Cloud Code

OpenAI Codex Masterclass — Vaibhav Srivastav & Katia Gil Guzman

Apr 29, 2026 · 1:01:59

Katia Gil Guzman and Vaibhav Srivastav showcase OpenAI's Codex as a full software engineering agent, not just a coding assistant, powered by models like GPT-5.4 and GPT-5.3 Codex Spark. They demonstrate plugins bundling skills and MCP servers, automations that run scheduled tasks (e.g., Slack or Gmail summaries), and code review that 100% of OpenAI pull requests use by default. Subagents parallelize work across up to 20 agents for tasks like reviewing 45 persona files, with each subagent configurable by model, sandbox mode, and MCP access. New features include Guardian approvals for privileged operations, hooks for custom triggers, and a Cloud Code plugin. The episode also highlights the milestone of 3 million weekly active users, tripled since January, and covers security scanning for vulnerabilities.

AgentCraft: Putting the Orc in Orchestration — Ido Salomon

Apr 25, 2026 · 11:18

Ido Salomon, creator of AgentCraft, argues that the bottleneck in multi-agent orchestration is not the agents but the humans managing them, and that the skills needed—borrowed from real-time strategy games—are already familiar to gamers. AgentCraft visualizes agents as physical units on a map that projects the file system, allowing users to see which files agents modify and detect collisions via a heat map. To reduce babysitting, Salomon implemented a campaign feature where agents autonomously decompose tasks and plan within containers, shifting the human role to review. Review bundles present changes with screenshots and videos for efficient inspection. Workspaces enable real-time collaboration between humans and agents across teams, with shared visibility and soft coordination. The goal is to raise the ceiling of collaboration through visibility, autonomy, and human-agent interaction.

Building pi in a World of Slop — Mario Zechner

Apr 16, 2026 · 18:25

Mario Zechner explains why he built pi, a self-modifying, extensible agent core, after growing frustrated with Cloud Code and Open Code for their buggy features, uncontrolled context management, and zero extensibility. pi strips away complexity, giving the agent just four tools (read, write, edit, bash) and a minimal system prompt, letting it adapt to the user's workflow rather than vice versa. Extensions are TypeScript modules that hot-reload, enabling users to build custom tools, chat rooms, or even play Doom without forking pi. He then attacks clankers — agent-generated pull requests and issues — that are destroying open source, sharing his own tactics like auto-closing PRs with a human-voice request and de-prioritizing OpenClaw interactions. In Act 3, Zechner argues that agents compound errors with zero learning, producing enterprise-grade complexity in weeks, and that long context windows and agentic search are hacks. His prescription: scope agent tasks, modularize codebases, cap generated code review, and always read every line of critical code yourself.

Don't Build Agents, Build Skills Instead – Barry Zhang & Mahesh Murag, Anthropic

Dec 8, 2025 · 16:22

Barry Zhang and Mahesh Murag of Anthropic argue that instead of building domain-specific agents, developers should build reusable Skills—organized folders of files that package procedural knowledge for agents. They explain that skills are progressively disclosed to protect the context window, use scripts as self-documenting tools, and have already grown to thousands in five weeks, including foundational, partner, and enterprise skills. Skills complement MCP servers by providing expertise while MCP handles connectivity. The future includes treating skills like software with testing and versioning, and enabling agents to create their own skills for continuous learning, ultimately creating a collective knowledge base that makes agents more capable and reliable.

Z.ai GLM 4.6: What We Learned From 100 Million Open Source Downloads — Yuxuan Zhang, Z.ai

Nov 22, 2025 · 19:39

Yuxuan Zhang from zAI details the technical roadmap behind the GLM 4.6 open-source model series, which has surpassed 100 million downloads and tied for number one on the LMSYS Chatbot Arena alongside GPT-4o and Claude 3.5 Sonnet. The training pipeline uses 15 trillion tokens of pre-training data, followed by 7 trillion tokens of code and reasoning data, repo-level contexts at 32,000 tokens, and 100 billion tokens of long-context agent trajectories up to 128,000 tokens. Zhang introduces SLIME, a hybrid synchronous/asynchronous RL framework that decouples agent-environment interaction from GPU training to avoid bottlenecks. He explains why single-stage RL at 64,000 tokens outperforms multi-stage approaches for preserving long-context abilities, and shows that token-weighted loss converges faster than sequence-average loss for code RL. The multimodal GLM 4.5V model handles native resolution images and video with temporal index tokens, enabling GUI agent capabilities. Deployment is supported via vLLM and SGLang, with an API at z.ai and an open-source coding assistant.