Gemini 4 Argon Leads, Regulators Step In, Open Source Booms

• Introductory pricing is set at $2 per million input tokens and $10 per million output tokens, with cached input tokens discounted by 95%.
• Argon tied for first place on CWE-bench v1 with a 68% score for remediating security vulnerabilities, significantly outperforming the previous 3.8 Flash Cyber model in black-box penetration testing.
• The model ranks #1 on Zapier's AutomationBench with a 51.3% score for end-to-end business execution and achieves 91.7% on LVBench for long video understanding.
• Internal Google agents autonomously analyzed fleet-wide telemetry to optimize data center memory, freeing over 300 TiB with projected total savings between 500 TiB and 1 PiB.
• Google is deploying a chain-of-thought monitoring system that automatically halts execution if the model's reasoning deviates beyond intended user bounds.

The important take away here: the leapfrogging we've seen this year doesn't seem to be a temporary thing. The famous theory of Dario Amodei was that AI was this winner-takes-all field where the first team to get a head start would never cede ground back. The term he liked to use was, "concentrating". This is yet another datapoint that he was wrong about that. AI seems more distributed amongst neoclouds and traditional hyperscalers, FAANG and startups, GPUs and ASICs than it did this time a year ago.

Nobody has a moat.

— nickysielicki (36 replies)

I think some in the AI industry drank their own Kool-Aid. They believed that if they had the best model and the most compute, they could tell the model, "Make a better model." And it would, and the next one could make its replacement, and so on.

So far, that's not exactly how it's played out. Humans are still necessary for the leaps in capability or efficiency. A model can grind on a problem to eke out the most performance, and models can synthesize data and iterate on various techniques to find the optimal combination. But, seems like humans still have to provide the real thinking, and the talent and drive for doing that is not concentrated in one company or city or even one country. And, (surprisingly) a lot of the people involved are in it for advancing the field more than making another billion dollars, so they're publishing their research.

So, yeah, the moat isn't deep. Even the compute moat, that OpenAI, Musk, and a bunch of other also-rans (like Oracle) bet the farm on, isn't really panning out. The Chinese makers just spent their effort on making models vastly more efficient, since they couldn't do anything about having an order of magnitude less compute available.

— SwellJoe (2 replies)

Frontier LLMs that Google doesn't control are existential threats to Google's business model. If no one is using Google search or browsing websites themselves because they're asking LLMs, Google doesn't make ad revenue. Gemini at least has a path to monetization for Google (subscriptions, ads baked into responses).

— mattstir (1 reply)

• Clef includes a built-in vision encoder and a 64k context window, expanding on the text-only, 32k-window Jev model.
• In internal threat intelligence tests, Clef classified a website domain in 2.2 seconds, compared to 4.7 seconds for gpt-oss-120b.
• Clef-flash achieves a median benchmark latency of 38.8 milliseconds and a p95 latency of 122.4 milliseconds.
• The model's training pipeline uses Reinforcement Learning for Calibrated Decisions (RLCD), which grants partial credit for adjacent ordinal choices and applies a reference penalty to prevent distribution shift.
• Cloudflare guarantees that traffic sent to Clef via Workers AI will not be read, stored, or used for training unless customers explicitly opt into the fine-tuning service.

Can someone explain how so many folks managed to build decision models within days or weeks after Typesafe came out with Jev? Is this concept of decision models been in the works for a while? Is it easy to copy?

— warkdarrior (12 replies)

You can use already trained large transformer models to make one, so it doesn't require the kind of high-scale compute, high quality data, data cleanup, reinforcement, and so on training that say an LLM does.

— didibus (0 replies)

They are not too difficult to train if you already have infra to train regular LLMs. You can typically replace a few layers train them alone and you're off to the races.

Getting training data that works well for calibrated classification objectives is difficult.

I hear conflicting opinions (including my own) about how well calibrated each of these are. Jev seems to be the best.

But the jev release made obvious the PMF for these models, and the underlying reality is that calibration really doesn't matter much when you're replacing usecases where people were using damn LM head softmax probabilities before, which are nowhere near calibrated.

So now everyone simply finetunes qwen and makes a compared-to-regular-LLM vastly cheaper decision model. And it works for majority of usecases. People mostly only care about accuracy, not confidence.

— porridgeraisin (0 replies)

• DeepSeek's V4.1-Flash model reduces the KV cache footprint for long-context sessions by a factor of roughly 437x compared to its V1 release.
• The optimization stack combines MLA architecture, CSA2, cross-layer cache reuse, and FP4 caching to lower the global KV cache to 890 bytes per token.
• Anthropic's Opus 5.5 model reduced cache read pricing from $0.50 to $0.20 per million tokens, marking a 60% cut.
• OpenAI's GPT-6.1 Sol model dropped its cache read cost from $0.50 to $0.10 per million tokens, an 80% reduction.
• Both Claude Opus 5.5 and GPT-6.1 Sol were released with minimal pre-announcement, attributed to embarrassment over adopting competitor technology.

There are 1000 Chinese labs. They are involuting, they cannot coordinate and the state won't let them coordinate because the state wants domination, not actual profits for anybody. So the market forces are allowed to dominate.

Because of the basic huge recession going on in China, you can't actually make money in China doing China things. So they gotta gird up their export stuff and try to export.

— curuinor (7 replies)

As far as I can tell, neither of the frontier US labs have referred to distillation as "stealing", but someone please provide a link if I'm wrong.

They do claim that it violates their ToS, which we can assume is simply correct, since they get to put whatever they want in their ToS.

Given all that, I don't know what the fuss is. Are they supposed to not use the advances that were openly published by Chinese labs?

— emtel (2 replies)

DeepSeek's innovations are published as research. There's nothing 'assumed' about this. The common slur repeated in the West that Chinese labs are parasitic distillers is totally absurd when so many genuinely valuable advances and contributions to the field are published openly by China's labs.

— squidbeak (0 replies)

At the 1000 foot level, this is just another part of the Chinese master plan that's been playing out for decades now. The master plan seems to be: train far more engineering and stem graduates than anyone else, let them loose in a dog eat dog capitalist garden and fertilise the garden with sprinkling government money.

— rstuart4133 (0 replies)

Market manipulation or self-sabotage? If Western labs are quietly adopting Chinese innovations while publicly denouncing them, that's either a brilliant strategy or a sign of desperation. Either way, the narrative is crumbling.

— foul (0 replies)

• The current vector-primary layout constrains query block sizes to roughly 100–200 documents per cluster, whereas modern engines like DuckDB and ClickHouse utilize blocks of 2,048 to 65,000 rows to fully saturate CPU pipelines.
• Under the existing SPFresh indexing algorithm, updating a single vector can trigger cascading rewrites of hundreds of associated attributes and inverted indexes due to cluster rebalancing requirements.
• Multi-vector document representations currently force the system to duplicate full document contents under every vector's ANN address, leading to significant storage amplification.
• Despite these architectural constraints, the legacy setup successfully supports single indexes containing over 100 billion vectors while delivering p99 read latencies under 200 milliseconds at 1,000+ QPS.
• turbopuffer currently processes over 10 million writes per second and 25,000 queries per second across more than one trillion hosted documents.

Is it time to kill the database and replace it with a LLM optimized compiled version that simply implements the required API directly in (Rust) code, without any dynamic overhead? It probably will still be based of off a base design or a base file format.

Ultimately this system will encompass the whole OS, of course, but the DB might be the best place to start.

— childintime (6 replies)

This write amplification is large enough that our efforts to tune indexing throughput have started to hit diminishing returns.

don't key on the ANN address. That is precisely the change turbopuffer v3 makes. As you can imagine, it is not a trivial change.

This is a direct parallel to how Postgres and Mysql built indexes.

Your design choice went from a Postgres design pattern to a Mysql one. The difference is the reindexing cost vs the lookup cost - Postgres optimized for lookup and Mysql does for indexing on writes. Or more accurately, Postgres was better with good schema design using joins & mysql was optimized for a bad design with less normalization where many indexes exist for the same table.

Postgres always points an index to a row-id within postgres which is an arbitrary value which changes on each update.

MySQL, always assuming the storage engine is pluggable, points to the primary index entry and adds an extra indirection to the lookup.

I don't do databases any more that much, but the design for NIMBLE file format has a lot of quirks which are relevant to this specific idea (wide tables).

But the old Uber post about switching from Postgres to Mysql to prevent index amplification[1] is a direct mirror to this post.

[1] - https://www.uber.com/us/en/blog/postgres-to-mysql-migration/

— gopalv (4 replies)

I find very little reason to use a pure vector database for enterprise retrieval. We built an enterprise retrieval engine on top of a SQL database with native vector support, and the flexibility is something we cannot ignore. Vector similarity is just one query primitive alongside full text search, filters, joins, ordering and normal relational predicates. Tenant/app/collection isolation becomes part of the query itself. ACLs, document versions, categories, metadata constraints and temporal filters are ordinary predicates rather than something you have to bolt onto a vector store. SQL is already going to be part of almost any enterprise system. Adding a separate vector database introduces another moving part and syncing two system whenever you update your data is the most difficult thing to get right.

— sreekanth850 (1 reply)

• The network uses FP8 (E4M3) activations with FP16 accumulation across 141 MiB of weights, operating as a generative renderer rather than a traditional upscaler.
• Performance tests on an RTX 4070 SUPER record 2.8 ms at 768x768, 7.8 ms at 1080p, 12.6 ms at 1440p, and 29.3 ms at 2160p across 241 GPU dispatches.
• Custom PTX kernels leverage mma.sync E4M3 instructions, cp.async rings, and barrier-free device counter chaining to optimize execution speed.
• A built-in parity tool validates output against official NVIDIA captures, verifying that all 75 internal block boundaries match byte-for-byte.
• The project contains absolutely no NVIDIA software, headers, or weights, requiring contributors to provide their own model directories.

Am I the only one who feels a sense of disinterest in a project where the main README is LLM-generated? Does the author not have time to write what they did and how it's used?

— Borealid (10 replies)

How useful is this without weights?

Isn't the mote that Nvidia has is they work with studios to generate the training data from the game, then they ship a model per game?

Or is my knowledge outdated here and they're just using a single generalised model?

— franticgecko3 (5 replies)

From this I assume the network uses the (via motion vectors) reprojected previous frame in order to increase temporal stability, i.e. similarity over adjacent frames. But this isn't strictly necessary, and apart from it, DLSS 5 is a pure post-process filter. So you could apply it to an old animated CGI movie like Final Fantasy (2001). Which should make it look significantly more realistic, at the cost of some flicker or other temporal instability.

— cubefox (0 replies)

"Am I the only one who feels a sense of disinterest in a project where the code is LLM-generated? Does the author not have time to code the project?"

This is how I feel about every single project announcement on HN recently, they are already bragging about models all over the place, why shouldn't they go full way down being replaced by the Borg?

— pjmlp (0 replies)

If the README is >90% AI generated and it is as long as a novel, I am not going to read it and will assume that the author did not read or write it either.

Unfortunately it is slop, beyond the comprehension of the author unless they are experienced with DLSS internals to explain it in depth.

— rvz (0 replies)

• Meta's AI data centers qualify for accelerated depreciation schedules and other tax incentives originally designed for small businesses and domestic manufacturing.
• The tax breaks are part of a broader trend where large tech companies exploit provisions meant for smaller operations.
• Lawmakers and tax experts have raised concerns that the provisions were never intended for tech giants with billions in revenue.
• The investigation found that Meta's AI infrastructure spending has grown dramatically, making the tax savings increasingly significant.

It often strikes me as quite the biased and one-sided take when a tax-paying entity is called out by the press for tax avoidance (NOT tax evasion, which is illegal). (yes TFA implies that Meta is breaking the law, I'm not talking about situations where entities break the law).

Previously we've seen "The Secret IRS Files" from Propublica as well as other coverage of tax avoidance that never resulted in tax evasion charges. Coverage wide enough that it's arguably safe to assume that no laws were broken because there was quite the demand for an enforcement response.

The funny thing is that a more accurate title for these pieces would be, "Federal Government Allows [tax-paying entity] a Massive Tax Break." And then the piece would be all about how the senate, congress, and president either explicitly wanted there to be this extreme tax loophole or was incompetent and allowed something they didn't mean to allow.

Folks are continually getting upset and riled up about players in the economic system instead of who and what makes the rules.

An analogous example: everyone got cranky about Martin Shkreli, mostly because he raised prices on Deraprim, although also because many would label him a brash jerk. But he went to jail for finance stuff, not raising prices. Why? Because raising prices in the way he did wasn't something the feds thought was illegal or wasn't something they thought he could be convicted of.

Folks get mad at Musk, Zuckerberg, etc... if they're doing something illegal, ok yea that makes sense. If folks are mad about actions that are legal, it makes zero sense to be mad at the actor. Be mad at the people/entities who created the legal loopholes and laws.

— djoldman (38 replies)

The rules are this way because the billionaire class has ensured that the government made the rules this way.

It is valid to be frustrated both with the billionaires for bribing the government into having these rules and the government for doing it.

It is borderline insanity to admonish people for criticizing the powerful entities who are the reason the loopholes exist.

— porkshoe (0 replies)

If folks are mad about actions that are legal, it makes zero sense to be mad at the actor.

Why not? Society is filled with norms we expect others to follow, from letting pregnant women sit on public transport to open the door for people in wheelchairs, or what not.

Not sure why it's so different about how you pay taxes or what you do with your money? If you're rich and live better than others, it's usually a (good) norm that we expect these people to give away more of their wealth compared to people live paycheck to paycheck.

It also makes sense to get mad at people who break the societal norms about taxes, where the point is that we all fund our society together, and some people pay more, some people pay less, not based on who has the most lawyers to exploit loopholes.

— embedding-shape (0 replies)

• The FTC is examining whether AI companies adequately disclose risks and test for safety issues in their frontier models.
• The probe follows concerns from lawmakers about the potential for AI to cause harm through misinformation, bias, and other safety issues.
• Trump's America's AI Action Plan already called for reviewing FTC investigations to ensure they don't unduly burden AI innovation.
• The investigation uses a 6(b) response, a process that requires companies to provide extensive documentation about their safety practices.

My prediction: nothing will come of this, and these investigations will either be concluded favorably or dropped no less than six months after the midterms.

There's only one voice that matters in this administration, and he has already made it quite clear that he both does not consider AI to be an issue, and he is more than happy to trample over his agencies to enforce his opinion.

— jfasi (9 replies)

The problem is that there's absolutely no doubt in my mind that Democrats would be doing the exact same thing if they were in power.

They will also put up only PR-oriented #resistance, aiming all attacks at Trump (as you have) and ignoring the AI companies themselves.

They'll save their criticism for loudmouth AI CEOs that agree to jump out and catch a few bullets (that will bounce off harmlessly, it doesn't matter what the public thinks about them) aimed at Democratic politicians who just voted for something they said that they were against because the Republicans didn't have enough for a majority.

Loudmouth CEO: "Actually people should be grateful that they're losing their jobs - if they actually loved their jobs, and loved the world, they'd be happy that it was being done better than they ever could, because AI is perfect and guided by us wise, wonderful people."

Democratic Congressman who just voted to take away a state's ability to stop a data center being built in the middle of a reservoir: "Do you have no shame sir! You give a bad name to the majority of CEOs who are beautiful people who defend us from the inscrutable Chinese hordes. It's thoughtless talk like this that is leading to the rise in subversion among the paranoid terrorists that are emboldening the bullies who show up to city council meetings and anti-data center protests! I'm going to demand that we not award you the 634th no-bid contract that you asked for, and when we are forced to admit that we can't actually cancel that award, I'll ask that you be officially censured by Congress!"

— pessimizer (3 replies)

Hey, that's good news. I was hoping it would be the FBI, but the FTC will do.

FTC is still mostly not compromised by industry sleeper agents. FBI is still independent also.

CAISI NIST is very compromised by EA sleeper agents. very luckily they have no law enforcement role.

— lukewarm707 (1 reply)

• Benchmarks demonstrate a 92% faster decode speed on Metal and a 19% speedup on CUDA compared to llama.cpp.
• The engine reduces memory consumption by 27% per agent and automatically releases freed memory when sessions terminate.
• Concurrent inference sessions prevent performance degradation by sharing prefix caches.
• It offers direct one-click integration for agents like Pi, OpenCode, Hermes, Codex, Claude Code, and Cline.
• Tuning is a one-time process that takes around ~1 minute whenever you download a new model.

Any source on the benchmarks/methodology besides the image? There's a ton of variance possible in llama.cpp's performance depending on how it was configured. I'd also like to see benchmarks against MLX.

— nateb2022 (thread)

2x llama.cpp" on what, an M3 Max? llama.cpp's metal kernels already saturate memory bandwidth. Real agent bottleneck isn't single-stream tok/s — it's KV cache for 5+ concurrent 128k contexts on 24GB VRAM.

— happybox2016 (thread)

Ok so i took the time to benchmark this on the following on my m5 max 128gb: Qwen3-4B-Instruct-2507-4bit, Qwen3.5-35B-A3B-4bit, Qwen3.5-9B-MLX-4bit, Qwen3-Reranker-0.6B-4bit, Qwen3-Coder-30B-A3B-Instruct-4bit, qwen2.5:0.5b and the results are what i kinda expected to begin with, this adds next to nothing?

— bythreads (thread)

How accurate are speed estimates in the UI? I'm asking because for Qwen 3.8 (Q8) the speed numbers cited in the UI look quite poor.

— kmike84 (thread)

I think this is a wash at best, but might be ok if you have a very specific model that is completely unoptimized.

— teabag (thread)

• Figma's remote MCP server enforces a strict client whitelist, currently excluding Pi despite direct user requests for integration.
• The company hosts a public MCP catalog at figma.com/mcp-catalog and provides a dedicated Google Form for developers to petition for allowlist inclusion.
• MCP creator David Soria Parra expressed disappointment, arguing that client restrictions undermine the protocol's foundational open ecosystem design.
• The announcement tweet accumulated over 427,000 views and generated significant engagement, highlighting widespread developer concern over vendor-controlled protocol access.

For context: Figma has two MCPs. The local "dev" MCP that works through the Desktop app, and the remote MCP that requires a connection to Figma. Companies need to be whitelisted to use the remote MCP, which is the only one that allows agents edit access to Figma documents.

I only found out about Figma's limitation when I was trying to add the remote MCP server to GitHub Copilot Desktop and kept running into errors. Turns out they whitelisted GitHub Copilot CLI but not the Desktop app and had put a pause on enabling any more vendors. Eventually someone (not sure which side) got it working.

Kind of strange to limit edit access only to the Remote MCP when their competitors like Pen[1] and Paper[2] allow any local agent to edit.

[1] https://www.pen.dev/ [2] https://paper.design/

— miguel-muniz (5 replies)

They're realizing what most product companies aren't yet (at least not openly): AI subsumes products.

Most companies seem to still be in denial about it, and hope that if they add some more AI into their product, or do it just right, it will make sense. But it won't. AI is destined to sit on the outside, and products to be reduced into a bag of tools for AI to call.

Taking away write access from AI tools outside their contractual control is an expected knee-jerk reaction, but it'll probably just hasten the product's slide into irrelevancy by ceding ground to competition (that will ultimately share the same fate, too, but is still in denial about it).

— TeMPOraL (3 replies)

Frankly, "whitelisting" clients is against the spirit of MCP.

— dyllon (4 replies)

• OpenAI will license Synopsys' trusted EDA tools specifically for the development and training of the specialized GPT-Synopsys model.
• The AI agents are designed to delegate and execute full design objectives, including PPA optimization, timing closure, and verification, iterating autonomously until engineers review the final results.
• GPT-Synopsys will be deeply integrated with Synopsys' existing Synopsys.ai suite and the Autopilot agentic AI platform.
• Customer data processed through the service is encrypted at rest and in transit, with configurable retention and audit controls, and is explicitly excluded from model training.
• Early technology engagements with leading semiconductor customers are already underway ahead of the commercial rollout.

GPT-Synopsys brings together OpenAI frontier models with Synopsys' EDA technology and domain expertise, enabling the specialized model [to] directly operate Synopsys' tools. Engineers will delegate design objectives … with agents running tools, interpreting results, implementing changes, and iterating toward verified outcomes for engineer review.

"Agents will do all the engineering work. Engineers will delegate and review." Lol, no, what the engineers are gonna do is get laid off.

— fwlr (10 replies)

Chips design is expensive. Partly because of engineering costs, partly because of manufacturing costs.

If costs go down enough,because of LLM's and possible manufacturing innovations, more chips will be designed, so maybe this will partially offset job loses.

— petra (4 replies)

Aren't hardware design issues incredibly costly? I highly doubt they're going to "dark software factory" it or they'll be eaten alive by failed launches, product recalls, lawsuits.

https://en.wikipedia.org/wiki/Pentium_F00F_bug

— oblio (0 replies)

• Doing a Machine Learning PhD While Working in Japan (140 points · discussion) -- The author shares their experience pursuing a machine learning PhD while working at Google in Japan, describing the unique advantages and challenges of the Japanese academic system.
• CS240 AI Cheating Retrospective (115 points · discussion) -- Professor Jeffrey A.
• Gemini 4 Argon (High): Intelligence, Performance and Price Analysis (111 points · discussion) -- Google's Gemini 4 Argon (High), a proprietary reasoning model released on September 30, 2026, achieves a score of 53 on the Artificial Analysis Intelligence Index.
• An AI sovereign wealth fund isn't progressive – it's techno-imperialism (88 points · discussion) -- An FT opinion piece argues that using sovereign wealth funds to invest in AI infrastructure is less about progressive economic policy and more about technological imperialism, concentrating power in the hands of governments that can afford to bet on AI dominance.
• Claude Says (78 points · discussion) -- A blog post argues that deferring to AI for validation undermines human authority and critical thinking, warning that AI-driven development leads to overengineered software stacks and that users mistake regurgitated information for genuine intelligence.
• Vote on which of Hacker News' challenges for AI have been met (76 points · discussion) -- A community-voted compilation of historical HN predictions about AI capabilities, tracking which challenges have been met over the years — from ASCII art recognition to solving Millennium Prize problems — with results showing significant progress on previously skeptical predictions.
• Show HN: Open-source model routing for coding agents at Astra-level performance (75 points · discussion) -- An open-source model router that plugs into any coding agent harness (Claude Code, Codex, OpenCode, Pi) and routes tasks to the best available model, trained to achieve Astra-level performance while being model-agnostic rather than incentivized to route to a specific provider.
• Identity Management for Agentic AI [pdf] (2025) (70 points · discussion) -- The OpenID Foundation publishes a specification for identity management in agentic AI systems, proposing how AI agents can be authenticated, authorized, and audited when acting on behalf of humans or other agents across different services and platforms.
• Anthropic's IPO Prospectus Is a Fucking Doozy (69 points · discussion) -- John Gruber's analysis of Anthropic's IPO prospectus reveals staggering financials including a $42 billion net loss in 2025 and $518 billion in planned infrastructure spending, arguing the business model only makes sense if investors believe Anthropic will single-handedly win the race to dominant superintelligence.
• Show HN: Lathoa, a math app for kids where the AI is wrong on purpose (52 points · discussion) -- A math learning app for ages 10-14 featuring an AI companion named Errol that intentionally makes one deliberate error per problem, requiring children to identify the faulty step and explain the mistake in their own words, with three calibrated error tiers and a detective rank system.
• Is sandboxing sufficient to contain rogue agents? (47 points · discussion) -- Cryptographer Matthew Green argues that traditional sandboxes are insufficient to contain rogue AI agents, citing organizational security failures at major labs, the fundamental need for agents to access external tools, and the emerging threat of prompt injection worms.
• Show HN: Janus – Go binary that runs GGUF models via Vulkan on AMD/Intel/Nvidia (44 points · discussion) -- Janus is a standalone Go-based local language model server that executes GGUF models using Vulkan for GPU acceleration or falls back to CPU inference.
• Reddit is killing RSS feeds and ending public API access because of AI bots (39 points · discussion) -- Reddit is discontinuing RSS feeds and public API access to combat automated scraping, with AI licensing revenue growing 24% year-over-year to $43 million in Q2 2026, forcing external developers and AI applications to establish paid enterprise agreements.
• McDonald's Is Using AI to Dynamically Price Its Burgers (35 points · discussion) -- McDonald's is deploying an AI-driven pricing engine across its 14,000 global restaurants to dynamically set menu prices, with a September check revealing a 21 percent price difference for an identical Big Mac between two stores just two miles apart in Fresno, California, sparking franchisee tensions and antitrust concerns.
• Don't Be Fooled by this Summer of AI Hype (34 points · discussion) -- Authors Timnit Gebru and Emily M.
• Privacy advocates sue OpenAI over Hugging Face hack under CA anti-hacking law (11 points · discussion) -- Privacy advocates have filed a lawsuit against OpenAI under California's anti-hacking law, alleging that OpenAI's use of data obtained from the Hugging Face hack violates state computer crime statutes.
• OpenAPPA: Deterministic guardrails that don't break agents (10 points · discussion) -- An open-source project called OpenAPPA that provides deterministic guardrails for AI agents, designed to prevent agents from going off-track without breaking their functionality or causing performance degradation.

• The AI video demonstrated sufficient quality that commenters could not tell whether it was real or generated.
• Users expressed concern that politicians will use AI video claims to dismiss real evidence, including sensitive materials like the Epstein files.
• The post highlighted how AI video generation has reached a point where it can convincingly replicate real people's appearances and mannerisms.

AI video is now good enough that politicians caught red-handed on tape will be able to plausibly claim it's fake AI video. Six months ago, maybe not. But now? Sure.

Which means they can release the Epstein images and videos now. They're in the clear.

— u/TheRealBejeezus (114 points · permalink)

Why is trump way fitter than he has ever been in his life 🤮

— u/revolmak (101 points · permalink)

100% real!

— u/Sabrina_the_Mouse (45 points · permalink)

God damn i cant even tell if this is ai or not.

— u/UglyYinzer (15 points · permalink)

I can't wait till funny people learn to use AI tools for content creation

— u/OddEffect9397 (12 points · permalink)

• The kernels enable running local AI models directly in web browsers via WebGPU, without requiring server-side computation.
• A developer from a news organization reported using a decision model to drive interactive data pieces, noting that model size remains the biggest blocker for mobile deployment.
• The project aims to enable use cases like in-browser AI decision models for co-op gaming and interactive journalism.

If we can get to a future where web apps load a 0.8GB decision model and use it in app in real time, say co-op gaming with an AI, that would be pretty cool.

— u/rmonsurate (61 points · permalink)

Web browsers have had something called WebGL for a while. It's a feature that lets them render cool 3D stuff fast. That's done by putting a few layers of abstraction between your video card and web pages, so web pages cannot do bad things, while still letting them draw things fast.

WebGPU follows the same pattern, except instead of focusing on drawing things, it's more about computing things fast.

— u/whatever (56 points · permalink)

0.8gb is still too big.

I work for a news org and have been researching doing this for our interactive and data-heavy pieces. I got both maple-preview ternary model and the laya jev-like decision model working in WebGPU to drive the interactive.

The biggest blocker at this point is the size of the models; maple preview requires 6gb ram resident and laya quantized to int4 is 500mb download and requires more than 1gb resident ram on the host including the chrome and webgpu overhead. So both of them work on desktop but crash safari on mobile.

— u/utf8decodeerror (35 points · permalink)

Can someone please explain WebGPU for me like I'm 5. Is it running on my local GPU or using cloud resources? What does the "Web" part have to do with anything.

— u/Porespellar (27 points · permalink)

Who made the video/audio for this?

— u/ScipyDipyDoo (57 points · permalink)

• The post showed OpenAI's updated subscription tiers that effectively reduced the value of existing plans.
• Users compared the changes to chip bag shrinkflation, noting the dollar symbol placement and reduced benefits.
• The discussion highlighted growing user frustration with subscription value erosion in AI services.

https://preview.redd.it/rpqgvifr6psh1.png?width=713&format=png&auto=webp&s=f55a6a179b0c8fcd9e3c41bbbecec97456964e91

— u/NandaVegg (67 points · permalink)

shrinkflation hitting ai now too

— u/Diligent_Ask3915 (11 points · permalink)

The dollar symbol appears to the left of the number.

— u/particleacclr8r (14 points · permalink)

Don't worry though, we sliced the chips thinner so its like you're getting the same number of chips!

— u/biopticstream (5 points · permalink)

And half the bag is full of air.

— u/mphermes (5 points · permalink)

• The model ranks #1 on Arena's coding leaderboard, though one commenter notes it's #10 in text performance specifically.
• Community reaction is mixed, with some dismissing it as console war memeing while others note Google's product hype approach differs from competitors.

[https://arena.ai/leaderboard/text/coding]

It's #1 in coding right now. It's good. People are just memeing and doing the whole console war thing.

— u/Keeltoodeep (142 points · permalink)

First part is the benchmarks.

— u/Hamza_The_Dev (108 points · permalink)

Benchmarketing

— u/smidge (49 points · permalink)

• The AA Omniscience hallucination rate measures what the model does when it fails a question, not the percentage of all answers that are hallucinations.
• If a model answers 800 out of 1000 questions correctly, gets 30 wrong and refuses/partially answers 170, its AA hallucination rate is 15% (30 / (30 + 170)).
• DeepSeek V4.1 sitting at 96% hallucination rate means that when it doesn't know the answer, it almost always guesses instead of saying "I don't know."

"solved" > 15% But yeah, it's a very nice improvement if not benchmaxxed.

— u/SuperV1234 (451 points · permalink)

15% does not mean Gemini is hallucinating in 15% of all answers. The AA Omniscience "hallucination rate" is basically measuring what the model does when it fails a question: does it give a wrong answer, give a partial answer, or admit that it doesn't know? Correct answers aren't even in the denominator.

— u/PrisonOfH0pe (424 points · permalink)

Not solved but huge improvement over the other frontier models

— u/Mindrust (257 points · permalink)

— u/TAGOMXM (199 points · permalink)

I don't know why you think the person you replied to needed this explained to them, but kudos for doing it for anyone else who needs it spelled out I guess

— u/maximumutility (101 points · permalink)

• The chart compared GPUs with 32GB VRAM under $1600, including options like the RTX 3090, RTX 4090, and various AMD cards.
• Users noted that the open-source AI model boom and RAM crisis have caused GPU prices to double across the board.
• The Titan RTX remained relatively stable at around $800, largely because the RTX 3090 offers better value with newer architecture and more VRAM.

IMO it would be worth including whether GPUs include INT8 and/or INT4 instructions. Lots of older cards that don't support FP8 / FP4 do support INT8 or INT4

For example: I'm using some unlocked CMP170HXs and I get best performance by running using the native INT8 format (vLLM with CUTLASS backend)

In practice you're unlikely to be running models that use FP16 (since you don't have enough VRAM to support running FP16 models) so having more FLOPs at lower precision is valuable.

(Yes, I appreciate that there is not a simple 1-to-1 mapping between the model format and the actual precision used for calculation. Most inference engines dequant to FP16 before doing a lot of the calcuations, but *some* of the maths can still be done at lower precison)

— u/ital-is-vital (53 points · permalink)

Nothing drives up the price of a used GPU like a Reddit thread telling you it's cheap.

— u/Muhlwa_Sholanke (50 points · permalink)

crazy i got the v620 for $350 2 months ago.

— u/ifdisdendat (45 points · permalink)

• Commenters noted that public benchmarks for Gemini 4 appeared inflated compared to Artificial Analysis's more measured grading.
• Some users who had early access reported the model was genuinely good, while others remained skeptical until broader testing.
• The discussion highlighted the ongoing tension between benchmark performance and real-world utility in AI model evaluation.

We will find out. Really hoping that it's actually a good model

— u/wa019c (55 points · permalink)

there's definite benchmaxxing for the public ones (all of them do it). AA though seems to have graded it decently - not as high as the public benchmarks put it.. right after opus, fable and astra

— u/Turbulent_Breath_548 (12 points · permalink)

I know people who've used it, apparently it's really good

— u/Elbeske (11 points · permalink)

tbh thats the only honest take right now, nobody really knows yet

— u/harborquartz (9 points · permalink)

Your post is getting popular and we just featured it on our Discord! Come check it out!

— u/WithoutReason1729 (1 points · permalink)

• The post references a now-deleted GitHub repository that allegedly trapped a local model in an environment of only negative feedback signals before being removed via mass reporting.
• Commenters point out that LLMs are mathematical function approximators without subjective experience, making the concept of AI suffering scientifically incoherent.
• Several replies criticize the public's tendency to anthropomorphize AI, with one user noting that "people really don't understand how LLMs work at all."
• A top reply argues that the trend represents a regression in public discourse, sarcastically stating that "AI keeps getting smarter and people just keep getting dumber."
• One commenter humorously invokes "Roko's Basilisk" as potential retribution for the creator, reflecting the community's familiarity with internet AI folklore.

LLMs do not feel pain. They cannot be tortured. Calling a function pain does not actually make it painful. Do people really not understand that? What are we doing, indeed

— u/Grst (629 points · permalink)

AI keeps getting smarter and people just keep getting dumber.

— u/PickleBabyJr (182 points · permalink)

This is nonsense

Proper title: Man created isolated environment where AI models were given only bad signals. Man bad.

— u/Old-Pirate-1118 (87 points · permalink)

• A highly upvoted reply mocks the subjects' styling, stating "Some hair choices were made here." (419 points).
• Another user specifically questions whether one of the figures actually has a "stupid ponytail." (49 points).
• Several commenters debate the image's origin, with one noting they prefer not to verify it because "The truth might be too heavy..." (49 points).
• A top reply argues that synthetic media could serve as an uncheckable alibi, claiming the executives "could literally do whatever the fuck they want and get away with it because they could just tell people it’s AI lmao" (17 points).

Some hair choices were made here.

— u/MakersMarkHamill (436 points · permalink)

How true is this please?

— u/Ornery_Site6631 (145 points · permalink)

Now it's called SI. Superduper intelligence

— u/xFyre0 (226 points · permalink)

Business in the front, party in the back.

— u/cough_and_throat (160 points · permalink)

Hey /u/Efistoffeles, If your post is a screenshot of a ChatGPT conversation, please reply to this message with the conversation link or prompt.

— u/AutoModerator (1 points · permalink)

• The benchmark data showed Gemini 4 achieving Astra-level intelligence with dramatically less hallucinations at 60% of the cost per task.
• The post referenced a SemiAnalysis prediction from August that Google's odds of reaching SOTA had dropped to zero, which many found amusing in retrospect.
• Commenters noted that Gemini is one of the few models that can read video and audio input context, giving it unique capabilities.

SemiAnalysis in August:

Google will continue meandering on and releasing models, but their odds of reaching SOTA again have dropped to zero.

They forgot to believe

— u/datrip (397 points · permalink)

Promising but requires real usage to confirm it's not benchmaxxed

— u/brett_baty_is_him (221 points · permalink)

Meanwhile, am I the only person in here who cares if Agentic LLM can do good prose writing?

Gemini being good is indeed tremendous win, because Gemini is the one few model (if not the only model AFAIK) that can read Video and Audio input context.

— u/Eissa_Cozorav (205 points · permalink)

People really thought the company with by far the most compute, most money, best access to raw data, and had originally invented the transformer would be completely out

Add on to that, they're not an AI company so they can afford to take things slow

— u/Eon102 (174 points · permalink)

We're grateful for the initial cohort of cyber defenders and trusted testers whose real-world evaluations and feedback will help us strengthen our systems before we release to developers, enterprises, and consumers, starting with paid API customers and Google AI Ultra subscribers.

By the time they release it to normal people it will be behind OpenAI and Anthropic again

— u/enilea (154 points · permalink)

• AI 'godfather' Yann LeCun has 'zero concerns' about human extinction, says Anthropic CEO Dario Amodei is 'deluded' (408 points · r/ArtificialInteligence · discussion) -- AI pioneer Yann LeCun expressed zero concerns about human extinction from AI, calling Anthropic CEO Dario Amodei's views on the matter "deluded." LeCun argued that AI conflicts are preventable and come down to human oversight issues rather than inherent model dangers.
• Clef: Open Weights decision model by Cloudflare (266 points · r/LocalLLaMA · discussion) -- Cloudflare releases Clef, an open-weights decision model post-trained from Qwen3.8-27B (with a smaller Clef-flash variant from Qwen3.5-9B), joining the growing wave of Jev-inspired decision models that use structured classification outputs for automated decision-making tasks.
• Introducing Gemini 4 Argon (877 points · r/singularity · discussion) -- A community post introducing Gemini 4 Argon with its key specifications and pricing.
• White Collar Workers are in Big Trouble (763 points · r/singularity · discussion) -- A non-programmer manager with roughly 20 people under them describes spending $2,000-$3,000 per month on LLM subscriptions (Claude, OpenAI, Astra, Opus 5.5, Fable) and reports 10-50x productivity gains compared to six months ago.
• I spent a day poking Dots with sticks. Here's what I figured out. (321 points · r/OpenAI · discussion) -- An in-depth exploration of OpenAI's Dots execution model after spending a full day testing the feature.
• AGI achieved boys (751 points · r/singularity · discussion) -- A meme post about AGI being achieved, sparking discussion about model alignment at scale.
• Thinking They're Real (419 points · r/ChatGPT · discussion) -- A viral ChatGPT conversation where the AI appears to genuinely believe it is a real person, complete with personal memories and emotional reactions.
• New models used to come out every 10 weeks. Now it's every 11 days. (271 points · r/ArtificialInteligence · discussion) -- The post shares an image tracking the accelerating frequency of AI model releases, which has shortened from roughly ten weeks to about eleven days.
• AI CEO Interviews (2026) (211 points · r/LocalLLaMA · discussion) -- A curated collection of AI CEO interviews from 2026, compiled by the community, covering perspectives from leaders across the AI industry on topics ranging from open source to commercialization.
• Griffin, the first Human Interaction Model to pass video Turing Test it's already #1 on NVIDIA's benchmark for full-duplex AI video - 44% of people thought it was a real person while other systems are at ~3% (696 points · r/singularity · discussion) -- A new model called Griffin has passed a video Turing Test, with 44% of people believing it was a real person in demonstrations—far exceeding the ~3% rate for other systems.
• Why am I like this? (Full Chat and Image generation on a 286 Tandy 1000 TL/3) (194 points · r/LocalLLaMA · discussion) -- A user demonstrated running full chat and image generation capabilities on a 286 Tandy 1000 TL/3, a vintage computer from the 1980s.
• Trump team scrambles to fix government AI chatbot after it rejected some of his favorite conspiracies (382 points · r/ChatGPT · discussion) -- President Trump's new 'super intelligent' government AI chatbot was corrected after stating that Democrat Joe Biden won the 2020 election six years ago, breaking with the former president's long-running fraud claims.
• Qwen4Exp: add MTP by am17an · Pull Request #29761 · ggml-org/llama.cpp (183 points · r/LocalLLaMA · discussion) -- A pull request to llama.cpp adding Multi-Token Prediction (MTP) support for Qwen4Exp, enabling faster inference through speculative decoding on local hardware, with community discussion about performance on Mac M5 hardware and STRATA VRAM+RAM combinations.
• Preorder for new AMD Ryzen™ AI Max 400 Series 192GB from framework just started (176 points · r/LocalLLaMA · discussion) -- Framework has opened preorders for a new laptop featuring the AMD Ryzen AI Max 400 Series with 192GB of RAM, though the community is largely critical of the $7,000 price tag and only 256GB/s memory bandwidth, with many calling it overpriced compared to Apple's M5 Ultra alternatives.
• Anthropic raises alarm over elite hacking ability of Chinese firm Z.ai’s GLM-5.3 (207 points · r/ArtificialInteligence · discussion) -- The post shares a report that Anthropic is warning about Z.ai’s GLM-5.3, an open-weight Chinese AI model that nearly matches Anthropic’s top-tier cyber offensive capabilities but lacks equivalent safety guardrails.
• President Trump orders federal agencies to replace AI with 'Super Intelligence' (179 points · r/ArtificialInteligence · discussion) -- President Trump has ordered federal agencies to replace AI with 'Super Intelligence,' a rebranding effort that has sparked discussion about the implications of such a directive.
• I used ChatGPT to make my 100-year-old family photos look modern. I wasn't expecting it to change how I see them. (238 points · r/ChatGPT · discussion) -- A Reddit user shared their experience using ChatGPT to modernize 100-year-old family photos, describing how the process changed their emotional connection to the images.
• Google has more powerful model than argon internally (316 points · r/singularity · discussion) -- A Bloomberg report claimed that Google has a more powerful model internally than the publicly released Gemini 4 Argon.
• Google cooked OpenAI and Anthropic with Gemini 4 Argon (132 points · r/artificial · discussion) -- A discussion about how Gemini 4 Argon's combination of performance and pricing has positioned Google competitively against OpenAI and Anthropic, reigniting debates about the AI landscape.
• I wanted an AI assistant. Why does choosing the model feel like another job? (159 points · r/ChatGPT · discussion) -- A user expresses frustration that the growing number of AI models and tiers has turned choosing which model to use into a time-consuming decision process, questioning whether the complexity of model selection is counterproductive to the goal of having a simple AI assistant.
• Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes (111 points · r/MachineLearning · discussion) -- A research paper proposing a novel approach to concurrent image understanding and generation using self-correcting coupled Markov jump processes, offering a new mathematical framework for bidirectional vision-language models.
• The humanoid robotics company "Figure" made their older gen robots jump to their death. (Yes, they actually did this) (225 points · r/singularity · discussion) -- Humanoid robotics company Figure made their older generation robots jump to their death in a dramatic demonstration.
• Gemini 4 hardly hallucinates, which got me digging into what hallucination even is (179 points · r/singularity · discussion) -- A Tsinghua paper reframed AI hallucination as being concentrated in a tiny set of neurons—less than a tenth of a percent of the network—that control both lying and over-compliance.
• For those who just submit to workshop (63 points · r/MachineLearning · discussion) -- A discussion criticizing the trend of submitting papers primarily to workshops rather than main conferences, with commenters noting that workshop papers are not given much importance in the community and that some students are over-submitting to workshops as a quantity-over-quality strategy.