How many tokens can we rip on this commute? https://t.co/q1JjOaTpB1

AI is becoming a research partner for scientists around the world. But as more researchers rely on AI for experiment design, reviews and analysis, an important question emerges: what happens when trust shifts from colleagues to machines? Science advances through verification, debate and human judgment. Those foundations still matter in the AI era. https://t.co/dMa1IRr6RP @ConversationUK @ConversationUS

Our cofounder @iamtimdavis built an AI storybook app using @BlackForestLabs' FLUX2 and @googlegemma 4 on Modular Cloud. Pick a character, make choices, and the story branches endlessly, with every page written and illustrated in real time. Tim has spent his career obsessing over inference latency, first at Google, now at Modular. Building something his kids use settled it: in a real-time generative app, the inference platform determines the experience as much as the model. The numbers back that up. From 24 hours of production traffic: first prose in 420ms, a full illustration in under 6 seconds, 85% of page turns in 48ms. Create your own story with Inkwell and share it. We're sending swag to our favorites: https://t.co/ocAP9ElBjE

We put our CEO in an Inkwell story. Chris Lattner: compiler legend and devoted Diet Coke hoarder. Someone stole his last case. The only way to get it back is to write a new programming language fast enough to track down the thief. Read it and choose your own ending: https://t.co/zWLwMqYrh3

Every Inkwell story stars a character you build. Pick the species, the hair, the outfit. Watch them come to life across an endless branching story, illustrated on every page. Make yourself the hero at https://t.co/RXe3DSArMf. Tag us in what you create. We're sending swag to our favorites.

For engineers: hit the </> button in Inkwell and dev mode stays on across every page. You'll see exactly how fast each image is generating in real time: latency, tokens/sec, and more. Powered by Modular Cloud. Try it at https://t.co/rNUKOjyRdx https://t.co/dYj7a8FSH0

Had an interesting exchange with roon of OpenAI last night over whether super intelligent AI would actually be able to navigate organizational challenges. https://t.co/2i9wYO24s1

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There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it. I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines. Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%. Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable! Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more. Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus. To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market. Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades. Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have). Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future! [Original text in The Batch newsletter.]

AfD bei 28%, inzwischen hat sich der Abstand zur Union (22%) auf 6 Punkte vergrößert. Gleichzeitig sinkt auch das Verständnis für die "Brandmauer". Deutschland will den Wandel - wir sind dafür bereit. https://t.co/ie1le8vakP

over the weekend, I had the opportunity to participate in the AI Engineer Hackathon, hosted by the 65labs team as part of the road to AI Engineer 🇸🇬 the event was a 7-hour open-canvas build day with hundreds of builders experimenting, prototyping, and pushing AI tools as far as we could. I decided to build a game: Prompt Bound Prompt Bound is a retro pixel side-scroller that teaches AI engineering through play. Instead of explaining concepts in slides, the game turns them into levels: prompting, context, retrieval, tool calling, and agents. players move through each trial, meet characters, solve agent-themed challenges, collect rewards, and gradually learn how to work with AI Agents more thoughtfully. i built this because AI engineering can feel abstract when you first encounter it. A game gave me a way to make the learning loop more immediate. the @65labslah team consistently create excellent spaces for builders to meet, learn, and ship things together, and this hackathon was no exception. And huge congrats to the finalists and winners! Your pitches were amazing 🤩 Prompt Bound is not live yet; more improvements need to be made 😊 AI Engineer Singapore is sold out, but you can still join the community around it: explore AI Engineer Singapore side events here: https://t.co/bVr1kKAvBq Learn more about the main conference here: https://t.co/T2RQH3Wvps also shoutout to Codex! @SherryYanJiang @agrimsingh @brianchew @unprofeshme @adlinzainal @darenstwt

Philosophy is used as a prop in 2026 AI. Claude is exactly the same as it was in 2023, but now it comes with a Super Bowl ad, a “Model Welfare Officer”, and Dario Amodei musing publicly about its “possible” consciousness. • Architecture: unchanged. • Marketing evolution: Alignment → Character → Personality → Selfhood. The only thing being built is narrative. Everything else is inference-time safety theatre. Full breakdown: https://t.co/2vBZmlAUMA #ai #consciousness #safetytheatre

AReaL is joining the @PyTorch Landscape🎉https://t.co/5IvYOhWcYW Fully async, modular, built for the agentic AI era. AReaL is a scalable, RL infrastructure designed to bridge foundation model training with modern agent-based applications. Explore AReaL: https://t.co/659iOG4Xbq https://t.co/m8veV5wY0x

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AReaL is joining the @PyTorch Landscape🎉https://t.co/5IvYOhWcYW Fully async, modular, built for the agentic AI era. AReaL is a scalable, RL infrastructure designed to bridge foundation model training with modern agent-based applications. Explore AReaL: https://t.co/659iOG4Xbq https://t.co/m8veV5wY0x

Arm has released a set of hands-on labs designed to help developers deploy PyTorch models using ExecuTorch across real edge devices. The labs walk-through: • Exporting models into lightweight .pte artifacts • Running optimized inference on Arm CPUs (XNNPACK + KleidiAI) • Offloading workloads to Ethos-U NPUs using TOSA and Vela • Visualizing model partitioning and performance behavior It’s a practical way to understand how models are executed across heterogeneous compute—and how to optimize for it. If you’re working on edge AI or exploring on-device inference, this is worth a look: https://t.co/cml4cBVF3Q #PyTorch #ExecuTorch #OpenSourceAI

🚨 WARNING: The self-spreading “Mini Shai-Hulud” worm compromised npm & PyPI packages tied to TanStack, Mistral AI, Guardrails AI, OpenSearch & more. The attack used GitHub OIDC token hijacking and cache poisoning to spread credential-stealing malware across 42 TanStack packages and 84 versions. Check your dependencies immediately → https://t.co/33fxlrOPzz

Don't tell me Reachy Mini isn't the most adorable robot ever! https://t.co/BOGd95ann4

Due to rising RAM prices and tariff-related costs, Reachy Mini pricing will change on June 1st. Good news: you still have a bit more than 2 weeks to order at the Early Bird prices. Order here: https://t.co/UcMxyyfYO9 https://t.co/Oq4LjBlWUl

Meet physics-intern🧑‍🎓, our agentic framework for theoretical physics. It takes Gemini 3.1 Pro from 17.7% to 31.4% on CritPt, a new SOTA on one of the hardest benchmarks for LLMs. Theoretical physics is hard for humans and LLMs alike. But physics-intern decomposes problems and dispatches them to a team of specialized agents, solving research-level questions far more effectively than the base model alone.

Need document parsing that stays fully local and private? 👀 Meet liteparse-server, a self-hostable, open-source HTTP server for parsing documents and generating screenshots from PDFs, Office files, and images. ✅ 100% self-hosted ✅ Private by default ✅ Open source ✅ Built for production deployments Deploy it as: 🐳 a @Docker container ⚡ or a serverless Express.js API It also integrates easily with: - @Redisinc for caching and rate limiting - @opentelemetry-compatible collectors for traces and metrics - observability tools like @JaegerTracing, @PrometheusIO and @grafana Read the full breakdown here: https://t.co/E3y2ZHvURm GitHub repo: https://t.co/K0d8XVEFGK

For our free newsletter this week, we discuss how AI-powered cyberattacks are a growing risk. And this morning a ton of hacks are being disclosed, as @emollick points out here. 
@IrenaCronin and I write this newsletter every week.   AI-powered cyberattacks are a growing risk because AI helps hackers move faster and create more convincing attacks. They can use AI to find software flaws, build attack tools, automate parts of cyber operations, and write phishing messages that look more realistic. Businesses need to respond with stronger security basics, including faster patching, better login protections, employee training, software visibility, and AI-aware defenses. Subscribe and read for free: https://t.co/HHwYy7NoAl

Cool idea from Nous Research. What if you could speed up long-context pretraining with a subquadratic wrapper that you remove before deployment? That is the idea behind Lighthouse Attention. The method wraps ordinary SDPA with a hierarchical, gradient-free selection layer that compresses and decompresses queries, keys, and values symmetrically, preserving left-to-right causality. Crucially, it can be removed near the end of training in a short recovery phase, so the deployed model still runs vanilla attention with no architectural cost at inference. Preliminary LLM experiments report faster total training time and lower final loss than full-attention baselines. Why does it matter? Most efficient-attention work either changes the deployment-time architecture or pays a quality tax to do so. A training-only wrapper that survives a clean recovery phase sidesteps both. If it scales, this becomes an important training-time speedup for long-context pretraining. Paper: https://t.co/9g5Ldnb1rV Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

We just crossed 1,000,000 public datasets on Hugging Face! That's petabytes of data available that millions of AI builders are downloading, analyzing, and training AI models on every day! What's interesting is that we see a clear acceleration since agents started to be good as the number of datasets doubled over the past 8 months (it took 4 years to reach the first 500k). It's becoming easier and faster to build, share and use your own datasets! Many are saying the next bottleneck for more people to build AI themselves (instead of relying on APIs) is better data so we're just getting started! Thanks everyone for your amazing contributions, we couldn't do it without you!

Don't tell me Reachy Mini isn't the most adorable robot ever! https://t.co/BOGd95ann4

Don't tell me Reachy Mini isn't the most adorable robot ever! https://t.co/BOGd95ann4

NEW paper from Google DeepMind. (bookmark it) AI Co-Mathematician is an agentic research workbench for mathematicians, and it just hit 48% on FrontierMath Tier 4, a new high score among AI systems evaluated. The system is an asynchronous, stateful environment that supports ideation, literature discovery, computational analysis, theorem verification, and knowledge development. It manages uncertainty, clarifies intentions, records unsuccessful attempts, and emits formal mathematical outputs. Early applications yielded solved open problems, fresh research angles, and recovered overlooked citations during active research sessions. This is one of the cleanest demonstrations that agentic AI moves the frontier on genuinely hard mathematical research, not just problem-solving but discovery support. The asynchronous stateful workbench design is interesting to adopt if you are building agents for any expert workflow. Paper: https://t.co/C1ro3mGPQi Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

https://t.co/a0sKQPZSz3

We've just hit 1M open datasets on the Hugging Face Hub 🎉 Open models need open data. Today we hit that milestone, together with the most incredible community in AI! 🤗 Onwards to the next million 🚀 https://t.co/PV6knP3XlJ

Expect your feed to look more and more like this in the coming weeks and months. https://t.co/kcnHnnCVya

Planning your trip to San Jose? 🛫 Join the #PyTorch community at #PyTorchCon North America, Oct 20-21. Register by July 31 & keep $400 in your pocket with early bird pricing. Secure your pass: https://t.co/AVHdaIFT20 https://t.co/JpGdQYzDDb

Reachy is mad, but RAM costs + tariffs are forcing our hand. Prices will go up on June 1st! Still at the early bird price until then though if you were looking for an excuse to get one now: https://t.co/veqPEwFIaP! https://t.co/UP45svdMr8

We just hit #1 trending on @huggingface Spaces 🎉 “The Ultimate Guide to RL Environments” dives into building & scaling RL environments for LLMs. If you're exploring RL + agents, this might be useful https://t.co/2bbwtic6xN

Unsloth just published MTP-enabled quantized GGUFs for Qwen3.6-35B-A3B. https://t.co/9iuepdo5AW