benchmarks
Measuring ghost-text latency honestly
Learn how to accurately measure AI code completion latency across all four components that affect the delay between typing and seeing suggestions.
2026-08-078 min read
Goatfied blog
Benchmarks, deployment notes, and hard-won patterns
Developer-to-developer writing. No fluff. Just what worked, what failed, and what shipped.
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benchmarks
A reproducible methodology for benchmarking multi-file refactors
A reproducible methodology for benchmarking AI refactoring tools using auditable pass criteria, version control, and automated validation across real repositories.
2026-08-068 min read
benchmarks
Why SWE-bench scores don't predict daily developer productivity
SWE-bench scores measure isolated patch generation on open-source issues, not the multi-file refactoring and codebase navigation that dominates real development work.
2026-08-058 min read
benchmarks
Latency versus accuracy in inline completion engines
Inline code completion engines must balance response time under 100ms with suggestion accuracy, as latency below 80ms matters more than high acceptance rates.
2026-08-048 min read
benchmarks
Measuring AI code quality beyond “does it compile”
Learn how to evaluate AI-generated code for production readiness using metrics beyond compilation, including performance impact, architectural patterns, and team conventions.
2026-08-038 min read
benchmarks
We pointed six AI coding tools at a 400k-line monorepo. Here's what broke.
We tested six AI coding assistants on a 400,000-line Python monorepo with real feature tasks to document how they handle cross-service changes at scale.
2026-08-028 min read
benchmarks
Context window budgeting under token pressure
Learn how to manage LLM context windows when refactoring large codebases without hitting token limits that cause agents to forget constraints.
2026-06-1211 min read
benchmarks
Goatfied vs Cursor vs GitHub Copilot: how to benchmark them on real PR tasks
A reproducible framework for benchmarking Goatfied, Cursor, and GitHub Copilot on real pull-request tasks — the metrics that matter, the harness to run, and how to read the results.
2026-05-2910 min read
benchmarks
Debugging AI-generated code with checklists
Learn how to build systematic debugging checklists that catch common failure patterns in AI-generated code before deployment to production systems.
2026-05-2313 min read