
Cursor 2.4: Subagents, Skills, and What They Mean for Your Workflow
Cursor 2.4 introduces subagents, skills, and image generation. Here's how these features fit into a structured AI development workflow.
We publish and curate research on agentic coding in enterprise environments. Our focus spans multi-agent orchestration, team adoption patterns, and the evolving best practices that help engineering teams ship faster with AI. Here we share what we learn from training teams and staying close to the cutting edge.

Cursor 2.4 introduces subagents, skills, and image generation. Here's how these features fit into a structured AI development workflow.

We built Cursor Gas Town—a Cursor CLI implementation of Steve Yegge's Gas Town multi-agent orchestrator. Built in collaboration with the Cursor engineering team, it brings persistent work tracking and multi-agent coordination to Cursor workflows.

Peter Steinberger doesn't design codebases for himself anymore—he engineers them so AI agents can work efficiently. Here's how to structure code for maximum agent productivity.

Steve Yegge's Gas Town runs 20-30 Claude Code agents in parallel. Here's how multi-agent orchestration is changing AI-assisted development and why workflow durability matters.

Daniel Miessler's Job vs Gym framework helps developers decide when to use AI and when to do the work themselves. Here's how to maintain the skills that matter while leveraging AI for everything else.

New research shows senior engineers accept 22% more AI suggestions than juniors. AI coding tools amplify existing engineering skill, not replace it. Here's what this means for teams.

Y Combinator asked their founders about AI coding patterns. One insight stood out: when AI produces garbage, git reset --hard and implement clean. Stop stacking fix attempts.