Production-tested patterns, product stories from Blink AI, Nexia Academy and Wallnetic, and the trade-offs we hit shipping real software. Written by senior engineers, not marketing.
Why Blink AI uses Socratic questioning instead of advice generation — how we built prompts that guide users to their own insights, the failure modes we hit, and what the GROW methodology adds.
Six failure modes we hit running RAG in production — chunking gone wrong, embedding drift, retrieval recall, prompt bloat, hallucination on missing context, and how to detect them before users do.
A practical comparison of pgvector, Pinecone and Qdrant for production AI workloads — when each one wins, real performance characteristics, and the migration path between them.
How we combine Sentry for error tracking with OpenTelemetry for tracing across our Node, React Native and Go services — minimum viable observability for production systems.
When headless WordPress (REST API or GraphQL + Next.js) makes sense, when traditional WordPress is the better choice, and how to decide before building.
Shipping a native macOS app in 2026 — SwiftUI vs AppKit, sandbox constraints, App Store entitlements, and what we learned launching Wallnetic on the Mac App Store.
When to ship cross-platform with React Native, when to go native, and the hybrid pattern most production teams actually use in 2026.
A field guide to App Store review for macOS apps — entitlement reviews, screenshot specs, notarization edge cases and review timing, drawn from shipping Wallnetic.
Three production-tested patterns for multi-tenant SaaS — shared database, schema-per-tenant and database-per-tenant — with the trade-offs we learned shipping Nexia Academy and Blink AI.
Why we run most production workloads on Hetzner instead of AWS, with real cost numbers, when AWS still wins, and how to migrate without bringing the operational complexity with you.
