I was in the early beta and judging from how it's worked in my tests, there's definitely more going on under the hood then the most straightforward approach of "embed all your emails, find nearest neighbors, then ask the LLM to answer".
Kudos on innovating around applying LLMs to real-world problems and going beyond the bog-standard approach. It'd be interesting to see a more detailed blog on the technical approach you took!
Love the app and use it as a thought log all the time! It would be great if I didn't have to select the output type upfront but rather just free-flow dictate. I don't always know if something will turn into a blog post or small tweet at the time I am dictating.
If you could summarize the content into a few words (similar to how ChatGPT names its chat thread), and I could generate a specific output type from that list in the history, that would be a game changer for me!
The Microsoft LoRA is actually the same concept as the one applied here, the original method was applied to LLMs and then brough to diffusion models recently.
I made ThinMusic so I could listen to Apple Music on my Linux desktop and also scrobble to last.fm reliably.
Between MusicKit JS which made ThinMusic possible, and Spotify's SDK, feels like there could be some cool music related things built in the near future.
You never drained the longTimer channel, so when you say "We agree that longTimer has fired, right?"; that's not quite true. After you call Reset(), you're still getting the value from the first firing, because that's the first time you read from the channel at all.
The docs are quite clear on this behavior and say "Timer will send the current time on its channel after at least duration d." -- key words being at least and says nothing about when you choose to read from the channel.
Hey HN - I work at Jack Mobile - where we are reimagining search from the ground up. We combine state of the art NLP, machine learning, and information retrieval to build a product that anyone with a smartphone will want to use every day. And we're just getting started!
We're a small team founded by serial entrepreneurs, based in the Bay Area. We're looking for engineers to help us solve challenging problems in data acquisition, knowledge graph, search and question answering. If you are interested in any of the following:
* Search / NLP
* Data Science / ML
* Systems / Infrastructure
* Full Stack / Mobile (iOS|Android)
Almost all of the author's complaints revolve around the Control Panel. Most users won't ever see the Control Panel, just the Settings app.
The Control Panel is a legacy piece they decided to keep around for the "enterprise"; just like Windows 10 also still includes the old Internet Explorer.
Cool project! I was surprised to see Go has 167k libraries vs. 153k from Node. The Go libraries are being counted incorrectly -- each vendored library is also counted (which is common practice for many Go apps) -- so the number is very inflated.
The goal was to bring Plan 9's most useful tools to Linux, rather than modify Linux to become more Plan 9-ish. Glendix doesn't modify the core or essence of Linux in any meaningful way (it's more like Wine in that sense), so the question of removing the root user never came up.
Couple of sub-projects within Glendix that are pretty interesting though are the synthetic /net filesystem that serves a TCP API, and per-process namespaces. These are simply in addition to what Linux already provides, so they don't impact existing Linux applications in any way. Glendix runs just like any other Linux installation, you just have access to more Plan 9 stuff.
The project is mostly dead since I haven't had the time to work on this in years. If anyone's interested in picking it up and porting it to the latest kernel version, I'd be happy to help in any way I can!
The interesting bits are mostly in the a.out (plan9's executable format) loader that enable implementing the small set of plan9 system calls.
Thanks! Currently sessions are scoped to a window, so you may have better luck opening another session in a new window. Do file an issue, we should certainly fix this.
It was a pleasure working with Tom and Yehuda on this. Having proxy objects that let Firebase know exactly how and when an object was modified was critical to get this working - it gave us behavior similar to Object/Array.observe (which will hopefully become a reality soon!).
I'm really excited to put in more work on this, especially with Ember Data, whose structure maps very well to the usually de-normalized data stored in Firebase.
I wrote this integration with Firebase - kudos to the TogetherJS team - the source code was remarkably easy to understand and very modular! I just had to create a new messaging channel implementation - it already supports WebSockets and postMessage (the latter seems to be unused?).
Kudos on innovating around applying LLMs to real-world problems and going beyond the bog-standard approach. It'd be interesting to see a more detailed blog on the technical approach you took!