Knowledge catalogue
AI Gateway now supports team-wide Zero Data Retention (ZDR). Building safely with multiple AI models means wrestling with fragmented data policies, per-provider negotiations, and the hope that develop
GLM-5.1 is Z.ai's post-training upgrade to GLM-5, now available on Together AI, delivering a 28% coding performance improvement through a refined reinforcement learning pipeline while retaining the...
🚀 GLM-5.1 is now available on SiliconFlow Start a task, go to sleep. GLM-5.1 plans, executes, and self-improves for 8 hours and delivers high-quality results by morning. Still open-source. Big kudos t
If you’ve encountered garbled output like this while using GLM-5 or GLM-5.1 on our official service, the issue is now resolved. We've patched the underlying inference-side bugs and will be releasing a
Introducing the Hermes Ecosystem Map I was an early user of Hermes Agent from @NousResearch and have been a power user ever since But as the ecosystem has grown, its been hard to keep up, so I did som
Nous Research's **Hermes Agent** is an open-source agentic framework by NousResearch that delivers significantly improved reliability when using GPT-5.x or Codex (OpenAI Codex subscription) models ...
We partnered with @Zai_org to bring GLM-5.1 to Modal. Free to try as an endpoint for the next month. GLM-5.1 further improves upon GLM-5's coding abilities and long-horizon effectiveness. Introducing
A lot of you are asking about the :cloud tag. Here's the deal: GLM-5.1 is a 744B parameter beast. To hit that 54.9 benchmark score without turning my M4 Mac Mini into a space heater, I run the cloud e
GLM-5.1 is Z.ai's (zai-org) next-generation open-source flagship model for agentic engineering, achieving state-of-the-art performance on SWE-Bench Pro and significantly outperforming its predecess...
GLM-5.1: Towards Long-Horizon Tasks Chinese AI lab Z.ai's latest model is a giant 754B parameter 1.51TB (on Hugging Face) MIT-licensed monster - the same size as their previous GLM-5 release, and shar
How can you improve your agentic search pipeline? I just wrote a blog post with @tech_optimist from @lancedb to answer exactly that. TLDR: - Parse files and take page-level screenshots with LiteParse,
INCREDIBLE GLM-5.1 weights are now opensource > i’ve had early access to the weights for the past few days > and yeah… this one matters a lot benchmarks? > SWE-Bench Pro: 58.4 > beats Opus 4.6 (57.3)
AlphaXiv introduced GLM-5.1 as the underlying model powering its research paper understanding features on the alphaXiv platform, enabling users to highlight any section of a paper to ask contextual...
Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making complex reasoning difficult📄 So we teamed up with @lancedb
DoorDash and AWS describe a live Bedrock-backed voice agent for Dasher support, reporting hundreds of thousands of calls daily.