GLM-4.7: Advancing the Coding Capability(z.ai)
404 points by pretext | 217 comments
Z.ai research blog post announcing GLM-4.7, a language model with improved agentic coding, terminal tasks, and UI generation, with benchmark results.
First screen, captured .
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GLM-5: From Vibe Coding to Agentic Engineering(z.ai)
367 points by meetpateltech | 195 comments
Z.ai blog post announcing GLM-5, a 744B-parameter (40B active) language model aimed at agentic engineering, using DeepSeek Sparse Attention and asynchronous RL training.
How GLM built its own inference infrastructure(z.ai)
373 points by whiteros_e | 261 comments
Z.ai research blog post describing how its GLM model was used to build its own inference infrastructure, framed as a step toward recursive self-improvement.
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GLM-5.2 is a step change for open agents(interconnects.ai)
344 points by vantareed | 197 comments
Nathan Lambert's Interconnects post arguing that the GLM-5.2 model marks a capability threshold for open-weight agentic AI.
You should write an agent(fly.io)
944 points by tabletcorry | 375 comments
Fly.io blog post arguing that every developer should build their own LLM agent to understand how agents work.
GLM-4.7: Advancing the Coding Capability(z.ai)
404 points by pretext | 217 comments
Z.ai research blog post announcing GLM-4.7, a language model with improved agentic coding, terminal tasks, and UI generation, with benchmark results.
How LLMs work(0xkato.xyz)
834 points by 0xkato | 228 comments
A blog post giving a math-light walkthrough of how transformer-based large language models work internally.
GLM-5.1: Towards Long-Horizon Tasks(z.ai)
593 points by zixuanlimit | 240 comments
Z.ai research blog post announcing GLM-5.1, a flagship model for agentic engineering with stronger coding and long-horizon task performance on benchmarks like SWE-Bench Pro.
Training LLMs from ground zero as a startup(yitay.net)
360 points by swyx | 92 comments
Yi Tay's blog post on the experience of training large language models from scratch at a startup (Reka), covering compute, infrastructure, and lessons learned.