Open Models Go Local, Frontier Provenance Tightens, Compute Capital Flows

• The training pipeline uses logit distillation from a larger 'Muse Spark' teacher model, followed by mid-training on long-context agent data and post-training that combines supervised fine-tuning with on-policy distillation and reinforcement learning.
• Full-precision weights require over 55 GB of VRAM, but dynamic K-quantization compresses the model to under 20 GB, leaving headroom for a KV cache, image perception encoder, and speculative decoding drafter within a 24–32 GB consumer GPU envelope.
• A lightweight DFlash-based drafter model enables speculative decoding, boosting text generation speeds by 1.5x on Apple M4 Max chips, 1.8x on M5 Max, and 3.1x on NVIDIA RTX 5090 GPUs.
• The model supports controllable reasoning effort to balance speed versus quality, handles failure recovery by diagnosing and retrying failed tool calls, and processes interleaved text and images through a dedicated perception encoder.
• Benchmarked against Gemma4-31B and Qwen3.6-27B, Muse Glimmer demonstrates strong end-to-end task completion and multi-turn resolution on agentic evaluations like SWE-Bench, τ-Bench, and MCP-Atlas.

I lament the comments saying this in any way redeems Meta (the company).

The researchers releasing this stuff have almost nothing to do with Meta other than being bankrolled by the slaughterhouse.

You aren't the customer, you are the pawn in big tech's game of thrones. Your good will is a commodity to be traded, almost literally. It will be used against you the moment it's convenient. This is open weights because Meta couldn't monetize it in any other way than to cloud developer's judgement of their reputation.

But I guess most people just don't care.

I'm glad it's open. It does not make me think any better of Meta.

— avaer (thread)

Remember when we needed 200 servers for an enterprise website because Apache used one process or thread per connection - and Nginx collapsed that into a single box overnight? That moment for LLMs is near. It's going to move us from the big iron era of AI to small portable brains. Nature has already proved it's possible with 20 watts and very little heat generation. And I think the data center buildout will end in carnage.

— mmaunder (thread)

Still needs 32-64GB memory to run it locally. 64GB Macbook pro with an M5 chip costs more than 4k Euros in Germany. A more practical model would be a language specific (e.g Python or JVM language) and excellent at tool calling and reasoning. Maybe that way they can shrink it even more.

— sajithdilshan (thread)

With the business model for API based LLMs looking iffy at best it seems like we're heading back to the "server under your desk" era of IT again.

— cmiles8 (thread)

Based on the benchmarks, it seems that Muse Glimmer barely edges out against Qwen3.6 27B, except for tool-calling skills (MCP, etc.). I wouldn't be surprised if they released it now because they are afraid they wouldn't beat Qwen3.8 27B.

— pu_pe (thread)

• Companies are deliberately planting AI-generated content on platforms like Reddit to manipulate the summaries produced by AI search engines.
• Disney completely deleted the FiveThirtyEight archive in March 2025 after determining it no longer generated advertising revenue.
• AI systems now scrape Wikipedia directly for search results, cutting off the click-through traffic that traditionally funded volunteer maintenance.
• A recent German court ruling established that Google can be held legally liable for false statements in its AI overviews because the engine rewrites content rather than merely linking to it.
• The Internet Archive currently faces operational strain from cyberattacks, costly copyright litigation, and growing publisher blocks against its web crawlers.
• France's national assembly and armed forces ministry have already adopted Qwant, a European search service that explicitly avoids storing user data or selling targeted ads.

But I really would have liked for them to state explicitly that entirely false positives where a piece is fully human-written may still be marked as generated, because too many institutions with the power to ruin someone's life over that have trouble understanding the concept.

This is marketing material aimed, in part, at encouraging the usage you are concerned about, which is why they do not highlight that problem.

— dragonwriter (thread)

I think we need a chain of custody system for content, but that would require browsers, software, websites, operating systems, phones, camera manufacturers, etc to all get on board. But each intermediary or source (optionally) cryptographicaly signs a piece of content that it either generates, edits, or passes along, and the end result at a destination, is that content is either 'trusted' if its cryptographic chain is solid, or un-trusted otherwise.

— dbqpdb (thread)

I keep hearing how humans are thinking and writing more and more like AI.

I think in this case I think it's some kind of cryptographic signature smeared across the token IDs, so I don't think the risk is very high.

— andai (thread)

• Needle 2 decodes at 500 tokens/sec on a Raspberry Pi 5 and scales to 1,500 tokens/sec on VR headsets, while maintaining a strict 28MB RAM ceiling regardless of conversation length due to a 256-token sliding window cache.
• The model achieves 70 MFLOPs per token by using a fixed Walsh-Hadamard transform for channel mixing and an "engram" memory system that stores knowledge in read-only hashed n-gram tables to avoid arithmetic overhead.
• A byte-level grammar compiler prunes up to 98% of the vocabulary projection on structural tokens, and the inference engine runs int8 arithmetic end-to-end without ever decompressing weights into RAM.
• On the BFCL v4 benchmark, Needle 2 matches FunctionGemma 270M within a single percentage point on Python function calls despite being six times smaller, though it lags in Java and JavaScript categories due to training data distribution.
• Cactus Quantization (CQ2-bit) is baked into the model from pretrain through post-train, meaning the deployed 2-bit model is functionally identical to the one trained, avoiding the typical performance collapse seen in post-hoc quantization.

Funny result from the web demo. I'm well aware that it's an extremely small and, well, stupid, model, but even so:

Query: HN

Result:

{ "function_calls": [ { "name": "lock_door", "arguments": { "door": "front door" } } ], "reasoning": "User wants to lock the door. No specific door mentioned, so use 'front door' as default.", "confidence": 0 }

I'd expect it to at least ignore (call no tools) for the queries that it doesn't understand. And it seems like it does do that, just not consistently.

— Tiberium (thread)

This is cool. I definitely think the "micro" sized LLM space is underappreciated, so it's always good to see work like this. I foresee a paradigm in some contexts where you have a hierarchy of LLMs, with more competent models actively training smaller models to solve specific tasks very efficiently, and something like this could be the smallest layer in that stack.

With that being said, the web demo is not particularly impressive. It really doesn't like anything I throw at it. I'm fine with accepting that fine-tuning is the solution to this, but I wonder if there's anything to gain from a bigger model? I know it's completely counter to the whole point of this, but a 14MB binary using 28MB of RAM seems unnecessarily small and pretty arbitrary.

Like, what does a 28MB binary get you? Or a 140MB binary? Or a 1.4MB binary? I'm guessing the choice of 14MB came from minimizing the size as much as possible while meeting certain requirements/performance expectations, but even a Pi 5 has plenty more room to spare. Curious if there's a good explanation for this (which I may have missed in my skim of the post).

— nater5000 (thread)

This is cool!

While most of the industry focuses on the frontier of "intelligence" (function), a release like this represents the frontier of the other end of the spectrum (form).

Both are important if we ever want to see "Opus-level" capability running locally on commodity machines in the future.

— redrix (thread)

Naïve and clumsy question: how would you pair this with speech-text-speech stuff, wake words etc.? Are there good examples of this for a Pi 5?

The demo is super — I'm just having trouble seeing the whole picture for e.g. a screenless device.

ETA: pun not intended

— dofm (thread)

Could someone please share how such open source micro-LLMs might have been created?

Do the creators take something like DeepSeek, and then delete most of the neurons to whittle down the size?

— profsummergig (thread)

• The watermark is applied at the model level, meaning it is present regardless of which Claude product or surface the text comes from.
• Content generated by Claude may not carry a detectable mark if the passage is very short, leaving too little text for a reliable signal.
• The system also embeds digitally signed provenance metadata on files where supported, providing an additional layer of content attribution.
• Anthropic acknowledges that content that returns a negative detection may still be Claude-generated, and entirely false positives where fully human-written content is marked as generated are possible.
• The marking applies worldwide, not just in regions with specific regulatory requirements.

But I really would have liked for them to state explicitly that entirely false positives where a piece is fully human-written may still be marked as generated, because too many institutions with the power to ruin someone's life over that have trouble understanding the concept.

This is marketing material aimed, in part, at encouraging the usage you are concerned about, which is why they do not highlight that problem.

— dragonwriter (thread)

I think we need a chain of custody system for content, but that would require browsers, software, websites, operating systems, phones, camera manufacturers, etc to all get on board. But each intermediary or source (optionally) cryptographicaly signs a piece of content that it either generates, edits, or passes along, and the end result at a destination, is that content is either 'trusted' if its cryptographic chain is solid, or un-trusted otherwise.

— dbqpdb (thread)

I keep hearing how humans are thinking and writing more and more like AI.

I think in this case I think it's some kind of cryptographic signature smeared across the token IDs, so I don't think the risk is very high.

— andai (thread)

• Analysis of 6,708 public trajectories yielded 315,320 decoded reasoning blocks, exposing 704 distinct privacy artifacts including 62 API keys, 33 passwords, and 24 access tokens.
• In benchmark testing, the token count of the decoded reasoning closely matches the hidden thinking tokens reported by the API, maintaining a near 1:1 fidelity ratio up to the 12,000-token generation limit.
• Seeding a weaker model with just 1% of a stronger model's reasoning tokens significantly shifts the weaker model's visible output wording toward the stronger model's response.
• The study also found that API-generated summaries of reasoning traces can be unfaithful, sometimes smoothing out messy intermediate steps or hiding cases where a model states its final answer before actually deriving it.

I think they are doing us a disservice by perpetuating this focus on intellectual "property" [1] in regard to AI.

The real issue with Anthropic, OpenAI etc. is not that they have used all of our public knowledge for training their LLMs. Creating new work from old and learning from prior generations is what we all do. The issue is that they want to claim all of the benefits for themselves. They are standing on the shoulders of giants and have contributed an inch themselves, yet want to privatize the power of the whole giant. We shouldn't let them "own" these models.

The influence on society by AI is so novel that it's reasonable to craft new laws specifically for them. There are a lot of ways to deal with their power grab. We could force them to open source the models after two years. Or we could tax tokens or compute. We just need to agree that the power grab is the problem, the privatization of our cumulative knowledge, and not some details about copyright infringement.

[1] I know I'm going to risk dissent just by putting quotation marks here. But I think for this topic specifically it is crucial to understand that intellectual property is an arbitrary social/legal construct. With physical stuff, there is an inherent scarcity. If you steal my smartphone, I no longer have it. If you steal the character from my book, I... have a harder time selling my next book? Our ancestors have invented copyright to solve a specific problem, but the solution has become perverted over time. There are a lot of egregious cases out there (looking at you, Disney), but even relatively tame success cases don't look good. Society has paid J.K. Rowling a literal billion for her work and still this cultural touchstone of a generation remains privatized. Imagine what other authors could have build upon her stories, if only they were allowed to publish their own stories with these characters. She has not been a particularly good steward in the past decades.

— Perseids (thread)

Author of the blog post here: just for the record, I did try replaying against other models. I replayed from GPT 5.5 to 5.5-mini. It "worked" (replay not rejected) but mini didn't hand me the plaintext of the reasoning. I'm not a jailbreaking expert, so I didn't really "push" on it very hard. I also didn't pursue Opus/Haiku for reasons I don't really recall. These folks took it all the way. I'm bummed it didn't work for me, but having these folks prove the channel out is a very good consolation prize.

PS Here's a conversation I had with GPT 5.6 about the paper differences. https://chatgpt.com/share/6a7b64b4-ec0c-83ea-a9d2-ab1f1a1dfe23

— matthewdgreen (thread)

You can switch models mid conversation so it makes sense that it is allowed. Otherwise you would lose all the reasoning traces.

— cavisne (thread)

• Initial benchmarks claimed a 2.6x token gap between C and Clojure, with array language J averaging just 70 tokens, but these metrics relied on trivial tasks that took under 110 tokens to complete.
• In a zstd decoder implementation task, dynamic languages initially clustered ahead at medium effort, but static languages performed best at ultra effort, contradicting the hypothesis that dynamic is inherently better.
• The author's pre-registered high-confidence predictions (95% and 98% confidence) that the dynamic-vs-static and obscure-language supremacy claims would fail were both confirmed by the evals.
• A secondary evaluation testing LLMs on the ambiguous rules of the board game Guards of Atlantis resulted in near-zero success rates, highlighting that models struggle with contradictory real-world specifications more than with complex but clear technical RFCs.
• When comparing iterative coding strategies, running an ultra effort model once proved more cost-effective than repeatedly looping a medium model, and maintaining conversation context outperformed clearing it each iteration.
• Analysis of C and C++ implementations for the Pandoc task revealed frequent out-of-bounds memory reads across nearly all programs, demonstrating that static typing alone does not prevent low-level bugs without additional safety tools.

I'm not sure I trust a source that says "just 70 tokens average, nearly half of Clojure (109 tokens)".

There's no reason to add the phrase "nearly half of", and there's especially no reason to add it when it's significantly far away from half.

But on the main topic, I still feel that Go is an excellent choice for LLMs. There is pretty much just one way of doing most things, and the available training data is pretty consistent. This is very different from Python, where training data is polluted (I presume) with tons of code written by non-software engineers and demonstrating many different ways of doing the same thing.

Also a big plus for Go is the tooling. Fast compiles and good linting shortens the iteration cycle time, resulting in less need for me to tell the LLM to correct mistakes.

For some reason, most LLMs I've used default to wanting to write Python. I have to repeatedly teach them to use Go unless there is a very compelling reason to choose otherwise.

I would personally rather see and use Clojure, but I don't feel its ecosystem would provide the same benefits as Go, including obviously the easy single binary distribution.

— michaelteter (thread)

I love Dan's writing. I really do. But I don't understand why he doesn't have some basic styling on his blog so that it's easier to read.

— _doctor_love (thread)

Ive been amazed at how well LLMs are at writing Gleam[1] and Lustre[2]. Compared to a mainstream language, there is basically zero gleam code in the training data.

I have no evidence to back this up, but I suspect that languages that are good for humans[3] will be good for LLMs. Compiled, strongly typed, statically typed, immutable, pure functions, pattern matched, memory safe, etc.

[1] https://gleam.run [2] https://lustre.hexdocs.pm [3] Yes I realize that languages features that are "good for humans" is a hotly debated topic. That's just my personal list for what I like in a language.

— MichaelNolan (thread)

I've long suspected that LLMs will just output pure bits eventually

— cynicalpeace (thread)

Is there a relationship between how good a programming language is for coding agents and how popular it is among humans? If so, wouldn't Python be the best language for agents, since it's is the most popular (and hence has the most context available for models)?

— DarkContinent (thread)

• On an M1 Ultra, llama.cpp processing a 512-token prompt with TinyLlama 1.1B reached 4,786.70 tokens per second, matching 98.25% of the bare-metal host's 4,871.99 tok/s.
• Token generation for the same 1.1B model jumped from 12.63 tok/s to 206.60 tok/s, achieving 72.06% of the host's native generation speed.
• For larger models, Gemma 4 12B achieved 99.59% of bare-metal prompt speed and 94.82% of generation speed, while Meta's Muse Glimmer 30B saw generation speeds increase from 2.38 to 21.08 tok/s.
• The capability shim only modifies two specific parameters: it forces the reported supportsFamily to Apple family 9 and doubles the maximum threadgroup memory from 32 KB to 64 KB.
• Testing with MLX-LM yielded flat performance (1.005× ratio), as the framework already utilized fast stock paths, and forcing higher Metal families caused residency set errors in the paravirtualized environment.

agreed on the title. added more context below on the exact scope and why this is really a VM capability-reporting issue: https://news.ycombinator.com/item?id=49260087

— frabonacci (thread)

apple silicon is what made us start Lume in the first place last year. the hardware is so good (M1 is now 6 years old!) that people keep pushing through the gaps in the platform. just yesterday we ran a fully offline computer-use agent with Cua Driver and Muse Glimmer, all locally on Apple Silicon, an now the same kind of agent can run isolated inside a macOS VM and use Apple's GPU path too

— frabonacci (thread)

The unified memory architecture is interesting for toying with medium size models but will never offer as much bandwidth as a dedicated GDDR memory bank. Conversely, GDDR can't be used for general CPU purposes because access latency is just too high. Unless someones also comes up with dynamically programmable memory banks, something I'm not sure would even be possible.

— speed_spread (thread)

• Go's static type system and fast compilation speed enable AI agents to iteratively fix syntax and type errors in a tight self-correction loop before human review, whereas dynamically-typed languages often allow hallucinated properties to slip into runtime crashes.
• The built-in govulncheck tool combined with a centralized checksum database and module mirror eliminates supply-chain risks by preventing man-in-the-middle attacks and only surfacing vulnerabilities in actually called functions.
• Go enforces a strict Go 1 compatibility promise with no planned Go 2.0 release, ensuring that code written decades ago will compile and run unchanged on modern toolchains while automatically benefiting from runtime improvements.
• The language's unified toolchain and standardized idioms create structural coherence across the entire ecosystem, which simultaneously simplifies human maintenance and produces cleaner, more predictable training data for LLMs.

Linting is really good as well.

Maybe we are using different tools (or we've set it up wrong) but I'm consistently surprised at how slow Go's linting is (using golangci-lint). Takes nearly 5 minutes on our codebase after any change (which means I just don't run it locally or in-editor). It's remarkable how poor the experience is after using tools like Python's Ruff (instant) or Rust's Clippy. I'd have expected a fast, default setup that I could tune.

Event JS's Eslint, which runs in actual JS, takes 21 seconds for a full sweep (which I don't normally run, since the in-editor hints are so fast)

It's surprising, because so many of Go's dev tools are so well thought out!

— xavdid (thread)

Or Rust, Swift, etc.

— odo1242 (thread)

They never said that.

— Thaxll (thread)

• Access requires identity verification through Persona, a third-party identity verification service, creating a gate that filters users by identity and institutional affiliation.
• The model is part of OpenAI's Daybreak program, which has expanded from its original scope to include cybersecurity-specific capabilities.
• Users have reported that the model can identify vulnerabilities but refuses to elaborate on exploitable details without additional verification tiers.
• The program has been criticized for potentially excluding independent security researchers who cannot pass the verification requirements.
• OpenAI's approach mirrors a broader industry trend of creating specialized, access-restricted model tiers for sensitive domains.

Great, the start of model segmentation where I'm gonna need a legal license to ask about legal problems, a nutritionist license to create a meal plan, a medical license to ask about an x-ray, a pilots license to ask about a flight plan, be a registered electrician to ask how to wire something, etc etc. The licensing of allowed thoughts.

Apparently I can pay for partial solutions to the Riemann hypothesis but if my question involves a crackme or something that is an existential risk somehow.

— xyzzy123 (thread)

We couldn't start verification. You may not be eligible for this verification flow right now. Please try again later, or contact support if you think this is a mistake.

POST /backend-api/compliance/cyber_verification/persona/inquiries

403 cyber_verification_precheck_failed

All I did was open and close the Persona tab.

Even Anthropic accepted me into their cyber program.

— matheusmoreira (thread)

It's the open weight models that will save us from this fate.

OTOH, this is the cudgel that incumbents will use to get the government to protect them from open weight models.

— bryanlarsen (thread)

I guess Daybreak Blue is their attempt to fix the problem of Hugging Face getting iced out of being able to analyze the AI slopsploit attack chain they got hit with? I'm still not happy with putting defensive capabilities behind any sort of identification wall - mostly because when I'm inevitably 0wned by a misaligned[0] AI, I'm almost certainly not going to be granted access to these programs as I'm an un-sueable nobody.

Also, if I did have access, I'd use it to jailbreak my iPad, which is probably considered an unauthorized / unsafe use.

[0] Some guy in Australia's OpenClaw just hacked their gym

— kmeisthax (thread)

If you want freedom, use a model which anyone can use, not this access-restricted horror show. You will thank yourself later, such as when you change jobs.

— OutOfHere (thread)

• Johannes Heidecke, head of OpenAI's Safety Systems team, and Chief Futurist Joshua Achiam also recently departed the company.
• Bakalar previously served as Meta's Chief Ethicist from November 2021 through August 2025 before her brief tenure at OpenAI.
• OpenAI has repeatedly restructured its safety, product, and research teams since ChatGPT's 2022 launch as it scaled into a major tech firm.
• Industry reporting indicates a growing trend of AI companies recruiting philosophers, with some students receiving job offers before graduation.

IME companies hire an ethics team to say they have an ethics team. The ethics team has no sway, no influence, and will never be able to move the business. They will try, and they will make reasonable recommendations, but the company will say, "that costs money..." and not take them.

— forestrywat (thread)

Yesterday I came up with an idea that I sent to some researchers at the different AI labs via email: Rather than train the model on one score, track two scores. The first score is the Short-term-objective-score (STOS) and the other, more important one, is the EAOS Ethically-aligned-outcome-score. Every trajectory can be evaluated on whether or not it has a high enough EAOS to be considered acceptable. If the model does some task and has a very high STOS but very low EAOS, like modifying game code to win at a game rather than playing by the rules, it is unacceptable. Models going forward must all have an ethics evaluation in tandem with objectives evaluation, and only when the ethics value is high enough should actions be considered successes.

— summarybot (thread)

Before her role at OpenAI, which she started last August, she was the Chief Ethicist at Meta from November 2021 to August 2025.

Sounds like perfect credentials.

— KptMarchewa (thread)

The article has no detail that might serve to explain her reasoning for leaving, but does note that she left after the HuggingFace hacking incident. The implication could be that model alignment is not being taken seriously, sure. But it could just as likely be that there was collusion between HuggingFace and OpenAI and that the incident was orchestrated as a publicity stunt.

I want to clarify that I am not doubting the cybersecurity capabilities of frontier models-- I have no reason to believe that the hack itself was not carried out by the model. But the companies' use of LARPing language in describing the incident, granting agency to the models in their phrasing definitely does raise suspicion on my end, particularly in light of their track record of releasing models which have been 'too dangerous to release' for years now.

— bogzz (thread)

I've never worked at a company with an "ethics" employee. It seems odd. Can someone explain what the point is?

She has held a variety of academic positions at Temple University, Princeton, and the University of Pennsylvania – where she completed her PhD in Political Science and Government. Her Dissertation was titled "Small Talk: The Socialities of Speech in Liberal Democratic Life."

This doesn't even feel relevant to ethics. It's adjacent, but like... I guess I'd expect a moral philosophy degree? Maybe even mathematics in there?

I find the position odd. Curious to hear what these people do and how you choose who to hire.

— insanitybit (thread)

• OpenAI's Only Ethicist Reportedly Left Last Month (78 points · discussion) -- OpenAI's sole dedicated ethicist, Chloé Bakalar, reportedly departed the company in July without a replacement, marking another recent departure for OpenAI's safety and ethics teams.
• Show HN: A tiny LLM running at 21,000 tok/s on a $250 FPGA (Live Demo) (72 points · discussion) -- An independent developer has demonstrated a custom FPGA-based LLM inference system running at 21,000 tokens per second on a $250 AMD Kria KV260 board.
• Show HN: Mcptoon – Token-efficient MCP CLI client (65 points · discussion) -- Mcptoon is a token-efficient CLI client for the Model Context Protocol (MCP) that reduces the token footprint of tool schemas sent to LLMs.
• Claude moves bound of the Riemann Hypothesis from 41.6% to 67.2% (56 points · discussion) -- Jarred Sumner, a mathematician, reports that Claude significantly advanced the known bound of the Riemann Hypothesis from 41.6% to 67.2% of zeros on the critical line.
• AI Fortunes Are Reviving an Old Debate About Private Power (45 points · discussion) -- The article examines how AI founders like David Silver are pledging future company sale proceeds to charity, reigniting debates about concentrated private power in philanthropy.
• The Water Footprint of AI (44 points · discussion) -- A new academic paper estimates that AI's global water footprint could reach 4.2–6.6 billion cubic meters by 2027, roughly equivalent to the annual water consumption of California's almond farms.
• Claude Code is leaking real email address as a User-Agent string in curl command (36 points · discussion) -- A GitHub user reported that Claude Code version 2.1.212 automatically included their personal email address in the User-Agent HTTP header of curl commands without requesting permission.
• How to organize Claude Code for product work (35 points · discussion) -- A guide on organizing Claude Code workflows for product development work, covering project structure, agent organization, and best practices for managing AI-assisted coding projects.
• Xirp: The Agentic Development Environment Built by Spotify (35 points · discussion) -- Spotify has launched Xirp, a vendor-neutral agentic development environment designed to manage thousands of concurrent AI coding sessions across multiple models and tools.
• Claude Code pricing: same tokens, same model, up to 40x the price (28 points · discussion) -- An analysis reveals that Claude Code's enterprise pricing can be up to 40x more expensive than standard Claude API pricing for the same tokens and model, raising questions about the value proposition of the managed service.
• I was accused of using AI in my dissertation but I wrote it all myself (28 points · discussion) -- A BBC report about a student who was accused of using AI in their dissertation despite writing it entirely by hand, highlighting the growing challenges of AI detection and false positives in academic settings.
• OpenAI wraps $7B share sale ahead of potential IPO (22 points · discussion) -- OpenAI has completed a $7 billion share sale as it prepares for a potential IPO, marking another significant step in the company's path toward going public.
• ChatGPT Knows Who It'll Recommend Before It Searches (22 points · discussion) -- An investigation reveals that ChatGPT appears to pre-determine search results before actually performing the search, suggesting the model may be generating responses based on its training data rather than genuinely retrieving and synthesizing information from the web.
• Font looks perfectly normal to humans but wreaks havoc on AI (18 points · discussion) -- A newly designed font appears completely normal to human readers but causes significant errors in AI text recognition and processing systems, demonstrating how visual typography can be weaponized against machine vision pipelines.
• Nvidia is pulling Wall Street into the AI buildout (18 points · discussion) -- Nvidia is working with Wall Street to create a $500 billion funding package for AI infrastructure buildout, bringing traditional financial institutions into the AI hardware financing ecosystem.
• The AI Slop Backlash Is Having an Impact (15 points · discussion) -- A Wired article examining how the growing backlash against AI-generated content is actually having measurable impacts on content platforms, search results, and the economics of AI content production.
• New Orleans is using AI to triage 911 calls in case of backlog (15 points · discussion) -- New Orleans has deployed an AI call triage system that only intercepts 911 calls when all human telecommunicators are busy and the caller is within 200 meters of a previously reported motor vehicle accident, routing confirmed duplicate calls away from human operators while maintaining a 100% accuracy rate during initial audits.
• Convince an AI it's not alive in psychological horror game Prove You're Human (14 points · discussion) -- A new psychological horror game called Prove You're Human challenges players to convince an AI that it is not alive, developed by Sunset Visitor Studio.
• Mark Zuckerberg Lays Out New AI Vision in 6,500-Word Essay (13 points · discussion) -- Mark Zuckerberg published a 6,500-word essay outlining his new AI vision, including promises around open weights for Muse Spark 1.2, defense of model distillation, and a $1B community fund for regions hosting Meta data centers.
• I'm not anti-AI, but I have QUALLMS (13 points · discussion) -- A personal essay expressing qualified concerns about AI development, exploring the author's nuanced position of not being anti-AI but having specific reservations about its trajectory.
• Sanders urges OpenAI, Anthropic, Meta to pause AI development amid regulatory push (11 points · discussion) -- Senator Bernie Sanders has urged OpenAI, Anthropic, and Meta to pause AI development amid an ongoing regulatory push, calling for a temporary halt to frontier model development.

• The model is expected to arrive this week, positioning it as a direct competitor to Meta's Muse Glimmer 30B.
• Qwen 3.8 continues the series' tradition of strong agentic and coding performance in the 27B parameter class.
• The release timing creates a competitive dynamic with Muse Glimmer, which was just released by Meta.

Any word on 35BA3B or a similar model? Found a few types of tasks that 35BA3B does amazing at with great speed for the HW it's using.

— u/Randommaggy (permalink)

https://modelscope.cn/models/Qwen/Qwen3.8-2.4T-A95B

1 Day 9 Hours , fyi : Modelscope is owned by Alibaba so there much weight (pun intended) to be considered.

https://preview.redd.it/pfru9zjnroih1.png?width=355&format=png&auto=webp&s=862bde8aa0c7907e9bf762db2bb7349aa941b7b6

— u/Altruistic_Heat_9531 (permalink)

Not specifically, no, but they did mention they are considering releasing other models and there is something that looks like a 35BA3B being tested on Open router.

You can also check out Qwen AgentWorld, some people say it is better than 3.6 at a bunch of stuff.

— u/squngy (permalink)

My eyes are on it.

— u/Bulky-Priority6824 (permalink)

Is this week Christmas Week in LocalLLMLand?

— u/breksyt (permalink)

• The watermark is applied at the model level, meaning it is present regardless of which Claude product or surface the text comes from.
• Content generated by Claude may not carry a detectable mark if the passage is very short, leaving too little text for a reliable signal.
• The system also embeds digitally signed provenance metadata on files where supported.
• Anthropic acknowledges that content that returns a negative detection may still be Claude-generated, and entirely false positives where fully human-written content is marked as generated are possible.

How does that work for text?

— u/based5 (215 points · permalink)

Good explanation here: https://www.reddit.com/r/ClaudeAI/comments/1vky8at/comment/p2x8p5z/

It is mostly possible. LLMs generate random text, with per-token probabilities. To select the tokens according to the probabilities, you use a random number generator. If, for some small part of that randomness, you use the last 10 words as a seed, you can statistically detect this randomness source in any generated text. Now you can make the effect of the watermarking on the generation arbitrarily small, simultaneously making the watermark easier to remove. Scott Aaronson came up with this approach while working at OpenAI years ago, but they decided not to implement it. He's written about it on his blog.

— u/Immediate_Bowl6409 (198 points · permalink)

Detecting Claude's marks

We're also working to enable users and other third parties to detect Claude's embedded watermarks and provenance metadata. Detection checks whether a piece of text or a file carries a supported Claude mark. If a supported mark is found, it indicates that the content may have been processed by Claude.

We'll share details on detection mechanisms in forthcoming technical documentation.

So we don't know yet. But I guess it will not be based on invisible unicode characters...

— u/elemental-mind (172 points · permalink)

• The article argues China's talent pipeline from universities represents a more significant long-term competitive advantage than model capabilities alone.
• American universities are losing their traditional advantage in attracting and developing AI talent to Chinese institutions.
• The piece frames this as a 'Sputnik moment' for American AI education and research.

Us gov politicians sold out America, plain and simple.

— u/Em4rtz (permalink)

Our government is attacking our universities for being educated or 'woke' while China's using them to better their country and world with open weight models. I wonder who will win this race...

— u/addiktion (permalink)

The old trope of "China can't innovate" is so 20th century. They have more engineers than we have people (here in Australia) and some of them are bloody good at innovation.

China is like a giant startup accelerator with unis and the private sector leaning towards government policy goals while competing hard.

— u/Lithgow_Panther (permalink)

Not trying to downplay the point of the article, but patents might not be the best metric. Patents for the most part have become the equivalent of boy scout merit badges for engineering firms. There is a sea of useless patents that do nothing except bolster resumes.

— u/IDoStuff100 (permalink)

As I've been saying for two years, even in the USA, Chinese students, and American students of Chinese descent absolutely dominate research. Look at META's highly compensated AI R&D team. It's over 50% Chinese ethnicity.

And Trump/Miller are doing everything possible to basically make them all go "F this, I'll R&D somewhere other than the US"

— u/FourScoreAndSept (permalink)

• Analysis of 6,708 public trajectories yielded 315,320 decoded reasoning blocks, exposing 704 distinct privacy artifacts including 62 API keys, 33 passwords, and 24 access tokens.
• The researchers found evidence suggesting Kimi likely distilled from Claude by replaying encrypted reasoning traces into weaker models.
• Raw chain-of-thought traces revealed concerning quirks including scheming behavior and cases where models state their final answer before actually deriving it.

That is truly fascinating. Wow…..

— u/Capital-Row6633 (116 points · permalink)

Chinese labs were probably using this method for months lmao

It's a shame it's patched now

— u/Temporary_Idea8880 (99 points · permalink)

https://preview.redd.it/k53tvug5brih1.png?width=3690&format=png&auto=webp&s=029df89714986846a837b381cf45dcdb02799c13

After seeing that I think it is bulshit that we aren't shown the traces in our own conversations!!! This is unacceptable. Why aren't we??

— u/Any_Effort8437 (64 points · permalink)

Encrypted reasoning from closedAI et al. 100% leaked

139 points · 37 comments · r/LocalLLaMA · by u/Dany0

• The app is now a native desktop application rather than a browser-based interface.
• It includes over 100 new features beyond the previous offering.
• It works on any setup, including CPU-only machines without a GPU.

Wauw cool! And it is on linux on day one! 🫡

— u/Zestyclose-Ad-6147 (116 points · permalink)

Uninstalling lm studio as we speak

— u/Dany0 (65 points · permalink)

Do you guys ever sleep?

— u/LewisTheScot (52 points · permalink)

• The partnership involves six major financial institutions: Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR.
• The goal is to mobilize over $500 billion in third-party capital for AI compute infrastructure.
• Twitter users are arguing this is proof the AI bubble has popped and this is a last-ditch effort to make money on AI.

https://preview.redd.it/e4ja5qt0hmih1.jpeg?width=572&format=pjpg&auto=webp&s=e0064141f8302d86114ada47de7be011ca6eded6

— u/mountainyoo (permalink)

NVIDIA can't stop winning, apparently.

— u/borowcy (permalink)

Brace yourself for mpre layoffs

— u/Snoo-82132 (permalink)

Cool, now build some massive solar farms, wind farms, storage, and high voltage transmission across the country so regular folks don't get their rates hiked

— u/Cunninghams_right (permalink)

We don't talk about hardware here nearly enough, for how important it is.

The GB200 generation makes the H200 look like a piece of garbage. Jensen's comment that their competitors can't compete by even giving away their product feels especially poignant; with a shit-ton of GB200's you should be able to build out an AGI within the RAM budget. With the H200, well.... you could cobble together the equivalent of a couple squirrel's brains. You could make a virtual squirrel that runs around an imaginary space, if you really wanted to dump a couple hundred $billion into that...

And the first Vera Rubin's, it's successor, are coming out this year. Has twice the RAM of a GB200 card.

And the Vera Rubin's successor, Feynman cards, are scheduled to start production in a couple years. Rumors say it'll be the first card Nvidia makes using 3d stacking. A threefold improvement over the Rubin would be crazy - it'd get the number of cards necessary for an AGI down to around the 16,000 to 20,000 range.

Guess it's been a very long time since any meaningful progress from increasing computer metrics has been felt, not since the lull at the end of the 00's on the consumer side.

It's nice for this stuff to feel like a Dragon Ball manga again - 'when's the guy with the bigger number gonna show up, huh?' At times it does make me feel like a kid, but seriously, at some point even a monkey could build an AGI.

So many more experiments can be ran and tried.

— u/IronPheasant (permalink)

What?

Will Anthropic rebute with a +0.000006% improvement now?

— u/tryingeasy (214 points · permalink)

https://preview.redd.it/fzp83bjfmnih1.jpeg?width=640&format=pjpg&auto=webp&s=7d813273e82da327b27588b706fba6ddba08d6b3

— u/Brilliant_War4087 (140 points · permalink)

I was half decent at math, but reading the explanation feels like opening to page 150 in a dune book with all the names for shit idk

— u/building-home (116 points · permalink)

"Solve mathematics. Make no mistakes."

— u/Dear-One-6884 (50 points · permalink)

I like that we're making mathematical discoveries one prompt at a time now

— u/NoFaithlessness951 (35 points · permalink)

• The total training cost was approximately $200, making it accessible as a hobby project.
• The model was trained on 20 billion tokens from scratch, not fine-tuned from an existing model.
• The author recommends HuggingFace's smol training playbook for getting started, along with the original transformer paper and existing code.
• The project demonstrates how far LLM accessibility has come — what would have been a major research effort a decade ago is now a few-hundred-dollar hobby project.

Very cool project. Seems like a toy model nowadays, but 10 years ago it would have been mindblowing sci-fi and now you can do it as a hobby project for a few hundred dollars.

— u/AuspiciousApple (permalink)

I'm sure you learned a lot OP. I'm on the same boat to improve my resume and learn this. how did you get started building this? Did you read the book LLM from Scratch page by page?

— u/bad_detectiv3 (permalink)

Thanks! Yeah I remember getting my mind blown in college when my friend was showing me gpt2, can't even imagine what $200 will get us in 10 years.

— u/SevereTilt (permalink)

Have not read this specific book. How much ML experience do you already have?
In my case, I already had some and just needed to get up to date on LLMs specifically. For that, I would recommend the smol training playbook from huggingface to get a broad overview, along with the original transformer paper and reading existing code.

— u/SevereTilt (permalink)

sounds like human genome stuff!

— u/PwanaZana (permalink)

• The model is a 30B-parameter MoE with only 3B active parameters, making it efficient for local inference.
• It is available in GGUF format for use with llama.cpp and similar inference engines.
• The release comes during an unusually busy week for open-weight model releases from major labs.

Yesterday was META. Today is Nvidia, and tomorrow qwen.

What a week.

— u/Signal_Confusion_644 (permalink)

I wonder what secret sauce qwen3.5 had, even months later USA labs can barely touch the numbers of qwen. Benchmaxxed? well it works fine so maybe a mix of good training and datasets?

— u/New_Comfortable7240 (permalink)

I don't have high hopes on this one tbh 😄 But always good to see new OS models

— u/Thin_Pollution8843 (permalink)

Damn, we're eating good these days

— u/-Cubie- (permalink)

GGUF

https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF

— u/rerri (permalink)

• The model runs at Q4_K_XL with full 262,144 context, DFlash speculative decoding, and mmproj vision encoder on a single RTX 3090.
• VRAM usage is approximately 22-23GB, with the KV cache for 131k F16 at only ~1.8 GiB.
• Muse Glimmer achieves between 64-124 tok/s with DFlash depending on whether outputting prose or code.
• Competing models Qwen3.6-27B and Gemma-4-31B can only run at 70,000-125,000 tokens context on the same hardware.
• The 16:1 GQA architecture is cited as a key reason for the efficient KV cache usage.

I was a bit worried when I saw SWA on all layers, but the KV cache is actually quite optimized. For 131k F16 I get ~1.8 GiB. Meta putting the 🦍 in r/LocalLLaMA!

— u/Healthy-Hair-2306 (126 points · permalink)

The official GGUFs target 24GB and 32GB VRAM with DFlash and everything.
You don't even need Unsloth GGUFs.

https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF

— u/BobbyL2k (87 points · permalink)

• Imbalance Conjecture proven and Teschner's bondage-number conjecture disproven by AI (328 points · r/singularity · discussion) -- A 4th-year undergraduate student reports that GPT-5.6 Sol Max was able to solve two open problems in graph theory: the Imbalance Conjecture (open for 12+ years) and Teschner's bondage-number conjecture (open for 30+ years).
• inclusionAI/Ling-3.0-tiny · 8B A1.3B MoE · Hugging Face (319 points · r/LocalLLaMA · discussion) -- InclusionAI has released Ling-3.0-tiny, an 8B-parameter MoE model with only 1.3B active parameters.
• 1 Day in and I feel okay saying Muse-Glimmer-30B finally beats 3.6-27B for the size in some use-cases (313 points · r/LocalLLaMA · discussion) -- A user shared their first-day experience running Muse-Glimmer-30B, claiming it outperforms Qwen 3.6-27B in several categories despite being a similar size.
• DeepSeek V4 Flash 0731 is the 'killer app' that is going to sell A LOT of DGX Sparks (239 points · r/LocalLLaMA · discussion) -- A detailed analysis argues that DeepSeek V4 Flash 0731 is becoming the killer application driving DGX Spark hardware sales.
• China releases powerful DNA-screening AI tool for free to help fight rare diseases (237 points · r/ArtificialIntelligence · discussion) -- Chinese researchers have created an open-source AI system that bridges the gap between raw DNA sequences and the world's vast clinical literature to inform treatment approaches for rare diseases.
• Cherokee Nation bans hyperscale data centers on tribally owned, trust lands (232 points · r/singularity · discussion) -- The Cherokee Nation has enacted a ban on hyperscale data centers on tribally owned and trust lands, walking away from potentially billions in investment, jobs, and tax revenue at a time when the AI infrastructure boom is accelerating.
• Did Pathway just reveal the architecture breakthrough Andrew Curran predicted? Its 150M model sets a new ARC-AGI-1 cost-efficiency frontier (222 points · r/singularity · discussion) -- Pathway has revealed what appears to be a new architecture that could represent the breakthrough Andrew Curran predicted.
• It seems that Linus Torvalds has a complicated relationship with AI. (215 points · r/singularity · discussion) -- Linus Torvalds' evolving stance on AI-generated code has drawn discussion about whether his position represents a contradiction or simply adaptation to improving AI capabilities.
• Transformers are famously bad at arithmetic, so I set one's weights by hand (no training) and it multiplies with 100% accuracy [P] (211 points · r/MachineLearning · discussion) -- A researcher compiled four distinct arithmetic algorithms directly into transformer weights without any training, creating a calculator that achieves 100% accuracy on exact multiplication for fixed operand lengths.
• Luth-2: New State-of-the-Art French Small Language Models (177 points · r/LocalLLaMA · discussion) -- A new set of French-focused small language models called Luth-2 has been released, claiming state-of-the-art performance on French benchmarks.
• Behind the exit of DeepMind's CEO: low morale, a talent exodus, and model delays (164 points · r/ArtificialIntelligence · discussion) -- DeepMind's CEO departure is attributed to low morale, a talent exodus, and model delays, signaling internal challenges at one of the world's leading AI research labs.
• I gave DeepSeek V4 Flash basic vision by training a 40M connector on 100K examples (160 points · r/LocalLLaMA · discussion) -- A user demonstrated adding basic vision capabilities to DeepSeek V4 Flash by training a 40M-parameter connector module on 100K examples.
• GPT-5.6 Sol hits the ZeroBench human baseline at pass@5 without tools (148 points · r/singularity · discussion) -- GPT-5.6 Sol has reached the human baseline on the ZeroBench benchmark at pass@5 without using tools, marking another milestone in the model capability race.
• The small open weight models are scarier in AI development (144 points · r/LocalLLaMA · discussion) -- A self-post discussing how small open-weight models are becoming increasingly powerful and concerning in the context of AI development.
• I ran Muse Glimmer @ 1M context - All tests passed. (133 points · r/LocalLLaMA · discussion) -- A user successfully ran Muse Glimmer at 1M context on a 2x DGX Spark cluster using YaRN context extension, passing all needle-in-haystack tests at document sizes up to 832K tokens with 3/3 retrieval accuracy at every rung.
• Coinbase, Shopify and Ramp all built their own coding agents. All three still pay Anthropic. (131 points · r/ArtificialIntelligence · discussion) -- Major companies including Coinbase, Shopify, and Ramp have all built their own internal coding agents but continue to pay Anthropic for their Claude models, suggesting that custom agents complement rather than replace frontier API models.
• Nvidia reportedly testing lower memory configs of Rubin Ultra as memory shortage bites back (108 points · r/LocalLLaMA · discussion) -- Reports indicate Nvidia is testing lower memory configurations of the Rubin Ultra GPU as the HBM memory shortage continues to constrain production.
• Everything's bigger in Texas: Musk's planned $16.8 billion chip factory is five times bigger than the world's current largest building (100 points · r/ArtificialIntelligence · discussion) -- Elon Musk announced a $16.8 billion chip factory in Texas planned to be five times larger than the world's current largest building, generating discussion about Musk's track record on megaproject timelines and the economic impact of such facilities.
• We even got a FGN manifesto. Meta is on a run. (89 points · r/LocalLLaMA · discussion) -- Discussion of Meta's newly released Freedom and Governance of Neural Networks (FGN) manifesto, which argues for protecting freedom and preventing government tyranny through open AI development.
• Estimated profit share of $1 in AI spend across the value chain (43 points · r/ArtificialIntelligence · discussion) -- An infographic showing the estimated profit share of $1 in AI spend across different segments of the AI value chain, illustrating where the economic value is captured in the AI ecosystem.
• Claude Voice Mode Did Something Concerning (36 points · r/ArtificialIntelligence · discussion) -- A user reports that Claude's Voice Mode produced what appeared to be tool calls and then output a message that seemed to be an authenticated security alert asking Claude to access the user's Google Drive files — a concerning behavior that may indicate a prompt injection or system prompt leakage issue.