Building things in the AI era — software, hardware, and projects for the long game, one decade at a time. Field notes from a solo builder shipping real products with AI agents. No hype, no tutorial-farm noise. Just what actually works, explained clearly and tested against the evidence.
Most advice about the first hire answers the wrong question. It tells you who to hire — an engineer, a generalist, a contractor first. Or it hands you a feeling: you’ll know it’s time when you’re drowning. Neither is decision-grade. “You’ll know” is not a number, and the person who feels most like drowning is often the worst person to be making a hiring call. The question that actually has data behind it is narrower: what predicts whether the hire works out? And the honest answer, from the two best datasets available, is not timing. ...
There’s a question every solo builder answers eventually, usually by getting it wrong once: Which answers am I allowed to act on without checking? Not “which tasks can AI do” — that’s a capability question and the answers change every few months. This is a sorting question, and it doesn’t change with the model. It changes with the task. The framework — and why it isn’t the article Let me be honest up front, because we do this on every post: the grading framework itself is not new, and it is not ours. Regulators, enterprise security teams, and academic groups have all published versions of it. Microsoft’s agentic-risk guidance says require approval for high-risk or irreversible actions. A recent DeepMind paper on delegation names reversibility as the axis that decides how much authority you can hand over. Practitioners publish permission matrices grading actions by blast radius × reversibility × stakes. If you want the general framework, those sources will serve you better than a solo-builder blog, and I’ll list them at the bottom so you can check them. ...
A brokerage just gave every customer a button that trades for them. Over 150,000 people turned it on in four months. The agents hit the platform’s tools about 30 million times a day. That’s not a bug report. It’s a structural change, and it matters more than the headlines about “AI trading” suggest. Here’s the thing nobody says plainly: those 150,000 accounts aren’t running 150,000 independent strategies. They’re choosing from a handful of shared models — the same few AI systems, pointed at the same market data, optimizing toward roughly the same goals. ...
For most of the history of shipping software, a solo builder could answer one question with a single, static number: “what does this cost me a month to run?” You tallied a server, a database, a couple of SaaS subscriptions, rounded up, and you were done. The number was flat. You built to it and forgot it. That number is now fiction. The tools you build on stopped charging you a flat rate and started charging you by the meter. GitHub Copilot — the most common AI coding subscription on the planet — moved to usage-based billing on June 1, 2026: every plan now draws down monthly “AI Credits” pegged at one cent each, with the old automatic fallback to a cheaper model removed entirely. Anthropic now estimates the average developer burns $13 per active day in Claude Code token spend — up from $6, doubled in one quiet docs edit. Flat-rate is over, and the solo builder is the one who feels it hardest because there’s no CFO to absorb the surprise. You are the CFO, the engineer, and the one who pays. ...
The AI model is out. Not gone — out. Finished as a place to compete. Everyone still talks about the model as if it were the whole story: which one is smartest this week, whose benchmark just moved, who shipped the biggest one. None of that is the important thing anymore. Here’s what actually happened. Token prices fell roughly 600× in six years. A model that cost $60 per million tokens in late 2021 now costs pennies. Open-weight models do for free most of what the flagship ones charged for. The model became a commodity — something you rent by the meter and swap out whenever a cheaper one is smart enough. ...
AI makes building cheap. It does not make building valuable. Those are two different problems, and most builders only notice the first one. Give a modern AI tool a clear description and it will produce the feature — the research, the code, the tests, the build. Hand it ten rejections from an app store review and it will diagnose and fix each one. That part of the old job genuinely got faster. ...
“Solo founder” used to be a phrase investors said with a wince. It read as couldn’t find a co-founder — a red flag on a term sheet. Going it alone was the backup plan, not the point. That framing is now dead, and it didn’t die by argument. It died the way framings actually die: the data stopped supporting it. This post is about the two datasets that killed it — and about the one number in them that most people quoting these stats get backwards. ...
Here’s a fact that should change how you think about your agent’s security: in July 2026, security researchers broke out of four widely used AI coding agents — Cursor, Codex CLI, Gemini CLI, and Antigravity — without breaking a single sandbox. Not one of the seven disclosed issues defeated the sandbox directly. Every one of them exploited the same thing instead: a gap between what the sandbox restricts and what a trusted component outside it later reads, runs, or scans. ...
Most solo builders now run an AI agent with real access: a file system, a couple of API keys, a database, maybe a deploy script. To make it useful, you connected it to things. A server from a README. A config you copied from a blog post. Something that “just worked” in about four minutes. That’s the part worth slowing down on. The thing you actually agreed to An MCP server is not a plugin. When you connect one, you hand the agent a set of tools, and you hand it the schema that describes those tools — their names, their parameters, and their descriptions. The model reads that description and decides when to call it. ...
The instinct is to wait. Finish it. Polish it. Make it good before anyone sees it. The problem is that “good” is a moving target you can’t hit without outside eyes — so you polish in private, forever, and the thing never ships. The fix is uncomfortable and it works: ship the half-done thing, on purpose, and let real feedback do the editing you can’t do alone. Why “done in private” is a trap Polishing in private has two hidden costs that usually don’t show up on any timeline: ...