AI is compressing how we build. Roles collapse, roadmaps expire quickly, and you end up rewriting the product every few months. So we thought we’d 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 It’s a great time to build. “There’s a radical desire to try new things that I haven’t seen in my lifetime.” If you don’t immediately get traction, there’s a problem. “When something works, it starts to work quickly.” 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! 🔥 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

+1 more

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

+1 more

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 🦞 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 🖥️ OpenAI compatible API servers: https://t.co/OypU5SYGxN 🎧 Qwen3 ASR CLI tool: https://t.co/knsZlastgQ 🎤 Qwen3 TTS CLI tool: https://t.co/1LKRapngVk

+1 more

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 Handmaid’s 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

🔥 Qwen 3.5 Series GPTQ-Int4 weights are live. Native vLLM & SGLang support. ⚡️ Less VRAM. Faster inference. Run powerful models on limited-GPU setups. 👇 Grab the weights + example code: Hugging Face: https://t.co/3MSb7miq68 ModelScope: https://t.co/LGHruBHP6Q

already on mlx :) https://t.co/NXxd7hAWMh

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: 🔹 𝗧𝗵𝗲 𝗚𝗲𝗻𝗔𝗜 𝗣𝗶𝘃𝗼𝘁: Why MLflow is being rebuilt for agents and real production systems. 🔹 𝗧𝗵𝗲 𝗠𝗲𝘀𝘀𝘆 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: Tackling evals, risky memory management, and governance that actually works. 🔹 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲: 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. 📺 Watch: https://t.co/TpLzUGNei0 🎧 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 features—including capabilities the web UI doesn't expose—from 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. 🔥🔥🔥 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.