Claude Code New Features, Explained
Claude Code works more like a dependable AI employee when a project supplies shared context, scoped tickets, review standards, testable feedback loops, recurring routines and explicit permission boundaries.
Context Engineering
Videos about structuring instructions, memory, project state, and reference material so AI systems can work effectively. 11 videos.
My top secrets to running an AI Agent Workforce
Proactive AI workforces need goals, broad but accurate context, permission to act within fixed risk limits and watchdogs that identify friction without making the human manage every task.
Three OpenAI Engineers Shipped A Million Lines. Your Ten-Hour Agent Run Starts Here.
Long-running coding agents work better when people progressively shape context through durable instructions, current state, project maps and review checkpoints.
Ex-Uber dev explains his Multi-Agent Workflow
AI agents become more useful teammates when a whole team shares their context, tools, memory and collaboration surfaces instead of operating isolated personal agents.
Ex-NASA dev reveals his Agentic Engineering Workflow
Maintainable agent-written software requires humans to decide product intent, architecture, program structure and testable vertical slices before agents implement the code.
Why Graph Engineering Improves Claude and Codex Workflows
Graph engineering turns a vague one-shot prompt into an explicit sequence of planning, parallel research, criticism, synthesis and human review that can be tested and improved.
Claude Skills: I Stopped Installing Them. Here's What I Do Instead.
Agent skills should encode trusted human judgment in instructions that agents can discover and use while people can still read, audit and revise them.
Jack Dorsey's Buzz Has Left Me Completely Speechless...
Buzz brings people, AI agents, repositories and local compute into one shared workspace so teams can collaborate with a common context instead of isolated assistants.
Paste This Into Claude, Never Hit a Token Limit Again
Nate B Jones shows that long AI sessions become cheaper and more reliable when users stop resending stale history and carry forward only accepted, task-relevant context.
Jack Dorsey's Buzz: Clearly Explained (and how to use it)
Greg Isenberg and Vince Canger present Buzz as an open shared workspace where people and agents can collaborate, build software and retain context across model changes.
Agentic Engineering, explained by a 10x developer
David Ondrej and Thorsten Ball argue that stronger coding agents shift software work from typing and model micromanagement toward product judgment, clear context and asynchronous verification.