Building 138,000 Lines of TypeScript in 21 Days
A technical breakdown of how I architected, coded, and shipped a full-stack AI education platform solo. From empty repo to production in one week.
AI engineering, full-stack architecture, and building production software faster than anyone thought possible.
A technical breakdown of how I architected, coded, and shipped a full-stack AI education platform solo. From empty repo to production in one week.
The difference between wrapping a chat API and building a real AI-native product. Prompt engineering, streaming, fallback chains, and production-ready architecture.
Why a single senior engineer with deep focus consistently outships a mid-sized dev team. Zero coordination overhead, maximum context, and compounding velocity.
Most founders overthink this. Here's how to make a defensible stack decision in under an hour, and avoid the mistakes that cost teams months of rewrites.
If your MVP has been almost ready for three months, the problem isn't your engineers. It's your scope, your process, or your handoffs.
Real cost breakdown: what drives the price of an AI-powered SaaS, tier-by-tier estimates from $5K to $500K+, and the agency markup problem no one talks about.
Speed, cost, quality, and risk. An honest comparison of when to hire a senior engineer versus an agency for your AI product build.
The actual playbook from a 21-day, 138K-line TypeScript SaaS build: architecture decisions, execution order, and what makes serious speed possible.
You need senior technical leadership but not a $400K full-time hire. What a fractional CTO actually does week to week, and when it's the right move.
Most RAG demos look great. Most RAG production systems fail silently. Chunking strategy, hybrid retrieval, reranking, confidence thresholds, and evaluation.
I ship production SaaS products weekly. Here's the decision framework I actually use for Next.js App Router vs Remix. Not a feature checklist.
I've shipped production systems on both. The right answer depends entirely on your data model, not on benchmark posts or Twitter takes.
How to integrate Stripe subscriptions, webhooks, and billing into a Next.js 15 App Router application. Real production patterns from shipping multiple SaaS products.
The most common Next.js 15 App Router mistakes I see in SaaS codebases, and how to fix them. From server/client component confusion to unnecessary re-renders.
Auth.js, Clerk, or roll-your-own JWT? The definitive breakdown of every Next.js authentication approach. Production patterns, middleware protection, and the mistakes that will burn you.
Most "AI features" are just API wrappers. Real agents have memory, tools, and can take multi-step actions. Here's the complete architecture: ReAct pattern, tool use, memory systems, and production concerns.
Most MVP cost estimates are wrong by 10x. Here are the real numbers, broken down by scope, team type, and tech stack. From someone who has shipped production platforms at a fraction of agency rates.
Intercom, Zendesk AI, Freshdesk bots, or build your own? I deployed a system that handled 700K+ monthly interactions. Here's the actual decision framework.
A fractional CTO is not a part-time software developer. Here's what the role actually covers, with real examples from client engagements, and how to know if you need one.
Beyond "be specific." The real techniques for production AI systems: chain-of-thought, structured outputs, prompt versioning, and confidence scoring. Based on going from 72% to 96.2% accuracy.
Most AI integrations fail silently. These five mistakes, from treating LLMs as deterministic APIs to shipping raw prompts into application code, are the ones I see most often in production systems.
The wrong answer wastes six months. A practical decision framework for founders covering validation stage, budget, buyer type, and execution speed. Includes a real case study.
Most founders evaluate AI engineers on the wrong signals. What skills actually matter, portfolio red flags to watch for, questions to ask in interviews, and what to pay.
Auth, payments, error handling, monitoring, analytics, legal, email, onboarding. The complete pre-launch checklist with specific tools and exactly why each item matters.
6 reasons AI chatbots fail in production: bad prompts, no context, hallucinations, retrieval problems, wrong model choice, no evaluation. Each with a concrete fix.