Software Development

Development work gets better when interfaces stay small, feedback is fast, and tooling serves the codebase instead of distracting from it.

The AI-assisted development posts here treat AI like a junior developer with useful speed and real failure modes. The goal is not novelty. The goal is better throughput without lowering standards.

Start Here

• AI Pair Programming: It’s a Junior Dev, Not a Wizard is the practical framing for coding with AI.
• AI Code Review Is Mostly Noise explains what AI review catches and what it misses.
• Running LLMs Locally: A Team Guide With Ollama and Go covers local-first tradeoffs.

Practical Rules

• Keep generated code behind the same review and test standards as human-written code.
• Use AI for acceleration, not for unstated architecture decisions.
• Prefer small, reviewable changes over broad rewrites.
• Make observability and rollback part of production development, not cleanup work.

Supporting Reads

• Observability-Driven Development: Instrument Before You Ship
• Most AI Developer Tools Are Not Worth Adopting Yet
• AI Engineering Is Its Own Discipline Now

Failure Modes

• Treating AI-generated boilerplate as production-ready because it compiles.
• Letting tools change architecture without explicit review.
• Adopting development tools before measuring the workflow they improve.
• Replacing feedback loops with prompt rituals.

Related Hubs

• AI Operating Systems
• Quality
• Developer Tools

References

• The Twelve-Factor App
• Go documentation
• Google SRE Book