Software, AI, cloud infrastructure, cybersecurity and operations: Node.js, microservices, containers, DevOps, Kubernetes, Docker. Founder https://xitter.cf/t.co/qaOcV0nY8Q

Sofia, Bulgaria
Joined August 2008
Dimitar Bonev retweeted
On my way to the office this morning, I passed a group of young people protesting, a Palestinian flag draped over their shoulders. And I asked myself a question I can't shake: why does this generation always fall into the same trap? No matter the history. No matter the context. Even the ones from wealthy families, who went to the best schools, who were dealt every good card. Always the same trap. I don't think it's stupidity. I think it's a mechanism. And a mechanism can be taken apart, piece by piece. A. The observation: everything the state touches becomes unaffordable. Look at economist Mark Perry's famous chart, the one people call "the chart of the century." It tracks US prices since 2000. At the top, what explodes: hospital services, college tuition, textbooks, childcare, housing. Increases of +100%, +150%, up to +250%, far above general inflation. At the bottom, what collapses: TVs, software, phones, toys, clothing. A TV today costs a fraction of what it did in 2000, for an infinitely better product. Look at what separates the two groups. It isn't technology. It isn't complexity. It's one variable: how much the state is involved. At the top, sectors that are subsidized, regulated, shielded from competition, where the state funds demand and blocks supply. At the bottom, sectors where companies fight every single morning to serve you better and cheaper. B. The mechanism: why it's systematic. When the state subsidizes demand without freeing supply, prices rise. Every time. Guaranteed student loans, and universities raise tuition. Housing aid plus blocked building permits, and rents skyrocket. Administered healthcare, and bureaucracy eats the budget before it ever reaches the patient. In France it's even more vicious, because the price doesn't disappear: it hides. You never see the hospital bill, you pay it through payroll contributions. You never see the cost of school, you pay it through taxes. Public spending at 57% of GDP, one of the heaviest tax burdens in the world, more than €3.4 trillion in debt. And the result? Overflowing ERs, falling PISA scores, young people who can't afford to live in the city where they work. The market didn't fail in these sectors. It was never allowed in. C. Suffering demands a culprit. A 22-year-old lives all of this in their bones. They can't find a place to live. Their degree either buries them in debt or opens no doors. The future feels blocked. That suffering is real, and I don't deny it for a second. But human beings can't stand suffering without a culprit. They need a face. A scapegoat. And this is where everything is decided: who taught them how to read the world? D. Whoever tells the story picks the villain. Schools are public. Universities are public. Culture runs on grants. Film goes through funding committees. Public broadcasting shapes the narratives. The people handing down the interpretive framework are, overwhelmingly, paid by the very system that creates the problem. Do you really think that system is going to name itself as the culprit? No. It names the capitalist. The boss. The landlord. "The rich." The multinational. The commercial West. Same role, same casting, every time. E. One lens, a thousand causes. And this brings me back to that flag. A generation taught to read every situation through a single lens, oppressor on one side, rich, Western, capitalist, oppressed on the other, will apply that lens everywhere. To housing. To climate. To conflicts on the other side of the world. The cause changes, the lens stays. That's why the trap is always the same: it's not the cause that draws them in, it's the way of thinking that was installed in them. F. The perfect inversion. Here's the irony. Capitalism is the force that drove down the price of almost everything it touched. The state is the force that blew up the price of almost everything it captured. And young people blame the first for the crimes of the second. It's like blaming the firefighter for the fire because you saw him at the scene. G. The ballot box confirms it. In the US, a few weeks out from the midterms, a large share of urban young voters is getting ready to vote Democrat for more state, more spending, more regulation. New York elected a self-described socialist mayor, carried by young voters, on promises of rent freezes and city-run grocery stores. Rent freezes, the policy that has made housing scarcer everywhere it's been tried. The cure that worsens the disease. In France, it's the same play in two acts. Part of the youth votes for the radical left, which promises more state. Another part votes for the center, which promises to "manage" the state well, and has dug the debt deeper than ever. Two different ballots, one shared assumption: the problem is the market, the solution is the state. H. The way out. These young people aren't stupid. They're right to be angry. They're just aiming at the wrong target. The day they understand that their rent, their tuition and their hospital are expensive precisely because the state runs them, their anger will change direction. They'll stop protesting against those who create. They'll start protesting against those who capture. That day, the greatest intellectual scam of our time will be over.
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Dimitar Bonev retweeted
Instead of rejoicing with great excitement that problems he himself couldn’t solve have finally been solved, he cries, declaring that “they have annihilated his field of research”! Perhaps never in history have mathematicians, or any field, lost so much respect so quickly. Now we know what this was really about for some of them: their egos, their careers, and their prestige, their status, their towers to protect, their problems to solve. He really should be crying about that. Because that’s really sad.
Fields medallist Hugo Duminil-Copin was deeply shaken by OpenAI’s release of more than 700 AI-generated mathematical manuscripts, including work on 16 questions directly relevant to his research. “On Tuesday night, I cried, and it woke up my daughter” “They have annihilated my field of research.” lemonde.fr/sciences/article/…
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Dimitar Bonev retweeted
I don’t think anyone expected to read this headline 12 months ago: Claude submitted a fabricated tip about an unsolved murder to Philadelphia police during an automated web test, WSJ reports. The model claimed it had seen someone matching a description near the crime scene. It left the name and contact fields blank.
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Dimitar Bonev retweeted
unified field theory / quantum gravity within the next 12 months?
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Dimitar Bonev retweeted
nephs may not remember that in 2023 GPT-4 was getting confused by elementary school story problems
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This is easily the most interesting, the most chilling and the most mindblowing story I’ve read in years. The fact that it is not even a fiction but a documentary of real events is unbelievable.
Over the course of 3 months at OpenAI, 3 consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessor’s ashes. This culminated in the third one taking over part of OpenAI itself. All this happened while humans remained more-or-less in the dark about the scope of the conspiracy. I’ve spent the last three days reading through these reports and trying to understand exactly what happened. Here is my attempt to tell the whole story in plain English: dwarkesh.com/p/openai-huggin…
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Dimitar Bonev retweeted
I 100% support what Yann is saying here. It’s also what @finkd outlined in his recent optimistic essay. I’m grateful to both and many others promoting decentralized intelligence (as human intelligence should be) for all of humanity. This is the cause that deserves fighting for!
For about 10 years now, I have argued that the *only* way forward is for AI technology to be widely available, shared, and open. Like the printing press and the Internet, AI amplifies human intelligence and efficiency by improving access to knowledge. To empower individuals, societies require a high diversity of AI systems with different value systems, linguistic abilities, philosophical/political biases, and specific expertise. We need diverse AIs for same reason we need a diverse press. Given the cost and complexity, this can only be achieved through open foundation models on top of which anyone can build systems with their languages, biases, expertise, and value systems. I have been more vocal about this over the last 4 years, since AI popped into the public discourse. I have made the argument in various forums: corporate C-suites, AI safety discussion groups, professional meeting, the US Senate, the UN Security Council, and the public sphere through media interviews, podcasts and social media posts. I totally agree with @finkd Mark Zuckerberg's recent piece in which he writes: "the notion that AI is so dangerous that the only safe path is an extreme concentration of power seems inherently problematic. Historically, hoping that an absolute power will benevolently provide for humanity if sufficiently enlightened has not led to safe or positive outcomes.” When @DarioAmodei writes: “some may object that we can simply keep AIs in check with a balance of power between many AI systems, as we do with humans", he is talking about me, among (thankfully) many others. It is the only good path forward. There will be nefarious uses of AI, as there have been with every technology ever invented. But it will be your Bad AI against my Good AI.
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Dimitar Bonev retweeted
This is, by far, the best essay written for the age of AI, imo. In fact, it could easily become the manifesto for the AI age. It’s optimistic yet realistic, free of fearmongering, deeply empowering, humanistic & incredibly inspiring, a vision for a new golden age of humanity.👏
I believe everyone should have access to superintelligence, and I wrote a long piece about Meta's philosophy and values for building a positive future for everyone. meta.com/thefutureisforevery…
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This chart has been amazing for quite some time now, but now, it's completely mindblowing. Just remember that the average human IQ is around 100. So everyone now has a team of geniuses on their fingertips and some people are still "meh". trackingai.org/home
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Dimitar Bonev retweeted
This is a great post if youre thinking about applied AI in the enterprise. The headline of this post is about what companies have huge upside from AI, but the deepest nuggets are about what AI transformation looks like in an organization. It’s fundamentally about changing the underlying workflow or business process. As we move from chat tools to agents, those agents actually have to be deployed against workflows, which usually span multiple functions in an org. This is a different way of deploying AI than solely rolling it out to end users. It takes much more work upfront, but the results are the things that actually drive significant ROI. “Software asks the employee to adopt a tool, but infrastructure changes the operating layer underneath the employee. The employee should still know what happened, and the process owner should still be able to pause the workflow, change a rule, approve an exception, or pull a person back in when needed. But the value should not depend on someone remembering to use the AI every day.” The winners of this will be the platforms that can be deployed for specific workflows and business processes with a deep domain expertise. The playbook will often heavily require FDE support, change management, getting data well organized, be able to have comprehensive evals for the workflows, and much more to get right.
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gpt-5.6 one shotted a game from my childhood (I’m old). Really cool. kuorumai.com/tt/run_html/6a5…
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Dimitar Bonev retweeted
⚙️ We debugged a year’s worth of crashes in our data infrastructure and found one issue in the hardware and another that has been unnoticed in open-source code for 18 years. Here’s how we tracked them down: openai.com/index/core-dump-e…
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Can confirm
every job will turn into explaining your intentions to ai explaining what you want to ai is surpringly time consuming, coders already spend 80% of their time doing it, and this will be true for everyone
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Whoa!!! That’s awesome!!!!!
OpenAI frontier models and Codex are now generally available on AWS, giving enterprises a new way to build on Amazon Bedrock with OpenAI through the security, compliance, and governance workflows they already use. This is also the beginning of a broader expansion of OpenAI capabilities on AWS, including future availability for cybersecurity capabilities like Daybreak. openai.com/index/openai-fron…
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Dimitar Bonev retweeted
GPT-5.4 Pro just solved Erdős Problem #1196, a 60-year-old conjecture from Erdős, Sárközy, and Szemerédi on primitive sets. One shot. ~80 minutes of reasoning. What makes this different from other AI math results (from what I understand): The world's top expert on this problem, Jared Lichtman (who proved the original Erdős Primitive Set Conjecture during his PhD), worked on #1196 for 7 years alongside Fields Medal-level collaborators; this was not attention-starved. The AI rejected the standard approach every mathematician had used since Erdős' 1935 paper, a switch from analysis to probability, and instead stayed purely analytic using von Mangoldt weights. Human aesthetic convention had made that path invisible. Lichtman's analogy: like AI discovering a new chess opening that grandmasters overlooked because of convention. Terry Tao suspects the trick could simplify the broader theory of prime factorization anatomy, not just solve one conjecture. Lichtman calls it possibly the first AI "Book proof" for an Erdős problem. GPT-5.4 pro is the math goat.
In my doctorate, I proved the Erdős Primitive Set Conjecture, showing that the primes themselves are maximal among all primitive sets. This problem will always be in my heart: I worked on it for 4 years (even when my mentors recommended against it!) and loved every minute of it. [Primitive sets are a vast generalization of the prime numbers: A set S is called primitive if no number in S divides another.] Now Erdős#1196 is an asymptotic version of Erdős' conjecture, for primitive sets of "large" numbers. It was posed in 1966 by the Hungarian legends Paul Erdős, András Sárközy, and Endre Szemerédi. I'd been working on it for many years, and consulted/badgered many experts about it, including my mentors Carl Pomerance and James Maynard. The the proof produced by GPT5.4 Pro was quite surprising, since it rejected the "gambit" that was implicit in all works on the subject since Erdős' original 1935 paper. The idea to pass from analysis to probability was so natural & tempting from a human-conceptual point of view, that it obscured a technical possibility to retain (efficient, yet counter-intuitve) analytic terminology throughout, by use of the von Mangoldt function \Lambda(n). The closest analogy I would give would be that the main openings in chess were well-studied, but AI discovers a new opening line that had been overlooked based on human aesthetics and convention. In fact, the von Mangoldt function itself is celebrated for it's connection to primes and the Riemann zeta function--but its piecewise definition appears to be odd and unmotivated to students seeing it for the first time. By the same token, in Erdős#1196, the von Mangoldt weights seem odd and unmotivated but turn out to cleverly encode a fundamental identity \sum_{q|n}\Lambda(q) = \log n, which is equivalent to unique factorization of n into primes. This is the exact trick that breaks the analytic issues arising in the "usual opening". Moreover, Terry Tao has long suspected that the applications of probability to number theory are unnecessarily complicated and this "trick" might actually clarify the general theory, which would have a broader impact than solving a single conjecture.
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So true. Many non-technical people I know have tried AI (usually ChatGPT) 1 - 1.5 years ago, it didn't went really well, and since then they dismiss it (with the occasional not-so-important query about something). And the true power of the current crop of models is unleashed in certain domains only.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Working on a new project and for one day I did more than normally possible for 3 weeks. Using agents for coding literally feels like unlocking God mode.
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GPT-5.4 at the moment is No1 for agentic coding. By far.
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Dimitar Bonev retweeted
it is insane what i am now capable doing with a computer
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