Recent submissions

Comment · 1 month ago
Yeah, it's a good question, let me know if you figure out a good answer :)

Honestly, my focus right now is to make the site useful rather than profitable. This is a side-project that spawned out of needing something like this for my own research, so haven't figured out how/if I'll monetize it yet. The one idea I have related to the graph structure that I think could be valuable right now is letting users "subscribe" to particular sections of the law, with alerts for when there are new bills in Congress that might affect or alter it. The graph currently goes from bills > public laws > USC > CFR, so users could monitor a regulation and get notified as a bill moves its way through congress

Comment · 1 month ago
Thanks! The primary goal is to make it the best place to research US federal law. All the features I have currently (cross-corpus search, version diffing, backlinks, law lineage, etc) will stay free and open.

Audience is primarily lawyers and legal researchers in fields that rely heavily on statutory law (tax, bankrupty, immigration, etc). I'm blessed to have a plethora of lawyers in my family/friends, so planning on using them as ~guinea pigs~ beta testers. If you (or anyone else reading this) think this is something you might use, I would love to talk more! [email protected]

In terms of monetization, I'm not really sure yet. My hosting costs are very cheap at the moment, so I'm not in any rush there, but I'm imagining I'll build in some more premium features like an API (for other legal-techs), change monitoring (for anyone relying on specific statutes in their work) or team research features (shared bookmarks, notes, collections etc). Open to suggestions here too! One very cool thing about my data pipeline is I've essentially built a connected graph of federal law with tens of millions of edges across millions across hundreds of thousands of sections, and I think there will be a lot of powerful applications of that dataset

Comment · 1 month ago
Comment · 1 month ago
Aw, thanks! I thunk it myself :)

The cross-referencing between different corpora has actually been very helpful. I was able to find a typo in the notes to a US Code section through it and submitted it to the OLRC. They fixed it, so now I will forever be bragging about having fixed an error in the US Code haha

Comment · 1 month ago
I'm building https://uscodex.org. In a nutshell, the goal is to collect all US federal law in version control, and cross-link any references between different sections. Currently I have the US Code, CFR, ~30 years of public laws and congressional bills and 25 years of executive orders.

Actually using raw git repos as the main data store which has worked out great since the US Code changes very little between new releases and so it compresses very well. Also makes it easy to generate diffs of sections: https://uscodex.org/usc/d/119-100/119-102not101/16/2201

Comment · 3 months ago
They're talking about the font on the shirt
Comment · 5 months ago
Kind of makes me wonder if the "more than 1,100 employees globally" actually means "1,111" employees. Talk about committing to the bit
Comment · 8 months ago
Comment · 2 years ago
Do you have an API?
Comment · 2 years ago
The constant "By the way..." from Alexa drove me nuts.

If you say "Alexa, stop by the way", it'll get the device to stop responding with follow-ups for ~24h. I ended up creating a routine that runs every night at 4am to lower the volume to zero, automatically say "stop by the way" to the device, and then raise the volume a minute later, and Alexa has stopped with the follow ups

Comment · 2 years ago
I've been tickled by the idea of a follow-up version, Send Me To Hell, where users instead throw their phones _downward_. High scores are awarded based on the maximum speed achieved as the phone hits the ground.
Comment · 2 years ago
The second example for each language sample where the generated squid ends up being “B4aajs” essentially reads as “P0ooop” to a Swedish speaker.

Which is fine, they don’t propose to filter “bad” words in other languages, but kind of funny when that’s one of the highlighted examples, right next to the goal of filtering words. Goes to show how hard it is to filter profanity generally for international audiences

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