deployment
Right-sizing GPUs for code completion workloads
Learn how to match GPU specs to your code completion request patterns, latency requirements, and team size without overspending on idle compute capacity.
2026-08-128 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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deployment
Deploying a coding assistant to Kubernetes without leaking source
Deploy AI coding assistants on Kubernetes without exposing source code through logs, volumes, secrets, or network calls to external model endpoints.
2026-08-118 min read
deployment
Zero-downtime model upgrades for coding assistants
Learn how to deploy new LLM versions to production coding assistants without dropping in-flight requests or causing retry storms during model transitions.
2026-08-108 min read
deployment
Cost modelling for self-hosted inference at team scale
Learn how to calculate actual costs for self-hosted LLM inference by modeling compute utilization, instance sizing, and traffic patterns for small engineering teams.
2026-08-098 min read
deployment
Running AI coding assistants in air-gapped environments
Learn how to deploy AI coding assistants in isolated networks using local inference, offline model management, and compliant monitoring infrastructure.
2026-08-088 min read
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
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
agent-loop
What 10,000 failed agent runs taught us about error recovery
Analysis of 10,000 agent failures reveals three core error patterns and the recovery strategies that prevent cascading failures in autonomous coding systems.
2026-08-018 min read