Run Local LLMs on Mac to Cut Claude Costs
A practical hybrid workflow that uses costly LLM APIs for planning and local models (via Ollama + OpenCode) for execution, guarded by deterministic evals.
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A practical hybrid workflow that uses costly LLM APIs for planning and local models (via Ollama + OpenCode) for execution, guarded by deterministic evals.
We recorded Warp traffic to see what gets sent back to the home base. Spoiler: It's everything.
AI coding adoption is high and trust is dropping. A testing pyramid for agents, plus reproducible production context that grounds AI in real behavior.
Trace-based testing uses OpenTelemetry traces as replayable test input so CI catches production regressions before deploy, not after incident review.
LLMs have collapsed the cost of custom internal tools. Here's the startup distribution problem I've watched kill companies — and how I vibe-coded my way out.
RBAC and DLP let developers access production data safely—without configuration drift or PII exposure. Here's how to design it right.
Record real LLM traffic from a FastAPI app, mock it locally, and replay it in CI with proxymock.
Production AI spend gets attention. Non-prod LLM calls in development, CI, and load tests often do not. Simulation fixes that.
AI-generated code is moving fast—but without behavioral validation, you're gambling with production stability. See how Proxymock changes the equation.