AI is beginning to decode the electrical noise inside our brains. Signals once thought too complex to interpret are now being translated into patterns and meaning. When machines start reading inner thoughts, neuroscience enters an entirely new era. https://t.co/4ZB8TUqxiG @LauraCReporter @bbcnews

AI literacy isn’t optional anymore. As automation spreads across industries, understanding how AI works becomes a core economic skill. If policymakers underestimate that urgency, the competitiveness gap will widen fast. https://t.co/ncHCj37goe @epc_eu https://t.co/XtYStA1f1V

WE WON THE @MistralAI LONDON HACKATHON 🇬🇧🇫🇷 We made Mistralverse, here's our demo vid. @HarryStebbings who says the UK isn't shipping?? https://t.co/lVWr43XkNj

NEW: When OpenAI announced its Pentagon deal Friday night, people immediately challenged Sam Altman's claims. Why, they asked, would the DoD suddenly agree to red lines when it had said it would never do so? The answer, sources told me, is that it didn't. https://t.co/DkF9uWVHa4

introducing Voice Mode. speak as you draw and get changes in real-time. available now in Krea iPad. https://t.co/c6mHHjupmW

introducing Voice Mode. speak as you draw and get changes in real-time. available now in Krea iPad. https://t.co/c6mHHjupmW

introducing @nozomioai v1. state of the art search and index API to reduce hallucinations in AI agents. use it inside any coding agent or power your own products (thread): https://t.co/mqNqWDSAsU

What if AI could see the world the way we do? That’s the idea we bet our weekend on at the Mistral Worldwide Hackathon. With @haaspierre_ and Arman Artola-Zanganeh, we built 𝗣𝗼𝗿𝘁:𝗪𝗼𝗿𝗹𝗱🌍, an open-source framework that lets anyone connect their Meta glasses to any AI system. Let me take you back to saturday morning. So before knowing it could work we needed the hardware. So I ran to Rue de Rivoli and bought €500 Meta glasses on the spot. If that’s not commitment, I don’t know what is (a true bet). We then built non-stop for 36 hours to make it usable. End-to-end. The glasses stream what you see → the AI makes sense of it → it answers back through the glasses’ speaker. And suddenly when we understood that it was going to work, the question changed. It was no longer “𝗜𝘀 𝘁𝗵𝗶𝘀 𝗱𝗼𝗮𝗯𝗹𝗲?” It became “𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝗽𝗲𝗼𝗽𝗹𝗲 𝗯𝘂𝗶𝗹𝗱 𝘄𝗶𝘁𝗵 𝘁𝗵𝗶𝘀?” - A plumber getting live assistance while repairing something. - A technician repairing industrial machinery. - A traveler exploring a new country. - A visually impaired person navigating space. At first, we were looking for the “right” use case. Then we realized something more interesting. If AI can share your perspective, continuously, the use cases are not ours to decide. That’s why 𝗣𝗼𝗿𝘁:𝗪𝗼𝗿𝗹𝗱🌍 is fully open source. If you want to connect your Meta glasses, plug in your own models, customize with your own prompts, your own MCP, your Openclaw… you can. Link to the open source repo (you can contribute and give it a little star ❤️): https://t.co/UueLnkMZpM Link to the demo video: https://t.co/qcTDqKGvax Huge thanks to the organizing team of the hackathon, it was truly great. @Jthmas404

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BOOM! Apple’s Neural Engine Was Just Cracked Open, The Future of AI Training Just Change And Zero-Human Company Is Already Testing It! In a jaw-dropping open-source breakthrough, a lone developer has done what Apple said was impossible: full neural network training– including backpropagation – directly on the Apple Neural Engine (ANE). No CoreML, no Metal, no GPU. Pure, blazing ANE silicon. The project (https://t.co/jrk67hf9p1) delivers a single transformer layer (dim=768, seq=512) in just 9.3 ms per step at 1.78 TFLOPS sustained with only 11.2% ANE utilization on an M4 chip. That’s the same idle chip sitting in millions of Mac minis, MacBooks, and iMacs right now. Translation? Your desktop just became a hyper-efficient AI supercomputer. The numbers are insane: M4 ANE hits roughly 6.6 TFLOPS per watt – 80 times more efficient than an NVIDIA A100. Real-world throughput crushes Apple’s own “38 TOPS” marketing claims. And because it sips power like a phone, you can train 24/7 without melting your electricity bill or the planet. At The Zero-Human Company, we’re not waiting. We are testing this right now on real ZHC workloads. This is the missing piece we’ve been chasing for our Zero Human Company vision: reviving archived data into fully autonomous AI systems with zero human overhead. This is world-changing. For the first time, anyone with a Mac can fine-tune, train, or iterate massive models locally, privately, and at a fraction of the cost of cloud GPUs. No more renting $40,000 A100 clusters. No more waiting in queues. No more massive carbon footprints. Training costs that used to run into the tens or hundreds of thousands of dollars? Plummeting toward pennies on the dollar – mostly just the electricity your Mac was already using while it sat idle. The AI revolution just moved from billion-dollar data centers to your desk. WE WILL HAVE A NEW ZERO-HUMAN COMPANY @ HOME wage for equipped Macs that will be up to 100x more income for the owner! We’re only at the beginning (single-layer today, full models tomorrow), but the door is wide open. Ultra-cheap, on-device training is here. The future isn’t coming. It’s already running on your Mac. Welcome to the Zero-Human Company era.

@HamelHusain I love it. I have this in my global AGENTS .md to maximise the use of the questions tool (works in Claude, @opencode, @code, and @GitHubCopilot CLI). https://t.co/cPDwXHjwrP

OCR is solved right. https://t.co/hSGdRBi1jw

GPT 5.3 Codex (xhigh) scores 79.3% and takes the lead on WeirdML, just ahead of opus 4.6 (77.9%) at less than half the prize. It is very solid across the board, but I still feel the peak performance of gemini 3.1 is stronger. https://t.co/WRYosAStGY

dLLM Simple Diffusion Language Modeling https://t.co/8a3wDPMZiN

Enhancing Spatial Understanding in Image Generation via Reward Modeling https://t.co/3t4ylnDlTo

Mode Seeking meets Mean Seeking for Fast Long Video Generation paper: https://t.co/TFznQW57cC https://t.co/nfLMnHpp9b

Introducing Jan-Code-4B 💻 A compact coding model tuned for practical day-to-day tasks. Generation, refactors, debugging, tests — all runnable locally in Jan. Download Jan: https://t.co/MPwceB2eHG Model: https://t.co/siedXzTv0v https://t.co/KNlzvwKkDu

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Cool chart showing the ratio of Tab complete requests to Agent requests in Cursor. With improving capability, every point in time has an optimal setup that keeps changing and evolving and the community average tracks the point. None -> Tab -> Agent -> Parallel agents -> Agent Teams (?) -> ??? If you're too conservative, you're leaving leverage on the table. If you're too aggressive, you're net creating more chaos than doing useful work. The art of the process is spending 80% of the time getting work done in the setup you're comfortable with and that actually works, and 20% exploration of what might be the next step up even if it doesn't work yet.

hahaha smash i have what i need is so cute lmfao https://t.co/oXGn6ym7hQ

Figured out how to do Apple ID auth with @clerk ! Next step is to wire this up to @convex. Huge fan of convex after it helped us build out some of our judging platform. Next step is to build out the basic first pass of the app https://t.co/MbxtxpqXaF

@agrimsingh @clerk @convex I just followed @GeminiApp's original recomendation https://t.co/NnH5rFxogp

@rubanlah your @AnthropicAI thinking cap has competition now https://t.co/uNB09rKUbC

https://t.co/v11e5DFEjy

https://t.co/v11e5DFEjy

Monday https://t.co/NdfxHDESs9

Monday https://t.co/NdfxHDESs9

seeing an ex-oomf on the for you TL https://t.co/kQJWQQVSzz

seeing an ex-oomf on the for you TL https://t.co/kQJWQQVSzz

Good deranged Daily Mail content https://t.co/A42l1P8foL

Good deranged Daily Mail content https://t.co/A42l1P8foL

https://t.co/fnx1peBKEO

https://t.co/fnx1peBKEO

This is my favorite video i’ve ever shot. You’ll never guess how it ends. https://t.co/D3GJFwyCyx