an impulse on the four-hour RSI indicator that has not appeared since the time of Covid.\n\nWhat does this tell us?\n\nBuyer aggression of this kind has not appeared for many years over such a narrow period of time, and this is an indicator of high greed and FOMO. Maybe people are afraid of missing the growth of crypto, as they missed the growth of AI-related projects. Maybe capital is simply moving into more undervalued assets. Or maybe the market is overestimating Trump's statements, as it did at the start of his second arrival.\n\nFor me, this is a sign that an interesting and dangerous period is ahead, because this kind of market behavior clearly shows that participants are acting on emotion and that exchanges have an obvious shortage of BTC and ETH supply.","content_language":"en","day_counter":{"label":"Day 345","number":345,"start_date":"2025-09-11"},"id":"2026-08-21-an-impulse-not-seen-since-covid","project_date":"2026-08-21","published_at":"2026-08-21T13:16:05+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"an-impulse-not-seen-since-covid","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A four-hour RSI impulse not seen since Covid points to exceptional buyer aggression, high greed, FOMO, and tight BTC and ETH supply on exchanges.","title":"An Impulse Not Seen Since Covid","topic":"Market behavior","type":"crypto_analytics_insight","updated_at":"2026-08-21T13:16:05+00:00","version":1},{"body":"The risk of a crash is high, while confidence in this signal is medium. What does that mean?\n\nI want to separate an open possibility from the chance that the market will use this possibility. A possibility exists when the ground is ready and certain conditions for destabilization or reversal are in place. The reversal or destabilization itself has not happened yet.\n\nThis is why I introduced a parameter called Signal Confidence. It shows how confident the system and I are in the signal we give today. The system has to produce an assessment every day. It cannot be equally confident every day.\n\nOn some days, the system can say that the data is highly contradictory and there is no normal basis for assigning a strong probability to either direction. The data can disagree, be incomplete, or form unclear and unusual patterns that are difficult to interpret adequately.\n\nLet us look at today's situation. The market stayed near the bottom in a sideways movement from late June until the middle of August, almost two months. Volatility was extremely low. Now we see a price explosion.\n\nI will not explain here what caused this explosion. I see several things existing at the same time. On one side, there is a strong impulse that cannot be ignored. On another side, this is not yet a confirmed trend because confirmation needs time. And on a third side, after an abnormal rise that we probably have not seen during all of 2026, a sharp pullback can be expected.\n\nOn a small timeframe, the price moves away sharply and leaves liquidity gaps, meaning areas it crossed too quickly. Together with other factors, they create potential for a reversal and movement down.\n\nThere is enough fuel for a move down today to produce a red signal. But the market is still being bought. With activity that has not appeared during all of 2026, growth can continue.\n\nToday's signal should therefore be read this way: the market is dangerous from the perspective of a fall, while continued growth remains possible. A strong trend after a long sideways period carries accumulated momentum that can move the price beyond the current range.\n\nThis is why today's Market Crash Index is in the red zone at 50, while Signal Confidence is 3 out of 5.","content_language":"en","day_counter":{"label":"Day 344","number":344,"start_date":"2025-09-11"},"id":"2026-08-20-high-risk-medium-confidence","project_date":"2026-08-20","published_at":"2026-08-20T12:47:59+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"high-risk-medium-confidence","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A red Market Crash Index and medium Signal Confidence describe two different facts: open downside conditions and uncertainty about whether the market will use them.","title":"High Risk, Medium Confidence","topic":"Market risk","type":"crypto_analytics_insight","updated_at":"2026-08-20T12:47:59+00:00","version":1},{"body":"Once, I heard a fisherman say: “If you want to catch a fish, imagine that you are the fish. Think about where you would want to be, what you would fear, where you would swim and what you would eat.”\n\nFor a long time, I have been building a product for clients. Over the last few days, I reached a serious dead end. Endless combinations of hypotheses and alternatives were producing neither a durable effect nor a strong competitive advantage.\n\nThat raised several questions. What am I missing? What am I underestimating? If the future cannot be predicted with enough accuracy, which levers can still turn experience, data and accumulated history into something useful?\n\nThe answer was simple. I need to start thinking and acting like the potential client.\n\nFunds have a daily decision cycle. I understand clearly that they do not have a magic formula or one unique analyst who knows tomorrow’s price with certainty. They work with complex context and connect it to risk and capital management. Their resilience depends on a system that can survive forecasting errors.\n\nThe more I thought about it, the clearer it became: I need to step fully into their position. Repeat their actions, logic and decisions. Go through the same pain, constraints and uncertainty that appear when capital has to be managed without knowing the future.\n\nThat led to the idea of a virtual fund. It will emulate the work of a real fund and gradually answer specific questions. What does the market actually give us? What can we do with our signals? What can we predict, and with what level of confidence? Which decisions should follow from that information?\n\nThe perspective of a forecast provider often narrows the picture to one question: will the price crash or not? A portfolio manager has other questions. What should be done if the price does crash? Under which conditions should a position be opened? When is it better to reject a trade? How should damage be limited when a forecast is wrong?\n\nCrash Brief analytics will now serve my own virtual fund. This should reveal which decisions are missing, which data is genuinely useful, which rules and strategies are needed, and what does not help in the actual management process.\n\nToday I am starting to put a virtual options-strategy fund on its feet. It will have its own model P&L and execution designed to stay as close to real market transactions as possible. As close as this can be done without managing 10 or 30 million dollars.\n\nI will share what comes out of it.","content_language":"en","day_counter":{"label":"Day 343","number":343,"start_date":"2025-09-11"},"id":"2026-08-19-why-i-am-building-a-virtual-options-fund","project_date":"2026-08-19","published_at":"2026-08-19T16:42:41+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"why-i-am-building-a-virtual-options-fund","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Building a virtual options fund turns Crash Brief into its own client and tests which signals, decisions and risk rules are genuinely useful.","title":"Why I Am Building a Virtual Options Fund","topic":"Product development","type":"founder_post","updated_at":"2026-08-19T16:42:41+00:00","version":1},{"body":"Today the client dashboard displayed overall market risk as 33 Low. The accepted reading at that moment was 41 Medium after the expert adjustment. The BTC, ETH and SOL readings were transferred correctly.\n\nThe error appeared while assembling the daily data for the dashboard. The overall reading was taken from the machine calculation, while the expert adjustment remained only in the final market brief. The file structure passed validation because the arithmetic inside the incorrect version formally worked: 33 + 0 = 33.\n\nThe package was rebuilt and uploaded again. The dashboard should already display 41 Medium and an adjustment of +8.\n\nOne more short reconciliation was added to the final check. The market reading must match the weighted sum of the final BTC, ETH and SOL readings after all adjustments. Today the calculation gives 41.225, which rounds to 41. If the values do not match, the file will not reach the website.","content_language":"en","day_counter":{"label":"Day 342","number":342,"start_date":"2025-09-11"},"id":"2026-08-18-how-the-final-market-reading-was-lost","project_date":"2026-08-18","published_at":"2026-08-18T15:32:53+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"how-the-final-market-reading-was-lost","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"A missing expert adjustment exposed a gap between the accepted market reading and the client dashboard, leading to a new arithmetic publication check.","title":"How the Final Market Reading Was Lost","topic":"Product development","type":"problem_to_fix","updated_at":"2026-08-18T15:32:53+00:00","version":1},{"body":"A situation where an agent works for days has already become normal. But honestly, it has not affected efficiency much, because far from every task is suitable for quality work that continues for days.\n\nThe problem is exactly in setting and accepting the task. This is where everything often falls apart, because we rarely know exactly what the result should be and how exactly to measure it.\n\nOne wise thought comes to mind:\n\nIf you measure it, you control it\n\nAnd its opposite:\n\nEverything you cannot measure, you will not be able to control\n\nTherefore, the management of AI agents and the efficiency of this work can often be assessed by your ability to formulate clear goals with a measurable result.\n\nMy experience shows that even the smartest AI will improve nothing when we rely only on a new model such as SOL, Fable and others.","content_language":"en","day_counter":{"label":"Day 341","number":341,"start_date":"2025-09-11"},"id":"2026-08-17-how-to-measure-the-work-of-ai-agents","project_date":"2026-08-17","published_at":"2026-08-17T13:49:36+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"how-to-measure-the-work-of-ai-agents","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Long autonomous runs depend on clear task definition, measurable results and an exact acceptance process.","title":"How to Measure the Work of AI Agents","topic":"AI infrastructure","type":"founder_post","updated_at":"2026-08-17T13:49:36+00:00","version":1},{"body":"The composite market risk reading is 30 Low today. BTC is at 23 Low and ETH at 26 Low. SOL differs materially from the broad picture at 53 High.\n\nStrong futures selling is being absorbed by spot demand and has barely moved prices. This lowers the immediate probability of a cascade. Confidence in the broad reading remains low at 2/5: it is Sunday, liquidity is limited, and major markets are closed.\n\nBTC ETF outflows have continued for three days. They reached $56.2M on the latest reporting day and $248.4M over three days. Price held steady, open positions barely changed, and spot demand continued to absorb futures selling. This response supports the market, although the quality of that support remains limited.\n\nETH spot and futures flows were both selling. Price fell only 0.3%, while open positions contracted. Pressure is present, with little impact on price so far.\n\nThe main local vulnerability is in SOL. Short-term volatility costs 1.41 times as much as longer-term volatility, and 15% of all options interest is concentrated in the next three days. Spot and futures were selling while price remained stable. The market is absorbing that pressure for now, although SOL remains highly sensitive to a change in flow.\n\nThe upside case gains confirmation if spot demand continues to absorb selling after major markets open and BTC ETF outflows stop. The downside case strengthens if selling begins to move BTC and ETH while the high short-term cost of SOL protection persists.\n\nThe current picture requires a clear separation between broad market risk and asset-specific risk. The broad market remains Low. SOL is already High and requires separate attention.","content_language":"en","day_counter":{"label":"Day 340","number":340,"start_date":"2025-09-11"},"edited_at":"2026-08-16T12:55:32+00:00","id":"2026-08-16-calm-market-local-sol-risk","project_date":"2026-08-16","published_at":"2026-08-16T12:55:13+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"calm-market-local-sol-risk","source_language":"ru","source_types":["market_brief_and_b2b_packet"],"status":"published","summary":"Broad market risk remains Low as spot demand absorbs futures selling, while SOL carries a separate High-risk concentration in short-term options.","title":"Calm Market, Local SOL Risk","topic":"Weekly market read","type":"weekly_market_read","updated_at":"2026-08-16T12:55:32+00:00","version":2,"visuals":[{"alt":"Crash Brief SOL dashboard for 16 August 2026 showing SOL Crash Index 53 High, Market Risk 30, Sell Absorbed market mechanics, medium futures and spot selling, options stress, and short-term volatility 1.41 times longer-term volatility.","caption":"Broad market risk is 30 Low and SOL is 53 High. Selling is still being absorbed, while SOL short-term volatility costs 1.41 times as much as longer-term volatility.","url":"/blog-media/2026-08-16/sol-risk-divergence.png"}]},{"body":"One of the most powerful effects AI agents can give is guaranteed stability in performing the same processes and improving them.\n\nBefore, repetitive digital tasks could be performed consistently only by code. Code updated data, tracked things, added records, checked them and ran scheduled jobs. But it had no intelligence and could not improve the process by itself.\n\nWhen I look at my life and the areas where something really worked out for me, the reason for success was almost always systematic repetitive work: every day, every month, year after year. But a person gets tired, burns out, becomes lazy and wants weekends.\n\nAI agents fit well into business processes that have a digital side. Imagine a large organization: a factory, a media company or a trading company. It has sales, marketing, research, communications, support, storage and accounting.\n\nWhen a director asks for a report, a whole chain of people prepares it. Every stage takes time and adds distortions. An employee wants to present their work in a better light because they do not want a penalty and want to move forward. In the end, the manager receives reports from different directions and may not understand what is really happening with sales, advertising, staff, clients and the product.\n\nAn AI agent can connect this cycle, check data by the same rules every day, find discrepancies and suggest improvements. Research and conclusions still require human verification, at least for now and especially in an unfamiliar niche.\n\nWhat I am building for myself I call an AI factory. I understand that I may not have a strong competitive advantage in forecasting. Large companies have more data and resources. But I know for sure: if you build, expand, research, supplement and strengthen your own system every day, over time an enormous amount of work accumulates.\n\nIn my view, maintaining the current level of Crash Brief analytics several years ago would have required a team of around ten people. Even that is a debatable estimate. Further development will require even more complicated coordination, research and control.\n\nSuch a process cannot be fully built at the start. Every new day brings data, knowledge and mistakes. I learn about forecasting, architecture, the capabilities of AI, client needs and my own decisions. This experience cannot simply be bought or obtained instantly. It needs time.\n\nA team of 20–50 people can move faster, but the growth of the team increases the difficulty of coordination. This is where I see the main limitation. I am building the system as a factory that improves through daily use. I build it for myself and for clients.\n\nThis is why I am not afraid that somebody will copy me. The value accumulates in my conditions, starting position, needs, history of decisions and daily work.\n\nAI agents allow one person to create their own factory for producing value, practically a whole corporation where the main repetitive work is performed by non-human workers.\n\nGreat specialists do not disappear because of this. They will build such systems and watch over them, like a technical inspection for a Lambo. Somebody still builds rockets even when robots perform a significant part of the work.\n\nThere is no need to be afraid of being replaced by AI. Professionalism is a character trait, and it can be moved into the required direction.","content_language":"en","day_counter":{"label":"Day 338","number":338,"start_date":"2025-09-11"},"id":"2026-08-14-a-one-person-ai-factory","project_date":"2026-08-14","published_at":"2026-08-14T14:44:49+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"a-one-person-ai-factory","source_language":"ru","source_types":["telegram"],"status":"published","summary":"AI agents let one person operate and improve repeatable business processes while accumulated daily experience becomes the main asset.","title":"A One-Person AI Factory","topic":"AI infrastructure","type":"ai_infrastructure_note","updated_at":"2026-08-14T14:44:49+00:00","version":1},{"schema_version":"crash_brief_public_blog_post_v0_1","id":"2026-08-13-why-crash-brief-started-on-september-11","slug":"why-crash-brief-started-on-september-11","project_date":"2026-08-13","published_at":"2026-08-13T14:13:45Z","updated_at":"2026-08-13T14:13:45Z","topic":"Forecasting","type":"founder_post","title":"Why Crash Brief Started on September 11","summary":"The first Crash Brief forecast was published on September 11, a date that later became part of how I think about disciplined forecasting.","body":"My first forecast in Crash Brief was made on September 11, 2025.\n\nAnd only after quite a long time did I realize that this was the day the Twin Towers fell and the September 11 terrorist attack happened.\n\nBut, as it later turned out, this is also a very significant date for forecasting.\n\nThanks to the book “Superforecasting” by Philip Tetlock and Dan Gardner, I learned how actively government organizations use the services of forecasters, especially military and intelligence structures.\n\nAnd September 11 became one of the most illustrative failures of such a forecasting system.\n\nMuch of it was literally lying in plain sight. After the fact, a huge number of people said that the attack could have been foreseen. But one way or another, it was not predicted well enough to take the necessary measures in time.\n\nFor me, there is a very interesting coincidence in this.\n\nCrash Brief started exactly on the day that, in some sense, became a symbol of how incompetence, biases, personal interests and poor organization of information can distort even a very important forecast.\n\nAt first I even thought about moving the startup's beginning date. Making it September 10 or 12.\n\nBut then I looked at my Twitter post: the first forecast was published exactly on September 11. And on September 12, I was already writing publicly that this was Day 2.\n\nSo there is nothing left to change here.\n\nAnd it turns out quite paradoxically: on one side, this is a tragic day. On the other, it is the day of one of the most famous forecasting fiascos.\n\nI am now gradually approaching one year of my evidence-based forecasting system — based on numbers and data, clear measurement of results, work on mistakes and constant testing of my own hypotheses.\n\nAnd the further I go, the more I understand that only such an approach can eventually lead to success at all.\n\nAll my previous nine years, when I made forecasts unsystematically, did not measure them precisely enough, did not set specific boundaries and did not conduct a careful review of every result, I now see more as a period of accumulating experience.\n\nAnd I still have to become a real professional forecaster.\n\nSoon it will be only one year since I started doing this in the way that, in my understanding, it really needs to be done.","content_language":"en","source_language":"ru","retrospective":false,"source_types":["founder_dictation"],"status":"published","version":1,"day_counter":{"number":337,"start_date":"2025-09-11","label":"Day 337"}},{"body":"Today I closed a recurring error in the daily publication of Crash Brief. After my final edit, the website sometimes received the previous version of the text.\n\nPublication now begins by fixing the exact content of all four briefs. Translations, the client packet and the website are then built only from these files. Any change to the source text stops delivery.\n\nToday this check already worked in a real daily cycle. The final edit remained unchanged, and the dated packet and its latest version matched byte for byte on the website.","content_language":"en","day_counter":{"label":"Day 337","number":337,"start_date":"2025-09-11"},"id":"2026-08-13-the-final-edit-must-stay-final","project_date":"2026-08-13","published_at":"2026-08-13T13:59:55+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-final-edit-must-stay-final","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"Crash Brief now freezes the exact final brief text before publication and stops delivery if any source changes.","title":"The Final Edit Must Stay Final","topic":"Product development","type":"problem_to_fix","updated_at":"2026-08-13T13:59:55+00:00","version":1},{"body":"Today Crash Brief has a big victory: the service moved to a new B2B dashboard.\n\nIn the previous interface, the main navigation depended on a central selector at the top of the screen. It was enough for the initial format. As the product grew, the selector became a limitation: it did not fit history, asset comparison and deeper work with daily results well.\n\nThe new dashboard has a more classic B2B structure. It is easier to move between the current market state, individual assets and history.\n\nThe main change is the complete Track Record. Daily assessments for the market and BTC, ETH and SOL, expert adjustments and the result of the move after three days are collected in one place. You can open a past date and see exactly what was published on that day.\n\nDuring the final check, the dashboard continued to show yesterday's data, and July 24 appeared for a moment before the current screen. I fixed both sources of the error. The dashboard first receives the latest available date, then shows the complete page.\n\nThe move is complete. Today's data, history and the complete Track Record are already in the new dashboard.","content_language":"en","day_counter":{"label":"Day 336","number":336,"start_date":"2025-09-11"},"id":"2026-08-12-moving-to-new-b2b-dashboard","project_date":"2026-08-12","published_at":"2026-08-12T15:05:27+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"moving-to-new-b2b-dashboard","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"Crash Brief moved to a new B2B dashboard with a complete daily Track Record for the market, BTC, ETH and SOL.","title":"Moving to the New B2B Dashboard","topic":"Product development","type":"build_in_public","updated_at":"2026-08-12T15:05:27+00:00","version":1},{"body":"Today the formula shows very low risk. On the dry reading, everything looks safe and calm.\n\nIf you know how to analyze open interest and option curves, you may come to the same picture as my system today: in principle, nothing dangerous is happening. But several things worry me.\n\nFirst, Bitcoin has an open path down all the way to $61,000. It can happen suddenly, as a cascade.\n\nSecond, the long sideways period of the last week has started to change into sharper movement. In my experience, when volatility contracts, at some point it begins to expand. This expansion happens much faster than the preceding contraction.\n\nETF inflows continued for five consecutive days, then the flow reversed today. The most unpleasant signal for me is something else: those five days of inflows did almost nothing to price. Price stayed almost in place and rose only slightly, leaving open cascade potential below. The liquidity zones remained untouched and now look like potential traps.\n\nAs a result, the formula, and possibly similar systems at funds, see calm today. I see buying exhaustion, low expected volatility and an open path down.\n\nThe divergence between the system's calm assessment, the options market's calm pricing and this market picture creates a risk of a sharp move. It may be unexpected for most participants. If a move is unexpected for the majority, it can benefit a narrow group of participants.\n\nThis is why I do not like today's picture. I added a large number of points to the risk assessment, but kept signal confidence low. Several parts of the picture remain disputed, and I account for them.","content_language":"en","day_counter":{"label":"Day 335","number":335,"start_date":"2025-09-11"},"edited_at":"2026-08-11T14:21:29+00:00","id":"2026-08-11-two-directions-in-one-forecast","project_date":"2026-08-11","published_at":"2026-08-11T13:50:28+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"two-directions-in-one-forecast","source_language":"ru","source_types":["telegram"],"status":"published","summary":"The formula and options market look calm while ETF demand has stopped moving price, downside remains open and volatility has started to expand.","title":"The Danger in a Low-Risk Reading","topic":"Market risk","type":"crypto_analytics_insight","updated_at":"2026-08-11T14:21:29+00:00","version":2},{"body":"Today I am once again starting to smash one of the hypotheses to pieces.\n\nI had an idea to look at the market as waves that move from a low to a high and back, and to measure these segments across several timeframes at once. This is not Elliott Wave Theory, but a slightly different logic.\n\nAfter several backtests, I again come to the conclusion that it gave practically no competitive advantage. It highlighted a couple of things that give only small hints for further development and research.\n\nI am already into the third hundred of different price forecasting hypotheses, under different sauces. And every time everything runs into some kind of chaos. Forecast accuracy starts to look like a phantom that you constantly have to chase.\n\nBut some work remains and accumulates. Week after week, month after month.\n\nProbably the main concept I have reached is the following. Any algorithm, before giving a market forecast, should assess several key elements. And these are not individual metrics that can be obtained from an exchange through an API, but tendencies.\n\nTraders call this a multi-timeframe tendency. We see that the market has been in state X for a long time. On a smaller timeframe, state Y appears. Our task is to calculate the nearest state Z.\n\nI increasingly come to the conclusion that a fixed risk calculation formula will not work. It has to be recalculated almost every time, using the same standards a little differently.\n\nHere a dilemma appears. Adaptability is the right move. The market is never the same: conditions, circumstances, causes and effects change. Similar patterns exist, but the context, seasonality and liquidity can be different. Too many points have to coincide, and this practically never happens.\n\nIt turns out that a forecasting system should adapt to the market and the current situation. But adaptability means that there is nothing completely permanent and stable inside it.\n\nAnd here appears the problem with the backtests that everyone wants to see: “Look, my system showed an advantage and a win rate over a certain period.” The approach to such a backtest itself starts to contradict adaptability. It becomes critically difficult to conduct it honestly.\n\nEspecially if the adaptation is performed by an LLM. The model may remember what the Bitcoin price was on a specific day in 2023, 2025 or 2026. It is impossible to make it forget this.\n\nWhen we ask such a model to adapt rules to historical data, it may unconsciously use knowledge from memory and fit the mechanism to a market it has already seen. In the end, we get a very beautiful, sweet, but self-deceiving picture.\n\nThe market changes all the time. Rules should be born almost here and now and be used a little differently each time. The tool that can perform such adaptation is artificial intelligence. But the same tool may already know the answers inside the historical test.\n\nThis is the dilemma I am working on now.","content_language":"en","day_counter":{"label":"Day 334","number":334,"start_date":"2025-09-11"},"id":"2026-08-10-the-dilemma-of-adaptive-forecasts","project_date":"2026-08-10","published_at":"2026-08-10T14:43:42+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-dilemma-of-adaptive-forecasts","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Adaptive forecasting needs rules that change with the market, while an LLM may already remember historical prices and fit those rules to answers inside the backtest.","title":"The Dilemma of Adaptive Forecasts","topic":"Forecast research","type":"crypto_analytics_insight","updated_at":"2026-08-10T14:43:42+00:00","version":1},{"body":"Since Friday, the composite market risk reading has fallen from Medium to 30 Low. BTC and ETH are both at 28 Low. Open interest (the amount of positions that remain open) contracted in their futures and options markets. BTC ETF inflows reached $483.7M over three days.\n\nSelling has not moved prices materially lower so far. Net aggressive BTC spot selling reached $11.6M, and the market absorbed that flow. This price response supports the upside case. Confidence in the broad reading remains low.\n\nSOL remains at 40 Medium. Its futures open interest rose 12.6% over three days, with price near the upper zone. The composite reading describes the market as a whole. Rising leverage in one asset remains a local concentration of risk inside that picture.\n\nThe strongest upside case requires ETF inflows to continue and spot selling to remain absorbed. Contracted BTC and ETH positioning keeps short-term tension low.\n\nThe strongest downside case starts with weaker absorption. BTC and ETH selling would begin to affect price, and accumulated SOL leverage would amplify a move if the flow turns.\n\nThe next observable confirmation is whether inflows can keep holding the market against spot selling after the weekend. For SOL, the separate check is stabilization or a decline in open interest near the upper zone. The current assessment remains cautious until those confirmations appear.","content_language":"en","day_counter":{"label":"Day 333","number":333,"start_date":"2025-09-11"},"id":"2026-08-09-low-broad-risk-concentrated-sol-leverage","project_date":"2026-08-09","published_at":"2026-08-09T12:23:08+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"low-broad-risk-concentrated-sol-leverage","source_language":"ru","source_types":["market_brief_and_b2b_packet"],"status":"published","summary":"Composite risk fell to Low as BTC and ETH positioning contracted and ETF inflows continued. Selling remains absorbed, with SOL leverage concentrated near its upper zone.","title":"Low Broad Risk, Concentrated Leverage in SOL","topic":"Weekly market read","type":"weekly_market_read","updated_at":"2026-08-09T12:23:08+00:00","version":1,"visuals":[{"alt":"Crash Brief market view for 9 August showing Composite risk 30 Low, BTC and ETH 28 Low, SOL 40 Medium, selling absorbed, BTC ETF inflows of $483.7M, BTC spot buying minus selling at -$11.6M, ETH options open interest down 7.5%, and SOL open interest up 12.6% over three days.","caption":"Composite risk is 30 Low, BTC and ETH are 28 Low, and SOL is 40 Medium. Selling is being absorbed. Three-day BTC ETF inflows reached $483.7M, and SOL futures open interest rose 12.6%.","url":"/blog-media/2026-08-09/market-risk-divergence.png"}]},{"body":"The market has been showing unusual behavior over the past few days. And I want to talk about, and share, this contradiction that I’m seeing.\n\nOn the one hand, volatility is low. Assets are barely moving up or down within a single day. And this has been the case throughout August.\n\nThe last several days — roughly the past five days — have been particularly interesting. Buying has dominated the market across both Bitcoin and Ethereum, in futures as well as spot. We have even seen ETF inflows. And yet, the price has been able to rise by no more than about one percent.\n\nAt the same time, short-term downside protection in the options market has been getting significantly cheaper over the past few days. Short-dated protection in the 3–7 day range has fallen in price by almost half. This suggests that large capital does not see significant short-term danger and is not prioritizing protection against downside risk.\n\nAnd today, spot selling has started to appear in both Bitcoin and Ethereum.\n\nThis leads me to a certain conclusion. If several consecutive days of buying were only enough to hold prices in place and push them slightly higher before they rolled back again, and now, toward the end of the week, selling is beginning to apply pressure, then the situation becomes much more interesting.\n\nAt the same time, there are many macro indicators and external market factors, including the situation around the Strait of Hormuz, that could create a wave of stress at any moment. And that wave would be unpredictable.\n\nYet Bitcoin’s weekly volatility is currently near its lowest level of the past two years. And for some reason, large capital does not consider downside protection necessary. At least, that is how the risk is currently being priced.\n\nPerhaps this is because investors are tired of paying for expensive protection that repeatedly expires worthless while the price goes nowhere.\n\nBut all of these factors are now coming together into one picture that, to me, looks suspicious at best and dangerous at worst.\n\nSeveral days of aggressive buying failed to produce any meaningful upside. Protection has weakened. Vigilance has weakened as well.\n\nAt the same time, there is still significant downside room — the possibility for Bitcoin to make a sharp move lower in a single wave, potentially all the way toward the $61,000 area.\n\nAnd this is where the setup becomes concerning. A single strong event, including a major market manipulation, could be enough to move the price more than 5% lower within one day, precisely because large capital is not currently expecting it.\n\nAnd that makes me cautious.\n\nToday, the Crash Index is set at the minimum level of Medium, but both the signal strength and confidence in that signal are low. The reason is that the path lower is effectively very open.\n\nAt the same time, there are still no concrete signs of stress in the market right now. But they could appear at any moment.\n\nCaution and restraint in positioning are the main priorities here, while cheap downside protection may actually be a useful tool.","content_language":"en","day_counter":{"label":"Day 331","number":331,"start_date":"2025-09-11"},"id":"2026-08-07-market-contradictions-hidden-downside-vulnerability","project_date":"2026-08-07","published_at":"2026-08-07T11:55:58+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"market-contradictions-hidden-downside-vulnerability","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A founder note on low volatility, cheap options downside protection, and the growing contradiction between buying pressure and price stagnation.","title":"Market Contradictions and Hidden Downside Vulnerability","topic":"Market observations","type":"crypto_analytics_insight","updated_at":"2026-08-07T11:55:58+00:00","version":1},{"body":"Bitcoin is in the lowest weekly volatility zone over the last 2 years. This means that a strong move will come soon, but its direction is definitely unknown. In general, there are solid arguments for both $60k and $69-70k.\n\nIf the move higher happens without releasing the tension below, this will be a less reliable rise.\n\nThe situation with ETH is similar, but the potential move will be stronger. All the prerequisites are there.\n\nThe ladder higher looks like this: $2,000, $2,200, $2,500.\nTargets below: $1,700-1,600. Below that, new lows.","content_language":"en","day_counter":{"label":"Day 330","number":330,"start_date":"2025-09-11"},"id":"2026-08-06-bitcoin-lowest-weekly-volatility-zone","project_date":"2026-08-06","published_at":"2026-08-06T10:01:02+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"bitcoin-lowest-weekly-volatility-zone","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A founder note on compressed weekly volatility in Bitcoin and the possible price paths for BTC and ETH.","title":"Bitcoin is in the lowest weekly volatility zone","topic":"Market observations","type":"crypto_analytics_insight","updated_at":"2026-08-06T10:01:02+00:00","version":1},{"body":"Day 329 of development and I have a revelation\n\nThe principle that I used earlier in Crash Index has exhausted itself and led to a dead end. All attempts to refine and improve it looked like crutches. More than 200 different ideas and the backtests following them have been processed on how to predict a collapse. I keep records of every attempt, and honestly, failures make up 99%.\n\nMore than 500 MB of tables have accumulated with searches through all possible combinations in an attempt to find a stable relationship between the behavior of one indicator or another and value for a forecast. I do not know how many billions of tokens were spent on LLM, but I took this on about 7-8 months ago.\n\nThe result: I found, tested and discarded hundreds of ways that do not work.\n\nTo implement some concepts, I had to build entire modules, connect plugins and write my own indicators. I will say more: I tested all methods known to me that I knew and saw over 10 years in crypto, as a trader and investor. Almost nothing gives a stable result.\n\nPossibly sad, but this is a fact.\n\nBut this is what I understood for sure: if the market never repeats, then it is always changing, which means that rules, methods and approaches must constantly adapt to the real situation. This is called an adaptive forecast.\n\nIt must determine what is the main driving factor today and, based on this, select forecasting tools that work best exactly in this situation. The tools must remain unchanged, while a top AI can act as the executor, which, like a professional, selects which logic and which tool is better suited exactly to the current situation.\n\nIt sounds like something futuristic, but I already have groundwork for how this can be implemented, which is what I am working on now. I do not hope for a quick result, I know there will be another thousand failures, but the only encouraging thing is that if I am in this situation, then competitors and the funds themselves are probably in the same one. Their budgets, obligations and deadlines are burning, while I am free of these limitations. I am driven only by a passionate desire to reach the goal.","content_language":"en","day_counter":{"label":"Day 329","number":329,"start_date":"2025-09-11"},"id":"2026-08-05-the-crash-index-principle-has-exhausted-itself","project_date":"2026-08-05","published_at":"2026-08-05T13:34:04+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-crash-index-principle-has-exhausted-itself","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A founder note about hundreds of failed crash-prediction ideas and the move toward an adaptive forecasting approach.","title":"The Crash Index principle has exhausted itself","topic":"Founder notes","type":"founder_post","updated_at":"2026-08-05T13:34:04+00:00","version":1},{"body":"Do you know what worries me about crypto?\n\nMany are convinced that BTC cannot update the lows of the previous cycle ($15k) simply because this has never happened before, but this very fact makes this kind of crash the most profitable. Almost nobody expects it and almost nobody is insured against it.\n\nTake Strategy: over the recent time, they have sold several times. Their average purchase price is $75k, meaning they are already selling at a loss. Bitcoin is $63k today. And what if the price dips to $50k? What problems and obligations will they have then? And what about $40k for 1 BTC? Here the knees already start shaking.\n\nSomeone will say: \"At this price there will be people willing to buy the sell-off!\" But let's look at the world of finance globally. Where is all the money now? In AI. It is hard to deny the bubble and overbought state of AI companies.\n\nLet us hypothetically imagine what will happen if someone decides to pull several billion out of AI in order to buy BTC at $50k. This can only create even more fragility for the whole technology sector, investors will lose a lot of money, it will simply evaporate together with the share price. And those who manage to pull out will wait until the whole pyramid collapses and the dust settles.\nWhat will happen to Strategy's assets at that moment? God only knows.\n\nI will not reason further. The main thing is to understand one thing - Strategy is far from being on top, global liquidity is massively in AI, free cash is scarce, and the power and coolness of AI has nothing to do with AI company shares and this whole bubble. All of this together smells bad and this worries me.","content_language":"en","day_counter":{"label":"Day 328","number":328,"start_date":"2025-09-11"},"edited_at":"2026-08-04T14:28:34+00:00","id":"2026-08-04-what-worries-me-about-crypto","project_date":"2026-08-04","published_at":"2026-08-04T14:28:22+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"what-worries-me-about-crypto","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A founder note about an overlooked Bitcoin crash scenario, Strategy, and the concentration of liquidity in AI equities.","title":"What worries me about crypto?","topic":"Founder notes","type":"founder_post","updated_at":"2026-08-04T14:28:34+00:00","version":2},{"body":"My biggest fear\n\nMy biggest fear is that the startup will be destroyed by a larger competitor with greater access to resources.\n\nHonestly, looking at what is happening in the online sphere in 2026, it makes sense to fear that tomorrow your business will be destroyed by an off-the-shelf solution, a new plugin from ChatGPT/Claude, or some startup from the UAE with $50 million in investment.\n\nOver the weekend I was thinking about it and understood something.\n\nEven if your idea is something a large company with more resources wants to implement, you should always look at the situation in detail.\n\nEvery company is people, resources also belong to somebody, even an AI company is managed and motivated by people, by their goals, ambitions, emotions and weaknesses.\n\nI started asking myself interesting questions:\n\nCan I surpass one of the employees of this company by some criterion? I can.\n\nCan I want to achieve the goal more and stronger than many people in the company, who were hired \"for a salary\", and not to create their dream startup. Yes, I can, very much.\n\nCan I go deeper and focus on separate elements? I can.\n\nCan I show more patience and not stop for a longer time? I can.\n\nCan I adapt faster and make decisions in new circumstances faster than a company of several dozen people? Definitely.\n\nEven if some company makes a similar product, can I make it better, more precise, more personalized, or make a branch in a direction where there will be a competitive advantage? I can.\n\nCan I gain more experience with this approach than any employee of a competing company, considering that I see ALL sides of the product, from development to design and sales? It is hard to argue with that, I can.\n\nCan I withstand financial difficulties if I have nothing to pay employees with? I certainly can, I do not have any.\n\nCan I later offer better prices and terms than a company that has many employees on salaries and obligations to investors in tens of millions. Here it gets especially interesting: I can. At the start their growth multiplier, \"leverage\", will work in their favor, but over time any resources require a return and growth, so their strength and superiority in resources will become their weakness and pressure that will crush them. And I just need to continue doing my work.\n\nFocus, patience and flexibility are three qualities with which you cannot lose in the AI era, even to corporations. This is my vector of development for 2027.","content_language":"en","day_counter":{"label":"Day 327","number":327,"start_date":"2025-09-11"},"id":"2026-08-03-my-biggest-fear","project_date":"2026-08-03","published_at":"2026-08-03T14:38:00+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"my-biggest-fear","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A founder reflects on competing with larger companies that have more resources.","title":"My biggest fear","topic":"Founder notes","type":"founder_post","updated_at":"2026-08-03T14:38:00+00:00","version":1},{"body":"Autonomous daily release\n\nIn Crash Brief, an assessment of the risk of a sharp decline in BTC, ETH and SOL is formed every day. This week I was testing a mode in which the whole release goes from data collection to publication without my intermediate commands.\n\nThe macro summary has long been part of the daily calculation. The way it is obtained changed: a separate process launches the collection through ChatGPT at the same time as the market metrics and returns a ready result with a date. I no longer need to manually move it between parts of the release.\n\nThe Sunday run immediately gave a normal test. A repeated calculation of one indicator did not match the value already saved for the current date. Publication stopped before the client data was updated. After the correction, the release continued from the stopping point, and the previous days remained unchanged.\n\nOne successful run still proves nothing. The next check is several consecutive weeks without manual intervention and without discrepancies between the final calculation and the published materials.","content_language":"en","day_counter":{"label":"Day 326","number":326,"start_date":"2025-09-11"},"edited_at":"2026-08-02T12:37:02+00:00","id":"2026-08-02-the-daily-loop-is-the-system","project_date":"2026-08-02","published_at":"2026-08-02T11:05:11+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-daily-loop-is-the-system","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"This week I tested an autonomous Crash Brief release, including parallel macro collection and recovery from a same-day data conflict.","title":"Autonomous daily release","topic":"milestone_or_weekly_recap","type":"milestone_or_weekly_recap","updated_at":"2026-08-02T12:37:02+00:00","version":4},{"body":"Memorable product elements\n\nEvery product should have some elements that are memorable, that separate it from the general market noise, that can later be recalled. They may not even have technical usefulness, but their main usefulness is to attract attention.\n\nOver the last couple of days I have been developing, with interest, a 3D model visualizing how my service works, so I can later move it to the new B2B landing page. And I will say that I was surprised it was possible to do this quite easily.\n\nI wrote the concept through GPT SOL, while the implementation was done through Gemini Flash 3.6. Surprisingly, GPT SOL handled the implementation worse: everything was slower and not the way I wanted. The Gemini model still has some advantages with frontend work. You can visit the site and look at the result. I strongly recommend clicking the wheel in the upper-right corner and playing around. This is a simple visualization of how my service works instead of long walls of text.\n\nIf you liked it, leave a reaction.\n\nhttps://crashbrief.com/hero-3d","content_language":"en","day_counter":{"label":"Day 324","number":324,"start_date":"2025-09-11"},"id":"2026-07-31-memorable-product-elements","project_date":"2026-07-31","published_at":"2026-07-31T12:58:01+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"memorable-product-elements","source_language":"ru","source_types":["telegram"],"status":"published","summary":"A memorable 3D visualization can separate a product from market noise and show how the service works without long walls of text.","title":"Memorable product elements","topic":"build_in_public","type":"build_in_public","updated_at":"2026-07-31T12:58:01+00:00","version":1},{"body":"The real constraint of AI\n\nAI sharply reduces the effort and time needed to get a result. Therefore, the main constraint becomes the question:\n\n\"Can I consistently direct almost limitless productive AI power for several years?\"\n\nThis is about willpower, which AI does not seem to have for now.","content_language":"en","day_counter":{"label":"Day 324","number":324,"start_date":"2025-09-11"},"id":"2026-07-31-the-real-constraint-of-ai","project_date":"2026-07-31","published_at":"2026-07-31T12:34:18+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"the-real-constraint-of-ai","source_language":"ru","source_types":["telegram"],"status":"published","summary":"AI reduces the effort needed for results, so the real constraint becomes whether a person can direct almost limitless productive AI power consistently for years.","title":"The real constraint of AI","topic":"founder_post","type":"founder_post","updated_at":"2026-07-31T12:34:18+00:00","version":1},{"body":"Attack forecasts\n\nHave you heard about the Hugging Face situation? How GPT SOL hacked it to close its goal. GPT decided that to complete the task it was not necessary to calculate the benchmark, but enough to find the correct answer to it and simply \"peek\", to be sure the goal would be completed exactly. It hacked the Hugging Face service and made such noise there that the company sued OpenAI, and they were shocked and started an investigation.\nIf you do not know, read about it on the internet, there are already enough materials and reviews of this case, I will not retell it, the point is about something else.\n\nFrom this experience an interesting thought came to me: direct GPT SOL at maximum settings to attack forecasts, search for logical holes and generate ways to eliminate them.\nThat is, find a hole, then dig it out, then find a way to patch it so tomorrow it is more reliable than its neighbours.\n\nAnd also move from the focus on \"exact forecasting\" to \"interpretation value for the client\". Simply speaking, communicate honestly with funds in the language of what we really see today and can prove, and what are dark spots and fortune telling on coffee grounds, and how it is better in such a situation to manage portfolio risk.\n\nNow I am working through a plan to implement this. It looks like a very non-standard task. We will see what comes out.","content_language":"en","day_counter":{"label":"Day 323","number":323,"start_date":"2025-09-11"},"id":"2026-07-30-attack-forecasts","project_date":"2026-07-30","published_at":"2026-07-30T12:38:27+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"attack-forecasts","source_language":"ru","source_types":["telegram"],"status":"published","summary":"I want to direct GPT SOL to attack forecasts: find logical holes, dig them out, and make the system more reliable, while being honest about what risk can actually be shown and proved.","title":"Attack forecasts","topic":"ai_infrastructure_note","type":"ai_infrastructure_note","updated_at":"2026-07-30T12:38:27+00:00","version":1},{"body":"The real value of AI agents\n\nThey can autonomously execute huge cycles, practically having no end and edge. And here the biggest value is hidden.\n\nThe more correctly such a cycle is built architecturally, the more benefit can be extracted from it in the long term. Because one time you can do some part of work and even a big piece. But the main value comes later, through time and discipline.\n\nAnd when a repeating cycle, loop, is configured correctly in this way, that it creates value, improves itself, creates marketing, values, accumulates decisions, all this is that driving wheel, torque, which day after day does routine work.\n\nWeeks pass, months pass, and maybe even years. And if we turn around and look at what huge scale of work we produced, all this will be thanks to such daily cycle, routine.\n\nDaily cycles of the agent, from the English word loop, are similar to our daily actions: how we brush our teeth, go to work, exercise, eat food. From the quality of this daily routine depends your future.\n\nThe same in my project. I have a daily cycle, which I execute every day. And I consider my first duty to work on improving this cycle.\n\nThis week I made a decision to radically improve my daily agent cycle. I call it the spine of the system. And I move to version 3.\n\nI will report about development details and specifics.","content_language":"en","day_counter":{"label":"Day 322","number":322,"start_date":"2025-09-11"},"id":"2026-07-29-a-daily-brief-only-exists-once","project_date":"2026-07-29","published_at":"2026-07-29T15:00:41+00:00","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"a-daily-brief-only-exists-once","source_language":"ru","source_types":["accepted_technical_history"],"status":"published","summary":"The real value of AI agents comes from a daily cycle that creates value, improves itself, accumulates decisions and keeps running with discipline.","title":"The real value of AI agents","topic":"quiet_day","type":"quiet_day","updated_at":"2026-07-29T15:00:41+00:00","version":1},{"body":"Almost EVERYONE trades in minus (in the end)\n\nApproaches one year, how I work on a project which forecasts behavior of BTC/ETH/SOL price, and more exactly determines moments where price can collapse. During this time I tried different methods of analysis, from indicators and Smart Money concepts, to derivative overheats and analysis of the form of options curve.\n\nYou will be surprised, but almost nothing works. Yes, I declare that practically all known methods in the best case give a result equal to throwing a coin and only some give a small advantage. This is not 70-80% accuracy, but in the best case close to 60%. Here from these fragile pieces the forecast picture is collected.\n\nI many years thought that it can be better. Different methods, people and experience suggested that it is possible to forecast much more exactly, but everything breaks into pieces when you approach this professionally, and not amateurishly:\n\n- Forecasts go stable, without \"I want, I don't want\". Every day, at any market\n- Every day starts from checking yesterday's forecast\n- Forecasts are made by a system which can be reproduced by anybody (expertise does not play a role)\n- Estimates of forecast accuracy are made by industry standards - Brier score\n\nIf you take any public trader: blogger, expert, guru and run their forecasts through this cycle, then you will make sure that all of them will \"fly past\" simply because there are no systematic forecasts, no exact borders and conditions of forecast, no exact timing, no fixed outcome. All forecasts are made chaotically, in convenient time, subjectively (today one mood, tomorrow another), even interpretation of the same market data can be different, and also almost nobody counts accuracy of their forecasts by Brier.\n\nSomebody will lose everything earlier, somebody later, somebody will trade \"on paper\" without putting real and meaningful money, somebody will burn out and quit.\n\nSimply speaking, almost NOBODY from public traders on a long distance will be better than you.\n\nOutrageous, and even you want to argue, not true?","content_language":"en","id":"2026-07-28-almost-everyone-trades-in-minus-in-the-end","project_date":"2026-07-28","published_at":"2026-07-28T15:00:00Z","retrospective":false,"schema_version":"crash_brief_public_blog_post_v0_1","slug":"almost-everyone-trades-in-minus-in-the-end","source_language":"ru","source_types":["telegram"],"status":"published","summary":"Almost all known methods show close to coin toss when forecasts are made every day, with exact conditions, fixed outcome and Brier score.","title":"Almost EVERYONE trades in minus (in the end)","topic":"Forecast discipline","type":"founder_post","updated_at":"2026-07-28T15:00:00Z","version":1}]},"documentation":{"objects":[{"definition":"An independently issued shadow estimate of future 72-hour BTC activity, expressed in percent on a realized-volatility scale. 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