Honest takes on building software, shipping products, and the realities of the tech industry.
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.
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.
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.
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.
What Happened When Anthropic Told the Pentagon No In February 2026, the Pentagon demanded Anthropic remove all restrictions from Claude, including for autonomous weapons and mass surveillance. Anthropic refused. What followed is one of the most revealing moments in AI history so far.
I Started Learning AI Engineering Two Days Ago. Here Is My Honest Take. Two days into learning AI engineering, I already have opinions. The demand is real, the path is clearer than I expected, and some of what gets marketed as "AI engineering" is genuinely confusing. Here is what I found.