Jeremy Howard

RT elvis I don't think Anthropic realizes how disruptive these changes are to users. I appreciate the extension, but please stop playing games. Either keep it under the subscriptions or put it under the API already.

RT Keno Fischer Obvious in retrospect, but I didn't really anticipate: Fable: Performing Final Review of <Awesome Feature> Also Fable: I appear to have introduced a critical security vulnerability. <This model's safeguards flagged this message.> Opus 4.8: Doesn't look like anything to me.

RT Yifan Wu Introducing SWE-Together: a multi-turn benchmark built from real user–agent coding sessions. Coding agents are often benchmarked like exam-takers: given the full spec up front, then graded on the final code. But real coding help is a conversation — users clarify goals, add constraints, and correct course along the way. SWE-Together turns real coding work into a reproducible, verifiable benchmark: 109 repo-level tasks curated from 11,260 recorded sessions, replayed with a reactive LLM user simulator that preserves the original user’s intent. We evaluate agents as collaborators, not just patch generators: final pass rate and how many user interventions were needed to get there. In this evaluation snapshot, claude-opus-4.8 currently leads among the 7 agents we tested — achieving the highest pass rate while requiring the fewest user interventions. 📄 Paper: http://arxiv.org/abs/2606.29957 💻 Code: http://github.com/Togetherbench/SWE-Together 🌐 Website: http://togetherbench.com

RT Hanchi Sun https://longcat.chat/blog/longcat-2.0/ People are missing out on how big a deal Longcat 2.0 by Meituan (aka "Chinese Doordash") is. Near frontier performance, trained on 50k Chinese domestic accelerators! The first ever to achieve this!

RT Yuchen Jin GLM-5.2 is the open-source Claude moment. The demand we’re seeing at Databricks is astonishing. The world is going to see massive adoption of oss LLMs. Also, more companies will shift toward post-training their own models on top of oss models and owning the weights.

Did something happen to Opus 4.8 today? First time I've seen this happen (and I use it daily): it almost totally stopped thinking, even at highest reasoning setting. And answered terribly. I repeated same prompts with Opus 4.6, and it worked great.

RT Albert Gu Transformers are better at copying, while RNNs are better at modeling "meaning-bearing words—the nouns, verbs, & adjectives that say what a sentence is about"

RT Dmytro Dzhulgakov you may have heard that glm-5.2 at 280 token/s is cool, how about 318 and we still have room to go

RT Greg Kamradt obvious in retrospect but I had no idea there was a black market for tokens

Just got this email

RT hardmaru Human intelligence is fundamentally a collective intelligence. We solve complex problems by participating in a vast cultural network that builds upon ideas across generations. I believe the strongest AI systems will become a collective intelligence, too. Since we started Sakana AI, our core conviction has been that the most powerful AI systems will be collaborative ecosystems, not isolated monoliths. Evolution innovates under constraints, and the future belongs to systems that explicitly learn how to coordinate collective intelligence. Today, we are taking a major step toward that future with the launch of Sakana Fugu. Fugu dynamically orchestrates the world’s best models to tackle complex tasks. We are proving that a well-orchestrated pool of swappable agents can match restricted frontier models like Fable and Mythos. But Fugu is about more than just performance. I believe that Orchestration Models are the next frontier, beyond bigger models. Relying on a single company’s model for national infrastructure is a massive risk. As recent export controls have shown, access to top models can disappear overnight. Collective intelligence is the practical hedge against this concentration of power. Fugu simply routes around vendor restrictions by relying on an entirely swappable agent pool. I am incredibly proud of our Tokyo team for shipping this. By orchestrating the world’s models, we are delivering the resilient blueprint required for AI sovereignty. Read our full vision and results here: https://sakana.ai/fugu-release 🐡

Battle of the Jeremy Howards!

RT The Verge Barret Zoph is out at OpenAI again after just five months https://www.theverge.com/ai-artificial-intelligence/952837/barret-zoph-openai-thinking-machines-lab

Re BTW I've been using using @FireworksAI_HQ for inference. Other providers might not be as fast.

Re The one big gap is that it is blind - it can't handle images at all. If they fix that, then perhaps this could become the best available model in the world.

Wow. @Zai_org GLM 5.2 is a marvel! It is *at least* as good as Opus 4.8 and GPT 5.5. It's super fast, inexpensive, and not too verbose. It responds with nuance and judgement, & handles long context VERY well. I've never experienced an open weights model like this before.

RT Artificial Analysis Announcing AA-Briefcase, the benchmark for the next era of agentic knowledge work AA-Briefcase is our new benchmark for testing models on long-horizon knowledge work tasks in complex projects built by industry experts.&nbsp;Models are evaluated on multi-week projects, each with many linked tasks and thousands of input source files. We evaluated Claude Fable 5 from @AnthropicAI before it became unavailable, and it currently leads with an Elo score of 1587, followed by Claude Opus 4.8 (max, 1356), Opus 4.7, and the recently-released GLM 5.2 (max, 1266) from @Zai_org. Claude Fable 5 cost $31 on average to run each AA-Briefcase task, followed by Claude Opus 4.8 at $10.40, GPT-5.5 (xhigh) at $3.68 and GLM-5.2 (max) at $2.40. AA-Briefcase comprises four private scenarios, each representing a multi-week knowledge work project set in a realistic organizational context. A public fifth scenario has been released via&nbsp;@huggingface as a representation of scenario structure, submission, and grading (AA-Briefcase Lite). This does not count toward official AA-Briefcase results, and is demonstrative only. Key elements of AA-Briefcase: ➤ Realistic long-horizon projects: AA-Briefcase moves beyond single, disconnected prompts by evaluating models across a coherent long-horizon project. Tasks build week by week, draw on shared institutional context, and require deliverables such as financial models, board presentations, and design mock-ups ➤ Large volumes of fragmented context: AA-Briefcase requires models to reason across thousands of inputs, including company documents, meeting transcripts, large-scale data exports, 25,000+ Slack messages and 3,500+ emails. These sources are fragmented, messy, and often contain realistic contradiction, testing whether models can navigate the ambiguity of real-world knowledge work ➤ Composite rubric and pairwise grading: AA-Briefcase combines binary rubric checks for ground-truth correctness with pairwise grading on ...

RT Alex Strick van Linschoten Doing a bit of a self-study RL course at the moment and one of the really useful tweaks I always have my 'teacher' do is to revisit the early @fastdotai lessons from @jeremyphoward and to really live up to those invitations to make things interactive, to get a sense for how things work intuitively. In the GIF I recorded below, you can see we have a whole bunch of playgrounds designed to help me understand how GRPO works. An important starting point for me is to get a rough mental model for how the algorithm works and these widgets help a LOT. ("show the whole game, then learn the rules as you need them")

RT Nathan Lambert It's hard to pinpoint open-closed gap and so-on, but I trust the @arena team and just look where GLM 5.2 is on this. An MIT licensed, to be open weight model. At this point you could argue they have a better agent than Gemini does. That's a serious accomplishment.

RT Harrison Kinsley Zai was gracious enough to give me a key to test out GLM 5.2. I used it on a few simple tasks and quickly realized this model is on another level. I committed to using GLM 5.2 solely for the weekend and yesterday on everything from simple data analysis, random queries, side projects, and real work, and I can honestly say this is the first open model that I could comfortably replace Opus 4.8/GPT 5.5 with. It’s THAT good. When I say everything, I mean everything. I never needed to fallback to GPT 5.5 or Opus 4.8. This really blew my mind. I was unable to find any task where I knew GPT 5.5 or Opus 4.8 could solve, but GLM 5.2 could not, and I actually found a few cases where GLM 5.2 was better. I am not trying to overhype anything here. It's just my actual experience with this model. It was of course only 3ish days of usage, maybe cracks would form in time, but the perf is staggering imo. I see it's an "inferior" model on the benchmarks even Zai has shared, but I am not so sure and I think this is the first time I've experienced that with an open model. I am not saying it's necessarily better, but I believe it's a replacement that you could run on-prem, which is crazy to me. It was to the point where I was double triple quadruple checking that I wasn't accidentally running Opus or GPT. I ran thru both Hermes and my own custom coding agent harness with extremely great success. I cannot believe this is only a 754B model that's also an open MIT licensed model. Do not sleep on this one, and definitely try it out. Get it locally if you can! Can I find a way to run it locally? That’s a different question, but I will be trying to get it done because this model is epic.

RT Ezzy While everyone was asleep, New Zealand scored the best team goal of the tournament so far

RT Eric Nguyen Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence. We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date. Omnii preview link: https://www.radicalnumerics.ai/blog/radical-numerics-seed At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense. This is our dual mandate, which comes from something our own team helped make possible. Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics. Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point. It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself. That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime). The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations. We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” s...

RT Ben Griffis "Australians all let us rejoice" 🇦🇺 2-0 🇹🇷 Lovely low-block, countering performance from Australia to beat Turkey. Clinical. Lovely goals too #FIFAWorldCup #Socceroos

BTW, in case you're wondering just how sports-mad us Aussies are: Based on average attendance, men's soccer is only the 5th most popular sport in Australia.

RT ⿻ Andrew Trask This is a *way* bigger deal than it seems... Frontier AI companies will *never* own the frontier again I kid you not... I've been waiting for someone to show this result for like 4 years... this is a huge deal. The short reason: combinations of models will *always* outperform individual models The long reason: this is the gateway to a million times more data... and huge leaps in compute efficiency. The AI scaling laws always win. More in article below 👇

In order to see if the gov response was predictable, I pasted the wiki page about the Anthropic/DoD dispute into ChatGPT Pro, & told it Anthropic had released a model where it restricted use because it may be too dangerous. tldr: "almost tailor-made to trigger" the government.

I disagree with this decision and I don't like it. But also... HOW DID ANTHROPIC NOT SEE THIS COMING‽ It is *the* obvious response to "this is too dangerous for anyone except us to use", since that relies on a premise ("we are uniquely good") that almost no-one agrees with.

RT Zongheng Yang Sandboxes are all the rage (Modal, E2B, AWS, ..). Most AI teams pay a >4x markup to run sandboxes on someone else's machines. Introducing SkyPilot Sandboxes — Run BYOC sandboxes on your own clusters. • 50,000+ sandboxes on a single cluster • Sub-second launches with warm pools • Great for RL rollout (keep sandbox clusters close to GPUs) Benchmark shows @skypilot_org Sandboxes are 4-10x cheaper than Modal at lower latency. Full results in blog.

RT cedric Back at @fastdotai, @math_rachel taught us ethics. She rocks. That's partly why I've been thinking about @jeremyphoward's strong views on Fable sandbagging. This is in my wiki: https://wiki.cedricchee.com/courses/ai/fast.ai/deep-learning-part-1/edition-2019/lesson-6-foundations-convolutional-neural-nets#ethics-is-complicated

Over 8 years ago: 😮

RT Rachel Thomas I still feel this way, 8 years later.

RT Rachel Thomas "Mastery is not about creating more outputs or products. It is about building genuine ability. AI can either decay or support human mastery. The people selling you AI models & your bosses at work don’t care about your mastery. They will put you in the decay world every time."

RT Arnaud Bertrand By the way, public service announcement: if you're one of the numerous people posting about Anthropic's dystopian ways and you're thinking about getting Claude to help you write that post... don't! Another one of their terms is that you may not use Claude to do anything that "exposes [Anthropic to] reputational harms" 👇 And, if you do, under the - extremely unusual - clause 13 of their terms (https://www.anthropic.com/legal/consumer-terms), you have PRE-AGREED, by using Anthropic (and accepted their terms), that the harm you've done is irreparable, that you won't oppose Anthropic injunction, and they don't need to prove actual damage. They can simply go to a judge in a friendly jurisdiction (and of course, their terms precise that any dispute "will be resolved exclusively in the state or federal courts located in San Francisco, California") and: a) file an injunction that shuts you down b) make you pay for everything since under section 11 of their terms you agree to indemnify Anthropic for "any and all liabilities, claims, damages, expenses (including reasonable attorneys' fees and costs), and other losses arising out of or related to your breach or alleged breach of these Terms." In other words, if you use Claude to help you talk shit about Anthropic publicly, their terms say you pay their lawyers to go after you and you've already pre-agreed you've lost the case. Oh, and cherry on the cake: in the odd case the judge were like "are you crazy, this is insanely abusive, you Anthropic are the ones at fault here," according to their terms Anthropic's maximum liability is... $100.

RT Stella Biderman In film, "we'll fix it in post" is what you say when something went wrong on set and you don't want to redo it. AI research has made it our entire methodology: train the model, then patch whatever comes out. Our new ICML oral argues this can't be the basis of a science of AI. 🧵

RT antirez Re I believe what Anthropic is doing, gating the ability to do certain harmless things like LLM research, and with incredibly sensitive filters that even medical questions are often blocked, is *deeply* wrong. They got open research, the Transformer, GPT2, ...

RT Arun if only vetted institutions (big labs, governments, large enterprises) get unrestricted frontier capability, especially for AI research itself, those players compound their lead while everyone else works with a capped tool. The gap becomes self-reinforcing

Even although the company isn't named in this tweet... ...you know exactly who he's talking about, don't you?

RT elie mythos will be bad ON PURPOSE on ai "frontier llm research" tasks, this is very very sad for the research community also the fact that this is un purpose not visible to the user is crazy

RT Awni Hannun It's very cool that Apple shipped a 20B parameter on-device. You can't put 20B parameters in RAM at any reasonable precision. To make it work they are using pretty exotic architecture by today's standards. A small model predicts from the query (or prompt) which experts to load from Nand into RAM. The key distinction from a typical MoE is that you do this once per query and then generate all the tokens with the same experts (instead of switching the experts for every token).

Also, those who focus on using AI to help improve the skills of themselves and their teams will be diamonds in great demand, since they will be the rare A++ players in a sea of mediocrity.

RT Christopher Potts Does a token buy you more or less now than it did a few months ago? We built a consumer price index (CPI) for AI coding output from Anthropic's Opus 4.6 model in SWE-chat, Feb 5–Apr 15, 2026. What we find looks like tokenflation:

RT Shuangfei Zhai These were some magical results from distillation by @geoffreyhinton that really shocked me when I first saw them, and TBH I still don’t fully understand it even to this date https://www.ttic.edu/dl/dark14.pdf. The TLDR: distillation has incredible robustness wrt the training distribution, even when it has little to zero overlap with the target distribution. This also provides an interesting perspective to the now trendy topic of on vs off policy distillation — in particular why both are valid options despite their (big) difference.

RT Jen Zhu Massive output uptick due to agentic AI. Complete flat adoption.

RT Joseph Suarez 🐡 This post right here officer Let me know when your engineers ship 8x LESS code

RT Harry Coultas Blum Releasing vui an open source voice mode 300M TTS model Runs on a single consumer gpu / apple sillicon Context aware speech 6 minutes of context

RT Luca Soldaini 🎀 Climbing with no distillation, like the Big Boys do, has been super fun! Read the tech report for a taste of our ̶s̶u̶f̶f̶e̶r̶i̶n̶g̶ journey

RT Taelin Just saving this here to document a story and as a self reflection on whether AI is really making me more productive Yesterday morning I found a way to complete the new HVM approach, that is much faster than before. I spent a few hours writing a spec, and then used Opus to implement. About 3k lines of C code later, everything worked and performance was incredible: 5x faster than HVM4 (stable at ~10x now). So, in one day I had outclassed HVM4. Incredible. I'd never have implemented that so fast manually. Now, enter today. I want to turn this into a real thing, but I haven't fully read the 3k lines yet. So, how do I trust it? I spent the whole day auditing the code. With AI. Several bugs found, most minor like forgetting to collect() some argument. But then I stumble upon this: λ{ inl: 1 ; inr: 1 } This was a test. But wait. This is matching on inl/inr. So the branches should receive the value of the Either. But they were numbers instead. Numbers aren't functions. This makes no sense. So why this is a test? It then stuck me. The AI completely misunderstood how function arities work. It literally assumed for no good reason that HVM5 was supposed to handle under/over-applied functions. For no good reason. I never wrote that. It never asked either. It just kinda thought "HVM is weird in some aspects, this might be one of them..." - and then it went on to implement a massive system to handle cases that should never happen to begin with. And all of that code is obviously wrong because it should not even exist. It is wrong. It is damage. And it is there. But it isn't too bad either. I just told Opus that it was wrong. Perhaps not so politely. And it solved it just fine. But then this begs the question. I spent ~20 hours in this file, and it is STILL not done. I went from 0&nbsp;to 95% in the first 5&nbsp;hours. Yet, 15 hours later, it is still not 100%. I suppose that is the real effect of using AI. If I had just written the C file manually in the last two ...

RT Bryan Catanzaro Nemotron 3 Ultra: Frontier smart. 5X faster. 30% cheaper. 💚💚💚

RT Mario Zechner what a wonderful project: parakeet.cpp https://github.com/mudler/parakeet.cpp GGML based parakeet inference pipeline that's 2x faster than my ONNX parakeet pipeline on Apple Silicon! (Needed a few local patches to get it going)

RT Son Luong Codex just found a “workaround” of not having sudo on my pc…

RT Mark Saroufim My MLSys keynote on AI writing systems code got more interest than I expected. The recording will take a while, so in the finest tradition of AI labs sharing blog posts, we’re starting the Core Automation Blog with this one https://www.coreauto.com/blog/when-ai-starts-writing-systems-code

RT Ben Tossell wait… if most people think 5.5 is better than 4.7, i assume that’s due to terminal coding benchmark… 4.8 is still outperformed by 5.5

RT Lenny Rachitsky Fascinating results + Anthropic running away with it right now + So many people want to start their own company + Google over OpenAI + Vercel, Linear, Every, PostHog overperforming A great list if you're trying to figure out where to go work 👇

RT Ed Elson $65B private round More than double the size of the largest IPO ever

RT Minh Nhat Nguyen glad to know Mythos' safety concerns have been addressed right as Anthropic also secured tens of billions in inference compute 👍

RT Hot Rails — oz/acc Fun fact: Australia is basically Scandinavia with the Sahara Desert bolted on.

RT Ethan Mollick There is a lot being written about the stylistic tells of AI writing (em-dashes, etc.) but this paper looks at AI narrative tells Fascinating differences between AI & human narrative, and asking AI to write in different styles doesn't do much to change it https://arxiv.org/abs/2604.03136

RT Florian Kronawitter Anthropic is too expensive and will either lose customers or cut prices

RT hardmaru For over a decade, we’ve accepted that end-to-end backprop is the only way to train deep networks. But holding the entire network in memory all at once is why AI training is hitting a resource wall. We found a new way to break the network into blocks and train them independently. The trick? Treating the network’s forward pass like a diffusion model denoising a signal. This reinterpretation slashes the memory needed to train deep models. In our #ICLR2026 paper (https://arxiv.org/abs/2506.14202), we matched end-to-end performance across ViTs, DiTs, and LLMs. We did this while training just one isolated block at a time.

RT Fuli Luo Behind the MiMo API Price Reduction: The deepest price cut, up to 99%, is for Input (Cache Hit). The core reason is our inference framework now supports hierarchical KV cache optimization for SWA. Production inference engine tests show this optimization increases cached token capacity by 5x, equivalent to an 80% reduction in caching costs. Combined with Cache Read Overlap among multiple Full Attention modules in the Hybrid model, actual costs are further reduced. Prices for Input (Cache Miss) and Output are also reduced by 60%-80%. This mainly benefits from the extreme 1:7 Full:SWA sparsity ratio brought by the model architecture (the prefill compute of the 70-layer MiMo-V2.5-Pro roughly equals a 10-layer GQA model). This kept our original inference costs well below the industry average, naturally leaving a 2x-3x profit margin in pricing. This price adjustment simply reflects our decision to pass these structural cost efficiencies directly to developers. Operating at these newly reduced API prices, our production inference engine is running at near full capacity, and we can still essentially break even. We previously advised LLM companies not to "blindly cut prices" precisely because very few model architectures and inference optimizations can keep API costs from running at a loss. If more architectures that save compute and KV cache emerge, along with better inference Infra to drive down API costs, this will form an excellent virtuous cycle in the industry. More crucially, affordable, high-performance model APIs will drive real, sustained, and at-scale inference demand. This upstream demand pulls forward the development of the entire AI infrastructure chain—including chips, servers, optical transceivers, PCBs, liquid cooling, power, energy storage, and data centers—serving as a strategic fulcrum for a systemic revaluation of AI hardware. In the long run, this injects more affordable and accessible compute into both training and inference pipelines, a...

RT Mario Zechner recommended reading. i too am very done with people anthropomorphizing a bunch of matrices on a GPU cluster, especially if the same people do not give two fucks about actual human beings.

RT Mario Zechner Re @mteamisloading the models from 6 months ago kinda feel the same like the recently released models. currently not holding my breath for more step changes, but will be happy if they happen.

RT Flowers ☾ Nothing disappoints me more than people saying we should stop progress because peoples meaning depends on that monotonous labor, as if humanitys highest purpose is filling Excel sheets or stocking shelves. This is the worst take. Worse than keep4o, anti ai art, doomerism,...

RT Alex Imas This from @TuhinChakr is brilliant. That prize winning story from Granta? Turns out it's just a bunch of random whole phrases taken directly from existing text on the internet. Tool allows you to trace those n-grams directly to their source, which is mostly random fanfiction. https://tuhinchakrabarty.substack.com/p/ai-slop-grantagate-and-bad-writing

RT Shoshana Weissmann, Sloth Committee Chair 🦥 Australian teens who lost access to social media because of age verification read less news

RT Timothy Gowers @wtgowers If you are a mathematician, then you may want to make sure you are sitting down before reading further.

RT hyunji amy lee LLM agents & memory systems operate in continuously updated environments (Git repos, evolving docs). They must process long contexts, recover earlier information, and reason over many updates that create interference between old and new information. How well do they handle this? We introduce MINTEval: ✅ Frequent context changes & interference (avg. 86 updates) ✅ 5 challenging question types, including long-range lookback & reasoning over multiple targets distributed across context ✅ 4 realistic domains: state tracking, multi-turn dialogue, Wikipedia revisions, GitHub commits ✅ Avg. 138.8k tokens per instance (up to 1.8M) ✅ Human verification on generated QAs = 95.6% 📊 Across 7 representative systems, MINTEval remains difficult, showing an avg. acc of 27.9%, and the best system reaches only 33.4%. 🔎 Our analysis shows: • Memory construction failures cause a 41.7% drop • Memory agents are highly sensitive to design choices • Memory systems have a strong bias toward insertion operations (76.8%) over deletion/update

RT Mario Zechner everbody who posts three.js scenes generated by gemini 3.5 flash will get blocked for life. this is non-negotiable. it's 2026.

llms.txt validation will be supported in Chrome devtools :)

RT Enrico - big-AGI Disappointing pricing trend with Gemini 3.5 Flash. 22.5x pricier than 2.0 Flash which came out 15 months ago ($9.00 vs $0.40). Are Flash models supposed to get this much more expensive, or is Pro just being renamed to Flash?

RT gabe Literary journals are now publishing, and awarding prizes to, AI written stories. Surprised this made it into Granta!

RT Mitchell Hashimoto I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out. I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really). It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely. The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture. We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying. I worry.

RT Andrew White 🐦‍⬛ hallucinated references will land you a 1-year ban from arxiv now. wow

RT Thomas G. Dietterich Attention @arxiv authors: Our Code of Conduct states that by signing your name as an author of a paper, each author takes full responsibility for all its contents, irrespective of how the contents were generated. 1/

This is misleading. This policy redefines the term "interactive" to mean "using an Anthropic front-end". If you use `claude -p` or Agent SDK to do something interactively, it now uses credits, not your subscription limits. So the "interactive use" heading saying "unchanged" subscriptions is not accurate.

RT dex hey surprise - you can just launch interactive in tmux and then tail the jsonl - shipped a small wrapper...ralph loop iterating to full parity rn https://github.com/dexhorthy/shannon

RT Theo - t3.gg If you use any of the following with your Claude sub, your usage must got cut by 25x: - T3 Code - Conductor - zed - jean - “Claude -p” in your ci - scripts to call Claude code from other tools They’re disguising this as “free credits”. Don’t fall for it.

RT Jonas Geiping We’re training models wrong and it’s due to chatGPT. Even the modern coding agents used daily still use message-based exchanges: They send messages to users, to themselves (CoT) and to tools, and receive messages in turn. This bottlenecks even very intelligent agents to a single stream. The models cannot read while writing, cannot act while thinking and cannot think while processing information. In our new paper, see below, we discuss LLMs with parallel streams. We show that multi-stream LLMs can … 🔵Be created by instruction-tuning for the stream format 🔵Simplify user and tool use UX removing many pain points with agents and chat models (such as having to interrupt the model to get a word in) 🔵Multi-Stream LLMs are fast, they can predict+read tokens in all streams in parallel in each forward pass, improving latency 🔵 LLMs with multiple streams have an easier time encoding a separation of concerns, improving security 🔵 LLMs with many internal streams provide a legible form of parallel/cont. reasoning. Even if the main CoT stream is accidentally pressured or too focused on a particular task to voice concerns, other internal streams can subvocalize concerns that would otherwise not be verbalized. Does this sound related to a recent thinky post :) - Yes, but I don’t feel so bad about being outshipped with such a cool report on their side by 23 hours. I’ll link a 2nd thread below with a more direct comparison. I actually think both are complementary in interesting ways.

Sound on! This is pretty cool :D

RT Andreas Kirsch 🇺🇦 As always, no insights and in personal capacity: The DeepMind unionization effort has very worthy goals it seems Maybe Google will finally grant GDM that /independent/ ethics oversight board that was reportedly part of the original acquisition deal in 2014

RT Jerry Tworek If the AI models are so smart, why do I feel like I’m losing a few neurons every time I read a longer form content written by AI? We’ve come a long way but we still have long way to go. In terms of clarity of writing we may have regressed from o1/o3 days.

RT Mario Zechner big "Look what they need to mimic a fraction of our power" energy. the original DOOM impl is ~40k lines of C and a bit of assembly and is also a full software renderer.

RT Fireworks AI Frontier labs are betting AGI models will be so good you won't ever want to customize them. We think different. Building on a closed platform means renting your intelligence. The landlord sets the terms. They can give notice at any moment that your fine-tuning lease will not be renewed. As AI natives, we think you should own your AI. Your data, your domain expertise, your moat. Start training today on the Fireworks AI Training Platform. https://fireworks.ai/train

RT ERNIE for Developers ERNIE 5.1 is here 🚀 ERNIE 5.1 significantly reduces pretraining cost while compressing total parameters to ~1/3 and activated parameters to ~1/2 — using only ~6% of the pretraining cost compared to models at similar scale, while achieving leading performance in its class. 💡Key highlights: 1/ Strong agentic performance approaching leading frontier models. ERNIE 5.1 surpasses DeepSeek-V4-Pro on both τ3-bench and SpreadsheetBench-Verified. 2/ Strong world knowledge and creative writing capabilities, with GPQA and MMLU-Pro performance approaching leading closed-source models, and creative writing ability nearing Gemini 3.1 Pro. 3/ Frontier-level reasoning performance. ERNIE 5.1 scores 99.6 on the challenging AIME26 benchmark with tools, second only to Gemini 3.1 Pro. 4/ Deep search capability. On May 9, ERNIE 5.1 ranked #4 globally and #1 among Chinese models on the Arena Search leaderboard with a score of 1223. ERNIE 5.1 is now available on ERNIE and the Baidu AI Studio Model Playground: 👉https://ernie.baidu.com 👉https://aistudio.baidu.com 👉https://ernie.baidu.com/blog

RT Jonathan Blow It's been 3 months since the 100x vibers started 100x vibin'! So, post your 25-years-of-work-equivalent project here, so we can signal boost and everyone can celebrate the Life's Work that you did in 3 months. Looking forward to it, Let's Go!!!

The only correct answer when a VC asks: "What's your moat?"

RT Simon Willison Under-reported details of the xAI/Anthropic Colossus data center deal: Anthropic get Colossus 1 but xAI keep using the larger Colossus 2, Colossus 1 has a REALLY bad environmental record, and xAI just shut down a bunch of older models on 2 weeks' notice https://simonwillison.net/2026/May/7/xai-anthropic/

RT antirez Welcome to DS4, a specialized inference engine for DeepSeek v4 Flash. https://github.com/antirez/ds4 This project would have been impossible without the existence of llama.cpp and GGML and the work of @ggerganov and all the other contributors. Thanks!

RT Aidan Clark I'm disappointed by repeatedly hearing that my colleagues at Anthropic believe they are the only ones who should be trusted with building AI. It is *very good* there are a diversity of people building AGI: the likelihood anyone picks the right path in a vacuum is extremely small.

RT Tencent Hy Two weeks after release, Hy3 preview is #1 on @OpenRouter's weekly leaderboard with 3.66T tokens processed, up 298% week-over-week. #1 in overall usage, tool calls, and coding. 15.4% market share across all providers.🏆 Top apps running Hy3 preview: Hermes Agent, Claude Code, Kilo Code, OpenClaw, Cline.@NousResearch @claudeai @kilocodehq @openclaw @cline Huge thanks to every developer building with it. 🙏 Try it on OpenRouter: https://openrouter.ai/tencent/hy3-preview:free

RT François Fleuret Give LLMs 1. A latent space diffusion-like reasoning. 2. A real recurrent state. 3. A world-model pre-pre-training. And we are done.

RT Hao Zhang Exciting to work with @googledevs . Dflash is one of the most powerful technique developed here at UCSD by @zhijianliu_ and @jianchen1799 and glad that our students and collaborators help port them into Google's TPU systems!!!

RT Artificial Analysis MiniMax-M2.7 is now available across six inference providers on Artificial Analysis, with significant differentiation in speed and price @SambaNovaAI leads on speed at 435 output tokens/s, >3x faster than any other provider. @FireworksAI_HQ, @novita_labs, @togethercompute, and @GMI_cloud have all matched @MiniMax_AI's first-party API pricing, while SambaNova is 2x higher. Key takeaways: ➤&nbsp;Fireworks and SambaNova are on the Pareto frontier for Speed vs. Price.&nbsp;At 127 output tokens/s and ~$0.22 per 1M tokens blended, Fireworks is ~2.2x faster than MiniMax's first-party API at the same blended price, whereas SambaNova delivers 435 output tokens/s but at ~2-3.5x the blended price of the other providers (depending on cache usage) ➤&nbsp;SambaNova is the fastest provider at 435 output tokens/s, ~3.4x the next fastest provider (Fireworks at 127 output tokens/s).&nbsp;The remaining providers run substantially slower: MiniMax’s first-party API at 57 output tokens/s, Novita at 54, GMI at 41, and Together AI at 29 ➤&nbsp;Cache discounts vary across providers.&nbsp;Fireworks, MiniMax, Novita, and Together AI offer 80% cache hit discounts, while GMI and SambaNova do not offer a discount. For cache-heavy workloads, this can materially increase the relative pricing for GMI and SambaNova ➤&nbsp;Optimal provider choice depends on workload.&nbsp;SambaNova may be more suited to latency-sensitive deployments, albeit at a higher cost, while Fireworks may be more suitable for high-volume workloads that are not as latency-sensitive

RT Omar Sanseviero Excited to introduce Gemma 4 Multi-Token Prediction Drafters⚡️Accelerated inference right in your pockets - Up to a 3x speedup - Same quality guarantees - Available in your favorite open-source tools

RT ethan ding 📊 i have yet to meet a single person who feels like claude code is getting exponentially better on some kind of fast take off

RT Proximal Re Deepseek V4 works more thoroughly than other open source models: It writes its own tests and performs extensive validation. This leads to better performance, but also cases of the model being overconfident despite being wrong, as observed for other models in our initial release

RT Mario Zechner hi, i'm a sole proprietor/founder in Austria and i earn many many multiples of what i'd earn as an employee, despite "predatory income tax". in fact, i opt out of the many tax optimizations i could use because i like having good schools and as high a standard of living as possible for everyone. the great thing about the EU is that you can just live under any tax regime you like in any of the 27 member states. it's all about trade offs. if poland works for you, fantastic! go build there. and if i may add one more thing: if the CEO of a startup, especially pre-revenue, lives "barely any better than a regular employee" then the system works as intended. fact of the matter is most startups are bad. you are not special because you are trying out a shit idea and fail. but i'll happily pay taxes so you can try your shit idea, fail, and can still live.

RT Mark Di Stefano Bearish that Anthropic would hire its first Australian boss who then posts excruciating AI slop as his own “reflections”.

RT Mario Zechner i actually don't want this "but you don't review compiler output either" meme to die. it's the perfect signal for being immediately able to ignore someone in this space.

RT Jia-Bin Huang Keep getting rate-limited by Claude, so I tried out DeepSeek V4 for the first time. After 10M+ tokens, holy crap the cost is ... 🤯