chroma-radiance-x0 result a true pixel space model combining JiT-x0 loss and pixnerd give it a go! github.com/comfyanonymous/Co… huggingface.co/lodestones/Ch…

Trump Phone Durability Test - Its worse than we thought...

A benchmark showing Haiku 5.5 beating DeepSeek v4.1 Flash 1017 is a joke not a benchmark lol

anyone got a spare change? ebay.com/itm/880041409723

Introducing Mol-1: Open Frontier Biomolecular Intelligence 🚀🚀🚀 📊 75.0% on FoldBench-AB, near IsoDDE's reported 75.58%!!! 🧬 60k-token assemblies on one single H20, outperforming Anthropic 8/10. 🎯 VHHs binding hit rate as high as 63% with best SPR KD at 43 pM. GitHub: github.com/molence/Mol-1 Try it: lab.molence.com

Meet Clover-35B-A3B-Preview 🍀 the first open-weight production diffusion language model (dLLM) with 35B total parameters capable of 680 tps on a single card at BF16. Clover exhibits strong performance against both dLLM and AR models and is available today at @huggingface @ModelScope2022 and at our website

How is the internet less private than in 2006, when the whole family shared one computer? Back then, the internet was weird, personal, and all of ours. So, we got your favorite meme legends back together to say it louder: What happened to the internet? 1/3

where is @MOSS_Global ! @ArtificialAnlys

Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model. 🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal. 🔹 Full-project coding: games in Unreal, Unity & Godot. 🔹 Excels at parametric 3D modeling in Blender & CAD. 🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%. 🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks. Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API. Open weights by Nov 4, 2026. 🔗 API: platform.kimi.ai 🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro

Hot take: Most autoresearch today is just glorified overfitting. Current autoresearch pipelines are essentially outer-loop searches for recipes that work in ONE specific setting. Try transferring a modded-nanogpt recipe to a different language or dataset. How much of that "optimal" recipe still holds up? We need to move beyond static recipes toward inner-loop autoresearch: optimizers, architectures, hyperparameters, etc., continuously adapting DURING training based on the training trajectory itself. Check out OPAL that enables this! Cool work with @SimulatedAnneal, Jia-Hao Liang, and @vincesitzmann.

DeepSeek might as well open source the whole company at this point. “We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.”

One must imagine Dario gnashing his teeth.

Nvidia, SGLang, and vLLM got mogged into oblivion

🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4

Announcing FlashMap: a new speculative decoding algorithm based on flow maps! >6x faster inference on Qwen3-8B 🧵 Project page: jshen.net/flashmap arXiv: arxiv.org/abs/2610.24365 w/ @Jay41969851 @_ryantlee @jonathanmay @cosmo_shirley

Today, we're open-sourcing 5 Ming-3.0 models across 3 speech families. - Ming-3.0-tts: 0.5B voice design & paralinguistic cues; Tiny 7.9B A1.3B long-form speech. - Ming-3.0-audio: Flash 126B A6.5B understanding & tools; Tiny 9.3B A2.7B efficient dialogue. - Ming-3.0-asr: Tiny 8.6B A2B dialects & context.

MiniMax M3.1 Flash: Space Performance + Bunny Speed 🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash. 💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed. API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens. Model weights will arrive on Oct 23. API: platform.minimax.io M Plan: minimax.io/m-plan MiniMax Code for desktop: agent.minimax.io/download

DeepSeek V4.1-Flash-1017 has released, beating GPT 5.6 Sol on Terminal-Bench while being ~17x cheaper. It improves the previous DeepSeek V4.1-Flash result by 35%, accomplished with "no algorithmic innovation" but instead by RL training it on multiple coding agent harnesses.