Coding with AI Agents
Get reliable, reviewable work out of AI coding assistants and agents, and know where they fail.
- For
- Developers using AI coding assistants and agents in daily work
- Assumes
- You write code professionally. No machine learning background needed.
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Stage 1 · Foundations
What the model is, and what it changes
The mechanics behind everything an assistant does, and the argument for where your value moves.
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Core AI Concepts
Tokens, context windows, and sampling explain most of the "why did it do that" moments in every later step.
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AI in Practice: The Skill Inversion
The argument for the whole path: when code is cheap, judgment is what's left to be good at. The rest of the path is how to exercise it.
Go deeper Core AI Concepts Diagrams
Stage 2 · Basics
Working with an assistant
Giving the model what it needs, and delegating on purpose.
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Prompt Engineering
Most bad agent output traces to context the model didn't have. This is how to give it that context on purpose.
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AI-Assisted Development
Where the speedup really comes from, and why it depends on the specifying and verifying you do around the model.
Stage 3 · Intermediate
Real projects, real boundaries
Discipline on a production rebuild, and the rules for what your tools may touch.
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When Discipline Makes AI a Force Multiplier
The basics on a real rebuild with no docs, no tests, and zero tolerance for regressions, including what went wrong before the discipline arrived.
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Assumptions-First Task Planning Skill
The case study's assumptions-first discipline as a skill you can install. Copy it, or use it as the model for your own.
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Model Context Protocol (MCP)
Connecting your assistant to tools and data means trusting a server. This is what that trust covers, and where it ends.
Stage 4 · Advanced
Beyond one session
Keeping long work, and many agents, on track.
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Beads: Durable Work Memory for Coding Agents
Agents forget everything between sessions. Beads keeps the work outside the context window, so long work survives.
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Scaling Generative AI Workflows
One good output and fifty consistent ones are different problems. This is how to split work across isolated agents without cost or quality collapsing.
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AI Batch Generation Pipeline Template
The previous step as files you can copy: a format reference, a plan file, and an orchestrator prompt.
Go deeper Gas City: Orchestrating Fleets of Coding Agents AI Agents Tool Calling