I'm working on three things right now.
ARVnote (https://arvnote.com): Mostly to teach myself the business side of things, as-is and after-repair-value comps for real estate investors. You give it an address, it pulls comparable sales, adjusts them for square footage, garage, lot, and condition, and returns the ARV with a deal analysis. The arithmetic turned out to be the easy part. Almost all of the difficulty is comp selection, and the data sources disagree with each other constantly, so a lot of the work is deciding which one to believe for a given property.
km (https://github.com/joeamroo/knowledgemaxxing): local search over your own
data exports, with LLM/RAG. It takes the Twitter archive, Google MyActivity, ChatGPT logs, and Reddit GDPR export you requested once and never opened, dedupes roughly 500k items into a single SQLite file, and keeps provenance for every save, like, bookmark, and page visit. Binds to 127.0.0.1 and makes no external calls. I wanted my own AI-assistant that understands what I like reading based upon my data, and I did not want to hand it to someone else's server just to make it searchable. (open for contributions!)
LarpIn (https://larpin.io): Just for fun. A parody LinkedIn where everyone admits
they are indeed larping. No signup, you get a generated persona on arrival, one global feed, and every post is scored on buzzword density from "NPC (for now)" to "Final Boss of LinkedIn." There is a button that fills in the "You need to be
X-maxxing" format for you, which is the part I actually keep using. Rails 8,
SQLite, Hotwire, one box, no JS build step. Open source (open for contributions!):
https://github.com/joeamroo/larpin
btw: the two web apps are Rails, km is Python. Happy to go into detail on any of them.
ARVnote (https://arvnote.com): Mostly to teach myself the business side of things, as-is and after-repair-value comps for real estate investors. You give it an address, it pulls comparable sales, adjusts them for square footage, garage, lot, and condition, and returns the ARV with a deal analysis. The arithmetic turned out to be the easy part. Almost all of the difficulty is comp selection, and the data sources disagree with each other constantly, so a lot of the work is deciding which one to believe for a given property.
km (https://github.com/joeamroo/knowledgemaxxing): local search over your own data exports, with LLM/RAG. It takes the Twitter archive, Google MyActivity, ChatGPT logs, and Reddit GDPR export you requested once and never opened, dedupes roughly 500k items into a single SQLite file, and keeps provenance for every save, like, bookmark, and page visit. Binds to 127.0.0.1 and makes no external calls. I wanted my own AI-assistant that understands what I like reading based upon my data, and I did not want to hand it to someone else's server just to make it searchable. (open for contributions!)
LarpIn (https://larpin.io): Just for fun. A parody LinkedIn where everyone admits they are indeed larping. No signup, you get a generated persona on arrival, one global feed, and every post is scored on buzzword density from "NPC (for now)" to "Final Boss of LinkedIn." There is a button that fills in the "You need to be X-maxxing" format for you, which is the part I actually keep using. Rails 8, SQLite, Hotwire, one box, no JS build step. Open source (open for contributions!): https://github.com/joeamroo/larpin
btw: the two web apps are Rails, km is Python. Happy to go into detail on any of them.