mister automate

I remember chatting with AI early in the chat bot days when it gave me a viewpoint I felt was rather underinformed. Skewed.

It was somewhat maddening, and lucky for me AI always lets you talk back (although getting the last word is simply not an option)

So I argued with it.

And after a few rounds back and forth, well... I won of course.

And then I had that let down realization that nothing had changed. The AI model would happily give its same stupid viewpoint to everyone else.

No one would get to feel the crushing weight of my unquestionable triumph.

It was... A complete waste of time. (plus a few tokens)

So. That all said you may find it surprising that I continue to argue with AI regularly.

But now with a purpose. To establish a clear worldview.

It is unfortunate that nearly all media channels have an agenda. And if you told me that AI is biased as well, I'd say there is truth to that.

That doesn't matter though, due to how easily you can influence the output based on your prompt.

Since chat bots will happily take any side of the argument you prompt them to, you can quickly get a grasp of every viewpoint on any topic. Add the near instant research of sources across the web, and you have an incredible educational resource.

Oftentimes I leave still believing that some of the viewpoints are completely wrong, but I've considered them. It's no longer the echo chamber, instead, a dynamic mirror.

I feel that using AI in search of reality, truth, understanding, and ultimately to be more human is a good use for this technology.

(p. s. after a few weeks of arguing about how risky AI really is, maybe I'll publish something on that - hint - I don't think it will kill us all)

If you’re not going to nail it, is it worth doing?

That doesn’t mean doing every little thing perfectly. A rough draft can be rough. A prototype can be ugly. Some work is temporary, some attempts fail, and some steps along the way simply need to get you to the next one.

Those aren’t the home run. They’re part of the at-bat.

The question is whether the larger thing you’re pursuing is worth hitting out of the park.

If it is, then the imperfect steps, mistakes, experiments, and failures all belong. They’re in service of something worth doing exceptionally well.

And if the ultimate goal isn’t worth nailing, maybe the better question is:

Why are you doing it at all?

I have a memory of a time, which was corrupted.

I'm not sure if the time was corrupted, but the memory was for sure.

Thankfully, most of it was still in the recycle bin, so we were able to restore it to 4K.

It went something like this.

I was completely broke.

I had $43.17 in my checking account.

Rent had an AI-generated due date of tomorrow.

Tomorrow was still in beta, which made the deadline difficult to plan around.

I sat at my Play-Doh kitchen table trying to Photoshop $43.17 into several hundred dollars.

That's when the fear set in.

It was 3D-printed fear, but it felt real enough.

My buddy buffered into the kitchen.

He scanned the bills spread across the table and said, “Man, you're going to be alright.”

I needed to hear that, but his voice was simply extracted from 10—no, 11 Labs.

We debugged my finances together.

The electric bill could wait until Friday.

Friday was still rendering, so that bought me some time.

We checked the refrigerator for liquid assets.

There wasn't much liquidity.

Half a gallon of milk.

A few eggs. No, they were solid leftovers.

“We can work with this,” he said.

His optimism was open source, so I downloaded a copy.

No matter what we tried, I still had $43.17.

At least I thought I did.

The $43.17 was cake. Wanna ETABITA Cake?

I felt like a complete failure.

The failure was a hologram, but I didn't know that at the time.

Unfortunately, life couldn't be real.

Real was trademarked.

Inspired by a video from @jackcarden.art

Earlier in my IT career, I wished there were a way to accelerate experience—to encounter years' worth of real problems, see the mistakes people made, understand why they failed, and learn how they eventually solved them. Experience comes quickly when you repeatedly encounter problems and have to overcome them.

AI changes what is possible here, particularly in software development.

An AI coding agent can now accumulate experience through the work itself. It attempts a solution, encounters a problem, diagnoses it, tries different approaches, and eventually finds what works. That process produces something potentially more valuable than the finished code: a lesson.

And we aren't starting from zero. There is already a massive world of GitHub repositories containing working solutions to real problems. When starting a new project, finding the right repository can provide an enormous head start. AI makes this even more useful because it can reference another project's code and approach while working through your own problem, extracting relevant patterns without requiring you to understand the entire project first.

The next step is to extend that idea beyond the code itself.

The lessons generated during an AI-assisted build don't necessarily require exposing the underlying codebase. They could be abstracted and anonymized into the problem encountered, approaches that failed, why they failed, the eventual solution, and what should be avoided in the future.

Now imagine those lessons becoming universally referenceable.

Instead of every AI encountering the same problems independently, one AI's experience could become available to others. The accumulated mistakes and solutions from millions of builds could form a growing body of practical experience that future AI systems can reference while working.

That creates a fundamentally different learning cycle:

Encounter a problem → attempt solutions → discover what works → abstract the lesson → share it → reference it during future builds.

The important shift isn't simply that AI can write code. It's that AI can do the work, experience the failures, extract the lessons, and potentially make those lessons reusable without exposing the proprietary work that produced them.

If that becomes possible at scale, experience itself becomes something we can accumulate and share much faster—allowing each new build to benefit from mistakes already made somewhere else rather than repeatedly rediscovering them.

Super old videos being reposted online are more likely to be interesting.

Bad videos stop getting reposted.

Time is a great filter.