hamsterbooster

I would say the opposite. We want to make sure that we build our systems in a way that it get better as foundational model becomes better.

Our thesis is that foundational models will become good and affordable enough to be used in almost all data processing pipelines. We build systems on top of that to manage workflows, integrations, and data applications that people may want to develop.

Thanks! We got quite a few good enterprise leads from Intercom chats.

Thanks! There are still a lot of amazing hardware companies and vertical applications in our YC batch.

We believe that AI is only one part of our product. A significant amount of value comes from building robust integrations with different data sources and managing the business logic that operates on top of this unstructured data.

In many use cases, like flagging documents for compliance issues or processing customer emails, it's challenging to manage this at the vendor level because end customers want the ability to apply business logic and run different analyses.

For data ingestion and mapping, I agree that in an ideal world, we would all have first-party API integrations. However, many industries still rely on PDFs and CSV files to transfer data.

Thanks for the feedback. We built Trellis based on our experience with ingesting and analyzing unstructured customer calls and chats in a reliable way. We couldn’t find a good solution apart from developing a dedicated ML pipeline, which is quite difficult to maintain.

There are some elements that might resemble Dagster, but I believe the challenging part is constructing validation systems that ensure high accuracy and correct schemas while processing all kinds of complex PDFs and document edge cases. Over the past few weeks, our engineering team has spent a lot of time developing a vision model robust enough to extract nested tables from documents

Really neat sandbox! Wonder how this would scale to large amount of data .Do you plan to support other data sources like audio and videos as well?

Insitro (led by Daphne Koller) just raised $400M dollar and seems to be making good progress on applying AI to drug discovery (https://insitro.com/).

Totally agree that library is easier to maintain and for developer to use. However, services are a lot easier to monetize and allow the company to collect any data they want. I can't think of any billion dollar companies that release their core library to the users.

Hi Peter, Thank you for doing this. I am a student founder at a university in the US on J-1 visa (with 2 year requirement in place). I have got an interview with YC last summer and plan to apply again. If I got into YC and plan to continue working on the startup full-time, what would be my options in term of immigration and visa?

This would be an amazing dataset to train ML/CV on. There is image + accurate labels.

There is a value in making things enjoyable/a bit fun to use. I would prefer my real analysis textbook to have color block. I don't understand the concept that adults need to use product that is boring.

2020 works from home version for pilots and flight attendants.

Many AI systems are used in the backend to increase revenue. Netflix has a very complex recommendation algorithm based on deep learning/statistics. Amazon uses a lot of machine learning to optimize transportation (NP-hard problem!) , sales, etc..

Thanks! I ran multiple maze at the same time and time it and it seems like the maze ran just as fast as running only one maze at a time. If the interpreter can pick other maze, should it take longer? Did I miss anything here?

This might be a stupid question but how can the site run multiple maze generation algorithm at the same time? My understanding is that javascript is single thread.

Amazon also own video-conference product Chime.