writing
Notes from the workshop.
Deep dives on full-stack development, AI automation, and agentic systems. No fluff, just practical knowledge you can use.
40% of AI Projects Get Killed. Here's Why Yours Might.
Gartner says 40%+ of agentic projects are at risk of cancellation. The real reasons agent projects die — unclear success criteria, no data access, eval drift — as a survival checklist.
Read the post →How to Calculate the ROI of an AI Agent (Without Lying to Yourself)
The ROI math everyone does for AI is wrong in a specific, optimistic way. Here's the honest version, including the costs and the one assumption that quietly inflates every estimate.
Read →Job Descriptions for Agents: A Template
The single highest-leverage thing you can do for an AI agent is write it a real job description before you deploy it. Here's the template we use, with every field explained.
Read →What Happens When Your AI Vendor Triples the Price?
Agent software spend is exploding and pricing is unstable. Here's how to architect so a vendor's price hike or a deprecated model can't hold your product hostage.
Read →Do You Need to Hire an AI Engineer, or Just Rent the Judgment?
The full-time ML hire everyone reaches for vs. what most early teams actually need. How to tell which problem you really have before you spend the headcount.
Read →How to Spot an AI Expert Who's Faking It
A founder's vetting checklist for AI consultants, agencies, and hires — the questions that separate real operators from prompt-jockeys with a good deck.
Read →The Agentic OS Maturity Model: 5 Stages
Most teams have no idea how far along they actually are with AI. Here are the five stages from one-off prompting to a real Agentic OS, and how to tell which one you're in.
Read →Guardrails: The Boring Work That Keeps AI Out of the Headlines
Every AI failure that makes the news shares a missing guardrail. They're the unglamorous constraints that turn a confident-but-risky agent into one you can actually trust.
Read →Why Your AI Demo Won't Survive Real Users
The demo was flawless. Then real users touched it. The gap between a demo that wows and a system that survives is the unglamorous work that decides whether AI ships.
Read →Evals for People Who Aren't ML Engineers
Evals sound like data-science jargon. They're just a way to test whether your AI is any good, on purpose, on a cadence. Here's how to build your first one with a spreadsheet.
Read →n8n vs. Custom Code: A Founder's Guide to Automation Plumbing
Low-code automation is faster until it isn't. Here's the honest framework for when n8n is the right tool, when custom code pays for itself, and how to combine them.
Read →Who's Accountable When the Agent Is Wrong?
Every leader weighing AI eventually hits the real question: when the agent makes a costly mistake, who owns it? If the honest answer is no one, you're not ready to ship.
Read →Cleaning Up Your Data Before You Automate: The Unsexy Prerequisite
Automating a process built on messy data doesn't fix the mess. It scales it. Here's what 'clean enough' actually means, and why it has to come first.
Read →What Does a Fractional CTO Cost? (And How It Compares to a Full-Time Hire)
What a fractional CTO actually costs — retainers, day rates, and projects — and how it compares to a full-time hire once you count equity, benefits, and ramp.
Read →Fractional CTO vs. Agency vs. Dev Shop: Which One Do You Actually Need?
Fractional CTO, agency, or dev shop? One sells judgment, the others sell hands. The honest comparison — what each is good at, where each fails, and how to choose.
Read →5 Signs It's Time to Bring in a Fractional CTO
Five signals it's time for a fractional CTO — from becoming the technical bottleneck to shipping AI that won't survive production. None are about headcount.
Read →The Context Layer: Why Your Agent Keeps Getting It Wrong
When an agent gets it wrong, it usually didn't reason badly. It answered correctly from incomplete information. The fix isn't a smarter model. It's a better context layer.
Read →Your AI Doesn't Have a Model Problem. It Has a Data Problem.
You upgraded the model and the output is still wrong. That's the tell. Almost every 'the AI isn't good enough' problem is a data problem wearing a model costume.
Read →When to Fire an Agent (and Hand the Work Back to a Human)
Everyone talks about deploying agents. Almost nobody talks about pulling one. Knowing when to take an agent off a job is a core management skill, not an admission of failure.
Read →Giving an Agent a Performance Review
Evals sound like an engineering chore. They're really the management ritual you already run: a regular, honest look at whether the work is good enough. Here's how to run one for an agent.
Read →Your First AI Agent Is a New Hire. Onboard It Like One.
Your AI agent isn't failing because the model is weak. You skipped its onboarding. Give it a job description, access, context, and a feedback loop like any new hire.
Read →The 70/30 Method: Building With AI Agents Without Betting the Company on Them
Why I let AI agents handle about 70% of the work and keep 30% for senior judgment — and how that ratio keeps AI projects out of the ditch.
Read →5 Signs Your Codebase Is Quietly Costing You Customers
The expensive problems in a codebase rarely announce themselves. Here are five symptoms I look for in an architecture audit — and what each one costs if you let it run.
Read →Do You Actually Need a CTO Yet?
A straight answer for founders weighing a full-time CTO, a fractional one, or no CTO at all — and how to tell which stage you're actually in.
Read →How I Build AI Agents That Actually Ship
Most AI agents die in the demo. Here's the process I use to get them into production — and the unglamorous parts that decide whether they survive contact with real users.
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