Writing
Notes on building software
Honest takes on shipping products, indie hacking, and the realities of the tech industry. No fluff.
MCP (Model Context Protocol): The Developer Guide That Actually Explains It
MCP has 97 million monthly SDK downloads and every major AI company has adopted it. But most developers still do not understand what it does, how it works, or why it matters for the way they build software. This is the practical guide.
TanStack Start vs Next.js in 2026: Should You Actually Switch?
Next.js has been the default full-stack React framework for years. TanStack Start is the first real challenger that made me reconsider. Here is an honest comparison based on building with both in production, covering routing, data loading, performance, and the tradeoffs nobody tells you about.
You're Probably Undercharging: A Practical Guide to SaaS Pricing for Indie Hackers
Most indie hackers set their price once, pick something that feels safe, and never touch it again. That one decision quietly caps their revenue for months. Here is what I have learned about SaaS pricing from my own products and from watching dozens of founders get it wrong, including me.
From Side Project to First Dollar: The Realistic Path Most Developers Never Take
Most developers have three to five abandoned side projects sitting in private repos. Not because the ideas were bad, but because nobody ever treated them like products. Here is the gap between building something and making money from it, and how to close it without quitting your job or burning out.
Learning to Code in 2026: What Actually Matters When AI Writes Code for You
AI can write functional code in seconds. The "computer programmer" job title is declining by 27 percent. Every tech influencer has an opinion on whether coding is dead. Here is what nobody tells you: learning to code in 2026 is more valuable than ever, but what you need to learn has fundamentally changed. The path that worked five years ago will waste your time today.
Technical Interviews in 2026: The Rules Changed and Nobody Sent a Memo
Meta now lets candidates use AI in coding interviews. Google factors AI usage into performance reviews. The interview format that defined developer hiring for two decades is being rewritten in real time. Here is what the new version looks like, what companies are actually evaluating now, and how to prepare for interviews that test a completely different set of skills.
The AI Productivity Paradox: Why Developers Who Ship More Code Are Not Actually More Productive
A controlled study found developers using AI tools took 19 percent longer to complete tasks while believing they were 20 percent faster. Teams with high AI adoption merge 98 percent more pull requests but PR review time increases 91 percent. The numbers do not add up, and understanding why is the difference between using AI well and just using AI.
Building Is the Easy Part Now: Distribution Is the Only Moat Left for Indie Hackers
AI made building fast and cheap. A chef built a media client in under a week. Developers ship production apps in 72-hour sprints. But almost nobody gets users. The best AI builder with no audience is worth less than a mediocre creator with 50,000 email subscribers. Distribution is the only moat left, and most indie hackers are still optimizing the wrong side of the equation.
Humanoid Robots Are Actually Shipping Now: What Changed in 2026
After decades of demos and prototypes, humanoid robots are being deployed in factories, warehouses, and even homes. Here is what is actually happening, who the key players are, and why 2026 is the year everything changed.
Vibe Coding in 2026: The Revolution That Is Rewriting How Software Gets Built
Vibe coding went from a Twitter joke to a legitimate development paradigm in under two years. Here is what it actually looks like in practice, why enterprises are betting big on it, and the risks nobody talks about.
AI-Generated Code Is Creating a Technical Debt Crisis Nobody Is Auditing
Forty-one percent of all new code is now AI-generated. Most of it ships without meaningful review. The result is a new category of technical debt that traditional tools cannot detect and most teams are not even looking for. Here is a practical framework for auditing your codebase before it catches up with you.
The Zero Employee Ops Team: How I Automated Every Non-Coding Task in My Solo SaaS
Running a one-person SaaS means doing sales, support, marketing, billing, and ops on top of actually building the product. I spent months automating 90 percent of the non-coding work with AI agents and workflow tools. Here is the exact stack and every workflow I built.
AI Brain Fry Is Real: Why the Most Productive Developers Are Burning Out First
A BCG study of 1,488 workers found that using more than three AI tools tanks productivity instead of boosting it. The developers who adopted AI the hardest are now experiencing the highest burnout rates. I have been feeling it too, and the research finally explains why.
Context Engineering in 2026: The Skill That Actually Makes AI Coding Work
The industry spent 2024 obsessing over prompt engineering. In 2026, the developers getting the best results from AI coding agents have quietly moved on to something different. Context engineering is about designing what the model sees, not just how you ask. Here is why it matters and how to actually do it.
AI Washing Is Real: The Layoff Lie and the Junior Developer Crisis Nobody Wants to Fix
Companies are blaming AI for layoffs that have nothing to do with AI. Sam Altman himself confirmed it. Meanwhile, junior developer hiring has collapsed by 73 percent. These two problems are connected, and the industry is ignoring both of them.
Spec-Driven Development in 2026: The Future of AI Coding or Waterfall 2.0?
Everyone is talking about spec-driven development. GitHub launched Spec Kit with 72k stars. AWS built Kiro around it. But some developers say it is just Waterfall wearing a new hat. I tried it on a real project and here is what I actually think.
Claude Opus 4.6 vs GPT-5.4 vs Gemini 3.1 Pro: Which AI Model Should You Actually Use in 2026?
The three flagship AI models of 2026 are closer in capability than ever, but they are not interchangeable. Here is an honest breakdown of where each one excels, where each falls short, and how to pick the right model for the work you actually do.
Rust in 2026: Why Nearly Half of All Companies Now Use It in Production
Rust has gone from a systems programming curiosity to a language used in production by nearly half of all software companies. Here is what drove that adoption, what the real experience of using Rust in production looks like, and whether you should learn it now.
Open Source AI Is Closing the Gap on Proprietary Models in 2026
The gap between open-weights models and closed proprietary AI has gone from years to months. Here is what the benchmark convergence actually means, who is leading the open source race, and how to decide when self-hosting makes sense.
Physical AI Is the Next Gold Rush and Everyone Wants In
Humanoid robots and physical AI are attracting billions in funding in 2026, with companies like Figure AI, Neura Robotics, and Boston Dynamics pushing the technology toward real commercial deployment. Here is what is actually happening and what the next few years look like.