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This dad has a good approach, teaching his child that no one can rely on anyone๐ https://t.co/JmUej6q1L4
This dad has a good approach, teaching his child that no one can rely on anyone๐ https://t.co/JmUej6q1L4
Today, we're introducing Spectre I, the first smart device to stop unwanted audio recordings. We live in a world of always-on listening devices. Smart devices and AI dominate our world in business and private conversations. With Deveillance, you will @be_inaudible. https://t.co/WdxmnyFq1I
Fantastic panel w/ leaders from Harvey, Replit, and Linear. My takeaway: traditional product roadmaps are too slow, all of the teams work in shorter, high intensity sprints. https://t.co/xdFScalkVS
Project Hail Mary is gonna be one of those blockbusters where people in 20 years will say, "man, i wish i could have seen this for the first time in theaters." https://t.co/e7g6XiIvc0
AI is compressing how we build. Roles collapse, roadmaps expire quickly, and you end up rewriting the product every few months. So we thought weโd give people a behind-the-scenes look. 21 Days to Launch, a Replit documentary. https://t.co/isWq9KtMIO
Everything sucks https://t.co/04ACtPHPZV
Packed house for @eladgil at @southpkcommons Itโs a great time to build. โThereโs a radical desire to try new things that I havenโt seen in my lifetime.โ If you donโt immediately get traction, thereโs a problem. โWhen something works, it starts to work quickly.โ https://t.co/oOhzlCZ58s
Netflix has bought InterPositive, a start-up founded by Ben Affleck that makes AI-powered tools for filmmakers. https://t.co/d0IFG0Pycc
You can now fine-tune Qwen3.5 with our free notebook! ๐ฅ You just need 5GB VRAM to train Qwen3.5-2B LoRA locally! Unsloth trains Qwen3.5 1.5x faster with 50% less VRAM. GitHub: https://t.co/2kXqhhvLsb Guide: https://t.co/JCPGIRo99s Qwen3.5-4B Colab: https://t.co/2Aj1mZ3f5j
I just implemented inference for Qwen3.5 0.8B based on https://t.co/W8bSA5TRiO, and successfully ran it on an M1 Pro. https://t.co/z0g1ynNlq3
BOOM! Appleโs 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 trainingโ including b
I just implemented inference for Qwen3.5 0.8B based on https://t.co/W8bSA5TRiO, and successfully ran it on an M1 Pro. https://t.co/z0g1ynNlq3
Rust-based OpenAI-compatible API servers for your local Qwen3 audio / voice models. Just replace your cloud API URL with http://localhost:8000/v1 This week, we have released agent tools + skills for Qwen3 ASR + TTS models. Those are zero-dependency CLIs that run @Alibaba_Qwen models locally on your @openclaw ๐ฆ But many existing apps still use cloud APIs. Now we have a LOCAL API server too. Perfect for local AI on devices such as Olares One from @BytetradeLab * Zero dependency binary distribution * Both /v1/audio/transcriptions and /v1/audio/speech API endpoints * Local 0.6B and 1.7B ASR + TTS models * Supports Nvidia GPUs * MLX support on Apple ๐ฅ๏ธ OpenAI compatible API servers: https://t.co/OypU5SYGxN ๐ง Qwen3 ASR CLI tool: https://t.co/knsZlastgQ ๐ค Qwen3 TTS CLI tool: https://t.co/1LKRapngVk
Introducing the Google Workspace CLI: https://t.co/8yWtbxiVPp - built for humans and agents. Google Drive, Gmail, Calendar, and every Workspace API. 40+ agent skills included.
Introducing the Google Workspace CLI: https://t.co/8yWtbxiVPp - built for humans and agents. Google Drive, Gmail, Calendar, and every Workspace API. 40+ agent skills included.
We're releasing our final update to Qwen3.5 GGUFs for improved performance. - Qwen3.5 GGUFs now use our new iMatrix data for better chat, coding & tool use. - New improved quant algorithm - Re-download 35B, 27B, 122B GGUFs: https://t.co/7Jmp13uYfU Guide: https://t.co/wjS1lMnbNp https://t.co/6lZKT6CSFf
Watch the trailer for #TheTestaments, a new story set within the world of The Handmaidโs Tale. Join this new generation of powerful young women. Streaming April 8 on @hulu and Hulu on Disney+. https://t.co/9Rr2lHV6gI
It's time to choose your own destiny. #TheTestaments premieres April 8 on @hulu and Hulu on Disney+. https://t.co/y7RDrDN2UZ
๐ฅ Qwen 3.5 Series GPTQ-Int4 weights are live. Native vLLM & SGLang support. โก๏ธ Less VRAM. Faster inference. Run powerful models on limited-GPU setups. ๐ Grab the weights + example code: Hugging Face: https://t.co/3MSb7miq68 ModelScope: https://t.co/LGHruBHP6Q
already on mlx :) https://t.co/NXxd7hAWMh
๐ Introducing the Qwen 3.5 Small Model Series Qwen3.5-0.8B ยท Qwen3.5-2B ยท Qwen3.5-4B ยท Qwen3.5-9B โจ More intelligence, less compute. These small models are built on the same Qwen3.5 foundation โ native multimodal, improved architecture, scaled RL: โข 0.8B / 2B โ tiny, fast, great
already on mlx :) https://t.co/NXxd7hAWMh
Meet GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill: a distilled powerhouse that brings elite reasoning to local machines. This GGUF model delivers Claude-level intelligence in a compact package, perfect for developers wanting high-performance AI without cloud costs. https://t.co/Q0HCPTI2oe
Back in January, the MLflow team sat down with @mlopscommunity to discuss why MLflow is being rebuilt for the "AI Engineer" era. As more teams move toward autonomous agents, this conversation is more relevant than ever. The highlights: ๐น ๐ง๐ต๐ฒ ๐๐ฒ๐ป๐๐ ๐ฃ๐ถ๐๐ผ๐: Why MLflow is being rebuilt for agents and real production systems. ๐น ๐ง๐ต๐ฒ ๐ ๐ฒ๐๐๐ ๐ฅ๐ฒ๐ฎ๐น๐ถ๐๐: Tackling evals, risky memory management, and governance that actually works. ๐น ๐ง๐ต๐ฒ ๐๐๐๐๐ฟ๐ฒ:ย Why MLflow remains the leading open-source standard for the next generation of AI. Don't build the next generation of AI on a legacy stack. ๐บ Watch: https://t.co/TpLzUGNei0 ๐ง Listen: https://t.co/VABLK7jqcC #MLflow #GenAI #LLMOps #AgenticAI
Claude Code acabou de lancar Voice Mode. Voce fala. O agente de IA codifica. "/voice" pra ativar. Rollout pra 5% dos usuarios agora, expandindo nas proximas semanas. Hoje: KREA AI Voice no iPad. Claude Code Voice no terminal. A era da programacao por voz chegou. https://t.co/9adiksDX0r
Comprehensive Python API for Google NotebookLM. Full programmatic access to NotebookLM's featuresโincluding capabilities the web UI doesn't exposeโfrom Python or the command line. https://t.co/5YQhAKiGuD
One of the more interesting and thought provoking research papers I've seen in a while. A system for reading and reimplementing NeRF papers, and it seems to work very well. Pretty easy to extrapolate out from here to what CVPR 2027 papers will look like. https://t.co/gokzG27mIT https://t.co/jPpRESdKkd
The legendary Donald Knuth just witnessed something remarkable. ๐ฅ๐ฅ๐ฅ After 30 years, a problem he posed about Hamiltonian cycle decompositions in a 3-dimensional Cayley digraph has finally been cracked. And the solver? Not a PhD student. Not a research group. Not a math department.
zoe was burning 24M+ opus tokens/day monitoring agents that weren't running. replaced her cron with a 2-layer system: - bash pre-check, zero tokens when idle - webhook fires opus only when needed. ~95% token reduction and more reliable output. details below. (set up a cron to watch this performance, if it works well I'll double down on this event driven stack, seems like the future)
i find it fucking hilarious how Apple "failing" at AI is now the exact reason they're about to win it: - watched everyone else burn $1.4T+ building models... then picked the winner (gemini) to use for... $1B - while everyone fights to grow users, apple flips a switch and 2.5 billion devices get AI siri tmrw. - $150B to splurge on the device / app layer. zero competition (because everyones spent their cash). - while openAI charges $200/mo subscriptions, Apple lets you run models on-device (cheaper, faster, private, personal) - while openAI struggles to build an AI device, Apple just dropped 5 powered by the best AI chips for hand-held devices. they "lost" the model race because they didn't need to win it in the first place greatest to (accidentally) ever do it.
nanochat now trains GPT-2 capability model in just 2 hours on a single 8XH100 node (down from ~3 hours 1 month ago). Getting a lot closer to ~interactive! A bunch of tuning and features (fp8) went in but the biggest difference was a switch of the dataset from FineWeb-edu to NVIDIA ClimbMix (nice work NVIDIA!). I had tried Olmo, FineWeb, DCLM which all led to regressions, ClimbMix worked really well out of the box (to the point that I am slightly suspicious about about goodharting, though reading the paper it seems ~ok). In other news, after trying a few approaches for how to set things up, I now have AI Agents iterating on nanochat automatically, so I'll just leave this running for a while, go relax a bit and enjoy the feeling of post-agi :). Visualized here as an example: 110 changes made over the last ~12 hours, bringing the validation loss so far from 0.862415 down to 0.858039 for a d12 model, at no cost to wall clock time. The agent works on a feature branch, tries out ideas, merges them when they work and iterates. Amusingly, over the last ~2 weeks I almost feel like I've iterated more on the "meta-setup" where I optimize and tune the agent flows even more than the nanochat repo directly.
TIL: There's a whole bunch of interesting skills in the oss codex repo: https://t.co/gNFHV3MD2j $skill-installer playwright-interactive (also /fast is sweeeeet, 1.5x codex makes a huge diff!) https://t.co/XTENPuZ9Ie
TIL: There's a whole bunch of interesting skills in the oss codex repo: https://t.co/gNFHV3MD2j $skill-installer playwright-interactive (also /fast is sweeeeet, 1.5x codex makes a huge diff!) https://t.co/XTENPuZ9Ie
