How teams use agents to iterate, review, and ship PRs with proof.
Showing 12 of 122 posts
Learn why semantic search outperforms grep for AI coding agents navigating legacy codebases with inconsistent naming conventions, and how it reduces token costs.
Learn how structured to-do lists enable AI coding agents to run autonomously for 45+ minutes without context overflow or tool call failures.
Learn why million-token context windows work best with upfront context loading rather than extended conversations, and how to eliminate drift in AI coding workflows.
Local embedding models like Ollama's nomic-embed-text now match OpenAI quality for codebase semantic search, enabling teams with strict compliance requirements to deploy without external data egress.
Why forcing every coding task through one AI agent costs more than switching - and how portable rules, short loops, and task-matched tools improve your workflow.
The gap between AI coding early adopters and the median developer is wider than the discourse suggests. Learn why adoption friction, not feature depth, determines which AI coding tools actually get used.
Generic AI coding modes optimize for broad applicability but fail on your specific codebase patterns. Learn why narrowing scope increases accuracy and how to tune modes to your conventions.
Learn why AI coding agents lose accuracy after 10-12 conversation turns and how context compression resets tool use reliability in agentic workflows.
Learn how AI coding agents can watch CI pipelines and iterate on failures automatically, eliminating context-switch tax and closing the loop from PR to green build.
Why synthetic benchmarks plateau at 97% accuracy while real GitHub issues reveal which AI coding agent actually ships code in your codebase.
Learn why comprehensive AI refactors fail and how the surgical pattern of single verified changes delivers reliable codebase improvements without breaking production.
Codebase indexing gives AI coding agents a search index, not compressed code knowledge. Learn why indexing accelerates discovery but the model still needs to read files to understand your code.
Cloud Agents review code, catch issues, and suggest fixes before you open the diff. You review the results, not the process.
