Tooling on Thede Technologies

Getting good output from Lens

Tue, 05 May 2026 00:00:00 +0000

A Lens daily summary derailed by reasoning artifacts: **People:**, **Patterns:**, **Drafting structure:**, “Constraint Check”, bleeding into the narrative

People pick up Lens, connect it to LM Studio, point it at whatever model they downloaded first, and expect polished daily snapshots. Sometimes they get them. Sometimes the same setup on a different machine produces output that reads like the model thinking out loud, or worse, fabricating events that didn’t happen.

Lens itself doesn’t change between those outcomes. The model, the quantization, and a few LM Studio settings do. This is the field guide I wish I’d shipped with the first build.

Four frontier models, one year of office sensor data, four very different dashboards

Wed, 29 Apr 2026 00:00:00 +0000

I have an Adafruit Clue sensor sitting on the desk in my home office. It has been logging temperature, humidity, pressure, light, sound, and color every thirty seconds for a little over a year. 425,000 readings. About 33 megabytes of CSV. (I wrote up the build itself a year ago: CircuitPython on the device, a Python pywebview gateway on the Mac, USB Serial in between.)

I finally fed that year of data to four frontier models and asked each of them to build me a single self-contained HTML dashboard. Same prompt. Same data. Four very different answers.

The move was upstream

Tue, 28 Apr 2026 00:00:00 +0000

I spent most of a day pointing at broken cases. Each time I pointed at one, the model patched it. The next regeneration would surface a different broken case in a different file. We’d patch that. Then a third. By late afternoon I was the one suggesting we slow down and return to first principles. The model wasn’t pulling me up a level on its own.

The bug itself was small. I’ve been wiring up a pipeline that turns transcripts of my conversations with AI tools into clean Markdown for an Obsidian vault. The reasoning part was working: extracting topics, summarizing, tagging. The rendering kept breaking. Stray characters, broken callouts, content that ended up harder to read than the original conversation had been. I was working it inside Gemini CLI, mostly on their fast tier with occasional escalation to their reasoning model. Twelve back-and-forth turns across roughly six hours of intermittent work, and we still hadn’t landed it.

Running real models locally on a Mac Studio that isn't new anymore

Thu, 23 Apr 2026 00:00:00 +0000

I have a multi-generational archive of handwritten family letters in my house, and I wanted to read it without sending the private content to a cloud provider. The first post is the why. This one is the how, on the specific hardware I already own, with the software I landed on.

My Mac Studio is almost four years old. M1 Max, 64 GB of RAM. It’s proven more than capable of running large language models locally. It keeps private content in the house, and it costs a lot less than any cloud provider.