China Tightens AI Controls as Markets Face Reality Checks

• Only 0.7% of internet users in the world's poorest countries have used ChatGPT, compared to 25% in the most developed nations
• Slightly more than a third of all smartphones shipped worldwide in 2025 were capable of running generative AI, projected to reach 45% by end of 2026
• An Arduino UNO Q ($50 device with Qualcomm chipset) runs a language model for mosquito detection using just 3 watts of power
• Both Google's Gemma 4 and Alibaba's Qwen 3.5 are cited as excellent open-weight models for small AI development

N_Lens (6 replies)

I strongly believe this premise in the article is correct - we will see a lot of tiny, hyper specialized models for individual tasks, and perhaps that will converge with an orchestration layer for a generalized intelligence that controls these specialized tiny models, that will be quite capable.

I don't foresee AGI arising out training bigger LLMs (Though investors won't realise that for a while yet).

It's actually how organic brains work - specialized tasks are offloaded to local cortical columns. The overall coordination between these sub-brains creates emergent skills/abilities.

SwellJoe (1 replies)

This is couched in prepper nonsense, but it's got LLM, WikiPedia, maps, etc. A bunch of genuinely useful stuff to keep on a USB stick or whatever: https://www.projectnomad.us/

But, the current model you really want for an emergency kit is Gemma 4 12B QAT 4-bit. At ~7GB on disk, it's small enough to run on a tablet or any modern computer, slowly if you don't have a GPU or modern Apple silicon, but exceedingly smart for its size, excellent vision capabilities, good tool user, surprisingly good reasoning.

chris_money202 (0 replies)

I think future is probably more similar to speculative execution (inference/decoding). A small LLM is used to speculate and a large LLM is used to confirm if needed. If the small LLM is accurate enough on N tokens it's cheap for the large LLM to say looks good and keep moving along.

andy99 (2 replies)

General purpose models are always more robust and generally better than smaller narrower models. My bet is that compute will catch up and any "small" model will still be generally capable, just smaller than sota, rather than intentionally narrow. The exception would be for very well defined tasks where the data distribution never varies, but these are rare and don't really need "AI" anyway when they do exist.

simianwords (3 replies)

No this will never work. Domain specific models will never be a thing because intelligence carries over and compounds.

Why didn't OpenAI release a math specific model? Why not a literature specific one? Why do they instead have generic models of different sizes? And how did all labs converge on this?

Why does Fable just not train on non cybersec and non biology data but instead have clearly costly and annoying classifiers?

• YC CEO Garry Tan claims to ship 37,000 lines of AI-generated code per day
• A developer used a single Claude session to review the downloaded website code and found numerous bloat and inefficiency issues
• The article frames the debate around whether AI coding tools increase quality or just quantity of code output

apimade (7 replies)

This is what happens when you give people tools that let them achieve an outcome, without necessarily giving them the judgement or expertise to know whether the outcome is any good.

If you asked me to build a house, I could probably assemble something that would stand for a few months. Hopefully. It might even keep the rain out. But it might also fall on my head, because I do not know enough about building houses to be confident that it won't.

And even if it didn't fall on my head under normal conditions, I also would not know when I needed to design for earthquakes. Or floods. Or fire. Or wind. Or grandmother-cosplaying wolves with very strong lungs.

But if all I need is shelter for a day, would I necessarily care whether it lasts more than a week?

That is effectively what a website like this is. It is not really a product. People don't depend on it. Tan's visitors are probably using MacBooks and iPhones on fast networks, and most of them will never notice how bad it is under the surface.

That does not mean it is good. It means it is good enough for the context.

arcticbull (1 replies)

I'd suggest looking at the review itself, there's an X-the-everything-app thread on it.

https://x.com/Gregorein/status/2038953944475472316

Note that Rails was built as a framework for making blogs, I'm having trouble understanding what 78,000 lines of ruby in the context of a Rails blog could ... do.

I'm sure there's some powerful ugly stuff in Office but in a good code that's calcified kind of way. It got that way over like 30 years of releasing to the public across platforms, not over a weekend.

I'd be surprised if microsoft.com is shipping their entire test suite unminified and their back-end posting rich text editor with index.html (with two title tags in the head) and rendering the entire DOM for desktop and mobile regardless of your platform.

I'm not critiquing Garry or the site. I think it's great people are using AI to build things that bring them joy, or that they find useful. I certainly do.

I am opposed to the idea that we've decided to go back to measuring work in terms of lines of code. It has always been the worst metric on earth as a proxy for productivity. Every line is a liability, and it always was. AI has not changed that, if anything it's amplifying it.

The best PRs remove code, not add, and the only companies that seem to have exponentially grown their revenues in line with AI-generated LOC are OpenAI and Anthropic. Everyone else seems to be rummaging around for an ROI.

pjc50 (1 replies)

I think this is where aggregate effects have to be considered. One person building an idiosyncratic house out of found materials: neat little project. One million people doing so: shanty town that can be seen from orbit and is a disaster waiting to happen.

The Web already had a problem with externalizing costs onto users. Both the simple cost of poorly executing websites (power, mobile data, time), and more subtle ones (social media). AI is a huge accelerant for that.

operation_moose (4 replies)

AI feels to me like having access to someone who got a D in literally every single course offered at a university. If you don't know anything about the subject they are smarter than you. If you do know the subject its unsettling how bad they are. Basically the Gell-Mann effect:

The phenomenon of a person trusting newspapers for topics which that person is not knowledgeable about, despite recognizing the newspaper as being extremely inaccurate on certain topics which that person is knowledgeable about.

They've improved from someone who failed every single university course a couple years ago. Maybe they'll get to a C or even a B in the future; maybe not.

blubber (8 replies)

"found numerous examples of bloat and inefficiencies in Tan's site code, and used a single (Anthropic) Claude session to review the files he downloaded from the website to confirm his observations"

1. I hope they never get hold of the code of MS Office or almost any other piece of real-world business software.

2. So anyone with claude access could arrive at the same conclusions ... and ask claude to fix it?

• Beijing is looking at curbing overseas access to China's top AI models (37 points · discussion) -- Reuters reports that Beijing is considering restricting overseas access to China's top AI models, including open-weight ones.
• The Making of Claude Code (29 points · discussion) -- Anthropic published a feature article on the development of Claude Code, their agentic coding product.
• Show HN: Shellular – run Claude Code, Codex, Pi from your phone (24 points · discussion) -- A mobile app that lets developers access their complete local development environment from a smartphone, including AI coding agents, a full terminal, local servers, and project files.
• Show HN: LLM Thought Visualization (22 points · discussion) -- A GitHub tool that visualizes Claude's J-space internal thoughts as the model reasons, based on Anthropic's Jacobian lens research.
• Yes, AI Will Take Your Job (19 points · discussion) -- An argument that AI will replace most jobs not because it outperforms humans, but because capitalist systems prioritize profit over quality, safety, and ethics.
• Claude Fable 5 Backlash Grows (17 points · discussion) -- Claude Fable 5 faces growing backlash after its July 1 re-release, with users claiming stricter guardrails have crippled the model's coding, debugging, and agentic performance.
• How ChatGPT Picks Sources (I Read the Network Traffic, Not the Outputs) (14 points · discussion) -- An SEO expert analyzed ChatGPT's raw network traffic to document how it selects and cites sources, revealing the internal pipeline including result_source labels (serp, labrador, bright, oxylabs) and fan-out query patterns.
• We're Living Through the AI Utopia and Can't See It (9 points · discussion) -- A Twitter thread arguing that AI's benefits are already transformative but remain invisible to those experiencing them.
• What's slowing down the AI buildout (8 points · discussion) -- An analysis of how electricity grid interconnection queues—not power generation capacity—are the primary bottleneck to AI data center expansion, with median wait times reaching 55 months.
• People Who Will Thrive in the AI Age (7 points · discussion) -- David Brooks argues that in the AI age, what will differentiate people is not intelligence but their relationship to mental effort—categorizing people as Productive Passengers, Reluctant Optimizers, and Mental Marathoners.
• Samsung profits jump 1,800% as AI chip sales soar (6 points · discussion) -- Samsung reported a 1,800% profit jump driven by surging AI chip demand, highlighting the continued hardware boom underlying AI infrastructure buildout.
• Big Tech Has Suddenly Flipped on the AI Jobs Wipeout Scenario (6 points · discussion) -- Wall Street Journal reports that major tech CEOs have shifted their public messaging on AI-driven job displacement, moving away from the 'wipeout' narrative that dominated earlier discussions.
• Hy3 (295B A21B), a reasoning and agent model with great cost efficiency (6 points · discussion) -- Tencent's Hunyuan team released Hy3, a 295B parameter mixture-of-experts model with only 21B active parameters.
• Treasury Has an Internal Report Warning About the Dangers of an AI Bubble (6 points · discussion) -- A draft internal Treasury Department report warns that the current AI market poses significant systemic financial risks, drawing comparisons to the dotcom bubble.
• China's DeepSeek developing its own AI chip, sources say (6 points · discussion) -- Reuters reports that China's DeepSeek is developing its own AI chips, reducing reliance on Western hardware.
• China may stop open-sourcing its most powerful AI models (5 points · discussion) -- A follow-up HN story suggesting China may restrict open-sourcing its most powerful AI models.
• Utah lets AI refill prescriptions. Doctors are wary (5 points · discussion) -- Utah has begun allowing AI systems to refill prescriptions, raising concerns among doctors about safety and oversight.
• Does Speaking to Agents Like Cavemen Save 65% of Tokens? We Test (5 points · discussion) -- A JetBrains blog post testing whether simplified 'caveman' prompting actually reduces token usage for AI coding agents.
• Whitepaper: The judgment problem in AI-generated API test suites (5 points · discussion) -- A whitepaper examining the judgment problem when AI generates API test suites and how to build adaptive coverage systems.
• FlowerBench: Benchmarking AI Agents on Real Enterprise Work (5 points · discussion) -- A new benchmark for evaluating AI agents on real enterprise workloads.

• A viral meme showing AI unemployment predictions being repeatedly pushed further into the future
• The post generated 466 comments discussing AI job displacement timelines

u/No_Aesthetic (1446 points · permalink)

Why did he grow a third leg in 2027

u/terrraco (578 points · permalink)

If I lost my software engineering job in 2025, does that make me 2 years ahead of everyone else?

u/daviddisco (211 points · permalink)

"unemployed" is somehow always a year away with AI doomers.

• A self-described former AI hater was converted after ChatGPT helped them buy a gaming PC for $580 on Facebook Marketplace
• The post generated 146 comments about AI as a learning and decision-making tool

u/CaptainScootiePants (248 points · permalink)

I'm a little concerned that you got a "really nice gaming PC" for $580. Unless you're playing solitaire..or it's used?

u/Turbulent-Armadillo9 (129 points · permalink)

It's a tool. I'm convinced Chat helped me learn a shit ton about some interests. I'm not saying I'm a good programmer and visual artist (pixel artist not ai) yet but I'm sure as hell a lot better than I was before i started using chat to teach me.

It's organizing info from the internet and making it digestible for me in more simple steps.

• User tested Gemini Omni's video understanding on personal phone footage
• Discussion centered on how quickly multimodal AI capabilities have improved

u/RoterRabe (78 points · permalink)

There were two dinosaurs in the first video. What were you trying to showcase?

u/nusodumi (42 points · permalink)

awesome. damn it's crazy how accurate the "just wait 3 years" people were

• Beijing is considering restricting overseas access to China's top AI models, including open-weight ones
• Community members express concern about the future of competitive local models if Chinese open-weight models become geo-blocked

u/unspecified_person11 (185 points · permalink)

The AI industry is just a bad news generator apparently, we just keep getting restricted more and more.

u/atape_1 (159 points · permalink)

Mistral, come one, we need you to step up, you are our last hope.

... for real though, their new datacenter near Paris should go online and day now and will allow them to train up to 10T sized models. Hoping for good things to come.

u/Euchale (83 points · permalink)

Lads it was fun while it lasted. Guess we aint gonna get any more competitive local models.

• The Supreme People's Court journal proposed a tiered system: basic open-source tools subject to simple filing, advanced technologies facing security reviews, and frontier models barred from public release.
• Reuters replaced scholars' words about 'national security tech' with 'frontier models' to make it appear that commercial AI was about to be locked away.
• The 'simple filing' system is actually a legal shield, since uploading code to GitHub technically counts as illegal export under old Chinese laws.
• Scholar Gu Lingyun explicitly warned against over-regulating open weights, saying strict controls would only be self-inflicted.

u/JayoTree (135 points · permalink)

Now it's confirmed: Beijing either is or isn't going to curb overseas access to its top AI models

u/Difficult-Top9010 (65 points · permalink)

the 'sources' are probably anthropic and openai

u/JustinPooDough (56 points · permalink)

There is no chance China will do this. Their models are doing a fantastic job popping the US bubble.

u/MustBeSomethingThere (26 points · permalink)

I feel like there are some Psy-Ops going on this topic. Probably from each side.

• Qwen 3.6 27B performs well on single prompts but fails at multi-turn agentic work
• The user found Qwen 3.5 122B more reliable for agentic workflows despite the 27B model's impressive single-shot performance
• Testing was done with Llama.cpp nightly on an RTX 6000 at 4-bit and 5-bit quantization

u/bradrlaw (99 points · permalink)

Have you tried this to get 3.6 27b to be more stable? Fixes some of the bugs for agentic flows:

https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates

u/xornullvoid (75 points · permalink)

Are you using the right inference params ? Preserve_thinking, for example?

Can you share which are your params?

u/DinoAmino (35 points · permalink)

I sense a ripple in the continuum as the disciples ponder if they should be downvoting the blaspheming of the vaulted 27B when you clearly praise the 122B. Let the rationalizations begin...

• The outage began approximately an hour before the post and was still ongoing.
• Affected users include both ChatGPT Plus and ChatGPT Go subscribers.
• Some users found that generated images still appear in the 'More > Images' section despite the conversation showing a failure message.
• The issue appeared to be a frontend display problem rather than a complete generation failure, as images were still being produced.

u/TheConfusedCat (27 points · permalink)

same +1

• User expresses surprise that people outside AI communities are largely unaware of AI hallucination problems
• Discussion centered on how free-tier model quality affects public perception of AI reliability
• Commenters noted that awareness of hallucinations correlates with level of AI usage and domain expertise

u/Flimsy_Meal_4199 (69 points · permalink)

I mean, if you don't use AI much you probably don't know how to sniff it out.

On the other hand, if you're anti AI you'll believe that it hallucinates way way more than it actually does

Also, if you're not using AI a lot, you're probably on free tier models, which are worse.

u/YaThatAintRight (20 points · permalink)

People are quick to point out where the AI hallucinations occur primarily in areas of topics where they have expertise or confident general knowledge. Without a baseline knowledge to question the AI output, they have no context to make a judgment.

The smart approach is to always question the validity of the output and confirm through an objective source. But most people are too impatient or the stakes aren't high enough to warrant that rigor.

• Built on a 30B Mixture-of-Experts (MoE) architecture that activates only 3B parameters per inference step.
• Supports a massive 1M-token context window and handles diverse audio tasks including understanding, speech recognition/translation, text-to-speech, text-to-audio generation, and speech-to-speech interaction.
• Maintains strong text-only reasoning, alignment, and agentic capabilities comparable to its predecessor, Nemotron-Cascade-2, despite the addition of audio modalities.
• Trained using a multi-stage supervised fine-tuning (SFT) and cascaded reinforcement learning (RL) pipeline to optimize both audio and text performance.
• Released under NVIDIA's Oneway Noncommercial License, with an accompanying technical report on arXiv (2607.05196).

u/AgeOfAlgorithms (16 points · permalink)

speech to speech generation?? that's really cool, im gonna have to check it out

u/killerstreak976 (6 points · permalink)

This is pretty interesting, getting the best of both worlds in 30B is actually pretty awesome. I also thought it was smart to use different codecs based on the complexity of the audio. I noticed in the paper that the final RL stage is mainly focused on text data tho, and am kinda curious about why they didnt look into audio specific gains in RL too. Then again, I'm not as cracked as the people behind this so I'm sure there's a reason.

u/FullOf_Bad_Ideas (1 points · permalink)

2B variant is also exciting. Non-braindead local speech-to-speech running on a phone should be within reach (still needs a lot of dev time to inference on a smartphone but the bulk of the work was done). It's beating PersonaPlex (braindead model) in S2S benchmarks by a mile.

• Anthropic published research on J-space, an emergent internal workspace in Claude where the model silently reasons
• The Jacobian lens technique reveals 'silent words' in the model's internal neural activations before they appear in output
• Neuronpedia partnered with Anthropic to provide an interactive demo of J-space on open-weights models like Qwen 3.6 27B
• The full paper is available at transformer-circuits.pub/2026/workspace

u/NandaVegg (38 points · permalink)

https://transformer-circuits.pub/2026/workspace/index.html

Full write-up is in Transformer Circuits.

I don't know, this whole thing sounds like more formal rediscovery of (well known) intermediate layer abstract representation.

I have been doing mid-training by upscaling for years and I think in a more naive sense, one could think of the thinking path of those LLMs as a marble falling down n-layer maze (but with geometry more akin to "basins" rather than tight labyrinths). In those intermediate layers the basins are not as defined as final layers, and the marble can easily flip into the opposite side with various momentum. But the final few layers there tend to be two wholly separate basins (base-model like corpus and instruct-model like corpus; at the end of the day, your model is a classifier as well) and the marble will continue to fall into the same side (along with a bit of nudge in each layer). If you duplicate intermediate layers, the model tends to just "work" with a bit of CPT, but it was very difficult to make it work by duplicating the final layers because you need very defined basins in those final layers, or the marble will jump over to the other side and suddenly starts to ramble like a base model in a middle of instruction output (in the lab I'm working for we did 80~100B tokens CPT for the final dupe layers with 1/5 mix of instruction data vs. base data, but the model still goes to crazy tangent, meanwhile the model works perfectly if the same recipe was done for intermediate layers).

u/QuackerEnte (27 points · permalink)

this confuses me like nothing else

i only asked about maths, didn't change anything

wtf is qwen thinking about man 😭

https://reddit.com/link/ow163fv/video/yv5cvryzxqbh1/player

u/SrijSriv211 (16 points · permalink)

I haven't looked into it yet just watched their video they put up on youtube and idk why but I feel like this J-Space consciousness-thingy thing is bullshit. I know I'm jumping to this conclusion way too early that too without looking at the code and other things.

The reason why I'm feeling this way is that in their video they explained that the model "thinks" of different words and all internally but I feel like it's more because of the Attention mechanism playing around. In attention we have multiple heads. Each head explores different words & concepts so that the final transformed hidden state is as useful to the FFN as possible.

I believe that in that process attention heads in Claude (and this sound very plausible to me cuz Claude definitely has 100s of heads) process different concepts which obv results in improved quality of our hidden state. After that when that transformed hidden state is passed to the FFN, it "thinks" of concepts that Anthropic might be talking about.

In very oversimplified terms we can say that Attention takes dot-product of input tokens with each other and FFN takes dot-product of those transformed tokens with all the tokens that are stored in the FFN. This results in the model "thinking" of multiple abstract concepts and tokens and after some n number of layers the model ends up with the correct tokens which we take as the output.

Again I might be wrong cuz I haven't read the code yet or even their article but I'm very done with Anthropic trying to use fearmongering to unnecessarily create more hype.

• Google is definitely cooking som good if these leaks are true (238 points · r/singularity · discussion) -- Leaked information about Google's upcoming AI models generated significant excitement in the singularity community.
• HuggingBay (144 points · r/LocalLLaMA · discussion) -- A new Hugging Face space that appears to be a model comparison or discovery tool, generating significant discussion in the LocalLLaMA community.
• China is considering restricting overseas access to its top AI models, including open-weight ones (130 points · r/singularity · discussion) -- Discussion on r/singularity about China's consideration of restricting overseas access to its top AI models, including open-weight ones.
• nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16 (99 points · r/LocalLLaMA · discussion) -- NVIDIA released a new Nemotron Labs 3 Puzzle model with 75B total parameters and 9B active, available in BF16 precision.
• Mimo & deepseek are really amazing at optimizing ai (92 points · r/LocalLLaMA · discussion) -- Discussion about how Mimo and DeepSeek have achieved remarkable cost optimization in AI inference, with 2x-3x profit margins that provide insights into efficient AI deployment.
• Anthropic just reported that LLMs have hidden thoughts they hold without saying. An internal 'J-Space' (73 points · r/singularity · discussion) -- Discussion of Anthropic's J-space research on r/singularity, including a user-built visualization tool (Subtext) that reveals the internal words the model thinks about before speaking.
• AI is scaling 3x faster than the internet wave and it's NOT slowing down (70 points · r/artificial · discussion) -- A post analyzing data suggesting AI adoption and investment is growing three times faster than the internet wave was in its early years, with no signs of deceleration.
• AI should be private and optional! (64 points · r/artificial · discussion) -- A post about DuckDuckGo's advertising campaign in Times Square advocating that AI should be private and optional, reflecting growing public concern about forced AI integration in search and other services.
• Chinese AI models are gaining ground with U.S. companies as OpenAI, Anthropic costs surge (59 points · r/LocalLLaMA · discussion) -- Report that Chinese AI models are increasingly being adopted by US companies as OpenAI and Anthropic pricing continues to rise.
• MIRA: Multiplayer Interactive World Models trained on Rocket League [R] (55 points · r/MachineLearning · discussion) -- Researchers from General Intuition, Kyutai, and Epic Games released MIRA, a 5B parameter multiplayer interactive world model trained on 10,000 hours of synthetic Rocket League data.
• I've spent the last two months tackling fully playable AI-generated instruments (54 points · r/singularity · discussion) -- A user shares their two-month project of creating fully playable AI-generated instruments, planning to release everything for free and open source.
• Scores in the currently ongoing AtCoder heuristics finals (50 points · r/singularity · discussion) -- Live scores from the ongoing AtCoder heuristics finals, which run until July 8th, showing AI performance on competitive programming challenges.
• Microsoft To Lay Off 4,800 Workers In Latest Wave Of AI-Led Job Cuts (48 points · r/OpenAI · discussion) -- Microsoft announced it will lay off 4,800 employees globally (2.1% of its workforce), with Xbox facing the largest share of cuts.
• [Paper] How much do language models memorize? (43 points · r/LocalLLaMA · discussion) -- A new research paper examining the extent to which language models memorize their training data, relevant to privacy and copyright concerns.
• Are We Betting the Economy on a Doomed Technology? (37 points · r/ArtificialIntelligence · discussion) -- A discussion questioning whether current AI valuations are justified, with commenters debating the technology's real-world value versus the speculative pricing.
• llama.cpp: Hy3 PR + GGUFs (36 points · r/LocalLLaMA · discussion) -- Early llama.cpp support for Tencent's Hy3 model with GGUF quantizations, showing coherent output at ~10-11t/s on a 5090.
• UPDATE: I built a tool to turn your Claude Code sessions into fine-tuning data for local models (35 points · r/LocalLLaMA · discussion) -- A developer released Claude-Converter v1.5.0, which can now convert Claude Code, Codex, and Pi sessions into fine-tuning data for local models.
• A trained fast-weight memory: a 3M-param transformer installs never-trained rules at inference, forward-only — where test-time training transfers nothing (30 points · r/LocalLLaMA · discussion) -- An indie researcher presents a 3M-parameter transformer that can install never-trained rules at inference time through a trained fast-weight memory mechanism.
• Got my Ascent GX10 two days ago, ran REAP-pruned NVFP4 DeepSeek-V4-Flash on a single Spark (29 points · r/LocalLLaMA · discussion) -- A user reports running REAP-pruned NVFP4 DeepSeek-V4-Flash on an Ascent GX10 with a single Spark chip, noting consistent performance at long context lengths.
• Trained a 117M parameters Silia model on an H100 in 5 hours. (29 points · r/LocalLLaMA · discussion) -- A researcher trained a 117M-parameter Silia architecture model on an H100 in just 5 hours, sharing the model and paper.
• Qwen3.6-27B: NVFP4/FP8 agent loops vs flawless BF16. Config or quant issue? (27 points · r/LocalLLaMA · discussion) -- A user with an RTX PRO 6000 Blackwell reports severe reliability issues with quantized Qwen3.6-27B (NVFP4/FP8) in agentic workflows compared to flawless BF16 performance, questioning whether it's a config or quantization problem.
• Pulpie Orange Small: Pareto-optimal HTML content extraction (27 points · r/LocalLLaMA · discussion) -- A new tool for HTML content extraction described as Pareto-optimal, generating discussion in the LocalLLaMA community.
• A Hippocampus for Linear Attention: An Exact Memory for What the Recurrent State Forgets (21 points · r/LocalLLaMA · discussion) -- A research paper proposing a hippocampus-inspired exact memory mechanism for linear attention models to retain information that the recurrent state forgets.
• Gemma 4 Technical Report (20 points · r/LocalLLaMA · discussion) -- Google's Gemma 4 technical report has been shared on the subreddit.
• Liquid AI - Antidoom (the doom loop remover) (18 points · r/LocalLLaMA · discussion) -- Liquid AI released Antidoom, an open-source method that removes the 'doom loop' failure mode in reasoning models, reducing error rates from 10-23% to 1-1.4%.
• UK regulator warns of 'arms race' to keep up with AI use in financial services (17 points · r/singularity · discussion) -- UK financial regulator warns of an AI arms race in financial services as institutions race to adopt AI capabilities.
• I tested Anthropic's new Jacobian Lens on open models, then it turned into a local-model hallucination router (15 points · r/LocalLLaMA · discussion) -- A user tests Anthropic's Jacobian Lens technique on open models and builds a local-model hallucination router from the results.
• PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference (14 points · r/singularity · discussion) -- A paper on efficiently compressing large MoE models through sparse expert merging and bit-packed inference.
• Databricks starts billing Genie usage under a pay-as-you-go model (4 points · r/ArtificialInteligence · discussion) -- Databricks announced a pay-as-you-go billing model for its Genie AI product.
• Blog post: Cliches in the age of the LLM (4 points · r/ArtificialInteligence · discussion) -- A blog post examining how LLMs are homogenizing language and creating new cliches in AI-generated text.
• Which cloud AI coding agents people actually run, by token volume on OpenRouter (3 points · r/ArtificialInteligence · discussion) -- OpenRouter usage data reveals the actual token volume rankings for cloud coding agents, with Roo Code leading at 6.16B tokens, far ahead of Ito at 3.58B.
• Researchers use machine learning on household surveys to optimize global antipoverty program targeting and costs (3 points · r/ArtificialInteligence · discussion) -- UC Berkeley researchers used ML on household surveys to optimize targeting and costs of global antipoverty programs.
• I tried making an AI World Cup commentator. It sounds real until the game gets fast (2 points · r/ArtificialInteligence · discussion) -- A user shares their AI World Cup commentary project, noting it sounds realistic until game speed increases.
• The AI race is becoming an electricity race (1 points · r/ArtificialInteligence · discussion) -- Discussion about how the AI competition is shifting from a model race to an infrastructure race, with countries competing on power generation and grid capacity.