Goatfied blog
Developer-to-developer writing. No fluff. Just what worked, what failed, and what shipped.
benchmarks
Learn how to create a reproducible multi-file refactor benchmark to compare AI code refactoring tools using controlled tasks, baseline capture, and scoring.
benchmarks
Learn why SWE-bench scores don't reflect real developer productivity and what AI coding benchmarks miss about actual software development work.
benchmarks
Learn how code completion engines balance response time and suggestion quality, and why both latency and accuracy metrics matter for developer productivity.
benchmarks
Learn how to evaluate AI-generated code for production readiness using metrics beyond compilation, including performance impact, architectural patterns, and team conventions.
benchmarks
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.
agent-loop
Analysis of 10,000 agent failures reveals three core error patterns and the recovery strategies that prevent cascading failures in autonomous coding systems.
agent-loop
Deterministic validation layers prevent agent failures by checking tool call arguments, resource states, and operation compatibility before execution happens.
agent-loop
Learn how LLM context compaction selectively removes or summarizes information when token limits are reached, keeping AI agents effective during long tasks.
agent-loop
Agents need clear escalation rules to avoid wasting time on stuck tasks - this post covers detection signals and handoff patterns that prevent expensive retry loops.
agent-loop
Comparing AI coding agent architectures that plan changes before execution versus those that edit first and validate afterward, with tradeoffs for each approach.
agent-loop
Learn how to implement exponential backoff, retry budgets, and circuit breakers to build resilient AI agents that recover from failures without wasting tokens.
agent-loop
AI coding agents fail on sequential edits because they lack validation checkpoints between changes, causing cascading errors that compound across multi-step modifications.
