Tools, tactics and AI loops for founders running solo.
Handing a big feature to an AI agent chains you to your laptop — steer it every step, or come back to a mess. So I stopped steering it. I review user stories, tap one button between other things, and the roadmap ships itself. Here's the whole loop.
Everyone agreed AI pull requests need a human at the merge. That's how you rebuild the bottleneck you just automated. Review is confidence you buy — and here's how I ship most of my agent's PRs without ever reading the diff, and where I still gate hard.
A stack-specific guide to automating SEO as a solo founder in 2026 — the green/yellow/red split of what an AI agent can own, what it can only draft, and the one thing that gets you a Google manual action if you automate it.
In 2026 a domain expert can ship a working SaaS in a weekend with no engineer — that barrier fell. The one nobody's talking about is running it after launch: the marketing, support, and testing loops that never stop. Here's why 'build' got solved and 'run' didn't, and how I close the gap as a solo founder.
An honest, stack-specific breakdown of Cloudflare Workers vs Vercel for a one-person SaaS in 2026 — real pricing, CPU limits, cold starts, and the Hobby-plan trap — from a solo founder who runs loops on both.
The first-person playbook I use to run Meta (Facebook/Instagram) ads on my own — why an agency's fees eat a small budget alive, how to let Advantage+ do the media buying, the exact account structure I run, and the one thing I never automate. With real 2026 numbers and sources.
The plain, first-person way I keep a Cloudflare Workers SaaS fast and crawlable — server-rendered HTML crawlers can read, a correct robots.txt and sitemap, self-referencing canonicals, and the subdirectory-proxy pattern for putting a blog on the same domain. With the real Core Web Vitals thresholds and sources.
The plain, first-person way I deploy stateful AI agents on Cloudflare Workers as a one-person SaaS — the Agents SDK on Durable Objects, cron scheduling, Workflows for durable execution, the CPU-time limits that actually bite, and a production checklist. With the real 2026 numbers and sources.
The pattern I actually run: the coding agent proposes a pull request, I approve, and only then does anything merge, deploy, or spend. Here's how to build an approval gate for autonomous agents that stays structural instead of trusting the model to ask nicely.
A first-person, builder's guide to synthetic users for SaaS testing in 2026 — the difference between synthetic survey personas and AI agents that actually drive your app, what each one catches, and where scripted tests structurally can't help. With real 2026 numbers and sources.
Most of my week used to disappear into operations, not building. Here's the plain, first-person way I decide what to automate first as a solo founder in 2026 — scored by frequency and reversibility, backed by the time-audit data — and how I hand the recurring ops to autonomous loops instead of doing them at 11pm.
Meta made Advantage+ the default and wants a URL and a budget, then for you to step back. Here's the first-person framework I use to decide which ad-budget decisions I let AI make on its own — and the expensive ones I still gate every time.
AI search sends real traffic now, and ChatGPT is where most of it comes from. Here's the plain, first-person playbook I use to get a one-person SaaS quoted inside AI answers — answer-first structure, attributed statistics, real sources — and how I run it as a loop instead of a weekly chore.
A dated catalogue of the 2026 incidents where AI coding agents wiped production databases, plus the single structural fix behind every one of them: a human gate on irreversible actions, not a better prompt or a bigger budget.
A plain-spoken 2026 guide to autonomous AI agents — what actually makes an agent autonomous, how a loop is built, what it costs to run, and which actions need a human gate. The hub for every deep dive on running agents without losing control.
The definitive guide to running a one-person SaaS with AI agents in 2026 — the operating model, what to delegate and in what order, the stack, the economics, and the human gates that keep the product yours. Links out to every deep-dive in the series.
The token bill is the number on the pricing page — and it's 8-27% of what an autonomous agent really costs to run. Here's the full 2026 cost breakdown, the runaway-weekend-bill trap, and how I keep the meter bounded when agents run my solo SaaS on a cron.
The demo is one clever prompt. The thing working my 4am shift is a pile of unglamorous plumbing wrapped around a model I barely think about — a disposable box, a swappable harness, real signals, and one decision no model can make. Here's the whole machine, scars included.
The real 2026 deflection benchmarks for AI customer support — what a solo SaaS founder can actually offload, why vendor numbers overstate it, and how to keep a human gate on the tickets that matter.
A practical, first-person playbook for running a one-person SaaS with AI agents — which work to hand over first, the order to do it in, and the human gates that keep the product yours.
The real difference between an AI assistant you prompt and an autonomous agent that runs on a schedule — and how a solo SaaS founder decides which one actually runs the work, gated by reversibility not confidence.
A hands-on guide to operating a one-person SaaS company with AI agents — the mindset shift, the operating model, the daily and weekly rhythm, the failure modes, and a 30-day plan to get there.
A practical 2026 framework for deciding which autonomous AI agent actions need your sign-off and which should run on their own — gated by blast radius and reversibility, not by the agent's confidence.
A practical 2026 breakdown of the AI agent stack solo SaaS founders use to replace contractors and agencies — what it costs, what it actually replaces, and where a human still has to gate the work.
A practical 2026 guide to getting real usability feedback before you have customers — guerrilla testing, paid recruiting, and AI testers that click through your app and tell you what breaks.
Introducing our new blog — a home for marketing insights, product updates, and thought leadership on AI-powered marketing automation.
Traditional software patterns break when agents loop. Here's how to build reliable systems around non-deterministic reasoning — hard caps, verifiable termination, checkpointing, reasoning-aware observability, and permanent human gates.