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Sunday, 23 August 2026 Reading Room

AI

Making AI Coding Agents More Efficient: My Local Toolkit

Giving AI coding agents more tools doesn’t necessarily make them more efficient. After using OpenCode and Claude Code heavily, I started focusing less on adding capabilities and more on controlling the information flowing into the agent. That means: searching before reading entire files choosing between text, structural, and semantic search reducing noisy command output keeping sessions focused…

dev.to ·

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The Coverage Loop: Turning Free AI Tokens into Verified C++ Tests

Unit tests are boring. Coverage is not optional. I asked a free AI model to write my tests. It failed. Then I built a feedback loop.

  • AI model generates C++ tests for string utility
  • Coverage measured with gcov, loop repeats until 90% achieved
  • Feedback loop refines tests based on missing lines

·dev.to

Debugging Is the Killer App for Free Model Tokens — Here's the Workflow

Most developers treat free model tokens as a code generation budget. They ask for snippets, refactors, and explanations, then wonder why the tokens disappear without making their codebase measurably…

  • Debugging is pattern-matching task, not code generation
  • Collect recent logs, extract error block and context
  • Python script automates workflow with free model API

·dev.to

Everything, All at Once

By the end of this post, you'll learn how I work faster with coding agents and run more work in parallel. Development looks very different form a year ago.

  • Agents speed up development process
  • Multiple tasks run simultaneously
  • Workflow allows trusting agents

·dev.to

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