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.
Latest insights on Agentic AI workflows, cloud native architectures, and performance optimization best practices from the Speedscale team.
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.
UI synthetics only tell you something is broken. Traffic replay per microservice isolates failures before any human walks up. Zero scripts required.
AI coding adoption is high and trust is dropping. A testing pyramid for agents, plus reproducible production context that grounds AI in real behavior.
Dark code is software no human has written, read, or reviewed. As AI tools accelerate, the gap between shipped code and understood code is widening fast.
Trace-based testing uses OpenTelemetry traces as replayable test input so CI catches production regressions before deploy, not after incident review.
Observability tells you what failed—but not how to recreate it. Why reproducibility is the missing fourth pillar, and what that means for incident response.
SaaS AI fails when agents need continuous access to your codebase and internal APIs. Here's why BYOC is the only deployment model that works at scale.
Learn a practical workflow to convert Datadog metrics, traces, and incidents into CI tests that catch regressions before deploy.
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.