Content History
RT John Lam Re @RhysSullivan i really like this slide from @mitsuhiko. writing small self contained libraries and composing them might be an interesting avenue to explore. deck is https://mitsuhiko.github.io/talks/ai-engineer-talk/#13 Original tweet: https://x.com/john_lam/status/2043794595700715885
RT Chris Hayduk I strongly suspect that Claude Mythos is a looped language model, as described in the paper "Scaling Latent Reasoning via Looped Language Models" from ByteDance The authors of that paper called out graph search as one of the areas where looping provides a huge theoretical advantage over standard RLVR. And look at where Mythos blows out its competitors the most Original tweet: https://x.com/ChrisHayduk/status/2042711699413926262
RT ben (is hiring engineers) every engineer at anthropic has been using mythos for ~1.5 months. meanwhile, their uptime is horrendous, claude code still has rendering bugs, etc. one could conclude that it won't be the end of software engineering. Original tweet: https://x.com/benhylak/status/2042051048261722467
1/3 Only just started reading this, but the two obvious errors/misunderstandings in an early paragraph bother me a bit -- makes me wonder if this wasn't fact checked as carefully as it should have been. Mailing lists didn't have a set font, and didn't rely on reply-all.2/3 This bit isn't exactly wrong, but is misguided - the idea that someone studying cryptography would use the same foundational technique (PKE) and programming language (C / C++) as everyone else in the field at the time really isn't "interesting"3/3 Similar issue here. Pretty much everyone in the field had similar concerns; anyone with substantial open source software had a mailing list; software updates generally followed a common format.
The mystery of Satoshi Nakamoto, the pseudonymous inventor of Bitcoin, has remained unsolved for 17 years. Not anymore. Read my 18-month investigation to find out who Satoshi really is. https://www.nytimes.com/2026/04/08/business/bitcoin-satoshi-nakamoto-identity-adam-back.html?unlocked_article_code=1.ZVA.5_s8.hTKeCkV97kow&smid=tw-share
View quoted postRT Stanislav Fort New post: We tested the Mythos showcase vulnerabilities with open models. They recovered similar scoped analysis! 8/8 models found the flagship FreeBSD zero-day, including a 3B model. Rankings reshuffle completely across tasks => the AI cybersecurity frontier is super jagged! Original tweet: https://x.com/stanislavfort/status/2041922370206654879
RT Maxime Rivest 🧙♂️🦙🐧 It seems like the day has come to leave Anthropic. Initially, I loved Claude Code. It was a good harness and a simple TUI... and I had learned to eat my tokens with a sauce of subsidy. Before joining the Max plan, I had paid $280 in one weekend of development on Attachments. Sadly, as time went on, Claude Code became a terrible flickering TUI mess. This is now my biggest north star in building: don't do feature bloat and accept half-working vibe slop like the Claude Code team. I really respect Boris and the team, I just see the result of their experiment and I don't like using it. So, I stopped loving Claude Code and started tolerating it. It was a good harness and a terrible flickering TUI. Then they started to mess with the prompt and behavior — it became an even worse TUI (because every week was worse) and a bad harness. I complained here. People told me Pi is great. I tried Pi. Pi is great. Now, they have blocked me from using Claude Code Max on Pi. Makes sense, but I learned to like my tokens with a sauce of subsidy. So I'll start to do prompt optimization on Codex. If it was not for the subsidy, I would make Gemini's edit tool work and use that with Grok 4.2 and some open-source mix. Claude is good, but Claude Code is bad, and token subsidies are better than both. On the subsidies: my bet is that by the time they stop, we will have models that cost about that price to operate at that quality. In my estimate, subsidies are just bringing that future ahead a bit. Original tweet: https://x.com/MaximeRivest/status/2041845510877708508
RT Ronan Farrow (🧵1/11) For the past year and a half, I've been investigating OpenAI and Sam Altman for @NewYorker. With my coauthor @andrewmarantz, I reviewed never-before-disclosed internal memos, obtained 200+ pages of documents related to a close colleague, including extensive private notes, and interviewed more than 100 people. OpenAI was founded on the premise that A.I. could be the most dangerous invention in human history—and that its C.E.O. would need to be a person of uncommon integrity. We lay out the most detailed account yet of why Altman was ousted out by board members and executives who came to believe he lacked that integrity, and ask: were they right to allege that he couldn't be trusted? A thread on some of of our findings: Original tweet: https://x.com/RonanFarrow/status/2041213917611856067
RT Maarten Grootendorst A Visual Guide to Gemma 4 With almost 40 (!) custom visuals, explore the new models from Google DeepMind. We explore various techniques, ranging from Mixture of Experts and the Vision Encoder all the way up to Per-Layer Embeddings and the Audio Encoder. Link below 👇 Original tweet: https://x.com/MaartenGr/status/2040099556948390075
RT Ross Wightman Okay LLM + PyTorch people, trunc_normal_, what the fuck! Many LLM inits use it w/ default cutoffs. It's either not doing anything or it's quite broken due 2 issues. 1. The a/b cutoffs in PyTorch are not in std-devs, they are absolute. So w/ a std=0.02, and -2/2 (default arg) cutoffs that's 100σ!! That is a normal distribution, trun isn't doing anything. 2. There are numerical issues. Even in float32, the truncation produces a handful of -2 (lower cutoff) values, 100σ!! That's incomprehensibly improbable. I doubt a float32 or even float64 algo could even produce it, but clamping a bad float value does. Olmo (@allenai codebases) appear to be one of the few that uses trunc_normal_ and bothered to set the cutoffs properly. It'd be nice to see more train code opened up as a default. We so often only end up with a sanitized version of the inference/fine-tune friendly model these days and may lose details like original init. I've known about #1 for ages, I have an alternate trunc_normal_tf_ implementation in timm for that reason. But I saw those -2's last week when I was debugging something and was a little surprised. Original tweet: https://x.com/wightmanr/status/2038634643843682366
RT Aakash Kumar Nain Alec is a once in a generational researcher, but saying that he invented pretraining is not only a bit of stretch, but it's also a disrespect to other people's work. Original tweet: https://x.com/A_K_Nain/status/2038230148282294665
Every LLM from any lab today traces back to this guy, who was the only person at OpenAI pushing for pretraining transformer language models. He built GPT-1. After that did others see the potential. He invented it, and almost none of the so called AI experts even know his name.
RT Jason Rosenfeld Every LLM from any lab today, including from OpenAI taces back to @jeremyphoward and @seb_ruder with their ULMFiT paper. The breakthrough for LLMs was transfer learning, not Attention. Original tweet: https://x.com/jrosenfeld13/status/2037991743883563225
Every LLM from any lab today traces back to this guy, who was the only person at OpenAI pushing for pretraining transformer language models. He built GPT-1. After that did others see the potential. He invented it, and almost none of the so called AI experts even know his name.
RT Matt Harrison For my friends who are still using UV and might be a little weary about recent compromises to PyPi packages, stick this in your pyproject.toml. You can let all of those pip users find and report the compromises... Original tweet: https://x.com/__mharrison__/status/2037621081771745388
"naming their next model after Cthulhu" 😒
Naming their next model after Cthulhu makes it hard to take Anthropic seriously as the good guys. It's fun at any other software company, not one that actually is flirting with extinction.
View quoted postGreat to see @xyflowdev joining the llms.txt party - and sharing some really helpful examples of how llms.txt helps agents get better results! 😎 https://xyflow.com/blog/llms-txt-agent-skills-ai-development
RT xlr8harder A tragedy is unfolding at AllenAI. Fs in the chat Original tweet: https://x.com/xlr8harder/status/2036476797080977801
RT Armin Ronacher ⇌ There will be more of this. And as much as we're joking about it, we're seeing a massive degradation of code quality right now and we're increasingly only catching it way too late. Original tweet: https://x.com/mitsuhiko/status/2036475951349948557
If anyone else is hitting this issue of Claude not using "extended thinking", be sure you're in the "Chat" mode, where you can enable it:Code mode doesn't have that option:
This is a fresh session. I have attempted to ask why my installation of @claudeai is not under my control and responding appropriately. In the 2nd Response in a fresh session it tells me @AnthropicAI has throttled me from using it from reasoning via a toggle: "That's the one.
If you're gonna be an asshole and use bots to reply, at least don't be a *stupid* asshole and have your both reply to the wrong thread. 🙄
RT Chris Rickard I'll be speaking @ AI Engineer Melbourne on "Legacy Software + Agentic Discovery". I’ll be sharing practical lessons from large-scale reverse-engineering projects: - recovering intent from code - where humans still matter most - what high-quality spec generation might look like ... and when I say large, I mean 12M+ LOC. ... and when I say legacy, I mean 25+ years old. --- 3-4 June 2026, Federation Square Melbourne & Online. Sharing the stage with some rad humans including @swyx, @GeoffreyHuntley, @jeremyphoward and stacks more. 🔗 Tix && discount link: https://webdirections.org/register/?eventName=aieng26&eventTitle=AI+Engineer+Melbourne+2026&selectedTicket=aieng26conference&promoCode=chrisraieng26conference%2Cchrisraieng26streaming Huge thanks @aiDotEngineer & @johnallsopp Original tweet: https://x.com/chrisrickard/status/2034432134539317258
RT Percy Liang In Marin, we are trying to get really good at scaling laws. We have trained models up to 1e22 FLOPs and have made a prediction of the loss at 1e23 FLOPs, which @WilliamBarrHeld is running. This prediction is preregistered on GitHub, so we'll see in a few days how accurate our prediction was. What we want is not just a single model but a training recipe that scales reliably. Original tweet: https://x.com/percyliang/status/2034367256277533100
RT Ali Behrouz This paper is the same as the DeepCrossAttention (DCA) method from more than a year ago: https://arxiv.org/abs/2502.06785. As far as I understood, here there is no innovation to be excited about, and yet surprisingly there is no citation and discussion about DCA! The level of redundancy in LLM research and then the hype on X is getting worse and worse! DeepCrossAttention is built based on the intuition that depth-wise cross-attention allows for richer interactions between layers at different depths. DCA further provides both empirical and theoretical results to support this approach. Original tweet: https://x.com/behrouz_ali/status/2033581834953453853
Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with
The combination of skepticism of anything containing the word "AI" plus skepticism of anything containing the word "Australia" is pretty tough... ;)
RT Timothy B. Lee I don't understand how Grammarly thought they could get away with this. Original tweet: https://x.com/binarybits/status/2032638328588194205
RT dr. jack morris pretty unsettling to see the disdain OpenAI employees hold for @karpathy, the most prolific educator of the AI era > median openai employee: gathers niche data + runs evals for GPT-N datamix > karpathy: teaches millions how to build these models who has more long-term impact? Original tweet: https://x.com/jxmnop/status/2032570044648218839
RT David Gwyer - AI Evals & RAG | ML Engineer I'm communicating with an LLM (SolveIt) via my handheld A4 whiteboard today! 😀 Feels like a really smooth and natural process. More. 👇🧵 Original tweet: https://x.com/dgwyer/status/2032423073312280851
New @answerdotai research by @R_Dimm & @alexisgallagher looking at whether there's been a clear jump in productivity based on python package releases. tldr: No. "Relax. You are not missing a party that literally everyone else was invited to." https://x.com/alexisgallagher/status/2032175544553570343
If AI is so great for coding, where are the apps? @R_Dimm and I studied the Python Package index to find an "AI effect". Here's where it is not, where it is, and thoughts on why. WHERE AI IS NOT. There's no clear AI effect on Python _package creation_ since ChatGPT.
RT Bryan Catanzaro Announcing NVIDIA Nemotron 3 Super! 💚120B-12A Hybrid SSM Latent MoE, designed for Blackwell 💚36 on AAIndex v4 💚up to 2.2X faster than GPT-OSS-120B in FP4 💚Open data, open recipe, open weights Models, Tech report, etc. here: https://research.nvidia.com/labs/nemotron/Nemotron-3-Super/ And yes, Ultra is coming! Original tweet: https://x.com/ctnzr/status/2031762077325406428
This is a real gold mine! 😎
Created close reading notebooks for almost every lesson of @jeremyphoward's fastai deep learning course (it's more than a course) Close reading is a technique for reading out of text, not into. Use a LLM, and you're in flow state for longer–you ask right there, with all context.
RT Larry Dial New NanoGPT Speedrun WR at 86.1 (-0.7s), by replacing partitioned hyperconnections with a simple idea: feed the exact same context vector into the last 3 attn layers, so late stage attn doesn't get polluted by prediction MLPs. Opinion: AI research agents are handicapped until they have a mech-interp toolkit. Many sub-3min architecture improvements came from analyzing weights. https://github.com/KellerJordan/modded-nanogpt/pull/241 Original tweet: https://x.com/classiclarryd/status/2030465730718908884
RT Noah Dasanaike I want to up the ante on this. If you have a large document collection, I will digitize it for you, for free (you pay for inference), on one condition: that we make the data publicly available immediately. Original tweet: https://x.com/dasanaike/status/2030286576052212175
Social scientists working with materials requiring digitization can only study what machines can read. In practice, that means printed Latin-script documents from well-funded archives. In a new working paper, I show that Vision Language Models used zero-shot outperform every
RT Noah Dasanaike Social scientists working with materials requiring digitization can only study what machines can read. In practice, that means printed Latin-script documents from well-funded archives. In a new working paper, I show that Vision Language Models used zero-shot outperform every existing OCR system across every script evaluated, and I propose a pipeline for deploying them on new collections. I apply it to six archival collections spanning 1.8 million pages across six countries for under $1,900. Original tweet: https://x.com/dasanaike/status/2030039366068772952
RT Chris Lattner Mojo🔥 has always had "peak perf" and "access to full power of the GPU"... but many want "peak perf" with high level code. "Structured Kernels" are simple and composable APIs that increase the usability of kernel programming - without losing perf, and with no template errors. Original tweet: https://x.com/clattner_llvm/status/2029639075196719574
You shouldn't have to choose between peak GPU performance and code you can actually maintain. We built Structured Mojo 🔥 Kernels to fix that. Performance, usability, and portability without the tradeoff. 14k to 7k lines. ~1.8k TFLOPS held. We wrote a 4-part series on how. Part 1
RT Acer Re Also, come on OpenAI. If you want an automated AI researcher, this needs to start going up, not down. Original tweet: https://x.com/AcerFur/status/2029624795113955357
RT Nanbeige In both LeetCode's Weekly Contests (Weekly Contests 489–491) and the HMMT February 2026 (Harvard-MIT Mathematics Tournament), Nanbeige4.1-3B's performance not only significantly outperformed that of Qwen3.5-4B but also surpassed Qwen3.5-9B. Original tweet: https://x.com/nanbeige/status/2029405267130220863
RT alistair BEAM is the correct virtual machine for agents, and Elixir and Gleam are the correct languages. The future is massively concurrent Original tweet: https://x.com/alistaiir/status/2029320569653739634
1/7 The specific contract language shared states "The Department of War may use the AI System for all lawful purposes". Although additional language is appended in that sentence, the language does not add practical constraints.2/7 The contracts goes on to reference an existing directive (DoD Directive 3000.09), but does not do so in a way that adds any contractual constraints.3/7 Based on this language, OpenAI's claim "even if those laws or policies change in the future, use of our systems must still remain aligned with the current standards reflected in the agreement" is not supported.4/7 Case law on this goes back >100 years. A 1911 case noted that that contracts must not freeze legislation, else “individuals and corporations could… in anticipation of legislation, render of no avail the exercise by Congress… of its power to regulate commerce.”5/7 This is also true of contracts with the government. In Winstar (1996) the Supreme Court held 7-2 that a clause referring to "all applicable statutes" required obeying future laws as they arose; it did *not* freeze the regulatory framework at the time of signing.6/7 Whilst mechanisms exist that in theory can freeze reference to legislation to a point in time, they are very rare, almost never used in government contracts, and would be unlikely to be enforceable in this case.7/7 Our legal analysis shows that this critical claim from OpenAI, that "use of our systems must still remain aligned with the current standards reflected in the agreement", is very unlikely to be supported by their contract with the DoW, or by the law.
RT Charlie Bullock I agree with Alan's overall claim in this piece (Anthropic will very likely sue and win), but I disagree with his analysis on one important point. I think that Anthropic's case is actually even stronger than Alan's and Michael's analysis suggests, because the statutory "judicial review bar" they're anticipating doesn't actually exist. The article suggests that the relevant supply chain risk statute "bars judicial review when the government limits disclosure of its determination." I previously thought that too, and tweeted analysis suggesting the same. But after further research, I've been converted to the @alasdairpr view: I'm pretty sure that the judicial review bar only bars review of the government's decision to limit disclosure, and does not bar review of the actual SCR designation. Basically, this means that Anthropic's legal prospects are even better than Alan anticipates. Anthropic won't have to rely on a constitutional claim or try to bypass the statutory judicial review bar with an ultra vires argument--they can just go straight to federal court and say "what DOW is doing is illegal." This is a big deal, because ultra vires arguments and similar bankshot strategies for circumventing judicial review are generally very difficult to win with. Original tweet: https://x.com/CharlieBul58993/status/2028528529537650997
A deep dive in @lawfare on the many legal problems with the Pentagon's designation of Anthropic as a supply chain risk.
RT Gergely Orosz On one end, the Anthropic team is a massive user of AI to write code (80%+ of all code deployed is written by Claude Code). They ship amazingly fast. On the other hand, seeing these beyond terrible reliability numbers suggests there might be a downside to all this speed: Original tweet: https://x.com/GergelyOrosz/status/2028465387570884640
RT Glenn Matlin Re @ch402 @sebgehr Too many to count. NatSec in general agrees with you @ch402. Jack Shannan’s background and placement in Operation Maven is noteworthy so his understanding of how critical Claude is to American military effectiveness is not just hot air. https://www.linkedin.com/posts/jackntshanahan_lots-of-people-posting-about-anthropic-activity-7432870987165077504-SCa7 Original tweet: https://x.com/GlennMatlin/status/2028244121178251503
As @bradrcarson explains, the contract language released so far does not restrict the gov from using AI to kill without human oversight.
RT dave kasten This is an important point from Logan Koepke: OpenAI is claiming that DoW lacks authorities to get commercial data at scale, despite extensive reporting that they have done so Original tweet: https://x.com/David_Kasten/status/2028137922529038487
@natseckatrina @David_Kasten @sama on point two, they have in fact done this and claim they have the authority to do this. • https://www.vice.com/en/article/us-military-location-data-xmode-locate-x/ • https://www.nytimes.com/2021/01/22/us/politics/dia-surveillance-data.html • https://static01.nyt.com/newsgraphics/documenttools/0117fa5f9ff7ae33/fe33e1ba-full.pdf
RT Leon Lang I understand that OpenAI employees might have been reassured by Sam’s tweet from yesterday. But this blogpost is so blatantly deceptive that I can’t grasp how you can have any other reaction than to be ashamed to work for your company. Original tweet: https://x.com/Lang__Leon/status/2027903509081821208
For those following the DoW AI drama, I highly recommend reading this post explaining how @OpenAI approached the negotiations with the DoW.
RT Andreas Kirsch 🇺🇦 I'm speechless at OpenAI releasing that contract excerpt and acting as if there aren't gaping holes that could be exploited far beyond their stated "red lines." I'm not a lawyer, but this is pretty obvious and common sense. (And to be clear: if Google had signed the same deal, I'd be saying the same thing internally. The issues here are bigger than friendly competition between companies.) OpenAI's "red lines" are: no mass domestic surveillance, no directing autonomous weapons, and no high-stakes automated decisions. They argue their cloud-only deployment + safety stack + cleared OpenAI personnel "in the loop" make violations impossible. They also claim the contract references the relevant laws/policies "as they exist today" so future changes won't weaken the standards. But the actual language they published is still full of obvious escape hatches. This is why Anthropic refusing to sign makes sense. Reporting on the Anthropic–"DoW"/Pentagon standoff described them saying the proposed contract language was framed as compromise but paired with "legalese that would allow safeguards to be disregarded at will." You don't need to agree with Anthropic on everything to see what they're reacting to: language that sounds like ethics but cashes out as essentially "subject to whatever the government decides later." ## Autonomous weapons The problem is that the restriction is conditional: it depends on what "law/regulation/policy requires human control" for. If policy definitions are weak (or later revised), the contract language itself doesn't read like a durable "no autonomous weapons" ban. It reads like "we'll follow whatever the current regime says requires human control." OpenAI says elsewhere that the agreement "locks in" today's standards even if laws/policies change. If that "freeze" clause is real and enforceable, sure, but it's not visible in the excerpt itself, so the excerpt alone doesn't justify the level of confidence they're projecting...
RT Xeophon If you are an (AI) researcher, it’s crucial to think about the implications about your research. I think this post from @giffmana is really thought provoking: Original tweet: https://x.com/xeophon/status/2027761750930567390
RT Bun am i a supply chain risk now??? Original tweet: https://x.com/bunjavascript/status/2027638567317737895
