🚀 Roll out GLM-5.2 for your entire engineering team with our Team Coding Plan! Most AI coding tools only serve solo devs. We built for engineering leaders: seat management, usage tracking, budget control & full code privacy. ✅ 1M context window for full-repo work ✅ Advanced agentic coding, full-stack support & code translation ✅ Team roles, usage analytics & budget caps ✅ Zero data/code used for model training 💰 Starting at ¥598/seat/month Try it on your codebase: Send us a real task for a 5-min benchmark test. 📩 Startups: startup@z.ai Enterprise: enterprise@z.ai #AI #LLM #DevTools #SoftwareEngineering

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GLM 5.2 now is live in Team Coding Plan as well～

Recent thoughts: The Shift to Long-Horizon Tasks The most likely breakthrough this year will be in long-horizon tasks. We are moving toward a stage where Large Language Models (LLMs) learn to complete extended, complex missions by interacting with Agent environments. This is perhaps where the true value of LLMs lies. Take cybersecurity as an example: imagine a model that continuously hunts for software bugs and vulnerabilities. While it sounds like a search process, it’s actually the model learning the high-level intuition and methodology of a professional hacker. Unlike humans, AI can run 24/7 without fatigue. It could potentially find exploits at a much higher frequwill ency and claim bounties on platforms like HackerOne or BugCrowd. It sounds fun, but fundamentally, it's a revolution that displaces the hacker. If even hackers are being "disrupted," one can only imagine the impact on general programmers. From One-Person to None-Person Companies Building on long-horizon capabilities, Autonomous Agent Systems (AAS) will inevitably become the next frontier. Last year, we were discussing the rise of the "One Person Company" (OPC). I didn't expect us to move so quickly toward the "None Person Company" (NPC). It’s an ironic twist—we might all end up as NPCs in this new ecosystem. Engineering the Impossible: Memory and Learning To realize the vision above, we must solve three technical pillars: Memory, Continual Learning, and Self-Judging. I used to think these would require massive paradigm shifts and years of research. However, the pressure from both the technical and application sides is so intense that we are seeing these capabilities emerge through ingenious engineering "tricks": Memory: Long context windows (1M ) and RAG have significantly bridged the gap. Continual Learning: While true continual learning remains difficult, the release cycles are shrinking. Global models are updated monthly; domestic models are catching up. If we reach weekly updates by next year, it will effectively function as continual learning. Self-Judging: This remains the most elusive, yet models like Opus 4.7 are already demonstrating early self-correction and judgment capabilities. The Self-Evolving Endgame The most difficult—and most promising—path is Self-Evolution. The current wave is incredibly fierce. I suspect that models like Claude may have already achieved a baseline for self-training: writing their own code, cleaning their own data, generating synthetic data, and then training on it. It might "waste" some compute, but it saves the most precious resources: human labor and time. In the LLM era, speed is everything. Rapid iteration is what creates the cognitive gap between leaders and followers. Claude’s rumored 2-million-chip cluster for next year is likely dedicated to exactly this: autonomous model self-training. Technical Summary: 1M Context: Necessary baseline. Memory & Continual Learning: Prerequisites, likely solved first via "tricky" engineering. Harnessing Environments: The breakthrough point. Self-Judging: The tipping point. Full Self-Training: The endgame. Redefining AGI and the Industry If this is the road to AGI, then AGI’s definition should be the sum of all human collective intelligence, not just an individual’s intelligence. It must possess the creative capacity to produce something as profound as the "Theory of Relativity"—meeting the bar set by Hassabis. During this transition, every APP will need to be reconstructed as AI-native. In fact, we might move past the concept of APPs entirely. The most significant challenge will be the reconstruction of the operating system itself. In the future, you won’t see a traditional desktop; you will see an LLM OS, where applications are "generated on demand." This challenges the 80-year-old Von Neumann architecture and represents a total upheaval of the computer science industry. The Irreversible Wave From completing long-horizon tasks to fully autonomous operations, every sector—Security, Finance, Law, E-commerce—will be reshaped. Many friends have reached out lately, asking how to transform their enterprises to keep pace with AI. But few truly realize that this irreversible process has already begun. As this massive technical wave hits, we must be prepared to act, but we must also start thinking seriously about how to regulate it.

The next phase of AI competition will be won in the enterprise where deployment, trust, and workflow integration matter more than model quality alone.

In AI, business models shape product behavior.If an assistant is meant to help you think, trust and alignment can’t be secondary to monetization.

This is where the market is heading: from model access to workflow integration. In the agent era, real adoption depends on how well intelligence fits into production systems...

Earlier this year, OpenAI and @amazon partnered to bring OpenAI’s frontier capabilities to enterprises, startups, and customers around the world. We’re taking the next step: making our models, Codex, and Bedrock Managed Agents available to @awscloud customers, in limited preview. Making OpenAI available on AWS means enterprises can get AI into production faster - across software engineering and other professional workflows. We’re excited to see what gets built! openai.com/index/openai-on-a…

Scaling laws push model capability forward. But whether that capability becomes reliable in production depends on how we handle Scaling Pain. z.ai/blog/scaling-pain In our latest blog, we share how we debugged GLM-5 serving at scale: reproducing rare garbled outputs, repetition, and rare-character generation; tracing and eliminating KV Cache race conditions; fixing HiCache synchronization issues; and introducing LayerSplit for up to 132% throughput improvement. We hope these lessons help the community avoid similar pitfalls and build more robust inference infrastructure.

VergeX × Z.ai GLM-5 Odyssey 5B GLM-5 credits consumed. 1.5B additional capacity, absorbed immediately. 1,500 new users onboarded.

We are thrilled to welcome @Zhipu_AI as a Gold Sponsor for #GOSIMParis 2026! 🇫🇷 paris2026.gosim.org/fr/ From groundbreaking LLMs to a thriving developer ecosystem, Zhipu is at the heart of the AI revolution. Stay tuned as we power the GOSIM Agentic Hackathon,（create.gosim.org/） where developers will build the future of open-source intelligence using Zhipu’s cutting-edge models See you in Paris! 🗼✨ #GOSIM #ZhipuAI #OpenSource #AI #TechInnovation

GLM 5.1 is coming huggingface.co/zai-org/GLM-5…. Coding is the cornersone and Long Horizon Task (LHT) is the new feature this time. focus more on 1. memory 2. evolving/continual learning 3. self judge/reflextion.

GLM5.1 x #Google Vertex console.cloud.google.com/ver…

Most models still break mid-task not because they’re not smart enough but because they can’t stay in the loop 8-hour runs start to change that this is how agents stop breaking.

impressive…

GLM-5V-Turbo from @Zai_org also comes with full set of Skills tools. 🧩 Image & video captioning 📝 Document-based writing 📍 Object grounding 📊 PDF to PPT 🌐 PDF to web 🛠️ PRD to app 🎨 Prompt generation 📄 Resume screening 🖥️ Web replication 📈 Stock analysis More than multimodal understanding — this is about turning vision into real productivity. Now open-sourced. Welcome to try: github.com/zai-org/GLM-skill…

Haidilao Qclaw Do you know if you go to Haidilao (the hotpot restaurant) in China now, you may receive free installation services about #qclaw ? @steipete

Big thanks to all our partners for the support.. We’re inviting the best startups to join now..let’s see who makes the finals 👀 Singapore · May 9 See you there.

Nice job ，which startup’s next? 🤗 If you’re building an AI-native startup, moving fast, and love GLM ❤️come join our startup community. Apply now!

Hard to nail everything when you’re not “Google” yet. Still remember “the Sora moment ”had everyone going crazy ..