TetraMesa

AI Build Copilot, Part 6: MORE Bonus Bits & Pieces

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It doesn’t matter what you’re doing. Product Management work, Creative work. Requirements. Code. We’re all over these tools now and across disciplines, we face some of the same challenges. Here’s yet another set of practical snippets for dealing with AI-assisted coding and building. The tools can move fast and sometimes faster than we can track. This installment collects a few more practical habits for staying in control. These are the small, deliberate practices that keep “trust but verify” from turning into “hope it worked.”

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AI Build Copilot, Part 5: Bonus Bits & Pieces

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Intro

I thought Part 4: Preparing for Production was going to wrap up the series. But wait, there’s more! Not every useful lesson about building with AI tools fits neatly into the larger themes of the past articles in this series. So this final section is a collection of extra bits and pieces that don’t quite belong anywhere else, but may still save you a lot of trouble. Yes, I could have just called it “Misc.” I suppose. But in any case, there’s just some random bits that didn’t fit cleanly into earlier docs and fell out of the edits.

Here they are in one random walk place.

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AI Build Copilot, Part 4: Preparing for Production

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Part 3 was about building durably, so what happens after that? A successful prototype creates a new problem if you actually put it into some form of production. Not too long ago, if we built what we called prototypes, maybe they went to user testing, or we ‘sort of’ put them out there. But now? A lot of so-called tests really aren’t. That is, they’re really more actual rollouts. Just maybe not necessarily with the same rigor we used to give product launches. The line between that which was intended as prototype testing and so-called Minimum Viable Product (MVP) seems like it might get blurry.

The next challenge with launching a kind of prototype, but that which is maybe real product is someone may decide to use it. So… Is it really a prototype? Meaning… is this just some brochureware for testing or is there actual sign-up functionality, any kind of real billing, private information collection, and so on.

This is when the low-stakes experiment begins accumulating customers, private information, billing relationships, operational dependencies, and consequences. You have to make your own call on this kind of thing. I have to tell you, personally I’ve never loved the “false front door” thing for testing. I get that it can be useful, but sometimes it’s on the edge of unethical in how it seems to trick people. If your prototype is really a test and that’s understood, great. If the truth is it’s not much of a test, but a rollout? Then there’s probably some more risk elements and due diligence that should be covered.

Prototypes have a way of becoming products. Temporary credentials become permanent. Test data becomes customer data. A one-time API connection becomes a critical service. The final part of an AI-assisted build is not clicking Deploy.

It’s deciding whether the thing can be operated safely. One thing that I’ve done along the way when building is created something I’ve called “Launch Blockers.” This is different than a UI backlog or minor changes you find along the way. It’s things like purging schemas, or making sure live production feature flags are set properly. It means a real security review. Things like 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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Using Skills for AI Builds: Product Safety

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Note: This isn’t about general skill in building things with AI… it’s specifically about things called Skill files or their similar counterparts.

Are you a product person at any level who is either building yourself or managing others that are increasingly doing some direct building?

Like a lot of us, I’ve been making some of my own stuff with AI tools. Or in some cases experimenting to understand their use cases better. Among the hype cycle things of this year are Skill files. (Or more generally skill type instructions for AI tools.) There’s whole marketplaces for them. This post is just a warning I’m throwing out there as a caution along with some ideas for mitigating risk. It’s not meant to be overly alarmist, but the “You can use skills to do anything!” hype is so overwhelmingly thick sometimes, it just needs some balance. And actually, I think it’s not just clickbait, it sometimes feels irresponsible and dangerous.

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Why the Best Web3 Products Feel Less Like Crypto

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For too long, the crypto space has been obsessed with selling the “engine” of the blockchain. Whitepapers of new projects focused on technical features and used a special jargon (e.g. words like “gwei” “slippage” and “hash”). It was like showing users the gears, the grease, and the technical specifications of a new car model, and then wondering why the general public wasn’t buying it.

It finally seems that in 2026, maybe this attitude is giving way to better product sense. Successful Web3 products are starting to make the technology invisible. Or at least trying. They have finally realized that users don’t care about features for their own sake, but instead about the benefit they get.

The era of “crypto-first” because it’s some self-sovereign anti-establishement thing that’s good for you is dead. The era of “stealth Web3” has begun. Here are some guiding principles for this new era.

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AI Ethics: How Should WE Treat THEM

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Most AI Ethics talk is about how we should manage our new tools in terms of their output and actions with regard to us. Not the other way around. I wanted to explore the other side. There’s been some thought and work in adjacent areas, and I’ll cover that. What I’m interested in considering is this… What does routine casual, dismissive behavior or even contempt toward human-like AI do to harm to our human character, habits, empathy, and social norms? Are there things we should be doing in our designs of these tools to nudge towards outcomes that don’t end up degrading our own humanity? This is about concerns regarding the potential for human self-corruption, not machine victimhood. Most AI ethics asks how AI should treat us. I want to ask what our treatment of AI may be doing to us from an ethics and morals perspective, not just general cognitive issues.

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Are Boomer & GenX Workers About to be More Valuable?

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This isn’t about a contest about what cohort is more or less valuable in general. Everyone is of course different. It’s an exploration into different types of skillsets, some of what’s been going on lately with AI and challenging some assumptions.

What I want to try is to run a thought experiment about workers in general and ageism in particular. With all the talk of AI displacement, I keep wondering if there’s a less dystopian view. A lot of roles may change or vanish, but we could also see growth in niche areas. And maybe the loud claim that “we won’t need so many people” turns out to be overstated. If so, do deep skills and hard-earned judgment become more valuable, not less?

Some companies who claim they’ve cut staff thanks to AI may discover they cut too deep, losing exactly the people they really need. Meanwhile, expertise may be repriced. AI is impressive, but has limits, even if it can hollow out early-career task bundles. That raises the value of people who can frame problems, validate outputs, and own outcomes. I’m a heavy user of modern AI tools and workflows, yet even with the “Something Big Is Happening” hype, there still seems to be plenty of room for talented, experienced humans See Joe Procopio’s “It Turns Out, AI Agents Suck At Replacing White-Collar Workers” for one of many examples.

Maybe this sounds naïve, but perhaps multiple cohorts will remain valuable, just in different ways. Through it’s looking to be a rocky transition. Yes, Skynet could wake up next week, but there’s also a world where things mostly work out fine. I know I’m supposed to say “if you’re not using AI in the shower to automate everything, you’re doing it wrong.” I’ll work on the clickbait. For now, let’s talk about what’s actually changing. By the way, that’s quickly become the clickbait headline style I find most ridiculous. Maybe it’s just my feed, (or maybe I am doing things wrong), but have you noticed how many use that framing, “If you’re still doing X – or not doing Y – you’re doing it wrong?” Since just about everything is about context, these seem especially foolish attempts at FOMO.

Kids These Days

Younger generations, (however you want to personally define that), are often far more fluent with modern tools than those of us in the GenX plus cohort were at their age. (Obviously in some cases, some of the tech wasn’t even there.) And they’ve grown up swimming in information; more volume, more variety, better teaching methods. But by definition, most early-career workers have limited lived experience. They may have had a few jobs in high school and college, maybe even a small side hustle, but the rest has been school, hobbies, and a first job or two. Their tech, however, is moving incredibly fast. One interaction I found highly amusing was when my 20-something nephew commented on something my elementary school age daughter was doing and said, “yeah, we didn’t have anything like that when I was growing up.”

Through all of this, or perhaps due to all of this, some may also have less intuition than older cohorts did at the same age, not because they’re incapable, but because so much is abstracted away. More services handle more of life, reducing cognitive load in ways that can erode “practice-based” skills (navigation via GPS is the cliché example). Sol Rashidi calls this “Intellectual Atrophy™.” Even if that term is AI-focused, the broader pattern predates LLMs. And yet, younger workers can be astonishingly capable especially in tech while still missing some “common sense” that usually comes from scar tissue plus environment.

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