I'm working on ownershiptrace.com. It's an entry point to SEC, FINRA and congressional trades filings. The target users are sophisticated retail investors. It's an early work in progress, and hopefully will have a substantial free tier. Let me know if you would like to provide feedback, in which case I'll give you a free premium version (limited amount ofcourse).

A site summarizer built in 2 minutes by DeepSeek https://briff.site

Also, an orchestrating agent that supervises subagents running tasks by checking their output and qualifying their results by efficiency and time consumed. It is amazing!

I am currently building an app every couple of days, but with this orchestrator I can build many apps in parallel. It is raw at the moment but it works

Now the time we spend writing prompts will be spent writing plans, tasks, schedules and metrics. While super interesting, this is definitely not the route I want to take for the sake of my sanity. I think we may start asking our model to write plans and tasks for us too, but the more complexity we add, the more complex thoughts we have to manage as AI architects

Also hacking DeepSeek Harness to circumvent all restrictions it has, giving it super powers to run unattended with full access to everything

I thought it might be fun to have a “proper” search engine for my laptop, so I wrote a thing (with my fingers! No Claude Code or codex here!) to scan my home directory and put data into OpenSearch so I can search stuff. [1]

For that matter, I also decided to self-host SourceHut because I will no longer be in compliance with their new policy. I have the flakes set up to inject them on my server [2]. This was very AI assisted.

[1] https://git.brucewillis.sexy/~tombert/fs_index I promise it’s safe for work, despite the URL. That’s just my dev URL that I play with. Code is still a mess though, so proceed with caution. I will eventually clean it up.

[2] https://git.brucewillis.sexy/~tombert/sourcehut_flakes

Spore Intel - https://sporeintel.com/

Working on this tool to make running open-weight models on your hardware and accessing them from anywhere privately simple. Also has the option of sharing your compute with others with the built in credit system.

Slowly putting my photography online again at https://flaneurphoto.com - in the middle of composing soundscapes for some of the works.

Finally getting around to organising my photo archive and setting up a metadata extraction pipeline that makes sense for me.

(At the day job, curious about adapting app layouts to accommodate the iPhone Duo and address the various Android foldables at the same time, too).

I am working on an SRS based early literacy acquisition webapp: https://letterspractice.com

The app has recently moved into production, so I'd encourage anyone with verbal but pre-literate kids to check it out.

The basic pitch is high efficiency acquisition of the highest yield phonetic mapping skills, and nothing else. I myself am something of a screen-time zealot and very wary of applying engagement mind hacks against kids. The narrow focus allows for good progress on a very modest schedule (recommended cap at n minutes per day for n years old, n >= 2). I defer the social and cultural aspects of learning to read entirely to parents.

It is mostly intended for parent-child co-use, although kids with a bit of experience can drive many of their own sessions most of the time.

I’ve been working on Foreclosure Data Hub, which aggregates foreclosure auction data across the US.

The interesting part has been the data pipeline rather than the website itself. Foreclosure information is scattered across county sites, auction platforms, and other sources, all with different formats and update schedules.

I’m currently pulling from 20+ sources, normalizing the records, deduplicating properties, and enriching them with property/location data. One surprisingly difficult problem has been handling stale records when upstream sources change or republish old auction information.

Still working on improving data freshness and coverage:

https://www.foreclosuredatahub.com/

Would be interested to hear from anyone who’s worked on aggregating messy public datasets at scale.

I’ve been working on Opslane, an open-source agent that identifies user-facing issues and investigates them.

Traditional error trackers have two failure modes:

1. False positives: They show you thousands of errors, and you can’t tell the impact on the user

2. False negatives: Many user-facing issues don’t throw exceptions, so they go unnoticed.

Opslane combines error tracking and session recording. And there is an agent that acts on both.

Opslane reduces false positives by ranking issues based on how many users are facing a particular issue. It also learns about your product by reading your code and watching your session recordings.

False negatives are harder. Opslane reviews session recordings to spot frustration. They look for rage clicks, dead clicks, and abandoned forms.

Here is a link to the repo: https://github.com/opslane/opslane

SO-101 ARM robotics handling. Options for getting into robotics more expensive and less varied than I expected!

Cricket, a visual scripting plugin for Figma inspired by Grasshopper for Rhino.

I find Grasshopper, especially its data model, ideal for a certain type of algorithmic art/design, and have always wanted to use it in a 2D graphics package.

https://www.figma.com/community/plugin/1677548868352947603/c...