18 models. One leaderboard. The biggest names in AI go head-to-head inside Hermes. The Hermes Index now compares 18 models across four benchmark suites, helping you see how they perform on real agent tasks. And a reminder: Hermes works with most of your AI subscriptions and providers, including Nous Portal, ChatGPT, Claude, OpenRouter, OpenCode Go, and many more. Connect your preferred providers to Hermes, then use the Index to help decide which models to run.

18 models. One leaderboard. The biggest names in AI go head-to-head inside Hermes. The Hermes Index now compares 18 models across four benchmark suites, helping you see how they perform on real agent tasks. And a reminder: Hermes works with most of your AI subscriptions and providers, including Nous Portal, ChatGPT, Claude, OpenRouter, OpenCode Go, and many more. Connect your preferred providers to Hermes, then use the Index to help decide which models to run.

By every measure, Opus 4.8 beats human experts as peer reviewers in a study of 124 Harvard social science papers covering a range from statistical analyses to formal models to qualitative work. As academics, we need to figure out how to adapt the practice of science to AI, fast. https://t.co/a9Q1pMd6E6

incredible work. im in awe of anthropic’s pace, and am grateful for the transparency of process+results i went through 3 out of the 5 case studies they released and wrote down my thoughts on each. in italics are LLM-summarizations for the cases for those who havent read them https://t.co/bLJoqExbjc

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EAGLE-4 is here. From EAGLE-1 to EAGLE-3, we’ve kept making inference faster. EAGLE-4 is our fastest yet. 961.6 tokens/s on @Alibaba_Qwen’s Qwen3.8-27B with TokenSpeed (BF16, 4× @NVIDIA B300, TP4, one request, T=0). Github: https://t.co/GwocZk1UTU https://t.co/GawgFNXKWj

Your OCR says it’s done—but did it get everything right? 🤔 In high-stakes workflows, even a small error can be costly. 🚀 Meet the MiniCPM-OCR suite: an open-source solution to parse fast and guard the results. Two new models: 🔹 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. Compact, accurate, and fast. 🔹 MiniCPM-OCR Jev — A 2B decision model that helps assess parsing accuracy, with performance approaching that of Qwen3.5-397B-A17B on JevOCRBench. ⚡ What’s new? Small model, SOTA results: MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. A sharp goalkeeper 🛡️: MiniCPM-OCR Jev is model-agnostic, checking text, formulas, and tables from different OCR parsers. It achieves 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8). Up to 128 pages per minute 🤯: With DSpark, MiniCPM-OCR runs at 1.49× the speed of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro. The SDK’s concurrency optimizations further boost throughput to as much as 128 pages per minute. Deploy where you work: Day-0 support for Arm, Houmo AI, Intel, and NVIDIA, plus 10 more chip vendors through FlagOS. Ready for your stack: vLLM, SGLang, llama.cpp, and Ollama, with quantized models, fine-tuning support, and Agent Skills. 🔗 GitHub: https://t.co/2SlATAJk08 🤗 Hugging Face: MiniCPM-OCR 1.0：https://t.co/i347di4Fju MiniCPM-OCR Jev：https://t.co/ayVzhCJenh 👉 Try the Demo：https://t.co/QsFAJT3AFy Use the API：https://t.co/dP01jBS2j2

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New in CodeRabbit Secure: Deep Scan now follows a vulnerability across your linked repos, then replays the exploit in a throwaway sandbox to prove it's real before it flags anything. Hendrik walks through this and everything else in the new update: https://t.co/i6ybcOrtGd

Introducing RL with Neural Feedback (RLNF). We have found reliable ways to train AI models to respond to human preferences purely from brain signals. The example below is a picture of a face that self-transforms into a more attractive version for the subject as they are looking at it.

Learn how to build evals, folks! It's one of the pillars of the intelligence stack. Agents are hungry for data and can get it in many ways, but you can't build anything useful in the real world without proper evaluation. Evals make agents useful and let you build truly differentiated products. Invest heavily here. Or reach out if you need help. We are also building evals to measure the frontier @dair_ai.

The secret to Modal's speed is our custom lazily loaded container filesystem, ImageFS. ImageFS is built on a massive tiered distributed filesystem, layering local NVMe disks, regional and p2p caches, and global blob stores. When we launched VM Sandboxes this month, we wanted to make sure they could take advantage of ImageFS in the same way our gVisor containers do. To do that, we built out a new filesystem stack that tricks a VM's ext4 driver into thinking it's talking to a normal disk.