Thank you, Clem, for bringing this perspective to the UN Security Council. Proud that GLM could support the Hugging Face team. “The biggest risk is not powerful AI, it’s asymmetry of powerful AI.” Wider access helps close that gap. That’s why we keep GLM open.

This is what a strong open-model ecosystem looks like. Built on GLM‑5.3, Aikido’s Altar‑1 brings near-frontier defensive security capabilities into a lightweight, deployable model—running on a single 4×H200 node. Open weights don’t just expand access. They enable builders worldwide to create specialized, sovereign AI for real-world industries.

AI is global. The next phase of AI adoption will be shaped by local infrastructure, local expertise, and strong ecosystem partnerships. Today, we're introducing the Sovereign Partner Program (ZSP) , a strategic program for partners looking to bring GLM to their countries and build local AI capabilities with ZSP partners will work directly with our teams across: → Early access to upcoming GLM model releases → FDE support and joint engineering → Joint go-to-market and enterprise engagement → Local token business development → Joint launches and market activation Our goal is simple: enable partners to deploy GLM on infrastructure they operate, serve customers locally, and build long-term AI capabilities in their countries. We're now selecting the first cohort of ZSP launch partners. Apply → #ZSP# #GLM# #SovereignAI# #EnterpriseAI# #AIInfrastructure#

GLM-5.3 is now open-weight. Our most capable model for agentic coding and cyber defense is now available to download, run, and customize. Weights: Tech blog:

Ox Alpha = GLM-5.3 Flash AA = 57 , 1/100 frontier price, Powered by pure Chinese chips. Delivered nearly 20% weekly token share (no. 1) on OpenRouter. Thanks to all for the support.

For enterprise AI, accessibility matters as much as capability. GLM-5.3 is now available on AWS Marketplace, making it easier for teams to evaluate, procure, and adopt through their existing AWS environment. Explore GLM-5.3 on AWS Marketplace:

This model is insane at 3D website design. I asked GLM 5.3 (left) & Fable 5 (right) to make me a 3d biking website. GLM nailed it while Fable failed. GLM cost $0.14 while Fable cost $2.21. More than 15x cheaper while being even better in this case!

GLM-5.3 is coming. Built to Code. Ready for Cyber Defense. We’re also launching “GLM Enterprise 100 ”program for Fortune Global 500 and leading enterprises with FDE support, stronger enterprise control, and a fast path to production. If your organization is ready to build with GLM5.3, contact us enterprise@z.ai

60 seats. 1 evening. GLM-5.2 in your hands. Jul 28, Singapore. Our first meetup with AI Singapore. Bring your laptop. Build your own live agent with one of the top open-source models with 1M context—at a fraction of the cost of closed-source models. API credits for every attendee.

Human + Agent Notes #004# Most people think AI will replace jobs. It may first replace departments. The real transformation isn’t one employee becoming more productive. It’s one person managing ten agents instead of ten people. The organization chart itself may change. Human → Agent Teams → Agent Organizations.

The RL framework behind GLM-5.2 is fully open source. The full post-training of GLM-5.2 ran on it in about two days. The same stack sits behind the entire GLM series, from 4.5 to 5.1. It is called slime, and it is built around one idea. Keep a single RL kernel, and push all the variety into data generation. Let me explain what that means. Every RL run has two halves. One generates experience, where the model produces responses and something scores them. The other learns from it by updating weights. The learning half is mechanical. It reads samples, computes a loss, and steps the optimizer, the same way whether the model solves equations or drives a browser. What changes between tasks is generation. A math run answers in a single turn and grades the result. An agent run loops through tool calls, reads results, and only then earns a reward. slime draws the line right there. The learning half stays fixed as one kernel, and everything that differs becomes a new way to generate data. Under the hood, it wires Megatron for training to SGLang for rollout, with a Data Buffer between them that owns prompts, custom data, and generation. Most RL stacks grow into a pile of disconnected trainers, rollout services, and agent frameworks. slime refuses that. Multi-turn tool use, sandbox interaction, environment feedback, and verifier rewards all enter as data generation, not as forks of the loop. So an agentic workload runs on the same loop a math run uses, and the kernel never changes. A few things follow. → It is battle-tested. The loop is validated by shipping real GLM models, and it also supports Qwen3, DeepSeek V3, and Llama 3. → Correctness comes first. RL bugs are silent, so slime keeps the dataflow explicit and treats CI, reproducibility, and fault tolerance as real engineering. The proof is the ecosystem on top of it. Dressage, Miles, vime, Relax, OpenClaw-RL, P1, and TritonForge all build on slime without touching the core loop. The lesson is not that RL needs a bigger framework. It is that the variety belongs in data generation, and the training loop should stay small enough to trust. GitHub repo: (don't forget to star 🌟) Since we're talking about RL, I wrote a full breakdown on fine-tuning LLMs with RL in 2026. Including how to skip manual reward engineering with automatic LLM-graded rewards. The article is quoted below.

GLM 5.2 x AWS GLM-5.2 is accessible via GLM API on AWS Marketplace 🚀 Powerful long-horizon autonomous workflows, top-tier coding & multi-step agent reasoning capability, delivered through a single unified API endpoint. Integrate seamlessly within your AWS cloud without self-host GPU maintenance. #GLM52# #ZhipuAI# #AWSMarketplace# #EnterpriseLLM#