Practical insights and guides on PHP/Symfony, React/Next.js, legacy modernization, and software architecture, drawn from 18+ years of building web applications for European businesses.
Running ALTER TABLE on a large, busy MySQL 8 table is how teams cause an outage. Here is how online DDL, pt-online-schema-change, and gh-ost keep schema changes safe.
Read moreAPI Platform makes shipping a Symfony API fast, and it makes N+1 queries and unbounded pagination just as fast. Here are the GraphQL, pagination, and query patterns that keep an API healthy at enterprise scale.
Read moreBlue-green deployment is usually pitched as a Kubernetes feature, which prices out smaller teams. Here is a single-server pattern with health checks and instant rollback that runs on any VPS.
Read moreMost teams skip secrets rotation because the tooling feels heavy. Here is a pragmatic rotation pipeline for Symfony and Next.js that runs without a Vault cluster to babysit.
Read moreCoolify, Dokploy, and Kamal all promise Heroku-style deploys on your own server, but they make very different trade-offs. Here is how to pick one for a small SaaS team.
Read moreModel providers have outages, rate limits, and slow afternoons. Here is how to design fallback chains, graceful degradation, and retry logic so your AI features stay usable when a provider fails.
Read moreBolting OpenAI onto a PHP app the quick way creates blocking calls, leaked keys, and zero observability. Here is a production-grade Symfony integration pattern with async handling, error boundaries, structured output, and cost tracking built in.
Read morePrompt caching can cut both latency and cost dramatically, but careless caching serves stale or wrong answers. We show where caching is safe, how to key it, and how to invalidate without losing the savings.
Read moreTraditional tests assume the same input gives the same output. Agentic AI features break that assumption. Here is how agent evaluation frameworks let you ship non-deterministic features with confidence instead of crossed fingers.
Read moreWhen a RAG system gives a bad answer, teams blame the LLM and start swapping models. The real fault is usually retrieval. Here are the evaluation metrics that locate the actual bottleneck before you waste a sprint on the wrong fix.
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