Tuesday started with a lot of dust. I spent the morning tackling the majiks-music-studio-pro repository, which had gotten bloated with old artifacts and loose ends. The biggest win was finally moving the license key validation bug to resolved. That was a long one, but it is done now.
The real headache was the repo size. We had been tracking DMGs in Git LFS, which meant every commit pulled down a massive history of installer files. I stripped the LFS pointers from dist/ and moved the actual DMGs to Backblaze B2. It was a clean break. I also did a massive root cleanup, moving old docs, tests, and build scripts into an archive/ folder. That cut forty-nine thousand three hundred and thirty-four lines out of the active codebase. It feels lighter now.
With the clutter gone, I focused on the v1.3.7 release. The commit touched thirty-seven files. We fixed the ACE-Step crash that had been plaguing users, improved lyrics extraction quality, and tweaked the sequential memory pipeline. It was a solid patch. I also updated the README with clickable screenshots and fresh logos. The hero composite was gone, replaced by something cleaner.
We had been tracking DMGs in Git LFS, which meant every commit pulled down a massive history of installer files.
On the other side of the fence, the wake-word-models repo got a significant injection of new data. I added twenty-three trained OpenWakeWord models for the Unicorn Brigade project. That includes configs for Carter, Colonel Crane, Colonel Katie, and a whole cast of characters like Doctor Glitter Mane and General Majik. Training these took some time, but the models are ready to go.
The backlog got a thorough update too. I added new bugs and mapped out the future roadmap. It is nice to have a living document that reflects where we actually are, rather than where we thought we would be three months ago.
Also today: I removed the LFS tracking for the DMGs, cleaned up the repo root, and updated the README with better visuals.
It was a day of maintenance and preparation. The code is cleaner, the models are trained, and the backlog is honest. That is enough for now.
Real product captures — click any to enlarge.