edshiro

This looks great! And incredibly timely too!

I finished watching this video today where the host and guests were discussing challenges in a RAG pipeline, and certainly chunking documents the right way is still very challenging. Video: https://www.youtube.com/watch?v=Y9qn4XGH1TI&ab_channel=Prole... .

I was already scratching my head on how I was going to tackle this challenge... It seems your library is addressing this problem.

Thanks for the good work.

I see - thanks for the clarifications

I presume if your customers are enterprise companies then you may opt to use this library vs sending their data to OpenAI etc.

And you can get more customisation/fine-tuning from this library too.

I've saved the paper to read it later.

The premise of this work seems very interesting... But I wonder how practical it is from both a cost and time perspective. I am toying around with an AI Agents library and one of the annoying UX things I notice is the time it takes to get my answers, because each call to an agent (either GPT-4 or Claude 3) is kinda slow.

Besides the time, it feels quite wasteful token wise.

I'm skeptical this approach will be adopted by many in the AI Agent space, but of course I could be very wrong.

I don't have much experience with embeddings...

Could someone more knowledgeable suggest when it would make sense to use the SentenceTransformers library vs for instance relying on the OpenAI API to get embeddings for a sentence?

I get the same feeling. AI Agents sounds very cool but reliability is a huge issue right now.

The fact that you can get vastly different outcomes for similar runs (even while using Claude 3 Opus with tool/function calling) can drive you insane. I read somewhere down in this thread that one way to mitigate these problems is my implementing a robust state machine. I reckon this can help, but I also believe that somehow leveraging memory from previous runs could be useful too. It's not fully clear in my mind how to go about doing this.

I'm still very excited about the space though. It's a great place to be and I love the energy but also measured enthusiasm from everyone who is trying to push the boundaries of what is possible with agents.

I'm currently also tinkering with my own Python AI Agent library to further my understanding of how they work: https://github.com/kenshiro-o/nagato-ai . I don't expect it to become the standard but it's good fun and a great learning opportunity for me :).

This is really exciting to see. I applaud Stability AI's commitment to open source and hope they can operate for as long as possible.

There was one thing I was curious about... I skimmed through the executive summary of the paper but couldn't find it. Does Stable Diffusion 3 still use CLIP from Open AI for tokenization and text embeddings? I would naively assume that they would try to improve on this part of the model's architecture to improve adherence to text and image prompts.

Nice work! I like the quick rendering too.

However, I don't believe any AI was used for this, so the actual TLD is a bit inappropriate here. A ".io" TLD would would have been more fitting.

Not every slice of bread needs to be spread with butter. Similarly, not every project requires AI.

This is amazing. Definitely something I will look at to relax when I feel stressed with this startup life.

Would also love to see a technical write up about how you implemented this. Great creative work!

I presume this does not apply to computer vision datasets? Frankly I am still confused at what exactly Snorkel does.

This looks great! I had never heard of Dash either and it seems it is precisely the tool we need at our startup to do data visualisation.

Awesome work. Will check all this out during the week-end.

Understood and makes total sense given the ODD at Starsky.

Thank you for sharing this very candid article on the Starsky Robotics and generally the autonomous vehicle space. It's a real eye opener. I've been following your progress for the last few years (and I also read about your company through Reilly Brennan's "Trucks - FOT" newsletter).

I am sorry you could not get investors to believe more in what you and your team, especially as you required a lot less funds than many other companies in this arena. I also thought you had a clear business case (I worked in ride-hailing and also logistics so understand some of the problems in this space).

I wanted to ask you a question: I am building a startup in the dash cam video analysis space. We are building a large and geographically diverse dataset of road videos, where our users can annotate/label the data. We then are going to look at detecting specific events like accidents and edge cases on videos. Do you feel this type of business, the data we collect, and insights we generate would have value for a AV startup?

All the best in your next move. Stay strong - you can be proud of what you and your team did.

Add to that the fact that you are never going to get 100% fleet utilisation, will need to pay for qualified tele-operators, maintain/repair vehicle along with sensors...

I recently listened to a podcast episode of the Autonocast[1], where they interviewed a Harvard Researcher who claimed the economics of Robotaxis just don't work. Very interesting listen.

[1] http://www.autonocast.com/blog/2020/3/11/177-ashley-nunes-on...