I thought AI would fix it. It didn’t.

I thought AI would fix it. It didn’t.

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Lately I’ve been spending time intentionally pushing AI into parts of a system that I know are not clean.

Not to see if it works.
To see where it breaks.

Because that’s where the truth is.

When everything is well structured, AI looks incredible. It moves fast. It produces clean output. It feels like you’re multiplying your effort.

But that’s not the real test.

The real test is what happens when the system isn’t perfect.

When boundaries are unclear.
When responsibilities overlap.
When things have grown over time instead of being designed end to end.

That’s where AI gets interesting.

Not because it fixes it.
Because it exposes it.

You start to see hesitation.
You start to see guesses.
You start to see it follow paths that almost make sense but don’t quite hold together.

And if you’re paying attention, that tells you something important.

It’s not struggling with code.
It’s struggling with the shape of the system.

That’s a useful signal.

It’s like bringing in a really capable contractor and watching where they slow down. They’re not the problem. They’re showing you where the structure isn’t obvious.

I’ve been leaning into that.

Using AI less like a tool to “fix things” and more like a way to surface where understanding breaks down.

Because once you can see that clearly, you can actually do something about it.

AI is great at accelerating clean systems.

But what I’m finding is that it’s even better at revealing where things aren’t as clean as you thought.

And that’s where the real work starts. ☕

Building in the Middle of the Noise

Building in the Middle of the Noise

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It feels like every day brings another AI headline.

New models.
New agents.
New capabilities.
New warnings about what jobs are disappearing next.

The pace of announcements right now is dizzying.

But when you’re actually building something, the experience feels very different.

While the headlines swirl around, most of my time is spent doing the same things builders have always done. Debugging systems. Reworking architecture. Testing assumptions. Trying ideas that fail and fixing them.

CoffeeBreak has been a good reminder of that.

AI can generate code quickly. It can suggest patterns and explore solutions. But turning those pieces into a coherent system still requires patience and judgment.

Real progress rarely looks like the headlines.

It looks like slow improvements, small fixes, and occasional breakthroughs after a lot of iteration.

From the outside, AI development looks like a race.

From the inside, it still feels like engineering.

The headlines will keep coming.

Meanwhile, the real work continues. ☕

Letting AI Try

Letting AI Try

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Lately I have been letting AI do more of the work while building CoffeeBreak.

Not because I think it is better than human developers.
Because I wanted to see what it would actually do.

I let it build a large portion of the backend. I knew the architecture wasn’t the way I would normally do it, but I let it ride. The point was to learn.

Eventually it broke.

So I nudged it.

AI tried to correct the issue, but it kept iterating around the edges instead of fixing the root problem. It was trying very hard not to break anything that might be running.

The problem was that it was already broken.

I nudged it again. Same behavior.
Another iteration. Another partial fix. Still broken.

After two weeks of watching it circle the problem, I finally stepped in and started fixing it myself.

One thing I have always believed as a developer is that when something is broken, the only acceptable outcome is fixing it. You cannot be afraid of making it worse. It already does not work.

Even if you break it further, it is still broken.
Fixing it is the only path forward.

AI struggles with that line of reasoning.

It tries to preserve stability even when the system is already unstable. It optimizes for not breaking things instead of restoring function.

That is an interesting lesson.

AI is incredibly useful. It can accelerate development, generate ideas, and help explore patterns. But it still does not replace ownership of the system.

Someone has to understand when the only option left is to grab the reins and fix the problem.

That part still belongs to us.

CoffeeBreak should be back online soon. ☕

Building While the World Swings

Building While the World Swings

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This week felt volatile.

Markets swung on AI headlines.
Layoffs were tied to automation.
Companies raced to release new agent capabilities.
Even a fictional research post managed to rattle investors.

It would be easy to read that as instability.

At the same time, we’ve been deep in alpha testing CoffeeBreak.

And here’s what struck me.

Inside the work, it doesn’t feel chaotic.
It feels incremental.

Bug fixes.
Edge cases.
Logging improvements.
Governance decisions.
Human review loops.

The headlines are loud.
The real work is quiet.

I’ve noticed this pattern before.

During big transitions, fear moves faster than clarity.
Markets react before systems stabilize.

But underneath it all, people are still building.

Still testing.
Still integrating.
Still trying to make things reliable.

That’s the part that doesn’t make headlines.

And maybe that’s the point.

☕

Rearranging Work

Rearranging Work

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Every few decades, we convince ourselves that this time is different.

This time, technology won’t just change jobs.
It will eliminate them.

We said it about the automobile.
We said it about electricity.
We said it about the internet.

Henry Ford didn’t eliminate work.
He rearranged it.

Yes, there were fewer blacksmiths and buggy makers.
But there were more mechanics, factory workers, road builders, traffic engineers, gas station operators, parts manufacturers, and logistics planners.

Entire industries formed around the new tool.

AI feels similar.

It will eliminate certain patterns of work. That’s inevitable.
But it will also create entirely new layers of responsibility, maintenance, integration, oversight, and design.

The mistake is assuming the surface change is the whole story.

The deeper story is value migration.

Work doesn’t disappear.
It moves.

The interesting question isn’t “Will AI replace engineers?”

It’s “What new responsibilities will emerge because of AI?”

That’s where the opportunity is.

☕

Responsibility Shows Up Eventually

Responsibility Shows Up Eventually

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We’ve reached an interesting moment with AI.

The conversation is shifting from “what can it do?” to “who is responsible when it does whatever it does?”

That shift was inevitable.

Every wave of technology eventually runs into the same wall.
Power scales faster than accountability.

Governance is starting to heat up as a topic around AI agents. Some people hear that word and think control, restriction, or slowdown.

I hear something different.

I hear maturity.

Governance isn’t about limiting capability.
It’s about deciding where judgment lives.

If AI agents are going to act, decide, generate, and integrate into real systems, then responsibility can’t be an afterthought.

We don’t get to skip that step.

And maybe that’s a good thing.

☕

Impressive Isn’t the Same as Satisfying

Impressive Isn’t the Same as Satisfying

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I watched the Super Bowl last night.

It wasn’t big or bold. It was boring.
Clean. Technically solid. Well produced.

I even watched both halftime shows at the same time. They were fine.
The commercials too. A few were funny.

The whole thing just didn’t stick with me.

Maybe it was because the Chiefs weren’t there this year. That probably played a role. But it was more than that.

That feeling has been showing up a lot lately.

In sports.
In technology.
In AI announcements.

There’s no shortage of impressive things right now. New models. Bigger numbers. Clever demos. Even an AI-generated compiler that can play Doom.

It’s cool. I appreciate the engineering.

But impressive isn’t the same as satisfying.

Satisfaction comes from coherence. From purpose. From systems that do something meaningful over time, not just once on a stage.

Lately I’ve found myself drawn more to quiet work. Marching towards a CoffeeBreak beta launch. Restoring old systems. Building things slowly. Making sure I understand what I’m creating end to end.

Not because I don’t value progress.
But because I want the progress to matter.

That’s where the real work is.
That’s where it has always been.

☕

Restoring Understanding

Restoring Understanding

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I’ve mentioned before that my first computer was an Epson Equity I+. I got it in 1987 and, unfortunately, got rid of it in the early 2000s. That decision has haunted me ever since.

Well, until recently, when I acquired one and started restoring it.

The restoration has brought back a flood of memories. I can feel the understanding growing every day, like a ten-year-old learning his first computer. Everything is new and fascinating. There’s a race to learn, to explore, to figure it all out.

As I dig into it, I’m constantly amazed. I can almost see the problems through the engineers’ eyes as they designed the hardware and software. There’s simplicity inside the complexity. When something doesn’t work, there aren’t ten layers of abstraction hiding the answer.

You can reason about it end to end.

That experience has been oddly grounding.

Modern systems are incredible, but they’re also opaque. We stack frameworks on platforms on services until even experienced builders rely more on trust than understanding. When something breaks, we hunt symptoms instead of causes.

Restoring this machine reminds me what it feels like to know a system again.
To see how choices connect.
To feel confident not because something is new, but because it’s clear.

That mindset has been showing up in how I think about CoffeeBreak.

AI tools are powerful. The progress is real.
But power without understanding doesn’t eliminate work. It just moves it around. Often onto people, quietly.

Unlike this restoration, with CoffeeBreak I’m not trying to build something nostalgic.
I’m trying to build something coherent.

There’s a quiet confidence that comes from knowing how a system works all the way through.

That’s the feeling I’m chasing, whether I’m restoring an old computer or building something new.

☕

You Don’t Get to Skip the Work

You Don’t Get to Skip the Work

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Here I am on a Sunday afternoon, watching the AFC Championship during a full-on snowstorm.

The Broncos are hosting the Patriots, and deep down part of me feels like Kansas City should be here. We’ve had a lot of success over the last decade. We’ve built something special. It’s easy, when that happens, to start believing this stage somehow belongs to you.

But that’s not how it works. Instead, the Chiefs sputter all year, and Patrick Mahomes tears the ligaments in his knee and the Chiefs are a footnote on the 2025 NFL season.

All of that is a big reminder that you don’t get to the next game based on last year’s stats. You earn it again, each year, in conditions that aren’t ideal. The Patriots are exemplifying that today. After a nearly 20-year dynasty and a recent trip into NFL oblivion, they’re back hoisting the Lamar Hunt Trophy once again.

That’s been on my mind as I work on CoffeeBreak.

Right now, I’m close. I can see the system. I can explain it. But I can’t quite get it to run end-to-end, on its own, without help.

In the past, I would have pushed harder. Added more features. Told myself it was “good enough” and that we’d clean it up later.

Experience has taught me to resist that impulse.

Right now, CoffeeBreak doesn’t need to do everything. It needs to do one thing really well, end to end, without excuses.

There’s no shortcut around that work. No entitlement. No reputation that carries you forward forever.

Not in football. Not in software.

And definitely not if you want the results to last.

Why I’m Waiting for One Thing to Work Really Well

Why I’m Waiting for One Thing to Work Really Well

JakeLeave a comment

This is probably overdue, but here we are.

The thing I’ve been indirectly writing about for the last few weeks has a name. It’s called CoffeeBreak. It’s an AI teammate designed to support the software development lifecycle, end to end.

I haven’t named it until now on purpose. Not because it’s a secret, but because the name isn’t the important part yet. The work is.

Over the years, I’ve learned that most software failures don’t come from ambition. They come from shipping something that almost works and asking users to fill in the gaps.

I’ve been feeling pressure lately. Not from investors or timelines, but from peers. People asking when they can try CoffeeBreak. That’s a good problem to have, and it’s also a dangerous one.

Right now, CoffeeBreak doesn’t need to do everything. It needs to do one thing, really well, end to end, without excuses.

I don’t want to ask people to imagine how it will work. I don’t want to say “ignore that part for now.” I don’t want users doing invisible work to make the system feel useful. And I certainly don’t want people to feel, “Pay no attention to the man behind the curtain” vibes.

I’ve shipped software fast before. I’ve cut corners before. And I’ve paid for it later with trust, rework, and long nights debugging or explaining why something behaved the way it did.

This time, I’m waiting until one workflow can stand on its own.
One task. One path. One outcome that doesn’t require hand-holding.

I’m close. I can see the shape of it now. But “close” isn’t the same as “ready,” and experience has taught me not to confuse the two.

When CoffeeBreak is ready to share, it won’t need caveats.
That’s the bar I’m holding myself to.