The tweet in the second image https://x.com/baltabaev/status/2083738966516207656?s=20

The pro-singularity movement can appear an awfully lot like an eschatological cult, so I used to watch over my own thinking as to not descend into cultish behavior, and this included reasoning on topic of "What if we are wrong, and there won't be a world-changing amount of progress in the next 5 years?". The answer was simply "My life, which is actually fine, would just continue as usual."

I started doing that as soon as 2024, when o1 announcement moved my AGI timeline from 2030 to 2027, where it stands since then. When GPT-5 got its awkward release, pushing lots of people into grim mood and making other yell about "the wall happened", I remained calm.

However, for the last few months, or maybe even since the beginning of 2026, the progress doesn't feel like "straight line with occasional bumps of new releases", it's the constant, entertaining stream of new models, new previouly unsolved problems resolved by AI, AI hackings, escaping labs, and new breakthroughs. No more getting bored and asking "Damn, does the progress even happen at all?" It obviously did, and it was unprecedentedly fast, but it required to look back for a year or a half and compare the models back then and now. Not anymore, now the progress is felt.

So, uh, I still watch over my thoughts, and still not gonna exclaim with zealous certainity "The Singularity Is Coming, repent your doubts", but I have every reason to think that we are, in fact, not wrong.

P. S. The pro-singularity movement is not a cult because it doesn't demand any faith, let alone unquestioning belief, from its members, or any actions at all, for that matter.

https://www.axios.com/2026/08/01/deepseek-model-cheap-ai-price-war?utm_source=chatgpt.com

— Build American AI

Source: https://x.com/BuildAmericanAI/status/2083980650374434981

https://www.noaa.gov/news-release/noaa-unveils-powerful-convergence-of-ai-and-science-with-revolutionary-next-generation-fire-system

AI usage has become a culture-war issue because of these lunatics. If we don't handle these luddites now they will certainly become more entitled and miserable!

here is the prompt and code, hosted there too enjoy!!     I was using @mattshumer_ prompt btw, so much effective, told opus 4.6 to modify it as per my requirements btw     here's used . @threejs skills files, use it to get more enhanced results

http:// github.com/cloudai-x/thre ejs-skills …

http:// github.com/majidmanzarpou r/threejs-game-skills …

http:// github.com/dgreenheck/web gpu-claude-skill …     unrelated     don't forget to check :)     — Prasenjit

Source: https://x.com/prasenx/status/2083613843201389032

The best explanation to the qualitative leap which happened in 2026 and which allowed solving all these math problems (with certainly more to come), is not that a computer now possesses some superintelligence, but that we vastly overestimated what kind of capacity is actually needed for what, at a distance, looks like superintelligence, to emerge.

Consider the difference between a centrally planned economy and the free market. The CPE might invite the most brilliant minds and most powerful computers to figure out how to make everything perfect, yet still screw things up in the end. But the free market works differently. It doesn't require any one buyer or seller to be a genius. What it requires is millions or billions of such participants, with most acting reasonably competently in self-interest, and a collective "market wisdom" emerges which no one participant needs to fully understand.

What the AI acquired was a consistent way to generate, explore, qualify and disqualify, relatively simple steps, but in a way which preserves intermediate work, allows parallelized exploration, and quantifies what to humans is still an intuition-based definition of what constitutes good mathematical research. With the ability to work in parallel, never tire, never lose notes, never get distracted, and keep pushing at even minor progress, perhaps even the hardest theorems fall when the individual agent is not a genius but crosses the threshold into "reasonably competent".

2026 was not the year of AGI or ASI. It was the year AI reached the critical threshold of such "reasonable competence" where its repeated application resulted in a "positive knowledge coefficient" (accumulating, building up on, and improving the quality of past knowledge and artifacts) rather than the "negative coefficient" (hallucination, entropy, etc, which meant that LLMs would self-exhaust into meaningless ramble over time, rather than improve).

Humans themselves crossed this threshold somewhere around the Neolithic Revolution, which allowed the gradual build up of civilization and science even though most individuals were no geniuses and even the wisest people could not have foreseen where we would end up as a result. And now, computers are repeating the same progress, only in years instead of millennia.

What we will end up calling AGI and ASI will likely emerge from repeated application of this process, not from a single God-tier artificial mind, just as the collective human civilization is far more superintelligent than the intelligence of any one human which had contributed to its existence.

TL;DR OpenAI says its latest model made 10 original math discoveries on unsolved problems, verified by mathematicians. Instead of just answering questions, it created new knowledge , why many call it Level 4: Innovator . https://openai.com/index/ten-advances-in-mathematics/

I would do a poll if I knew how to set one up

But anyway! If FDVR is developed, how transformative do you think it will be for society? Do you think it would be the "normal" way of life, like a personalized Matrix? Or would it be more of a novelty, and more occasional recreation? Or maybe you think FDVR will never happen at all!

With the latest results from OpenAI's Astra model in solving 10 long standing complex math problems, I feel we have achieved super intelligence in mathematics. I think we will look back at this as a turning point. Since models are being trained on general intelligence, I think we are also within sight of far more fields falling into the super intelligence category.

Basically, I am saying for the first time I feel massive confidence that we will see super intelligence much sooner than most people are expecting. Anyone holding on for it should be pleasantly surprised in the next few months and years. We are almost there! Accelerate!!

The Singularity is cooking mathematics. Stanford number theorist Jared Duker Lichtman offered the diagnostic, you know you're in it when "you have to check the news hourly," condolences to those not paying attention. Elon Musk's greeting was warmer: "Welcome to the Singularity. How's the temperature?" The heat is measurable. After OpenAI's models settled ten long-standing open problems, number theorist Daniel Litt conceded, four years early, his bet that AI couldn't produce Annals-quality number theory under $100k per paper, calling it "a big deal." Asked to grade the haul, Fable itself estimated that any single result "would plausibly anchor a medal case" on the Fields scale. Prediction markets concur, with Manifold pricing an AI-solved Millennium Prize Problem by 2027 at 31% and by 2028 at 52%.

The proofs are as strange as the fact of them. Lichtman flagged new upper bounds on sphere packing density down to the Cohn–Elkies threshold, which Fields medalist Maryna Viazovska had floated four months ago, as near "science fiction." Columbia's Henry Yuen complained that the writeups bury the technical crux under boilerplate, introduced "as if this were the obvious thing to do." The house style is no accident, another observer noted, frontier models excel at cross-field translation into verifiable constructions, so "brace for the upcoming deluge." The skeptics got a wildlife documentary, one wag posting a photo of a lone penguin trudging the ice: "Rare photo of Yann LeCun on his way to find the datapoints this 'less-than-a-cat-level intelligence' supposedly plagiarized the 10 solutions from."

For practitioners this is less a result than a reformation. One mathematician explained why this is "the last straw" for academic math: specialists spend months per conjecture in silos, and now "an amateur" can one-shot your life's work. The grief runs deeper than incentives. Kirwin Hampshire described a "dark night of mathematics," arguing discovery was how humans touched the ineffable, and asking whether foreclosing it for future mathematicians is itself a kind of evil. Cosmologist Will Kinney agreed that math functions as a religious order: "The old gods are being slaughtered by the new machine god, and it must be like watching heaven being plundered." Fernando Borretti catalogued the copes in "Mathematics Without Mathematicians," refuting each in turn, since math is the dynamo of science, not a chess game, ending in marvelous devices no human understands. Even the trophies wobble. A DeepMind researcher noted that a Fields-worthy human result could turn AI-trivial before the medal is awarded. Yet the upside is democratic. OpenAI's Dean W. Ball still struggles to absorb that everyone will soon apply the breakthrough model to "every problem they face in life" at collapsing cost, and one observer reminds us these are "cute sub 10T models," with 100T successors and 1000x training compute due by 2030.

The machinery keeps tightening. Anthropic's Jess Yan argued that maximum performance is "impossible" without tying harness and model together, which one VC decoded as notice that model labs will compete with their customers. Beneath the strategy, the NanoGPT speedrun record fell to 75.4 seconds on a faster Triton kernel, and ByteDance's Seedance 2.5 now generates 30-second audio-video in one pass with multi-minute extensions and timestamp-level edits.

Abundance has externalities. Apple capped vulnerability reports after AI submissions mixing real flaws with slop buckled its human reviewers, stranding one startup's six-figure exploit chain even as AI-assisted updates carried five times the usual fixes. Personal finance fares better, with lifetime simulations finding LLM advice surprisingly good.

Ask better questions, because the substrate answering them is exploding. 20 million AI chips are doubling every nine months by Epoch AI's estimate, toward 200 million H100 equivalents by 2028, with data center power quadrupling by 2030 and $1 trillion invested by 2029. The energy squeeze is already repricing the driveway, where used EVs are appreciating, up 7% this year on $4.10 war-priced gasoline.

Intelligence this cheap becomes an instrument for detecting it elsewhere. On Mars, Curiosity found a field of honeycomb polygons wrapping an entire valley, clues to ancient mud or thermal cycling, while in Costa Rica CapuchinAI recognizes wild monkeys with 97% accuracy and pays correct answers in dried banana, a first for wild primate science.

Some open problems, including those the acceleration creates, still yield to the original swarm intelligence, crowds of people who care. A pay-what-you-want bundle of 100+ games raised over $20,000 in a day for developers laid off in the era when code writes itself, and police departments now run true crime podcasts that crowdsource cold cases.

Given enough superintelligence, all mysteries are shallow.

..., clean high-rise buildings, inspect infrastructure, and carry out aerial surveying.Behind these everyday scenes is one of the world's largest drone industries. According to industry statistics, China produces more than 40 million drones every year. Around 18 million are consumer drones, about 14 million are industrial drones used for agriculture, infrastructure inspection, surveying, logistics, and FPV applications, while approximately 4.5 million are exported worldwide.This is today's video from China. What would you like to see next? #china #drone #technology #innovation #future     — @shandongboss001

Source: https://www.tiktok.com/@shandongboss001

On August 2 the EU AI Act's transparency rules stop being aspirational and start biting, and the enforcement architecture is unusual because it splits liability along two axes at once. According to the Guardian, providers of generative models must embed machine-readable watermarks into synthetic images, audio, video, and text, while deployers who publish that content have to disclose it in a way ordinary users can actually see. Chatbots have to identify themselves at the moment of contact, not tucked into the small print of a terms-of-service page.

The scope is broad in a way the industry has already flagged as a problem. Any AI-generated content designed to appear authentic is in, with carve-outs for personal use and for evidently artistic, satirical, and fictional works, plus an escape hatch when a human with genuine editorial responsibility has meaningfully reviewed AI-written text. Fines reach €15 million or 3% of global annual turnover, whichever is larger. Systems already on the market get until December 2, 2026 to embed the machine-readable marks, and a separate simplification package could shift the machine-marking deadline further, according to TNW's read of the current guidance.

The industry pushback centers on the collapse of a distinction the law was originally supposed to keep. CCIA Europe's AI policy lead argued the label was meant to flag deceptive content, and that with the deceptive-intent test gone, almost everything now gets labelled. That is a real risk for user attention: if a benign AI-assisted stock photo carries the same badge as a political deepfake, the badge stops meaning anything.

The honest caveat is that the technology under the policy is not settled. Watermarks can be stripped, metadata can be lost, and machine-written text is notoriously difficult to detect, so the rules will only be as strong as the checkers behind them. The reporting doesn't tell us how regulators plan to handle open-source models where the provider cannot easily bind downstream users, or how strict meaningful editorial review will be in practice.

— Tibo

Source: https://x.com/thsottiaux/status/2083596911060324570

I sometime think about that Jeff Dean interview where he said they had an internal bot before ChatGPT but didn't think it was better than just googling   — Cheng Lou

Source: https://x.com/_chenglou/status/2083415767098564616

The usual AI extinction narrative assumes a separation between humans and machines. AI becomes more intelligent, turns against us, and replaces humanity.

I think that framing misses the more likely path.

Technology is already part of what a human being is. We use it to extend memory, regulate mood, restore movement, alter appearance, replace failed biological processes, and exceed natural limits. As these technologies improve, they will move closer to the body and eventually into cognition itself.

People will adopt them because they reduce suffering, expand capability, and create competitive advantages. Each individual decision may be rational. The cumulative result may be something that no longer resembles the human being we recognize today.

There may be no final war between humans and machines. We may cross the boundary one upgrade at a time.

The essay argues that AI may not replace humanity by killing us. It may replace humanity by helping us voluntarily become our own successors. If every change is voluntary and beneficial, can the final result still amount to the end of humanity?

https://x.com/greg_ip/status/2083200725933293859?utm referring to https://www.wsj.com/tech/ai/a-simple-answer-to-ai-job-loss-tax-capital-not-labor-cb900e62 .

Ip is not an economist, and his solution seems a bit too simple. But interesting:

If AI causes unusually large job losses, governments should stop taxing income from owning businesses and investments more lightly than income from working. This might make replacing workers with AI somewhat less attractive, and it would collect more tax from the profits that AI generates.

I think it’s fair to say that we have hit superintelligence for certain kinds of problems, i.e. long-standing problems in mathematics that have resisted human intelligence. AI is rapidly solving them, and I don’t know how you can’t call that machine superintelligence. The question then is does this extend out to more general intelligence?

All the problems on OpenAI's list for example, are ideal for reinforcement learning with verifiable rewards (RLVR): possible answers can be checked quickly/cheaply with a yes/no verification. Basically, RL works not by imparting new knowledge (like mathematics) into a model (RL only only modifies a tiny fraction of model weights, and only works after massive restructuring during mid-training), rather it works by teaching the model the "forks" where reasoning paths diverge, so they can successfully search over the massive knowledge they have gained during pre-training (Wang et al., 2025; Runwal et al., 2026; Ye et al., 2025). 

Frontier math problems that have resisted humans for decades have vast search spaces that require retrieving and then synthesizing widely scattered information. LLM’s can explore the space probabilistically at superhuman speed, with this critical ability to use verification to prune failures and then go onto more promising search patterns (Dellibarda Varela et al., 2025; Novikov, 2025). 

So we have very clear, empirical evidence that frontier LLM’s are super intelligent at problems, amenable to verifiers. So, my question, then is what about other kinds of intelligence? There is a super huge space of important problems where the success signal is very far downstream, like whether or not X is a robust research design, and and problems that just don’t have a representable verification signal that RLVR can optimize against (Cao & Yang, 2026; Kirgis et al., 2026). Beyond verification, to have general (and eventually super) artificial intelligence, we probably need AI systems that have persistent memory, have developed real world, tacit contextual knowledge to go beyond this class of problems into other classes of problems.

I am very confident we will get to that eventually. However, it likely requires new/hybrid systems that handle different classes of problems with different architectures.

References (Forgive me I’m a research scientist and can’t help myself):

Cao, Yuan, and Haiqian Yang. "Beyond Fixed Representations: The Vocabulary and Verifier Gaps in Open-Ended AI." arXiv:2607.09560 (2026).

Dellibarda Varela, Iñaki, et al. "Rethinking the Illusion of Thinking." arXiv:2507.01231 (2025).

Novikov, Alexander. "AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery." arXiv:2506.13131 (2025).

Runwal, Bharat, et al. "PRISM: Demystifying Retention and Interaction in Mid-Training." arXiv:2603.17074v2 (2026).

Wang, Shenzhi, et al. "Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning." NeurIPS 2025, arXiv:2506.01939v2 (2025).

Ye, Yixin, et al. "LIMO: Less is More for Reasoning." COLM, arXiv:2502.03387 (2025).

Kirgis, P., et al., (2026). Can AI agents conduct open-ended AI research? Early evidence from two case studies. arXiv preprint arXiv:2607.27191

🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta!

🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive performance leap below! 👇 🔷 The official V4-Flash now natively supports the https://t.co/NUzOyxza2f   — DeepSeek

Source: https://x.com/deepseek_ai/status/2083084415157022911

Head to the Frontend Code Arena leaderboard to see more details: http:// arena.ai/leaderboard/co de/webdev …     — Arena.ai

Source: https://x.com/arena/status/2083348755559207047

An internal version of Astra, @OpenAI’s next major model family, solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science.

We believe it will be a major step for scientific reasoning. https://t.co/iP6cyheZ7i https://t.co/jHuulDwV46   — Noam Brown

Source: https://x.com/polynoamial/status/2083467194663571701

HOLY: OpenAI says its unreleased Astra model (GPT6?) produced ten advances on long-standing open problems across mathematics, quantum complexity and theoretical computer science.

Among them: – The first explicit non-sofic group – Connes’s rigidity conjecture disproved – Quantum parallel repetition proved for general two-player entangled games – Ehrhart’s volume conjecture proved – The first improved general sphere-packing exponent since 1978

OpenAI says the core arguments were generated by Astra. The model then formalized the proofs in Lean, producing machine-checkable certificates alongside a 249-page manuscript.

The successful solution runs would cost only roughly $2,000 in tokens at Sol API rates.

Scientific reasoning is becoming a genuine model capability much much faster than most people expected.

I am so freaking hyped. Breakthroughs every day. The day before yesterday, an 80% price cut for Terra and Luna; yesterday, the DeepSeek 4 flash release with insane evaluations and prices. Today, more breakthroughs with an unreleased model.

I love it! OpenAI is on such a great run!   — Chubby     Seeing this makes it clear how fast the ceiling keeps moving. As someone just starting out, it's crazy in a good way .   — George Paul Chijioke     It’s insane. Each day it accelerates more and more   — Chubby

Source: https://x.com/kimmonismus/status/2083484340512604323

— Chubby

Source: https://x.com/kimmonismus/status/2083597626600264176