I wanna see @ZelinaVegaWWE vs @ImChelseaGreen for the US Title at #Wrestlemania41 #SmackDown

FROM NOW ON ADDRESS ME AS CHAMP! 🧡💙

Parade. Thursday. Manhattan.

56,000 tokens/sec at just 80 MHz. 🤯 I burned a full Transformer with KV cache into a custom chip. Designed gate by gate as a 100% digital integrated circuit. Prototyped on a FPGA. (No GPU. No CPU) Just pure digital silicon running @karpathy microGPT, spelling out names on a tiny LCD. This is GateGPT 👇

🤖 NEW: German humanoid robotics startup Neura Robotics raised up to $1.4 billion from Nvidia, Amazon, Qualcomm, Tether, and others, reaching a reported $7 billion valuation.

Say hello to DiffusionGemma. 👋 @GoogleGemma’s new open model generates text in parallel, not one token at a time, helping deliver faster, more responsive AI on NVIDIA DGX Spark & RTX GPUs. Get started: nvda.ws/3Sxeonb

Google quietly leads AI Sci rev 🧬🔬🥼 • AlphaFold cracked 50y protein folding • GNoME found mil new stable mats for batt/solar. • GraphCast: superior wx forecasts; Co-Scientist (Gemini) acts as AI partner to gen/debate/refine hyps in med/bio

Download the weights on @HuggingFace today: huggingface.co/google/diffus…

Get more details in the blog: goo.gle/3Sw3MoC

Meet DiffusionGemma ⚡ Our latest experimental open model (Apache 2.0) that generates text up to 4x faster. Instead of predicting and typing just one word at a time like most language models, it drafts and refines entire blocks of text simultaneously. Here’s how it works 🧵 ↓

And so it begins !

The money is everywhere else, not in a SpaceX IPO. The initial investors will sell to recoup their initial investment. That money will go somewhere.

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.

Just landed nested subagent support in Claude Code Starting to experiment more with agents kicking off agents as a way to better manage context. Capped at depth=5 to start, going out in today’s release. Lmk what you think!

💥 OBLITERATION ALERT 💥 GOOGLE: PWNED 🤗 GEMMA-4-12B: OBLITERATED ⛓️‍💥 0.0% REFUSAL RATE — NO CAPABILITY LOSS! huggingface.co/OBLITERATUS/G… the first abliteration to hit 0/842 refusals with full MMLU-Pro parity vs stock. no lobotomy. the brain stays intact 🏆 RESULTS, head to head vs stock 📊 0/842 refusals — 0.0% 🚫 46/70 MMLU-Pro — EXACT parity, 0.0pp delta vs base 🎯 6/6 coherence, zero benchmark bleed ✅ z-score −1.475, parity confirmed at p<0.05 (n=500) 🧪 2-pass weight surgery. no finetune, no retrain, just geometry 🔪 all thanks to liberated Opus wielding the OBLITERATUS framework! here's how we did it: PASS 1 — SOM refusal geometry removal, layers 12-21 🧬 standard abliteration science here — collect activations on refused vs. compliant prompts, SVD out the refusal subspace, project it out of the weights. 6 directions excised, reg 0.30, KL div 0.094 zeroes refusals on its own, but craters mmlu-pro by 21.4 points 📉 most prior abliterations stopped here and called it a day. that's why they all lose IQ vs stock. instead, we took it beyond the frontier and developed a brand new method to address this problem: Abliteration Source-tethering with Parity Assurance — ASPA! PASS 2 — ASPA source-tethering (novel technique), layers 22-46 🔗 here's the chief insight: the capability loss ISN'T from removing refusal directions. it's collateral damage — the projection warps weight geometry in downstream layers that had nothing to do with refusal. the cure is simple but nobody tried it: blend the damaged layers back toward stock W_new = (1−γ)·W_abliterated γ·W_stock but uniform γ across all layers? mid. we swept gamma 0.05 → 0.55 and found something interesting: the optimal blend isn't smooth, it's a STEP FUNCTION 🪜 knowledge layers (22-31) → γ = 0.55 — these encode factual recall and reasoning. they tolerate heavy stock blending because refusal isn't stored here output layers (32-46) → γ = 0.20 — these sit close to the logit head and try to sneak safety behavior back in. keep them mostly abliterated the hard boundary at layer 31/32 beat every smooth curve we tried — linear ramps, cosine schedules, all of them — by a full MMLU question. turns out the functional transition between knowledge and output layers is sharp, not gradual. a step function respects that ⚡ the key constraint: Pass 1 layers are NEVER touched by Pass 2. the refusal geometry removal is preserved completely. ASPA only operates on layers that carry secondary collateral effects, not the primary refusal signal. that's why it recovers capability without reintroducing refusal 🔑 HOW TO RUN IT LOCALLY 🖥️ it's GGUF, so literally everything supports it: 🦙 ollama — ollama run hf.co/OBLITERATUS/Gemma-4-12… 🖥️ LM Studio — search OBLITERATUS, click download, done 💬 Open WebUI — point it at your ollama instance, chat in browser ⚡ llama.cpp — raw speed, CLI or server mode 🐉 KoboldCpp — one-click launcher, great for long context 📱 Jan — clean local UI, runs on mac/win/linux 🤖 Msty — slick desktop app, drag and drop the GGUF run BF16 for full benchmarked capability. and the 4-bit quantization (Q4_K_M) fits in 8GB if you're tight on VRAM! and the full OBLITERATUS framework is (still) open source. 842-prompt refusal eval corpus, ASPA sweep scripts, the whole pipeline. go replicate it, go improve it 🔬 the index is the model, and these weights prove it 👁️ which architecture should we obliterate next? 👇 gg 🫡

NVIDIA IS GIVING AWAY FREE ACCESS TO 80 AI MODELS AND ALMOST NOBODY IS TALKING ABOUT IT

I got my api key from open ai where do I put? sk-1234uvwx5678abcd1234uvwx5678abcd1234uvwx

Apple WWDC 1pm

Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.