a5huynh

Side-note, if anyone wants to really dig into all the data available about bills (including votes, attachments, etc.), this is a great place to start: https://github.com/unitedstates/congress

There's excellent documentation on the formats and how to access all the data.

Not op but one my favorites is: https://www.youtube.com/@Settledrs

Very entertaining to watch and explains things so even non players can understand the sheer absurdity of some of the attempts.

I don't think its too misleading because in both cases that's how many vehicles are affected at very moment by the recall.

Toyota shipped/sold more as mentioned in the article, but those are unaffected. Likewise with the Tesla, any shipped afterward the defect was discovered are unaffected.

Just to add context since I was curious about "Toyota had it wheels literally falling off".

That was a recall from 2022 (https://www.cnn.com/2022/10/06/business/toyota-bz4x-wheel-fi...) for 260 vehicles (their model BZ4X electric SUV).

The Cybertruck recall affects 3,878 vehicles (https://www.caranddriver.com/news/a60538687/2024-tesla-cyber...).

This reminds me of Floneum (https://github.com/floneum/floneum), this open-sourced tool for graph-based workflows using local LLMs.

More for personal use and not quite as polished but a decent alternative for those looking to play around with the idea locally.

I've been using a combo of LLMs + live transcription to build a passive assistant that keeps track of talking points and can pull out data/tasks from a conversation you're having (https://sightglass.ai or here's a demo of me using it: https://www.loom.com/share/0220ca03bce341669d314d4254872226)

So far this is being used for:

- Sales -> guiding new recruits during more complex client calls

- HR -> Capturing respones during screening interviews

If you'd like to try this out feel free to DM me or email me at andrew at sightglass.ai, we're looking for more testers!

It's in San Francisco. It was announced Sept. 6th on their blog: https://openai.com/blog/announcing-openai-devday

A problem with fine tuning based on organization data is that if the underlying data changes, you'd need to fine-tune the model again each change. This might be okay for one-off changes (such as the name of the model in the example) but if it costs $300 each time (not to mention the time spent) and you have 100s/1,000s of changes per month, it's not really viable.

If you want to play around with Stable Diffusion XL: https://clipdrop.co

Hey HN!

We've been building a semantic search engine for audio content focusing on podcasts. We're getting an early version for feedback.

You can search or ask questions about a particular podcast episode or feed and get back an answer as well as link to the relevant podcasts.

We also let you follow your favorite podcasts and receive summaries in your inbox whenever a new episode comes out

If you're running the text-generation-webui (https://github.com/oobabooga/text-generation-webui) it has the ability to train LoRAs.

It'll require a beefy GPU but I've seen some fun examples like someone training a LoRA on Skyrim books.

I recently found this list of models that works with llama.cpp: https://rentry.org/nur779 (with dl links, albeit given llama's licensing gray area, use at your own risk)

The latest so far would be Vicuna, whose weights were just recently release.

As an alternative for purely local LLMs, I've been having fun with this setup: https://github.com/oobabooga/text-generation-webui

Note that what they released are the delta weights from the og LLaMa model. To play around with it, you'll need to grab the original LLaMA 13B model and apply the changes.

  > We release Vicuna weights as delta weights to comply with the LLaMA model
  > license. You can add our delta to the original LLaMA weights to obtain
  > the Vicuna weights.

Edit: took me a while to find it, here's a direct link to the delta weights: https://huggingface.co/lmsys/vicuna-13b-delta-v0

You can calculate them yourself as well! huggingface has a great article on this: https://huggingface.co/blog/getting-started-with-embeddings

tl;dr, use: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v...

I've run into similar issues and found that the `thiserror` crate (https://crates.io/crates/thiserror) combined w/ anyhow makes a lot of that pain go away

Shameless self-plug, I've been building some similar that you can run locally as an app: https://github.com/a5huynh/spyglass

You can define some basic rules & it'll go out and crawl those particular sites. Or use one that someone else has built. It can also sync with your Chrome/Firefox bookmarks. Would love feedback from folks who get a chance to use it !

In addition to React/Vue, you have the ability to use Rust client-side frameworks such as Yew[0] or seed[1] for a truly full-stack Rust experience. I've been using Tauri + Yew to build an cross-platform app (shameless plug: [2]) and it's been a pleasure. There are some rough edges, but most of those have been due to platform specific issues (notifications, Windows vs Unix paths, etc.) and not Tauri/Yew itself.

[0] https://yew.rs/

[1] https://github.com/seed-rs/seed

[2] https://github.com/a5huynh/spyglass - Create your own personal search engine by crawling & indexing files/docs/websites that you want.

For those looking for an alternative to that, I've been building a self-hosted search engine that crawls what you want based on a basic set of rules. It can be a list of domains, a very specific list of URLs, and/or even some basic regexes.

https://github.com/a5huynh/spyglass

Thank you! Feel free to ping me on Github/Discord if you run into any issues, always looking to make it better :)

A bit different, but I've been building something similar that runs locally: https://github.com/a5huynh/spyglass

You create some rules for topics you want to index and it'll go out and crawl them. Searching through it is a global hotkey away.

Thanks for the feedback! I’ll keep that in mind as this is built out. Fortunately the initial bootstrapping uses data from the Internet Archive and the crawls afterwards is to check for updates (at a reasonable rate). The number of URLs being hit is much much lower in the end than you would think.

Hey HN, I'm building an open source search platform that lives on your device, indexing what you want, exposing it to you in a super simple & super fast interface.

I took the idea of adding "site:reddit.com" to your Google searches and expanded on it with the idea of "lenses" to add context to your search query and give the crawler direction in terms of what to crawl & index. This means that all queries are run locally, it does not relay your search to any 3rd-party search engine. Think of it as your personal bookcase at home vs. the Library of Congress.

It's still in a super early state but would love for people to start using it and providing some feedback and see what sort of lenses people want to build and search through!

Some details about the stack for the interested:

    * All Rust w/ some HTML/CSS for the client.

    * Client is built w/ yew + tauri

    * Backend uses tantivy to index the web pages, sqlite3 to hold metadata / crawl queue

Thanks in advance!

I believe Saudi Aramco and PetroChina have passed the $1T mark.

Historically, adjusting for inflation there are also some really big ones: https://www.fool.com/investing/general/2012/08/22/a-history-...

While not incredibly high-resolution, ground penetrating radar (https://en.wikipedia.org/wiki/Ground-penetrating_radar) is often used in archaeology to try and find buildings/artifacts without digging anything up.

Electronic Medical Record(s)

I believe LIDAR and LADAR are interchangeable since it's based on a portmanteau of `light`/`laser` and `radar`

It is much, much more than feeling sad. Please take a look at what it means to be clinically depressed: https://en.wikipedia.org/wiki/Major_depressive_disorder

I personally used the OFA Facebook application and the intent is incredibly different. Notably, the folks who authorized the OFA application knew that the data about their friends was being collected. Because that was entirely the point! We were using it to send out requests to our friends to persuade to them to vote/register to vote.

If you'd like a more in-depth explanation, I found this post by the former Chief Scientist of Obama For America campaign (2012) that goes into the key differences between how the data was collected & used compared to Cambridge Analytica: https://medium.com/@rayid/why-what-cambridge-analytica-did-w...

One of my favorite classes in grad school was learning to write about complex topics in a clear and concise way.

Hopefully these reading materials are still useful!

- "Science of Scientific Writing" https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.p...

- "Style: Lessons in Clarity and Grace" https://www.amazon.com/Style-Lessons-Clarity-Grace-10th/dp/0...