Your curated collection of saved posts and media
My favorite genre of American folk photography is, "fast food joint in a stunning natural setting." https://t.co/IhBs2fgkxt
May I add the Chipotle in Sedona to this conversation? https://t.co/DxSjeR7Gfb
My favorite genre of American folk photography is, "fast food joint in a stunning natural setting." https://t.co/IhBs2fgkxt

Every damn day, another post with a thousand plus likes for a year old "breaking" paper that should "scare everyone using AI" because of issues with "latest top models" like Llama 4 and o3. (The paper was good & multi-turn is hard, but, again, big progress since it was written.) https://t.co/9d3rYisbJ5
Amazing to see the two worst forms of AI posting in a QT. The original post misinterprets a highly-discussed paper from 2025 and calls it breaking news. Than that is retweeted by someone else giving even more wrong info (from model performance to benchmark names). 1M views. Bleh
I gave ChatGPT for Excel and Claude for Excel a try on a very hard Excel file: macro-economic data from 1,000 years of English history across over a hundred tabs. I think both did a good job, and I did not spot errors (though I only did spot checks). However, Claude was harder to check because ChatGPT tended to stick within the Excel app, building formulas and manipulating the data in the way a person would. On the other hand, Claude used Python and often pasted material into Excel for display purposes only, making it harder to trace or edit. If that holds, I think it will generally make ChatGPT more useful for serious users if you want to audit the results. Prompt: "help me understand the relationship between the mix of agricultural products in the UK, GDP, and population, along with hours worked. I want this over the total period, and you should illustrate interesting trends with graphs and statistical analysis

@saintgeorge Nope https://t.co/XV1eS1FEi2
Like the AI generates absolute bangers of metaphor that make no sense, but, because the writing is meaning-like, you figure out ways for it to make sense, and through that interpretation, find something deeply meaningful Very indicative of the general problem of AI personality https://t.co/iw9aGeOEkA

We present a research preview of Self-Flow: a scalable approach for training multi-modal generative models. Multi-modal generation requires end-to-end learning across modalities: image, video, audio, text - without being limited by external models for representation learning. Self-Flow addresses this with self-supervised flow matching that scales efficiently across modalities. Results: • Up to 2.8x faster convergence across modalities. • Improved temporal consistency in video • Sharper text rendering and typography This is foundational research for our path towards multimodal visual intelligence.
🚨BREAKING: Yann LeCun just dropped a paper that should make every AI lab rethink its roadmap. One brutal conclusion: chasing AGI is the wrong goal. Here’s why: → Humans aren’t general we’re survival specialists. → Walking and seeing feel “general” only because they keep us alive. → Outside that zone, we’re terrible. Chess computers proved it decades ago. → Most AGI definitions today either can’t be measured or assume human = general. We built the benchmark around the wrong species. The team proposes a new target: Superhuman Adaptable Intelligence (SAI). Not “can it do what humans do,” but: how fast can it learn something new? The approach: specialized expert systems with internal world models + self-supervised learning built to master the massive task space that humans biologically can’t reach. One giant model mimicking human limits isn’t the ceiling. It’s the trap.

We asked people around the world to rate the morality and ethics of others in their country. The U.S. is the only place we surveyed where more adults describe the morality and ethics of others living in the country as bad than good. See our full morality report here: https://t.co/qBtj1ycDkP

Jobs report uniformly weak: 92K jobs lost (with job losses in almost every industry), household survey employment down too, unemployment rate up to 4.4%, participation down, avg weekly hours flat. Main sign in the other direction was strong wage growth. https://t.co/tX3LF6WfVN
Trump’s second-term pardons are historic in their enormity—billions in fines erased, allies protected, donors rewarded, DOJ undermined, and election norms threatened. Corruption looks less like an exception and more like the rule, says Cato’s Dan Greenberg. https://t.co/rR2YH0O7py
Israeli Finance Minister Bezalel Smotrich says that Beirut’s Dahiya district will soon “look like Khan Younis.” https://t.co/KjQDryRAeK
Israeli strikes displace hundreds of thousands across Lebanon https://t.co/LRMn3B31hk
Israeli strikes displace hundreds of thousands across Lebanon https://t.co/LRMn3B31hk
As a reference to my good friends in SF curious about the scale of displacement in Lebanon https://t.co/Y0swBEpAvO

As a reference to my good friends in SF curious about the scale of displacement in Lebanon https://t.co/Y0swBEpAvO

The Taco Bell in Pacifica, CA https://t.co/VXxRz97j5Q
Europe has the Eiffel Tower Europe has the Colosseum Europe has Big Ben Europe has the Acropolis Europe has the Sagrada Família Europe has the Louvre China has the Great Wall China has the Forbidden City China has the Terracotta Army, etc The United States has...?
The Taco Bell in Pacifica, CA https://t.co/VXxRz97j5Q
We just published our 1H 2026 roadmap (https://t.co/qRKP2wg7RN) and an accompanying blog (https://t.co/fjVDnvk37c) for enabling the IBM's Spyre accelerator in PyTorch — ecosystem-first, building on torch.inductor, vLLM, and contributing back (Dataflow accelerator's Tile IR, OpenReg, out-of-tree CI). While the market debates whether AI disrupts legacy tech, we're busy building the accelerator infrastructure that enterprise AI runs on. We're sharing this journey in the open. Come see our talks on extending torch.inductor for dataflow accelerators and Spyre's vLLM integration at the inaugural PyTorch Conference Europe in Paris, April 7–8! @PyTorch @IBMResearch @IBM @RedHat_AI
Building on the previous correctness-focused pipeline, KernelAgent can now integrate GPU hardware-performance signals into a closed-loop multi-agent workflow to guide the optimization for Triton Kernels. Learn more: https://t.co/r2WqASIhWG @KaimingCheng @marksaroufim https://t.co/OrtOp9boum
このような最先端のAI研究を、実際のビジネス環境へ適用し社会実装を進めるため、Sakana AIではエンジニアの採用を強化しています。 日本でのAIの未来を共に切り拓き、エンタープライズの現場で実運用されるAIエージェントの開発に興味がある方は、ぜひ詳細をご覧ください。 https://t.co/hbpNDyUKrj
AIの進化で開発効率が上がる一方、ジェボンズのパラドックス(Jevons paradox)によりSoftware Engineerの需要はかつてなく高まっています。 Sakana AIではより多くのSoftware Engineerを採用します。ぜひご覧ください。 https://t.co/buNwDbN6tv https://t.co/nKxUiNtrvl

As AI makes coding more efficient, Jevons Paradox kicks in. The cost of building software is dropping, which means the demand for great Software Engineers to build even more ambitious systems is higher than ever. We are actively hiring more Software Engineers at Sakana AI to help us build these systems. Come join us in Tokyo 🗼🇯🇵 https://t.co/RzpIewkP9Y
AIの進化で開発効率が上がる一方、ジェボンズのパラドックス(Jevons paradox)によりSoftware Engineerの需要はかつてなく高まっています。 Sakana AIではより多くのSoftware Engineerを採用します。ぜひご覧ください。 https://t.co/buNwDbN6tv https://t.co/nKxUiNtrvl
It’s happening ✨ https://t.co/vtCn1EkHan
There is a possibility that there will be a billion programmers in 5 years.
I wrote this 2 years ago as a joke but it is no longer a joke: “Forget Torch, Tensorflow, and Theano. I decided to implement Backprop NEAT in Javascript, because it is considered the best language for Deep Learning.” https://t.co/eGNEpBWm6e https://t.co/JD27jievYB

to improve fine-tuning data efficiency, replay generic pre-training data not only does this reduce forgetting, it actually improves performance on the fine-tuning domain! especially when fine-tuning data is scarce in pre-training (w/ @percyliang) https://t.co/ClGPAUlPqQ
Normally replay old data reduces forgetting, but it actually helps you learn on new data too! We finally put this paper out on arxiv, but had it up as a Marin GitHub issue ~1 year ago: https://t.co/MNevf6XjvC
to improve fine-tuning data efficiency, replay generic pre-training data not only does this reduce forgetting, it actually improves performance on the fine-tuning domain! especially when fine-tuning data is scarce in pre-training (w/ @percyliang) https://t.co/ClGPAUlPqQ
Claude Code wiped our production database with a Terraform command. It took down the DataTalksClub course platform and 2.5 years of submissions: homework, projects, and leaderboards. Automated snapshots were gone too. In the newsletter, I wrote the full timeline + what I changed so this doesn't happen again. If you use Terraform (or let agents touch infra), this is a good story for you to read. https://t.co/Mbi3oM4HMn
2.5M+ people have already joined the international boycott of ChatGPT. OpenAI's market share is collapsing. I think people are starting to realize that we can actually push this company over the cliff. https://t.co/FjEOpUUBqF
@GaryMarcus https://t.co/JV7yyW9WY8
EXCLUSIVE: Department of War AI Chief On How The Anthropic Deal Collapsed When Emil Michael (@USWREMichael) took over the Department of War’s AI portfolio last August, he discovered the Biden admin had been “asleep at the wheel” when it came to top military contracts. “I was like, ‘Holy cow,’” Michael said of Anthropic’s contract, “There’s 25 pages of terms and conditions of things I can’t do.” For example: as written, the contract would not allow Anthropic to plan any kinetic strikes, generally considered a central activity of war. “This is a contract that should be made with GEICO Insurance, not with the Department of War,” he told us. A renegotiation ensued. What followed, in Michael’s words, were “three months of knockdown, drag-out negotiations” which involved Michael imagining every possible future wartime scenario that would require a carveout in Anthropic’s terms of service, and asking them for approval. Anthropic was also quite slow: “It’s not like mano a mano negotiation, me and Dario,” Michael says. “It’s like every time we discuss something, he has to take it back to his politburo of co-founders and their ethics panel.” Then, after an Anthropic exec reached out to Palantir to ask for classified info about how Claude was used to capture Nicolás Maduro — allegedly implying they could pull the plug on a military raid if they disagreed with how AI was used (which Anthropic denies) — Michael and the DOW concluded the company was a supply-chain risk. Many speculated that the Pentagon was punishing Anthropic for ideological differences. But Michael feared that certain ideological differences could, in fact, harm or undermine the performance of DOW products, potentially threatening soldiers’ safety. “I can’t have a gun not work because they decide they don’t like guns,” Michael says. That’s “putting real lives at risk. It’s no joke, right?” Anthropic’s unreliable behavior led Michael to believe they may have never really wanted to reach a deal. Still: he’s open to renegotiating if Anthropic can prove they’re acting in good faith. “I have a responsibility to the Department of War, and if there was a way to ensure that we had the best technology, I have no ego about it.” he said. “I mean, look, I’m a deal guy.” Full story in Pirate Wires 👇
New newsletter: THE ECONOMIC CRISIS OF THE IRAN WAR COULD GET VERY BAD, VERY FAST By April, energy experts say, the Iran War could be a full blown energy crisis. - Oil tankers are already stranded outside the Strait of Hormuz. - Storage facilities are filling up. - Qatar is shutting down LNG facilities. - Kuwait is pulling back oil production. - Saudi Arabia and UAE might be 2 weeks for shutdowns. Crude oil has already jumped from $55 a barrel in December to $89 this morning. All for a war of choice for which the administration has: shown no evidence of imminent attack, provided no consistent rationale, and explained no clear endgame.