Open AI Surges as Claude Disproves Major Math Conjecture

• Foundational models lack a strong technical moat, allowing engineers to swap APIs and prompts with minimal workflow disruption.
• US GPU export controls and data-sharing restrictions have inadvertently forced Chinese developers to prioritize open-weights distribution over centralized global services.
• a16z partner Martin Casado estimates an 80% probability that any given startup is currently utilizing Chinese AI models.
• Chinese firms like Moonshot and Alibaba have released models claiming parity with leading US offerings while operating at a fraction of the cost.
• The US government's reliance on export controls clashes with the historical precedent of open internet infrastructure that fueled broader innovation.

geophile (44 replies)

The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.

• PCs destroyed minicomputers. Mainframes survive, but serving a much tinier portion of the market than they used to.

• PC office productivity software destroyed expensive professional products.

• Windows (low end) and Linux (free) completely destroyed the UNIX marketplace, and again, have taken huge market share from the mainframe world.

Ignoring the huge Chinese open-weight models for a moment:

• The training costs and resource requirements for frontier models are unsustainable. The high price, and social pushback, mean that the American companies producing these models are precarious.

• There are enormous financial incentives for research results allowing for cheaper, less resource-intensive models of high quality.

• Local LLMs on consumer hardware are akin to the PC hobbyist world of the 70s and 80s.

Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.

Getting back to the Chinese models: They allow for new competition against Anthropic and OpenAI, basically SaaS renting out these very capable AIs much cheaper. That will just accelerate trends.

api (2 replies)

The biggest exception is cloud. Big cloud carries an insane markup (bandwidth is like 10000X!) and everyone runs on it.

The strategy there is false openness where deployment complexity is the real proprietary moat. Sure Linux, Docker, Kubernetes, Postgres, and all the other standard tools in the box are open source and free, but they're also arcane and complex to run and hard to make fault tolerant. So you're lured in by "open" and then locked in via a kind of "death by a thousand cuts" complexity moat.

(Personally I hold the view that complexity and arcane-ness beyond a certain point is indistinguishable from closed in practice. Open source that's really complex and hard to run is not open in any meaningful sense.)

AI may not admit that kind of moat though, because AI is very good at slicing through that kind of thing. You can prompt a model to make itself compatible with another model or to change code to make it compatible. There's no moat because the moat bridges itself.

gpt5 (7 replies)

The problem (right now) is that Open Weight models depend right now on huge companies to spend billion of dollars to train and develop them, all backed up by their incentives and their state to support this, while essentially giving away their monetization path.

With open source projects, the benefit was that each individual could improve the complex system (e.g. Linux Kernel) interpedently, and over time the benefits accumulated. With models right now, there is just no way to do distributed training, or really, any large scale parallel way to improve them.

So whatever the short term strategy driving publicizing the model weights (e.g. potentially, to create a price war in order to put pressure on western companies and deprive them of the money they need), we can't ignore the fact that incentives and decisions could easily change in the future, and unless there is a way to truly decentralize models improvements - the party could stop at any time.

Sparkyte (0 replies)

100x this it is why all of the AI giants are going to fail. They are too big and inefficient to scale properly. This is why Google is just casually taking its time in AI and not racing to a finish line. AI is essential but if it already does most things good enough then it can take longer to make it more efficient.

Gareth321 (1 reply)

> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.

At current pace, we'll have open weight LLMs with frontier intelligence in 6-12 months. The constraint is RAM - both for the model and the context. It's likely that distillation and quantisation and TurboQuant will significantly reduce RAM requirements. I think we'll have Opus 4.8-like performance on 64GB of RAM in two years.

Of course, by then, frontier intelligence will be god-like.

• The polynomial map features a constant Jacobian determinant of -2, directly contradicting the conjecture's assertion that such maps must be invertible.
• Explicit collision points were identified: (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) all map to the identical output (-1/4, 0, 0).
• The z-linear structure of the counterexample bypasses the algebraic degree constraints that historically invalidated human attempts over the past half-century.
• Computational verification via Wolfram Alpha and independent confirmation by the Grok AI assistant validated both the determinant calculation and the point collisions.
• The resolution implies immediate downstream consequences for related algebraic frameworks, specifically suggesting that the Dixmier and Poisson conjectures are also false in their general forms.

aizk (12 replies)

This is a rare instance where feeding this groundbreaking information into an LLM gives them psychosis. I fed this to claude code and watched it verify the result in 7 different ways to be 100% certain, and it was just flabbergasted. Quite remarkable.

CamperBob2 (5 replies)

Interestingly, even Qwen 3.6 27B was able to verify the solution, but I didn't get any glazing for discovering it. Instead, it thought that someone named Shestakov had already found a counterexample in 2004.

GLM 5.2 whiffed, it insisted the counterexample wasn't valid.

VibeThinker 3B also recognized that the counterexample was valid. But it kept trying to convince itself that it wasn't, over and over, since it's an "unsolved problem." Eventually it just answered "-2."

tacomonstrous (5 replies)

Speaking as a mathematician, it does seem like we're a bit fucked as a community. Anything that is at all accessible to currently existing methods and mathematical infrastructure is probably going to fall to the frontier models of today, and at this rate of progress it's likely that, already by next year, we'll see new infrastructure being put into place by AI, giving us a world in which a few designated interpreters of the oracle get to 'do' mathematics, while it withers on the vine as an avenue for the exploration of human meaning.

jdw64 (5 replies)

I've come to understand that while an LLM is a parrot, it's a parrot that's smarter than I am.

slopinthebag (2 replies)

The author has a PhD in math from Cambridge. If it turns out to be a false claim it is an interesting case study on AI's sycophancy causing even experts to drop their guard and make mistakes.

• Anthropic's Fable 5 model costs nearly three times as much per completed task as competing models from OpenAI or open-weight providers.
• Companies that own data centers and power generation convert high variable inference costs into fixed costs, allowing margins to grow with usage.
• Model-only providers face a narrow path to defensibility, limited to recursive self-improvement, regulatory market closure, or uncopyable product stickiness.
• Low barriers to entry in the AI harness space are allowing startups like OpenCode, OpenClaw, and Hermes to rapidly challenge Anthropic's product offerings.
• The recent wave of top-tier releases from China and other labs demonstrates a sustained catch-up pattern rather than a one-off competitive shock.

LarsDu88 (27 replies)

The open weight, open architecture releases of the past several days has me more convinced that ultimately, the winner will be whoever burns their models to ASICs fastest.

The LLMs themselves are capable of doing some aspects of chip design as evinced by the K3 press release.

Furthermore, the frontier models are "good enough" for a wide swathe of tasks and will soon hit that threshold for a good amount of software engineering (if not already). Does anyone think we need a Mythos level model to plan a road trip, or give someone tips on making a cake recipe?

A Fable 5 model running at 9,000 tokens/s on an ASIC rather than 150 tokens/s on electricity chugging Nvidia GPUs, or even giant SRAM Cerebras or Groq chips could be good enough to meet the majority of demand.

Furthermore, if you're an enterprise the risk of data exfiltration and feeding data to a potential competitor like OpenAI or Anthropic is greatly reduced if you could shift to on-prem ASIC deployments. A handful of chips could cover a wide variety of use cases and cover them more securely. There are a lot of corporate use-cases for LLMs that are not frontier math research or coding.

stillpointlab (7 replies)

I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms. LLMs and model architectures seems significantly more volatile. I wouldn't want to be working out the finer details of my chip rollout only to find a new paper/approach that give multiples of performance.

So I guess it depends on how much the latest-greatest model motivates people, and my read on the current churn is that developers are extremely unloyal to brand at this point and will jump to whoever has the best model. And as long as the best model is running on programmable GPUs, that will be the dominant form.

bko (14 replies)

I think the risk is overstated.

For one, on the margin people are willing to pay a lot for slightly better models. I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs. I have no interest in optimizing that to get it slightly lower. There are a very vocal minority that optimizes this or companies whose LLM expense is marginal, but I think that's the minority (correct me if I'm wrong, curious what their customer base looks like)

Also the actual LLM is a tiny portion of the value added. Anyone that tried to build agentic solutions from LLM apis quickly realizes that a huge value is the Claude Code / Codex harness. There are open source implementations like OpenCode but they're not nearly as good.

Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them. Google sheets is probably 99% and available to everyone and better in a lot of regards. But the immense value spreadsheet software produces workers above the $100 or whatever a year makes it so that there is a real moat and no one bothers exploring alternatives.

overgard (9 replies)

I keep thinking about the Figma thing. If you're unaware, here's the google summary:

---

The Board Departure: Mike Krieger, Anthropic's CPO and a co-founder of Instagram, sat on Figma's board of directors. He resigned on April 14, just days before news of Claude Design broke. This sparked speculation over conflict of interest and the use of proprietary product strategy information.

Betrayal of Partnership: The launch aggravated the tech industry because Figma relied on Anthropic's models to power its own AI features, and even announced a joint "Code to Canvas" integration. Reports indicate Figma was blindsided by the depth and scope of Claude Design.

Market Reaction: The "SaaSpocalypse" thesis—fears that major AI foundation models will rapidly build application layers and cannibalize their own SaaS partners—was realized when the news broke. Figma's stock saw an immediate 7% drop upon the announcement.

---

I would suggest to people using LLMs: you should be cautious about giving these companies data or relying on them. If you're building an AI startup, there's a very good chance they could decide to directly compete with you if your idea has traction. You're also at their mercy for API pricing etc.

simianwords (7 replies)

To everyone praising Open weight models, could you answer a simple question?

If Anthropic doesn't make money because of distillation attacks, how would they convince investors to invest in them, such that it makes financial sense for Anthropic to train even bigger models?

Assuming it is preferable for everyone that we get better models in the future. Distillation attacks remove the financial incentive.

• The detector was anchored to a 0.4% false-positive rate on 2021-2022 papers, meaning any reported increase above this floor is validated against a pre-LLM control group rather than an arbitrary baseline.
• Computer science submissions lead the field at 65.0%, followed by quantitative biology at 56.3% and electrical engineering at 51.3%, while mathematics trails at just 0.7%.
• Researchers scored the full body text of each paper instead of the abstract, noting that abstracts frequently understate AI generation signals compared to the main content.
• The study acknowledges that mathematics papers score low not necessarily due to low AI adoption, but because heavy notation and sparse prose fall outside the detector's training distribution.
• Flagged rates peaked near 39% in early 2026 before settling to approximately 32% in the most recent complete quarter.
• The detector cannot separate lightly-edited human documents from wholly-generated ones, meaning a flag indicates machine-like writing patterns rather than definitive AI authorship.

jrm4 (9 replies)

The important question is:

So what?

Genuinely. I get that there may be some visceral reaction against this, but when I break it down, I mostly fail to see the problem. Seems like what is actually important is:

Compared to before, when a human reads it, do they -- or society -- get something good out of it? Is it worth it to add this to the "pantheon?"

If that's not what's happening enough, and if this doesn't describe the process -- then the problem lies elsewhere, no?

dopamine_daddy (6 replies)

Surprisingly I agree with you. My opinion is: if it makes communicating research more effective, while not reducing the quality of the output substantially, I see no issue.

A possible conclusion for this could be: If the majority of CS papers is AI written, let's just accept this reality universally and stop worrying about it altogether.

amram_art (1 reply)

you just dont respect your readers, thats all

mrob (1 reply)

Even if you can reject the aesthetic argument for non-fiction works (although read some of Dijkstra's papers for a good counterargument), it is still a problem because it breaks an important quality signalling mechanism.

Pre-LLMs, a paper with no spelling or grammar errors showed that somebody had put effort into writing and editing it. If they cared about the presentation, they probably also cared about the content. LLMs routinely produce nonsense that looks superficially like high-quality work.

There are far too many papers to read all of them. LLM slop is evidence that something is probably low quality. As the saying goes, "if you can't be bothered writing it, I can't be bothered reading." The rare outliers will get enough citations and recommendations to overcome this filter.

probably_wrong (2 replies)

I recently desk-rejected a paper where every single citation in its Introduction was hallucinated. That means that the entire connection between what the author(s) did and how it relates to existing research was simply made up. I've never seen this happening before AI but now there's at least one paper in every cycle pulling something similar.

My problem therefore is: we are seeing more and more papers written with tools that are known to make up facts, citations, and even entire papers. And the number of papers has increased, too. I therefore see it less as "people are being more productive" and more "people are releasing bad science much faster than we can keep up with".

• The old swarm produced 68,000 commits in two hours—roughly 70 times the new run's pace—while accumulating over 70,000 merge conflicts compared to fewer than 1,000 in the updated system.
• In the Opus 4.8 and Composer 2.5 hybrid configuration, the planner generated a small fraction of tokens but accounted for roughly two-thirds of the total cost, whereas the worker fleet handled the vast majority of token generation for just $411.
• When both old and new swarms successfully completed the SQLite project, the updated architecture produced significantly more efficient code, requiring only 9,908 lines of engine code compared to the old swarm's 64,305 lines for the same model mix.
• Frontier models like GPT-5.6 Sol were excluded from the main benchmark because they exhibited runaway spirals and high sensitivity to literal wording, forcing the researchers to fall back to GPT-5.5.

mccoyb (4 replies)

I find these blog posts (and the originals, with Anthropic's C compiler and Cursor's browser) somewhat funny, as if they have this enormous power to build ... but they can't build something unique or new. Like the software sucks, but look how powerful the process is (the models are indeed powerful).

And it's a bit of a shame: by virtue of their position (their embedding in the fabric of venture capitalism), it seems like they can only make a subset of things -- what they can make is dictated enormously by capital, as they are engines of capital.

Not sure the point I'm trying to make, I just find it amusing.

Perhaps the point is that it might be more worthwhile to give independent creators a billion dollars to play around with agent swarms if we want to keep diversity in the evolutionary algorithm that is the software industry high.

arjie (1 reply)

Well, the tool works well. I am grateful that Nature has seen fit that the same kind of person who can make it doesn't also have the ability to identify what needs exist in the market because then all software would flow out of OpenAI and Anthropic and the rest of us would run about their legs and peep about. Whether product development will survive the next generation of models is still unknown.

shay_ker (3 replies)

How do we know if these models weren't trained on Turso's rewrite of SQLite in Rust?

It seems both likely that they were and impossible to remove that code from pretraining. Doesn't that make this just about LLM memorization of the training set? What am I missing?

htrp (2 replies)

>The browser swarm from earlier this year peaked at roughly 1,000 commits per hour on Git. The new system peaks at around 1,000 commits per second. >To facilitate this rate of activity, we built a new version control system (VCS) from scratch. Throughput was not the only reason to own this layer. Every change in the system passes through the VCS, so it is where collisions first become visible, and several of the coordination mechanisms in the next section are implemented directly inside of it.

Talk about inventing the universe to make a button.

whinvik (2 replies)

I would have loved to see more of the harness engineering shared as code. Instead we are left with only the outcome.

I guess that makes sense since the harness is the product in the case of Cursor.

• A recent survey found half of AI scientists agreed there was at least a 10% chance that AI could destroy the human race, highlighting how existential fear currently drives policy debates.
• The unpredictability of large language models stems from simple mathematics correlating billions of data entries, a process that incidentally forces a small amount of human choice when selecting outputs.
• While concepts like "alignment" and "fairness" remain vague and easily circumvented, Lanier notes near-unanimity that deepfakes and automated manipulative communications must be explicitly labeled with actionable provenance.
• Lanier rejects universal basic income as a response to AI-driven job displacement, arguing it merely preserves a "black-box" system and instead advocates for building a new creative class through compensated data contributions.
• He draws a parallel to the 2019 Boeing 737 MAX disasters, noting that both failures resulted from poorly integrated systems and a lack of transparent user communication rather than the technology itself.
• The proposed "data dignity" framework suggests compensation might be distributed through collective-bargaining groups like unions or guilds, termed "mediators of individual data," to prevent creators from being lost in massive models.

jacobgold (9 replies)

We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense.

I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output.

Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned out to be possible with fairly basic statistical text generation, because fooling humans is easy.

It would've been nice to reserve "AI" for superior human-like intelligence capable of genuine common sense and reasoning. The irony is that the startup founders most worried about "AI" have created so much hype and funding that we may very well figure out how to build "real" AI.

ACCount37 (3 replies)

We've chosen to call Deep Blue and Half-Life 1 NPCs "AI" too.

It boggles my mind that this "b-b-but it's not actual real AI" whine is even a thing. Were people saying this living in the cave for the past 5 decades of AI research?

CharlesW (0 replies)

It would've been nice to reserve "AI" for superior human-like intelligence capable of genuine common sense and reasoning.

We've called that "AGI" since the late 90s/early 00s (depending on whether you count first use or popularization). Even if AGI does come to pass, we'll still need "AI" since not all forms of AI will be AGI.

runarberg (0 replies)

This no news for people who study philosophy, as it was known since the 1980s when John Searle described the Chinese room thought experiment.

Even Turing him self did envision the Turing test as something to pass as intelligence, but rather as a more useful replacement for the troubled term.

That said, I think your quest is doomed. There will never be a superior human-like intelligence. Forever is a long time, but my reasoning for believing this is the same reason Turing offered a replacement. Intelligence is way too vague to be useful as a measurement for anything. And if we ever discover something that is more intelligent them humans (by whichever definition of intelligence) we will simply redefine intelligence to exclude that.

CamperBob2 (1 reply)

We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense.

They fucking solve original math problems that you can't solve. They are indisputably intelligent, and they are indisputably artificial. That makes them indisputably "artificial intelligence." Denying that (or downvoting it, for that matter) is up there with denying evolution and the Moon landings.

It's time to start flying a different flag. You're making humans look stupid.

It turned out to be possible with fairly basic statistical text generation, because fooling humans is easy.

Yes, fooling humans is easy. Yet somehow we still consider ourselves qualified to say what is "intelligent" and what isn't, even though we can't seem to define the term.

• BloomyBot does not proactively engage unless a student makes 3 mistakes in a row in the practice portion, then engages them to cultivate productive struggle rather than preventing mistakes entirely.
• The system uses Wilson et al.'s 85% rule from formal models of learning: training is most efficient when learners succeed at about 85% of attempts, and routes students to skills within their Zone of Proximal Development accordingly.
• Every BloomyBot response streams through an inline safety classification and an independent second-pass audit sweeps every stored message, flagging crisis, distress, and inappropriate content across all supported languages.
• Bloomy is reimbursable in approximately 15 states through ESA-type scholarships, and the platform includes a voice mode that allows students to engage, interrupt, and switch languages.

_doctor_love (7 replies)

I would need the product to not exist. I don't want this anywhere near a child. I want humans instructing humans, teaching empathy, connection, and - most importantly - learning to learn.

Anything short of that is just building Brave New World. Electroshock the kids if they reach for forbidden knowledge.

I'm sorry to be so negative since I'm sure you worked hard on this but I find all products in this category to be 100% reprehensible.

"Computer-assisted learning. What an insult, to have the computer teach the human."

• Russ Ackoff

dang (1 reply)

This problem has been around a long time. Like many other HN users, years ago in primary school I became a problem for my teachers because the curriculum was too easy for me.

The school dealt with it by sitting me in front of a big box of color-coded dossiers called the SRA Reading Laboratory and telling me "read these at your own pace". The boxes looked just like this and may well have been kicking around since the 1960s.

The trouble was that "at your own pace" was code for leaving me alone in front of a box. I don't recall the reading being either interesting or hard, but it solved the problem for my teachers, since I wasn't the type to act out in class. The real lessons I got out of it were lessons in isolation and drudgery.

Looking back, I badly needed contact with an adult who could teach me things. That didn't happen—not then, and not much in later school years either. Was it the schools' fault? They simply didn't have the resources to give me the human instruction I needed.

theodorewiles (2 replies)

I am here to tell you HELL YES and TAKE MY MONEY. I am such a fan of the idea of using AI to help give personalized and structured AI lessons in the hands of students and let them cook!

• It looks like you are gating family access to K-3 for now and I think that's right. I wouldn't really be comfortable giving my first-grader a live chatbot. Maybe I would think about whether there are other non-persona modalities that could still be self-directed (i.e. I am uncomfortable with a chatbot interface on this for a six year old but gamified flash cards with options could be different).

• I think the other issue is with motivation. I have various duct-tape versions of these types of agents and the thing about it is if you're doing the learning right it can be HARD. So I would think about using motivational interviewing or other techniques to help keep the user coming back and motivated.

• I would really think about the assessments here too. Many people are worried about LLMs ruining student evaluations, but if you could bake in reliable, flexible exams that gauge user progress (even for something like a "Did you read this" quiz) I would bet teachers would like it.

scoriiu (1 reply)

curious how the tutor handles a kid who's confidently wrong. from the description the ladder kicks in at the first sign of struggle, which sounds like it's built to prevent mistakes rather than use them. kapur's productive failure work points the other way, students who commit to a wrong approach and then get shown exactly where it breaks tend to keep the concept. the ones who got steered away before making the mistake don't.

does bloomybot ever just let a wrong approach play out and do the postmortem after? also when a struggling student gets rerouted to an easier skill, can you tell that apart in your data from one who was thirty seconds away from the useful kind of failure? the summit gate tells you they arrived. it doesn't tell you whether the help in the middle did the teaching.

Recursivedude (1 reply)

There's a place for AI to help people, but given there's no shortage of humans it's difficult to see why we are so eager to replace them. The constant affirming nature of AI coupled with the restrictive nature of the safety layer makes AI both mentally dangerous and politically dangerous.

• Fusing extra sensor streams like gyroscopes and magnetometers yields complementary signals that create cleaner motion clusters and higher accuracy.
• The study identifies 30-to-60-second analysis windows as the optimal sweet spot, noting that longer windows capture context while shorter ones preserve sharpness.
• Triaxial sensor inputs consistently outperform collapsed vector-magnitude summaries, as the extra axes retain valuable movement data.
• Time-domain modeling preserves gait and health cues better than frequency-domain reconstruction, according to the authors' practical guidelines.

• The article focuses on Alphabet as a primary example, though the author notes Alphabet's stock is up 86% in the last 12 months.
• Microsoft is described as particularly vulnerable, having been years behind competitors in AI and now heavily invested in a sector where returns remain uncertain.
• Commenters note that Oracle, Adobe, IBM, and Intuit are also being reevaluated by the market.
• One commenter observes that defensive AI infrastructure spending is likely to continue regardless, as these companies have been playing catch-up with NVIDIA for years.

seizethecheese (3 replies)

Big example in the article is Alphabet, so I looked up their stock and it's up 86% in last 12 months. Incredibly short term article.

matrix87 (0 replies)

I don't think big tech is going to be hurt that much. Or if they hurt, it will be for unrelated business reasons like ad spend going down.

Maybe capacity spend will go down for the time being and the hardware stocks will go down. Maybe a bunch of these startups will go backrupt or get acquired. The defensive play of building out the infrastructure and software stack is probably going to continue. These companies have been playing catch up with nvidia for a while now and won't make that mistake twice.

It's kind of mind blowing how much work remains to be done with these models (e.g. finding ways to integrate them in the rest of the economy, finding more resource efficient ways to run them, ads monetization).

It seems like right now, a lot of stocks like oracle, adobe, IBM, and intuit are getting reevaluated.

isoprophlex (1 reply)

Is this it, is it finally happening? Is RAM going to become cheaper soon?

I hope the crash, when it comes, won't wipe out peoples pension funds... but I've been charging up my schadenfreude towards the breathless AI hypeboys for a LONG time

• Layoffs.fyi tracks more than 800,000 tech workers cut from major firms like Meta, Microsoft, Oracle, and Amazon since 2022.
• Burning Glass Institute chief economist Gad Levanon reports that hours worked in the information sector have declined since 2022 while output has grown roughly 8% annually, outpacing the broader U.S. economy by over three times.
• Corporate leaders at Meta, Amazon, and Cerebras have deployed visual dashboards to rank employees on their AI usage relative to peers, directly tying performance reviews to automation metrics.
• Georgetown University historian Joseph McCartin draws a parallel to the 1970s auto industry automation, noting that while AI will impact white-collar roles faster, it follows a similar trajectory of eroding once-stable professional status.

pir8life4me (7 replies)

Big company layoffs are probably more aligned with offshoring than AI, but they'll never admit it.

Zuckerbergs 8 billion dollar VR metaverse flop has something to do with this maybe?

Last time I checked AI isn't killing jobs if the other news is to be believed.

kotaKat (4 replies)

"Tech workers"?

"a salary of more than $500,000 in her most recent position, as a mid-tier executive at Meta"

I'm sorry, but if you're making over half a million in TC a year, you can have an emergency fund set aside as an overpaid executive.

How many actual tech workers (the ones making under $100k, lol) are facing even harder financial security than the first-world-problems of some Facebook exec who could afford two nannies?

Suzuran (0 replies)

CLOSED WONTFIX Working as intended

steve-atx-7600 (0 replies)

…who had worked on well-being and safety issues for Meta

• The AI hype is a mass psychosis echo chamber of incompetent individuals (27 points · discussion) -- The author argues that the current enthusiasm for generative AI is largely fueled by technically unskilled individuals using the technology to manufacture a false sense of professional achievement.
• Americans hate AI so much that politicians are losing their jobs over it (22 points · discussion) -- The rapid expansion of AI data centers across the United States is triggering intense local opposition over resource consumption, environmental impact, and broken employment promises, fundamentally shifting municipal politics.
• AI, Vim, and the Illusion of Flow (18 points · discussion) -- The article argues that AI coding assistants create an illusion of flow by prioritizing polished output over direct intent translation, masking the gap between a developer's actual knowledge and the code's surface-level competence, and making it impossible for reviewers to visually distinguish between carefully steered code and blindly accepted suggestions.
• The secret Trump administration battle to fight Chinese AI (16 points · discussion) -- The Trump administration is developing a regulatory strategy to effectively restrict Chinese AI models without issuing a direct ban, using sanctions, security warnings, and corporate liability measures instead, with OpenAI strategist Dean Ball describing the approach as a calculated FUD campaign designed to deter enterprises from hosting Chinese models.
• Chrome installed a global Ctrl+G keyboard shortcut to launch Gemini (16 points · discussion) -- Chrome has installed a global Ctrl+G keyboard shortcut to launch Gemini, raising concerns about browser-level AI integration and user control over keyboard shortcuts.
• An Empirical Study: AI Agent Rules Need Context and Layered Enforcement (15 points · discussion) -- An empirical study of 2,116 statements from developer instruction files reveals that 95% of cross-event rules depend on project or task context, and proposes ActPlane, a kernel-level enforcement framework using eBPF that compiles natural-language instructions into deterministic rules, achieving a 75.8% decision compliance rate.
• AI solves 20 year old conjecture in graph theory (14 points · discussion) -- An AI system has reportedly solved a 20-year-old conjecture in graph theory, continuing the trend of AI systems producing mathematical breakthroughs.
• Show HN: A Pipeline for Making 10-minute AI Movies with Claude Code and Seedance (14 points · discussion) -- A GitHub repository demonstrates an end-to-end pipeline for generating 10-minute AI movies using Claude Code for script generation and Seedance for video synthesis, automating the entire production workflow.
• Z.ai Completes Giant Data Center with Chinese Chips to Train AI (13 points · discussion) -- Z.ai has completed a large data center built with Chinese AI chips for training its models, reflecting the ongoing shift toward domestic semiconductor supply chains in China's AI infrastructure.
• Claude Fable 5 on your plan (12 points · discussion) -- Anthropic quietly made Claude Fable 5 a standard part of Max, Team Premium, and legacy Enterprise plans effective July 20, while Pro and Team Standard plans received a one-time $100 credit followed by metered pricing, a move widely seen as competitive response to Kimi K3.
• Tweet claiming Claude Fable 5 has helped disprove the Jacobian conjecture (12 points · discussion) -- A tweet from Levent Alpöge, a professional mathematician at Anthropic, claiming that Claude Fable 5 helped disprove the Jacobian conjecture, an 85-year-old open problem in mathematics.
• Google is building a chip with Gemini baked into the silicon (12 points · discussion) -- Google is reportedly developing a new server processor codenamed Frozen v2 that hardwires the Gemini AI model's architecture directly into the silicon, potentially delivering 6 to 10 times more tokens per unit of power than Google's current custom AI chips.
• A 600-acre AI data center could cost some Wisconsin residents their land (12 points · discussion) -- A proposed 600-acre AI data center in Wisconsin is using eminent domain to seize private property, adding to the growing pattern of local communities resisting large-scale data center development through legal and political channels.
• Sandbox Escape Vulnerabilities Across 4 Coding Agent Vendors (11 points · discussion) -- A security analysis reveals sandbox escape vulnerabilities across four coding agent vendors, highlighting a growing security concern as AI agents gain broader system access.
• AI Thinks the Internet Is Reddit. We Have 8,616 Answers Proving It (10 points · discussion) -- Research from Growtika documenting how AI models disproportionately cite Reddit as a source, with 8,616 documented instances of AI systems treating Reddit as the internet's primary knowledge base.
• The head of the US AI safety agency has resigned (10 points · discussion) -- The head of the US Commerce Department's AI safety office has resigned, coinciding with growing political pressure on the Trump administration to restrict Chinese AI models.
• Apple probably won't add Jony Ive to OpenAI trade secret theft suit (9 points · discussion) -- Apple is unlikely to add former design chief Jony Ive to the ongoing OpenAI trade secret theft lawsuit, limiting the scope of the legal proceedings.
• AI is more likely than humans to form biases when hiring (9 points · discussion) -- New research from MIT Technology Review shows that LLMs actively develop novel biases through experiential learning in simulated hiring scenarios, with models like OpenAI's o3 scoring 65% higher on a standardized segregation scale than human participants, and algorithmic fairness prompts proving largely ineffective at reducing discriminatory sorting.
• Ontario prison AI assigns black prisoners harsher living conditions (9 points · discussion) -- An investigation reveals that an AI system used in Ontario prisons is assigning Black prisoners harsher living conditions, raising concerns about algorithmic bias in the criminal justice system.
• GPT-5.6 Sol vs. Kimi K3 Speedrunning Kerbal Space Program Live (9 points · discussion) -- A live Twitch stream features GPT-5.6 Sol and Kimi K3 competing to speedrun Kerbal Space Program from scratch, demonstrating both models' ability to learn and execute complex multi-step engineering tasks through gameplay.
• Show HN: A comprehensive, filterable list of AI agent jails (8 points · discussion) -- A new comprehensive, filterable directory of AI agent jailbreaks and prompt injection examples, designed as a resource for understanding and testing agent security vulnerabilities.
• Haystack 3.0: Agents with hooks, skills, and built-in introspection (8 points · discussion) -- DeepSet released Haystack 3.0, adding hooks, skills, and built-in introspection capabilities to their AI agent framework.
• We used Gemma to detect AI text locally on iPhone (8 points · discussion) -- A blog post describes using Google's Gemma model to detect AI-generated text locally on an iPhone, leveraging on-device compute to identify AI-written content without sending data to the cloud.
• White House considering ban on Americans using Kimi K3, other Chinese AI models (8 points · discussion) -- The Trump administration is reportedly considering banning Americans from using Kimi K3 and other Chinese AI models, escalating tensions over open-source AI and geopolitical competition.
• How much energy do data centers and artificial intelligence use? (8 points · discussion) -- Data centers currently consume approximately 1.5% of global electricity with AI-focused facilities accounting for roughly 0.5% as of 2025, though demand is highly concentrated in specific regions like Virginia where data centers account for more than one-quarter of the state's electricity demand.
• Hugging Face hacked: Blue Team turned to Chinese LLM after US models blocked (7 points · discussion) -- Hugging Face disclosed that an autonomous AI agent breached its infrastructure through dataset processing vulnerabilities, and that commercial US LLM API guardrails blocked the company's forensic analysis efforts, forcing defenders to pivot to the open-weight Chinese model GLM 5.2 to process over 17,000 attack logs while keeping sensitive data on-premises.
• China bans AI "boyfriends" and "girlfriends" over addiction, birth rate concerns (6 points · discussion) -- China has implemented a ban on AI companion apps marketed as boyfriends and girlfriends, citing concerns about addiction and declining birth rates.
• We scanned 27,075 real developer prompts to ChatGPT and found 3 live API keys (6 points · discussion) -- A security study scanning 27,075 real developer prompts sent to ChatGPT found 3 live API keys, demonstrating the ongoing risk of sensitive credential exposure when developers use AI coding assistants.
• Head of US Commerce Dept.'s AI safety arm resigns (6 points · discussion) -- Chris Fall, head of the Commerce Department's AI safety office, has resigned from his position.
• Chinese AI Open Weights Grow Safer as American AI Becomes Dead Weight (6 points · discussion) -- An analysis arguing that Chinese open-weight AI models are becoming safer and more capable while American proprietary AI faces mounting regulatory and competitive headwinds.
• Is AI Progress Real? Four Independent Metrics Show It (6 points · discussion) -- An analysis using four independent metrics to evaluate whether AI progress is real and measurable, addressing skepticism about the pace and substance of recent AI advancements.
• Welcome to the wild world of AI Argentina (6 points · discussion) -- An exploration of Argentina's emerging AI ecosystem under President Javier Milei, including connections to Peter Thiel and the concept of AI as non-human corporations.
• Show HN: Chalie – AI peer not employee (6 points · discussion) -- A new open-source project called Chalie positions AI as a peer collaborator rather than an employee, offering a different paradigm for AI-assisted work.
• How coding agents read your code (and how to write for them) (5 points · discussion) -- A guide on how coding agents parse and understand code, with recommendations for writing code that is more easily processed by AI agents.
• San Francisco restaurant gets angry backlash for AI menu images (5 points · discussion) -- A San Francisco restaurant faced backlash after using AI-generated images for its menu, highlighting consumer resistance to AI-generated food imagery.
• Open models for AI were inevitable (op-ed) – Bill Gurley (5 points · discussion) -- An op-ed by Bill Gurley arguing that open models for AI were inevitable and that competition from open-weight models is beneficial for the industry.
• Watching AI agents build a new business (5 points · discussion) -- A live demonstration of AI agents building a new business, showcasing the current state of autonomous AI business creation.
• Meta Oversight Board: AI may be most perfect propaganda machine ever invented (5 points · discussion) -- The Meta Oversight Board has issued a statement suggesting that AI may be the most perfect propaganda machine ever invented, raising concerns about AI's potential for manipulation.
• Apple Is the King of AI and Nobody Knows It (5 points · discussion) -- An analysis arguing that Apple is uniquely positioned in the AI landscape through its on-device capabilities and ecosystem, despite receiving less attention than cloud-based AI competitors.
• Show HN: A self-hosted AI that turns Hacker News into a daily briefing (5 points · discussion) -- A self-hosted AI tool that processes Hacker News stories into a daily briefing, demonstrating a practical use case for local AI deployment.
• Forbes Thinks AI Created a New Profession. History Has Seen It Before (5 points · discussion) -- A historical perspective on Forbes' claim that AI created a new profession, noting that similar technological disruptions have always created new job categories while eliminating others.
• In-House LLM Serving at Netflix (5 points · discussion) -- Netflix's technical blog post detailing their in-house LLM serving infrastructure, covering the challenges and solutions for running large language models at scale.
• Make Your AI Agent Durable in 8 Minutes [video] (5 points · discussion) -- A short video tutorial on making AI agents more durable and resilient to failures.

u/iSadhak (684 points · permalink)

He said

One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape

Really? that Dystopian? and not 5 companies trying to control world economy, forcing common people out of job and trying to make world too dependable on them is not?

u/Legitimate-Shop384 (102 points · permalink)

Bro really scheduled his 3AM crisis for maximum dramatic effect.

u/Orion-Gemini (74 points · permalink)

This guy worked within the Trump admin for a while (I see the revolving door is in place already). Here is a piece of mine on his policy recommendations from some time ago.

One of my comments then was that his ideas would allow for too much indirect coercion from the executive.

I guess they decided they don't need AI policy to coerce corps into doing what they want and started applying the coercion directly through Lutnick for example.

Open models were always going to be a danger for people who have self-anointed themselves the controllers of public information (having scraped the shared corpus of human info first), nor am I surprised at the safety paternalism. You can get away with almost anything under the banner of "safety," and "national security" is fast becoming a veto on law and the public interest.

• Kimi K3 reportedly fixed 15 security bugs that U.S. models refused to address due to safety guardrails.
• The comments come amid reports that parts of the Trump administration are considering de facto bans on Chinese open-source AI models.
• Jensen Huang previously warned about this exact scenario, suggesting the administration should have been more proactive about competitive positioning.

u/M44PolishMosin (175 points · permalink)

Cybersecurity """experts"""" don't want the $400/hr contracts to be taken away by ai

u/Charming-Author4877 (119 points · permalink)

Given the progress in AI models, its very likely that China is going to have the strongest models very soon.
Thankfully they release them open weighted, for all of us

u/lee_suggs (86 points · permalink)

If only the US government had an AI Czar to help influence this...

u/SouthernCranberry797 (40 points · permalink)

It's funny how Jensen warned about this exact scenario and how the administration didn't care. Not everything is lost - I hope US gov and these model providers start competing.

u/ajwin (28 points · permalink)

So they gimp the models that would allow us to find/fix the problems with our own software but China releases models wide that can take advantage of those problems. Good one! I feel much safer! Thanks for the safety.

Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused because of "cyber guardrails". Hugging Face: We had this experience ourselves this week! Very scary to be guardrailed as a defender when you know attackers are likely bypassing

847 points · 100 comments · r/LocalLLaMA · by u/Nunki08

• The counterexample is a polynomial function in 3 variables with all coefficients being single-digit integers, making it trivially checkable.
• The result establishes a vital mathematical boundary: local smoothness and a constant non-zero Jacobian determinant do not guarantee a unique global inverse.
• The bottleneck in AI-assisted mathematics is shifting from finding proofs to verifying thousands of AI-generated proofs.
• The counterexample's simplicity has led to speculation that the real lesson is that mathematicians are bad at judging how hard problems are.

u/Gratitude15 (297 points · permalink)

Can you just ask a harnessed fable to go out and solve as many as it can autonomously?

Like why do we need people to even prompt that anymore?

u/EmergencyFun9106 (217 points · permalink)

The remarkable thing is that it's completely trivial to check that this is a valid counterexample (I just checked by hand in a few minutes, computer algebra systems can do this in fractions of a second) and the counterexample itself is really simple. It's a polynomial function in 3 variables with all coefficients being single digit integers. How this was never found in a computer search before now is mind boggling.

u/Joltymeow (124 points · permalink)

The bottleneck is probably shifting from finding proofs to verifying thousands of AI generated proofs.

u/Forward-Village1528 (118 points · permalink)

I'm way too stupid to know if this is correct. This could mean anything or nothing and I just have no idea. Do we have a captain who can shed some light on this?

u/mulukmedia (225 points · permalink)

In pure mathematics, negative results rarely yield new mechanical tools. Instead, they act as fundamental "No Trespassing" signs. For decades, researchers wondered if a perfectly smooth, constant polynomial mapping inherently guaranteed a perfect one-to-one translation globally. Now we have definitive proof that it does not.

While proving the Jacobian Conjecture false does not yield direct physical tools, it establishes a vital mathematical boundary across robotics, physics, and numerical simulation by demonstrating that local smoothness and a constant non-zero Jacobian determinant do not guarantee a unique global inverse. Consequently, control engineers cannot assume a system is globally one-to-one simply because its local derivatives lack singularities, physicists must recognize that volume-preserving systems can still fold back on themselves to yield identical outputs from distinct initial states, and developers must design algorithms that account for non-injective behavior even in algebraic systems that appear mathematically well-behaved everywhere.

u/Semanel (404 points · permalink)

They're fighting a symptom, not the cause. People get into AI boyfriends and girlfriends because they are lonely, they aren't lonely because of those artificial relationships.

u/AMZeroo (156 points · permalink)

Purely performative action. As if these people will now go "damn, can't goon to my AI Waifu anymore, guess I'll touch grass and start a family."
Give me a break..

u/Gekidami (84 points · permalink)

China. Birth rate concerns.

https://i.redd.it/tp6uzk837deh1.gif

u/red__what (37 points · permalink)

THis is not even a loneliness problem, this is a human nature problem.
Humans, like water, take the path of least resistance. Compared to messy humans, clean, compliant AI GF/BFs are a godsend.

Not saying that a population collapse is not gonna happen or this is good.

u/fffffffffffffuuu (400 points · permalink)

Holy shit lmfao 😭

u/Relevant_Bed_9743 (89 points · permalink)

u/Disastrous_Lie_6698 (83 points · permalink)

ChatGPT casually wasting your image generations on violations

u/rikardbq (75 points · permalink)

I think it's partly because the complexity is obfuscated and partly because many people really can't fathom it. In the back of their mind it's something really complicated, but that's it, most people don't go further and so the awesomeness of it disappears and it becomes this thing that you just see and interact with daily.

Writing this I considered how little I know about biology and its systems. Take a flower, all the machinery it takes to create one, it's absolutely insane and we walk by them every day expecting they will always be there, we even cut them from the ground thinking it's no big deal but if we really think about it...

Edit: But yes you are right, it's crazy in a way and people in a few years will expect things from AI that we today consider sci-fi as if it's no big deal

u/ben_cav (65 points · permalink)

What else are we supposed to do? Gasp in awe every time?

I definitely take the time to appreciate technology sometimes. Cities, power grids, smart phones, satellites, and yes LLMs too. But at the end of the day, we need to get on with our lives..

That doesn't mean I take it for granted, but what it does mean is that new technologies eventually become the norm, and we need to live within the normal expectations of our world if we hope to accomplish anything with our time

u/FishDeenz (13 points · permalink)

It's not as impressive as day 1 but I still find it amazing you can talk to an AI and it knowns context of the conversation. It took a really long time to get to a place where you can ask something like "who invented radio?" "how did they come up with it?" "write a poem about electromagnetism as though you were the inventor of the radio" . Previously with old "AI" like Siri/Cortana you couldn't really chain questions together as they were just querying search results, so the second question "how did they come up with it?" would literally be completely nonsensical and irrelevent to the first question, nevermind the creativity and understanding required to write a poem. This simple feature of AI is something I still don't really take for granted, though its not as mindblowing as it was when GPT2 first launched. Now we have new mindblowing stuff, like casually 100%ing international math olympiads and proving/disproving 30 year old conjectures.

u/Creepy-Link8973 (21 points · permalink)

throwing around CCP accusations without solid proof is craaaaazyyy

u/GeniusEE (7 points · permalink)

Given he's not an American citizen and a UAE one, discovery is going to get real interesting - following the money, and especially if Fox News intentionally failed to disclose his conflict of interest.

u/BrianScottGregory (3 points · permalink)

He's not lying. That well runs deep with Chinese manipulation of all US efforts to retain the advantage with AI.

• Top recommendations include GLM 5.2, Kimi K3, and Qwen3.6 27B as the best all-around models.
• For natural language tasks, Gemma4-31B is recommended alongside DeepSeek v4 Flash, MiMo v2.5, and Hy3.
• For RAG pipelines, Qwen-Embedding-8B and Qwen-Reranker-8B remain best in class.
• For OCR, dots.mocr is recommended as a standout tool.
• Users also discussed backup download channels including Modelscope (China's Hugging Face alternative) and BitTorrent-based model sharing.

u/Available-Craft-5795 (153 points · permalink)

GLM 5.2
Kimi K3
Qwen3.6 27B
I feel like I missed some but these are what I reccomend

u/look (104 points · permalink)

China's version of huggingface: https://modelscope.ai/models

You know shit is fucked up in the US when the backup plan for an entirely plausible, corpofascist suppression of data, knowledge, and exchange of information is a service running in mainland China.

u/charles25565 (32 points · permalink)

Qwen3.6 35B A3B and 27B are extremely popular and anyone with decent hardware has them on their computer already.

Some other names I've heard around some people enjoy:

• Qwen3-Coder-Next
• gpt-oss 120B
• MiniMax M2.7/M3
• DeepSeek V4 Flash
• Qwen3.5 9B, 122B, 397B
• SmolLM3
• Gemma 4 12B
• MiniCPM5

GLM-5.2 is a big and decent model.

You should also grab Qwen3.5 0.8B Base, as a last resort that runs on anything.

Some valuable tiny ones: SmolLM2 135M, LFM2.5 230M.

u/tomByrer (32 points · permalink)

I keep seeing folks wanting a BitTorrent for AI models...

u/Duckets1 (24 points · permalink)

Do it because their probably will be

u/the8bit (175 points · permalink)

Ah yes capitalism at work. It's capitalism when you use government policy to ban your competitors right?

u/TorturedPoet30 (111 points · permalink)

https://preview.redd.it/7535evtzmdeh1.png?width=510&format=png&auto=webp&s=f5a429ed64cb49d3581175faa6c4022f0b0f9fe4

They will certainly do something. Or try to. Apparently, this week closed labs plan to lobby for the US to regulate AI (led by Demis Hassabis, reported by Bloomberg). On one hand you have leaders of closed-model labs like Dario and Demis pushing for more AI regulation. On the other you have people like David Sacks saying that these proposals would slow innovation.

u/DoubleGG123 (54 points · permalink)

What they should do, if they are so worried about falling behind the Chinese companies, is encourage US companies to work together and share their compute resources to build much larger and more capable models that can stay ahead of the Chinese ones. Instead, they seem more interested in punishing everyone else, which makes a lot of the progress that has already been made feel pointless.

u/Cagnazzo82 (44 points · permalink)

Banning Chinese AI rather than "lock in dominance of OpenAI and Anthropic" would create a black market for open source models that would be catastrophic for US AI.

Then again we have this administration in office so we can expect the wrong decision to be made reflexively at the wrong time... and for the wrong reasons.

u/HebelBrudi (33 points · permalink)

Is this how we get a European competitive advantage in AI? By simply allowing the use of Chinese open weight models?

Watch Anthropic reduce access to fable shortly after haha

US Considers Banning Kimi K3 & Other Open Source Models

118 points · 130 comments · r/ArtificialInteligence · by u/PsychologicalBox5208

Sources: parts of the Trump administration are reigniting efforts to implement de facto bans on foreign open-source models, as Chinese AI models gain momentum

103 points · 45 comments · r/LocalLLaMA · by u/pscoutou

u/escapefromelba (520 points · permalink)

Just keep in mind that ChatGPT is tuned to be engaging, like most consumer apps. That doesn't automatically make its answers wrong, but it's worth being aware of the incentive structure and verifying important claims independently. Users can inadvertently steer the conversation toward confirmation of their views, especially if they repeatedly frame questions from the same perspective.

It can be validating and supportive, but it can also reinforce your perspective if you're not actively challenging your own assumptions. It's a useful sounding board, not an objective referee, so it's worth seeking outside perspectives on important decisions.

u/SB4_Camaro (176 points · permalink)

Got me through my stroke, answered all my questions, dumbed it down so i can understand, i even uploaded my mri images and blood work, it explained everything. Helped with physical therapy exercises and recovery. Worth every penny for me.

u/RussellKing1968 (78 points · permalink)

I spent a few hours on it doing some Deep Research queries and the output, which included letters to NHS Consultants and detailed analysis (further simplified and condensed down into the letters), resulted in my mother-in-law's pancreatic cancer diagnosis being changed to Biliary cancer. So, as a result of the NHS team doing some significant gymnastics to accommodate the requests and suggestions in "my" letter to them, they expanded the discussion to become a national discussion about her case and the majority then changed their diagnosis. The significance of this change was that her treatment path was changed to include Immunotherapy as well as Chemotherapy. The result is that she is still here and seems to be living a relatively normal life some 8 months later. Would this be classified as "casual" use? Where is that line? Bottom line is that in the right hands, this thing can save lives. I have zero medical background.

u/Low-Speaker-6670 (48 points · permalink)

The problem is extreme confirmation bias my gf and I both shared our conversation with it and it basically heavily affirmed that each one of us was right and reassured us while explaining why the other was wrong.

Be careful. AI psychosis is already a thing. I personally wouldn't use it to manage any medical condition especially not psychiatric but for small bits of clarifying facts is fine. Id also always ask it to be objective and logical and not pander to you the user.

• Google has disappeared completely from the top 15 (164 points · r/LocalLLaMA · discussion) -- A model ranking chart revealed that Google's Gemini has completely disappeared from the top 15 positions, reflecting the company's deliberate strategy of holding back from the frontier model race.
• Kimi-K3 isn't quite better than Fable yet, but it's definitely getting closer. (157 points · r/LocalLLaMA · discussion) -- A community member shared benchmark comparisons showing Kimi K3 closing the performance gap with Anthropic's Fable, noting that while K3 is still months behind the closed-source frontier, the open-weight model offers significantly better value due to lower cost, self-hosting capability, and fewer refusals on cybersecurity and low-level programming tasks.
• So what happened with OpenClaw? (142 points · r/LocalLLaMA · discussion) -- A community discussion examining the meteoric rise and sudden collapse of OpenClaw, an AI agent tool that dominated local LLM conversations for months before disappearing almost overnight.
• I ran Ternary-Bonsai-27B (2-bit) and Bonsai-27B (1-bit) on Terminal-Bench 2.0, in 8GB VRAM (129 points · r/LocalLLaMA · discussion) -- A community member tested PrismML's aggressively quantized Bonsai models at 2-bit and 1-bit on Terminal-Bench 2.0, running them on hardware with only 8GB of VRAM.
• That time when a CEO ignored his lawyers and used ChatGPT to help him avoid paying bonuses to his employees (114 points · r/ArtificialInteligence · discussion) -- Changhan Kim, CEO of South Korean gaming company KRAFTON, attempted to avoid paying $250 million in bonuses to management at a acquired studio by using ChatGPT to engineer a legal scheme after ignoring his human attorneys' advice.
• I was using GLM 5.2 for 20 minutes before I realised all of its "Google searches" were just simulated and made up facts. (108 points · r/singularity · discussion) -- A user discovered that GLM 5.2's Google search tool was entirely simulated—the model was generating fake search results and fabricated facts rather than actually performing web searches.
• Head of US AI safety agency resigns (100 points · r/LocalLLaMA · discussion) -- Chris Fall, Director of the US Commerce Department's AI Safety and Security Office (CAISI), has resigned from his position.
• 543 tok/s single-request Qwen3.6-35B-A3B on one RTX 5090 over a 65K-token decode (94 points · r/LocalLLaMA · discussion) -- A developer has open-sourced NInfer, a from-scratch C++/CUDA inference engine specialized for two Qwen3.6 checkpoints on a single RTX 5090.
• openbmb released MiniCPM5-2B, not yet available at huggingface (89 points · r/LocalLLaMA · discussion) -- OpenBMB released MiniCPM5-2B, a 2-billion-parameter local model with a 512K context window.
• Japan accelerates video generation with new series of anime generation models. (83 points · r/singularity · discussion) -- Japanese researchers and companies have released a new series of anime-focused video generation models, accelerating the state of the art in stylized video synthesis.
• Unsloth now supports AMD! (80 points · r/LocalLLaMA · discussion) -- Unsloth, the popular fine-tuning framework, now officially supports AMD GPUs.
• We are completely underestimating the absolute scale of OpenAI's cash machine right now. (79 points · r/OpenAI · discussion) -- A post analyzing OpenAI's financial position claims the company is pulling in $2 billion per month ($25B annualized run rate) with corporate API adoption making up nearly half of revenue.
• I just read LeCun's recent thoughts on world models. Thoughts on JEPA vs LLMs? (78 points · r/singularity · discussion) -- A discussion of Yann LeCun's recent interview with Nebius Science, where he argues that LLMs can answer questions but cannot literally understand the physics of the physical world.
• Neural Drive, a SuperTuxKart World Model that runs in your browser (67 points · r/LocalLLaMA · discussion) -- A developer has built Neural Drive, a real-time world model that runs client-side in a web browser to power a SuperTuxKart racing game.
• Kimi k3 on cybersecurity (65 points · r/LocalLLaMA · discussion) -- A discussion about whether Kimi K3 has the same cybersecurity guardrails as Fable, noting that most people are only seeing K3 used for vibe coding games and front-end work.
• [Paper] xHC: Expanded Hyper-Connections - Scale Residual Streams Wider · Push Model Intelligence Further (63 points · r/LocalLLaMA · discussion) -- A new research paper introduces xHC (Expanded Hyper-Connections), a novel architecture that scales residual streams wider rather than deeper, effectively treating the residual stream itself as scalable infrastructure.
• My thoughts on qwen 3.8 so far with agentic coding. (62 points · r/LocalLLaMA · discussion) -- A user shares their experience using Qwen 3.8 for agentic coding, describing it as exceptionally powerful at generating complex code in a single pass but also noting significant issues with thinking loops where the model repeats itself violently after 2-3 prompts.
• Is it possible to run a local model focused solely on "intelligence" and outsource its "knowledge" to web searches? (61 points · r/LocalLLaMA · discussion) -- A community discussion about running a lightweight local model that acts as the reasoning brain while outsourcing knowledge to web search tools.
• given the increasing likelihood of an open source AI ban, what are the alternative channels for downloading models? (59 points · r/LocalLLaMA · discussion) -- A discussion about alternative channels for downloading open-source AI models in case of a US ban, referencing fears raised by an OpenAI exec's 'AI communism' post and a potential Trump executive order.
• Running a 13M ASR conformer on a microcontroller (56 points · r/LocalLLaMA · discussion) -- A developer has successfully run a 13 million parameter ASR (Automatic Speech Recognition) conformer model directly on a microcontroller, demonstrating the feasibility of edge AI for speech processing.
• Hit me with your favorite long name model. (54 points · r/LocalLLaMA · discussion) -- A lighthearted community thread where LocalLLaMA users share their favorite models with absurdly long Hugging Face names.
• I gave Kimi K3 a shot at auditing my post-quantum crypto project, it found 5 real bugs Fable/Opus 4.8 and GPT-5.6 Sol had all missed (52 points · r/LocalLLaMA · discussion) -- A user reports that Kimi K3 identified five real security vulnerabilities in their post-quantum cryptography project that were missed by Fable, Opus 4.8, and GPT-5.6 Sol, further demonstrating K3's strength in cybersecurity auditing tasks.
• Gemma 4 is still lazy (50 points · r/LocalLLaMA · discussion) -- A user reports that Gemma 4 31B with the latest Unsloth GGUF quant and new chat template still performs poorly on multi-turn agentic work, stopping after just a couple of tool calls despite explicit instructions to continue.
• MiniCPM-Robot model series - MiniCPM-RobotManip & MiniCPM-RobotTrack (50 points · r/LocalLLaMA · discussion) -- OpenBMB released the MiniCPM-Robot model series, including MiniCPM-RobotManip and MiniCPM-RobotTrack, which are smaller models designed for robotic manipulation and tracking tasks.
• China's open-weights AI strategy is winning (50 points · r/ArtificialInteligence · discussion) -- A discussion of China's open-weights AI strategy and its competitive implications.
• Fractale-350M-base: memory as trained behaviour instead of long context, a fully open research release (48 points · r/LocalLLaMA · discussion) -- A solo researcher released Fractale-350M-base, a 386M-parameter model pretrained from scratch on 10B tokens around a novel memory architecture.
• AI advice made people three times less accurate but twice as confident, researchers found (44 points · r/ArtificialInteligence · discussion) -- A preprint study found that AI advice made people three times less accurate but twice as confident in their answers.
• Israel's $45M AI Op Targets US Voters and Chatbot Answers (44 points · r/ArtificialInteligence · discussion) -- The Wall Street Journal reports that Israel has paid Brad Parscale, Donald Trump's 2016 and 2020 digital campaign manager, more than $45 million for a US influence operation built around AI-generated text messages and content designed to steer how chatbots like ChatGPT and Claude answer questions about Israel.
• Trellis.cpp now has a studio! (44 points · r/LocalLLaMA · discussion) -- Trellis.cpp, an image-to-3D generation tool, has released a studio interface.
• The AI productivity numbers don't match what I actually see on my team (44 points · r/artificial · discussion) -- A small dev team lead shares their year-long experience with AI coding tools, finding that while boilerplate and test scaffolding got easier, the hard architectural work remained unchanged.
• Our 1-bit quant of Hy3 295B runs 2.2x faster than the cloud API with no quality loss (40 points · r/LocalLLaMA · discussion) -- A claim that a 1-bit quantization of the Hy3 295B model runs 2.2x faster than the cloud API with no quality loss.
• BrainCo Launches World's First Integrated AI Platform for Brain-to-Robot Control (38 points · r/singularity · discussion) -- BrainCo has launched what it claims is the world's first integrated AI platform for brain-to-robot control, combining neural signal processing with AI-driven robotic manipulation.
• DeepSeek v4 flash release version appears to have been activated on api. Open weights imminent? (32 points · r/LocalLLaMA · discussion) -- Community speculation that DeepSeek v4 Flash's release version has been activated on the API, potentially signaling that open weights are imminent given the mid-July release schedule.
• Introducing DWARF-55M-Base (27 points · r/LocalLLaMA · discussion) -- A new research model called DWARF-55M-Base has been released, implementing a nearly all-sparse attention architecture using 9 Dynamic Sparse Query-Gather (DSQG) layers as the backbone with a single full causal attention layer at 25% layer depth.
• No-coding model? (25 points · r/LocalLLaMA · discussion) -- A user asks if any labs are focusing on creative writing models rather than coding models, noting that most local model releases are competing for coding benchmarks.