🚀 Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here. 🔹 SOTA on HLE (44.9%) and BrowseComp (60.2%) 🔹 Executes up to 200 – 300 sequential tool calls without human interference 🔹 Excels in reasoning, agentic search, and coding 🔹 256K context window Built as a thinking agent, K2 Thinking marks our latest efforts in test-time scaling — scaling both thinking tokens and tool-calling turns. K2 Thinking is now live on kimi.com in chat mode, with full agentic mode coming soon. It is also accessible via API. 🔌 API is live: platform.moonshot.ai 🔗 Tech blog: moonshotai.github.io/Kimi-K2… 🔗 Weights & code: huggingface.co/moonshotai

Gentlemen I need your full attention. Python is introducing lazy imports. I repeat. Python is introducing lazy imports. inb4 the flood of `treewide: adopt lazy imports` +123,244 PRs

Introducing Cursor 2.0. Our first coding model and the best way to code with agents.

We’re updating olmOCR, our model for turning PDFs & scans into clean text with support for tables, equations, handwriting, & more. olmOCR 2 uses synthetic data + unit tests as verifiable rewards to reach state-of-the-art performance on challenging documents. 🧵

New paper: You can make ChatGPT 2x as creative with one sentence. Ever notice how LLMs all sound the same? They know 100+ jokes but only ever tell one. Every blog intro: "In today's digital landscape..." We figured out why – and how to unlock the rest 🔓 Copy-paste prompt: 🧵

We made Amp Free. It's powered by great tokens and tasteful ads. Agentic coding is now free for everyone.

AMD Strix Halo: DenseAI compute(BF16) - 110tops 128GB 256GB/s memory 4TB Nvme 10GbE LAN x86 64MB cache $2,300 Nvidia DGX Spark: Dense AI compute(BF16) 125tops 128GB 273GB/s memory 4TB Nvme 10GbE LAN ARM 24MB cache $4,000

New paper: You can make ChatGPT 2x as creative with one sentence. Ever notice how LLMs all sound the same? They know 100+ jokes but only ever tell one. Every blog intro: "In today's digital landscape..." We figured out why – and how to unlock the rest 🔓 Copy-paste prompt: 🧵

Sometimes we forget that NVIDIA wins because it's a software company. DGX Spark is a reminder of that. It's a CUDA dev machine that's beautiful enough and small enough to be on my desk and with enough memory to fit a truckload of params. It's not the fastest or best at anything, but it's great to develop on and transfer your final training run to a H/B200, final robotics policy to your Jetson, final inference to {nvidia/apple/amd/[favorite vendor]}.

🚀 SGLang In-Depth Review of the NVIDIA DGX Spark is LIVE! Thanks to @NVIDIA’s early access program, SGLang makes its first ever appearance in a consumer product, the brand-new DGX Spark. The DGX Spark’s 128GB Unified Memory and Blackwell architecture set a new standard for local AI prototyping and edge computing. We're thrilled to bring these cutting-edge performance insights and software support to the developer community. Our review dives into how to efficiently deploy and accelerate large models like Llama 3.1 70B, GPT-OSS using SGLang's EAGLE3 speculative decoding and @Ollama on this beautiful piece of engineering. 👇 Unboxing video and tech blog in the thread #SGLang #NVIDIA #SparkSomethingBig #Blackwell #DGXSpark #AIInference #LLMServing

So it turns out people who care about performance already turn all Python imports into local ones under the hood: both Hudson River Trading, and Meta's production python auto lazy import: