Practical posts on AI, full stack development, and building things that actually ship.
Validating an AI product idea? Here's exactly what a 5-day MVP sprint looks like — features, scope, stack, and realistic cost expectations.
Add AI to your existing product without a rewrite. A guide for founders on integration points, build vs. buy, and shipping AI features safely.
Wrong hiring decision at early stage kills runway. When to hire a freelance AI engineer, use an agency, or build an in-house team.
Math.random() is not secure for tokens or passwords. Learn how to use crypto.getRandomValues() and Node.js crypto for cryptographically secure randomness.
Your AI product architecture will make or break scalability. How to design an AI SaaS system that scales without a full rewrite.
Type OpenAI and Vertex AI SDK responses in TypeScript — typed prompts, structured outputs, Zod validation, and patterns that prevent runtime surprises.
Most AI features fail not because of the model — but because of poor problem definition. Here's what product-minded builders do differently.
OWASP-aligned password security for web developers — hashing, storage, reset flows, timing attacks, and the mistakes that leave your users vulnerable.
Bcrypt password hashing in Node.js — pick the right cost factor, test hashes live with the bcrypt generator, avoid OWASP security mistakes, and use bcryptjs correctly.
Deploy a Node.js + Vertex AI app to Cloud Run — Dockerfile, authentication, Artifact Registry, and continuous deployment step by step.
A practical guide to building autonomous AI agents with LangChain.js in Node.js — tools, agent executors, memory, and real working code examples.
Prompt engineering for production LLM apps — system prompts, temperature, context injection, few-shot examples, and structured output validation.
A practical comparison of Chroma, Pinecone, and pgvector for production RAG pipelines — covering setup, query performance, scalability, and cost.
AI costs out of control? Cut OpenAI and Gemini API spend with model selection, caching, prompt optimization, and batching — without degrading user experience.
Most devs give the 3-step answer. The full version — DNS, TCP, TLS, load balancers, app servers, databases — with the depth that gets you hired.
The junior dev job market isn't broken — it's a filter. What junior developers must do to be in the 2 that stay, get hired, and build real careers.
Most developers are experts at HOW to build — but WHY and WHAT to build are where real product thinking lives. A Design Sprint changed how I see this gap.
Monitor LLM apps in production — track latency, token usage, error rates, and hallucinations with practical tools and observability strategies.
Choosing an AI platform for your product? A practical comparison of OpenAI and Google Vertex AI covering cost, reliability, and model quality.
Set up @google-cloud/vertexai in Node.js — authentication, getGenerativeModel, generateContent, streaming responses, and production patterns.
A practical guide to building Retrieval Augmented Generation (RAG) pipelines using JavaScript, LangChain.js, and OpenAI — with working Node.js code examples.
Learn how to set up AI workloads on Google Cloud with a step-by-step guide. Train and deploy AI models using AutoML, Cloud Functions & Cloud Run.
JavaScript vs TypeScript: advantages, disadvantages, and when to use each — a practical guide for web developers choosing between the two.