TetraMesa

Archives for July 2026

AI Build Copilot, Part 3: Building a Durable Workflow

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In Part 2, we went over Staying in Control of your build session. Now it’s time to start looking at consistency and reliability issues. AI conversations are useful working spaces. However, they’re not reliable long-term project memory. Sessions end. Context fills up and you have to switch chats. Maybe you switch models from Fable 5 to GreekTragedy-7.6 or ElonsNextWife-10.4. Files are edited elsewhere, etc.

An agent’s understanding of the project can become stale while sounding completely confident. That’s why the project must survive the chat and there’s ways to do that.

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AI Build Copilot, Part 2: Staying in Control

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We started in Part 1 with what the bright shiny What AI (Site builder) Demonstrations Leave out… and it was fairly high level. Now we’re going to dive a bit deeper and yes, somewhat into some technical things. My goal remains: to help level up those in generally more non-technical roles, especially product management. (Though of course, what that technical continuum looks like varies widely.) Let’s start with some basics, and drill down from there…

The dangerous thing about AI coding assistants is that they are often very good.

Huh? How could that be dangerous? It’s dangerous because if they were obviously terrible in spots, we would remain more alert as we used them. Instead, they’re helpful enough to earn trust and inconsistent enough for us to experience times where we clearly shouldn’t have trusted them.

The interaction feels conversational. The agent explains what it is doing, uses reassuring language, and often anticipates the next task. Over time, it becomes easy to stop evaluating each recommendation independently.

That’s the lull.

The assistant is called a copilot for a reason. Do not become its passive passenger.

Disclosure: I did use AI in this series for spelling, grammar and to fill out a few bullet lists with minor items I missed. Generally, I draft, do outlines, and write everything; then have AI check spelling, grammar, and completeness; maybe clean up a few items. Here I did miss some bullets though and those are filled in. Where that was true, I checked sources on ‘truthiness’ for everything. The point is, every high-level thing I go into here are things I’ve personally run into – recently – while working on real projects. So it may seem like a lot, but none of it’s fluff. And you ignore them at some degree of risk.

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AI Build Copilot, Part 1: What Demos Leave Out

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I’d recently put up an article about a joke site I build for fun while helping create some overall scaffolding for a more serious project. At the outset, I’d pointed out part of my motivation for writing about some of these things is to try to offer some tips and mention some of the traps that go with the reality of using these tools as product people. The bubbly happy path posts and videos seem to mostly gloss over some of the real speed bumps and risks along the way. I get that demos are often to help people just see what’s possible. But lately there’s such a hard sell to some of these things, and so full of clickbait as to often be somewhat disingenuous.

As “non developers” use these tools more, it’s obvious enough that AI coding tools can be astonishingly productive. They can also be confusing, unreliable, expensive, overconfident, and occasionally dangerous. As product people at all levels from Individual Contributor to Senior Managers, VPs, etc., start getting deeper into these things, (not as full developers, but just as useful parts of our new toolkits), we’ll need to get real about some basics. The Happy Path feel good posts on LinkedIn and demos on LinkedIn rarely go over the many real blockers.

So I’m going to do some of that here, in several parts.

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I Built a Joke Site with AI – Then It Became a Production System

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This started as a joke that got out of control, but the build produced a few lessons worth sharing.

Most of my work is product-focused rather than production-focused. Product and marketing people should spend their time with customers, markets, competitors, business models, and worthwhile problems. Becoming absorbed in production can turn attention inward instead of outward.

Still, sometimes you need or just want to build something yourself. Like everyone else, I now use AI for all manner of things. But there’s a difference between a throwaway prototype for quick user testing and something that might approach production. For one project, I had to move beyond simple prototypes and build a modern AI-assisted production pipeline without experimenting on a client’s product, a company’s core offering, or anything containing important customer data. So I needed something harmless, but sort of real, to test with. To borrow from my woodworking hobby, I practice new techniques on cheap material before touching fine red oak. The same principle applies here: make early mistakes where failure costs essentially nothing.

So I built a joke website, but the tools and methods behind it are no joke. They are genuinely powerful. At the same time, the more serious project benefits from using this as a no risk practice platform. So here’s where we come out:

  • This article: I’ll just tell you what I built and some of the tools I used to do it.
  • Next Up: A series on AI Build Copiloting in general for “semi” technical users. That is, product folks with some basic engineering familiarity, who need to dive a bit deeper into these areas, even if only part of the time.
  • After that: Another series focused on building in databases with AI assistance.
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