I have a good understanding of how to create likely-correct software: https://www.osequi.com/studies/list/list.html

Now I'm learning AI/LLMs from the perspective of correctness. So far I have two 'maxims' to guide me:

- AI shines where humans struggle (for prompt engineering)

- An LLM is nothing but an API call (for software engineering with AI)

Have been building a design tool for Spatial Computing [0] Me and my co-founder struggled for over a decade working in XR and wanted to simplify the design process for us and everyone else. We've been focused on reducing steps between design iterations by as much as humanly possible. So the tool works live, multi-user, in real-time and immediately across all compatible devices.

Here's a recent example of whats possible from one of our users [1], and a recent project I made for fun that you can try on your iOS [2] or Meta Quest [3] device.

[0]: https://ordinary.space

[1]: https://www.linkedin.com/posts/matt-rice-sennep_mixedreality...

[2]: https://app.ordinary.space/#/share/b2d3d7

[3]: https://app.ordinary.space/#/share/96b9cf

Writing a book on my unique theory on human psychology based on cynicism, military theory, and evolution.

Is has helped me to address all of my problems, from mental health to productivity to learning across all domains like economics, writing, coding, marketing.

Writing daily blog posts, and then piecing together everything into the book. https://www.moderncynicism.com/

Working on an embedded DSL for writing typed LLM workflows in Haskell: https://github.com/outervation/promptyped . It handles the "LLM in a loop" structuring for e.g. forcing an LLM to keep making changes in a loop until unit tests pass, and custom validation so the LLM is forced to retry a task if the validation fails (validation can include using a LLM to check the result, or plain Haskell function, or e.g. running a compiler/unit tests). It also allows structured context management, allowing the user to specify which files are initially provided as context to a task (which may itself be the result of a LLM query), as well as providing the LLM tools to open files it deems relevant, and prompting it to close the least important file when it has too many open (and compressing non-recently-modified source files when many are open).

Currently heavily WIP though and only has a working Go compile/test backend (Go seems particularly easy for smaller LLMs like o3-mini to work with due to being a relatively non-complex language).

Reimagining personalized food, prepared on demand and directly from fresh ingredients, as an always available urban utility. A vertically integrated ready to eat food retail and logistics network with order of magnitude improvements to value proposition, unit economics, scalability and risk profile. Consolidating nearly a decade of R&D for US go to market, our systems are way ahead of the competition by footprint (2m²/20ft²), cost per location (~$25K), degree of automation (complete) and drone integration (integrated multi-drone airport with autonomous handover, recharge, precision recapture). Think last mile delivery aggregator with zero people in prep/packaging/pickup/(post FAA BVLOS normalization ~2Q 2026 in some areas)delivery, meaningful personalization, fresher food, and 24×7 operations. Rough complexity ~10K parts BOM. Internal manufacturing. We aim to grow at ~2× historic QSR rates right out of the gate, then accelerate. Simultaneously, spinning off a few industrial ventures filling gaps in what's available on the market we've had to build for ourselves. Prior unicorn. Currently raising for US GTM, email in profile.

Busy building some personal infrastructure around information. Think mix of scrapping and filtering, scoring and summarization.

Clearly all of the info space is getting ever more polluted and I just don’t trust anyone else (with their own agenda) to manage and filter that for me. If one is to abdicate that sort of responsibility to a system wholesale then I think it has to be fully under one’s control, own data, own design, self hosted etc

I've been slowly building https://www.hamradiolog.net/

As part of that, I made an ADIF (ham radio logs) parser to learn go. It's more than 2x faster than parsing the same data in json format with the go standard library.

https://github.com/hamradiolog-net/adif

We are working on a fast reading font which works by using just opentype features (no server or other script required)

https://github.com/Born2Root/Fast-Font

We also created some emoji fonts: https://github.com/Born2Root/Feature-Fonts

I’m wrapping up the final audio plug-in in my “Loudness Series” which was designed to bring compressors and limiters into modernity by upgrading their circuitry to using LUFS and True Peak in place of RMS/Peak.

The final plugin is an equalizer which will be focused on loudness but expand the filtering to include more advanced equal loudness contours.

https://apu.software

Writing a science fiction book this year - after a year of planning (while working on other projects).

Also hoping to find some time to add a feature or two to my Video Hub App

https://videohubapp.com/ & https://github.com/whyboris/Video-Hub-App