Properant
California Property Intelligence for Investors
What it does
Properant gives real estate investors access to property intelligence & distress signals across all 15 million California parcels in one place. Search foreclosures, ownership, liens, equity, tax data, property details, and more without piecing together multiple data sources. What makes Properant different is the combination of statewide coverage and deep parcel-level intelligence. Find an opportunity, then immediately research the property and owner behind it — all in one platform.
Search every parcel in California: foreclosures at every stage, notices of default, tax delinquent properties, liens, true equity, auctions, and owner records — the county record, searchable in plain English.
Find foreclosures, tax delinquencies, liens, vacant properties and high-equity owners — then see the entire story in one click. USPS-verified vacancy, and owners whose mail goes somewhere else. Every notice of default and trustee sale statewide — date, opening bid, trustee’s phone. Tax, judgment, mechanics and HOA. Amounts, parties, and whether each is still open. The year they stopped paying, and what they owe to the dollar. Every notice of default and trustee sale statewide — date, opening bid, trustee’s phone. The year they stopped paying, and what they owe to the dollar. USPS-verified vacancy, and owners whose mail goes somewhere else. Tax, judgment, mechanics and HOA. Amounts, parties,…from properant.com
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Commerce · 26d ago · equitybee.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io


- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com