Blog
Deep dives on accelerating AI delivery, scaling production workflows, and news from the Rev.AISomething build crew.
SOLID for modern software: single responsibility, open/closed, Liskov, interface segregation, dependency inversion—clean interfaces, swappable parts, safer fallbacks, testable pipelines.
How to run your own LLM inference service with a small footprint, clear latency budgets, token-cost accounting, and end-to-end tracing.
A practical RAG build for devs on a budget: ingest once, store embeddings in SQLite/pg, add a rerank pass, and deploy on Fly or Render with sensible perf defaults.
How Token-Oriented Object Notation shrinks prompt payloads, improves LLM accuracy, and fits directly into vibe coding workflows.
A practical caching playbook for devs: request deduping, semantic caching, TTL tuning, and lightweight benchmarks to keep AI API spend under control.
Part one of our Vibe Coding architecture series on designing scalable systems when AI models co-author half the codebase.
Vibe Coding pairs intent-driven prompts with AI orchestration so teams ship faster, more consistent software with happier developers.
Only 9% of engineers trust AI-generated code without review—here’s why skepticism persists and how to pair AI speed with safer workflows.
Visa, Cloudflare, Mastercard, and Amex are piloting the Trusted Agent Protocol so verified AI shopping agents can transact without getting blocked.
How to tier your systems, design replayable data paths, and keep user workflows alive even when a cloud region fails.
A pragmatic framework for turning vibe-coded prototypes into systems that hold up in production.
Why we exist, the projects we take on, and how we help vibe-coded AI apps reach the finish line.
