✨ Log in to AutoClaw next week and get 40 Million Tokens 👉 autoclaw.z.ai 📅 Campaign Period Oct 26, 00:00–Nov 1, 23:59 (UTC+8) The reward expires at 23:59 on Nov 1 (Beijing Time). Claim it early to get the most out of it.

We plugged Beam into @opencode, asked it to look at @UnslothAI, and create a fine-tuning notebook for the latest and smallest Gemma-4 model on a Text2SQL task – an out of distribution domain for Beam. The model successfully researches the model cards, Unsloth documentation, and available READMEs, and implements the fine-tuning logic and surrounding machinery. As a result, Beam is able to successfully finetune Gemma, increasing Gemma’s accuracy on its heldout test set by 66.5%.

Kimi K3.1 Pro from @Kimi_Moonshot is live on OpenRouter. A 1M context, multimodal open-weight model, post-trained from Kimi K3 for long-horizon coding and agentic work. $2/M input tokens, $10/M output tokens. Try it: openrouter.ai/moonshotai/kim…

Big news: @Kimi_Moonshot is back in the top-4 labs in the Code Arena: WebDev with the release of Kimi K3.1 Pro! This model just landed #8 in the Code Arena: WebDev with 1692 pts, matching Claude Opus-5 (Max) at 70% lower cost. This is a 9 place jump (+37 points) from Kimi K3 (Max) at #17 with 1655 pts. Kimi K3.1 Pro improved from Kimi K3 across all categories. Highlights: - #7 in Brand & Marketing (up from #9) - #9 in Simulations (up from #20) Cost efficiency is another upgrade. Priced at $2/$10 per million input/output tokens, Kimi K3.1 Pro (Max) has a median cost per task of $1.23 (29% less than Kimi K3). See its placement compared to the Pareto frontier in thread. With open weights coming soon, Kimi K3.1 Pro would be the new #1 open model in the Code Arena: WebDev, overtaking DeepSeek-v4.1-Flash. Stay tuned! See @Kimi_Moonshot’s ranking by lab, and 3D generations from Kimi K3.1 Pro below in thread. Huge congrats to the @Kimi_Moonshot team on this release!

Today I’m excited to introduce OpenDocRouter - a unified API for document parsing with the latest frontier and open-weight models. There are a lot of VLMs and OCR models that can be used for document parsing: we have over 130+ models on ParseBench, and a HuggingFace search for “ocr” turns up thousands of results. It’s extremely time consuming to choose between OCR vendors. You need to figure out the right prompts, handle rate limits, manage deployments, integrations with all models you’re using, and benchmark new models as they come out. OpenDocRouter provides a comprehensive, transparent set of models along the price-performance frontier. It manages a unified API to transcribe documents to markdown. It serves all frontier and open-weight models “at-cost”, with a small transaction cut. It handles rate limits with all models to ensure you can put massive volume through. It even offers bounding boxes and layout as a service, so that you can add grounding to any model that you’re using. When new OCR candidate models ship, we will benchmark them on ParseBench and immediately add them to OpenDocRouter. We’re adding a lot more models very quickly, and also adding some extremely exciting feature improvements (e.g. automated routing) as we speak. We welcome your feedback! Check it out: opendocrouter.ai/ Blog: llamaindex.ai/blog/introduci…

Adit Abraham of @reductoai on why everything in AI is "state-of-the-art all the time," and how to find what actually works for you: "One of the unfortunate things about the AI industry... is benchmarks have become marketing over time." "There's a public benchmark out there that we tested where if you take the same documents and you add a little bit of noise, like you tilt it one degree, you add a little Gaussian blur, the provider that published the benchmark gets way, way worse than everyone else, but everyone else is kind of consistent." "Our recommendation is actually LLM as a judge is quite strong today if you're thoughtful about how you think about a rubric and how to approach it." "We want to see the industry move closer to people testing on their own data, with tools maybe to make that evaluation easier." "Instead of trusting a generic benchmark, you really should be at a point where you know what works best for you." @aditabrm

We're announcing the public beta of datavendor.ai We spent the last two years building the best solution for people buying and selling data, whether you're a researcher in a lab or a company trying to sell data. Data is the fundamental bottleneck for AI progress, and DataVendor will be the infra that solves it.

Introducing Orgo: computers for agents. Give every agent its own desktop in the cloud, ready in seconds and always on. Already used by 1,000+ companies, and launching today with @ycombinator.

Today, we're launching @firegoat_ai CLI right here from @resend forward! We believe every developer should have an easy way to ship agent-ready products. Run an agent on your SDK, CLI, API or docs, see where it gets stuck and ship fixes. Try it out: ☄️ 𝚗𝚙𝚡 𝚏𝚒𝚛𝚎𝚐𝚘𝚊𝚝 𝚒𝚗𝚒𝚝 Ps. Publishing this as @zenorocha x @rauchg are finishing their talk :)

Today we're announcing @with_kiwi_ai: your GTM agent's data engineer. Kiwi syncs your GTM stack into a data lake, shows the SQL behind every agent step, and turns every approval, edit, or decline into an eval. Backed by @ycombinator. Learn more at withkiwi.ai

A few things we shipped last week: • New connectors for Feishu, Lark, and DingTalk • Gmail search can now be scoped to Inbox or Archive • Follow-ups on the same goal are more likely to continue in the same task • Fixed an issue loading some conversations with link previews

135k I believe.