nnechm

Had a similar experience with meta. Extremely opaque decision making, terrible UX. Account permanently disabled ... did not follow community standards, literally on sign up to get a dx account. It's difficult after an experience like that to see how they are so successful. Is it because their users are so addicted and ad sellers will do anything to get onboard ? It's probably just a few dark patterns here and there to bump up impressions at will.

Feels more and more like it's the best of times and the worst of times...

Things feel off,but you have (mostly well-off) people talking about how great things are going to be and are. I suspect a lot of this correlates to the stock market.

For the median millenial atleast, whatever you were taught growing up is just the wrong guide to understanding the world today.

I think social media by its very nature, has to be understood inverted. A high number of posts about how great things are suggests otherwise. Comparisons with Europe have gone exponential that tells you more about how the posters feel than anything else...

Thank you for the advice, will implement accordingly.

I am assuming this is on Cofin and not on beatandraise.com, if so, please let me know, I shall fix it. You have 20 chats etc.

I don't think excel can be defeated :) I certainly do not expect financial professionals to switch to a chat interface.

When you login into capitaliq or factset or look at a bloomberg screen, you access data, you can then do the same here. Excel plugins go on top which can then get this data into excel to build models. The api that powers this app, can also send data into excel for instance.

Data can be copied already directly from the chat box into excel, maintaining the table format for e.g

Regarding standardization, I think data was standardized not to enable comparison but simply to fit into the same schema for every industry. Most companies within the same industry report the same way. REITs do not report like SaaS, but the existing datasets put it all into one set. Raw data from source is always better as you can convert it to standardized but you can't go back...

I think you are referring to the more hyped version where somehow LLMs can figure out how to get the most relevant information from 10Ks and do most of the investment process. My mental model is simply that we have an assistant, an amazing assistant that can read pretty well, and we can use them in the investment process.

Yes, information retrieval is hard :) A lot of people ask for 'can you get Apple's revenues by product category for all of 2020', how do you get the smallest piece of text that has the most information about product category and revenues ?I can get a lot of text, but that would mean I probably run over the context limit and so on :)

A few things on prompting: 1. Get me google cloud revenues fails, because somehow gpt4 thinks i am talking about an entity called google cloud and not google :) 2. So in order to fix it, you can either ask for Get me google's cloud revenues or get me google cloud revenues from google's results...

As you can see, inspite of all the training in the real world, gpt4 thinks google cloud is more of an entity than google is, based on that question :)

Yes, they are definitely not primitive, but considering how the breakthrough in LLMs happened, i.e MSFT, GOOGL were working on it, even as late as 2021, Google's BERT was not really there (at least the one they showed the public in the blog from Google research). So the sudden jump from ok to great thanks to openAI would probably mean a lot of existing systems also need to do that.

Hi, Thanks for bringing that concern up. I shall keep it in mind and change it based on feedback from customers if it is an issue. Typically, people search for a company after a price move rather than before. And these are searches on publicly available data, ie data that is not proprietary and already filed with the sec. Needless to say, none of the chat traffic is used or will ever be used to feed any trading signals for anyone.

The functions tend to fail when the prompt is complex and the user asks for a lot of fields, and typically the last field in the json is not closed i.e missing a }, i guess openai is aware of it. It doesn't fail that often to have to write a workaround, atleast not yet.

So like a lot of applications, the problem boils down to being able to serve the right text. You have something that can read and do basic inference .... You need to tell it what to read so that it can answer your question. But it can only read 16k tokens (20k words at best). So that's the basic problem. As it's universal, i.e a problem across applications, its going to get better and information will be a lot easier to get access to...