I lost a management job. About ten weeks later I was rejecting a 4 billion parameter model I trained on my own PC.

That sentence still sounds ridiculous to me.

I'm Jesse. Before Eterna, most of my working life was sales, customer service, management, hiring, training people and solving whatever problem was in front of me. I did not think I was technical.

Then I lost a management job and ended up in a rough stretch where I had no idea what came next.

The thing that started all of this was embarrassingly small.

I was trying to solve a problem in a game. Google's AI gave me an outdated answer. I knew it was wrong, so I argued with it, gave it better context and kept pushing until the answer improved.

That was the moment AI became interesting to me.

Not because it was right.

Because it was wrong and I could still work with it.

The next day I started asking about business ideas. One of the first was basically: could software help people photograph and organize everything they own?

I pulled on that thread.

Now, a couple of months later, I'm running Eterna Clarity.

Clarity App is the current software side. The idea is simple: people and businesses already have the mess. Photos, receipts, PDFs, records, random files. They should not have to become professional archivists before software becomes useful.

Custom Clarity is the project side. Bring the rough version. If the problem is real but the spec is not, that is still enough to start.

Then Eterna created a third problem for me.

The company itself became too complicated to keep inside AI chats.

Chats forgot things. Different conversations disagreed about what was current. Tools could report success while the actual result on screen was wrong. I built more orchestration to fix it. Some of that made Eterna stronger. Some of it made Eterna slower and harder to use.

One startup path I measured had grown to 21 tool calls and 291 seconds. The version I eventually kept needed three authoritative reads.

That internal operating system is now becoming Clarity Workspace, an AI Adaptive Workspace built around a principle I learned the expensive way:

The model can be temporary. The work cannot.

And then there is the local model.

I trained a 4 billion parameter candidate on the same Windows PC I use to run the company. After several rejected experiments it finally hit 24/24 on the benchmark I had been chasing.

I still said no.

It scored 11/12 on an older adversarial test, which meant it had not earned the next gate. I did not even open the final sealed 60 case evaluation.

The part I am proud of is not the 24/24.

It is that I wanted that result to mean I was done, and I had built enough discipline around myself to stop anyway.

That is probably what this account will be about.

Not AI hype. Not pretending a young company is bigger or more finished than it is.

The actual work.

The weird breakthroughs, the systems I delete, the things I get wrong, the tests I refuse to cheat, and what Eterna becomes because of all of it.

I am still early enough that a lot can change.

Good.

I want the record before hindsight makes it all look cleaner than it was.