Comment by niles | Hacker News Reader

Comment by niles | Hacker News Reader

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I was hoping for a tool that would reference "all" news, mainly historical news, and give some reasoning based on past patterns. My query on Kamala does not seem to factor in the news from just before the last Trump win over Hillary and how that time period contributed to a surprise win. It also doesn't seem to incorporate Regan, or past high inflation periods and how those news stories influenced the elections of the time.

It seems like this is a tool that "if you could read all the news of today, what would your gut tell you", and that is helpful but not thorough.

For example, can you apply the cureent reasoning to news articles from previous time periods and use the prediction on a past result. Would your prediction be accurate? If no, maybe re-reason.

https://aipredict.fun/prediction/1074cf90-c818-44fd-be0b-c00...

This is great feedback! Thanks for giving it a try.

You're right that we're limited in how much news we can pull. Generally, we can only look about 90 days into the past for news articles.

I'd definitely like to expand the corpus of information that we can pull from. Getting access to reliable historical data is high on that list, as it will dramatically improve base rate estimation.

Basically what I'm describing is "backtesting" from stock trading space, where a trader comes up with a hypothesis on what will come in the future. Then they retry that scenario in slices where the same conditions happened in the past and see how it would have played out. Importantly, the "algo" sees in real time based on when you start it, so it can not cheat. It makes it easy to see whether or not your intuition is based on actual data.

I kinda feels like you are using the LLM to assign "weights" or important properties of an algo and then directly translating the basic arithmetic accounting of those factors into a prediction. What I expect is that the LLM would also be used to read all past news to find similar patterns and then create time slices where its weights could be tested against a control. It can then backtest its own weights to better tune what factors really led to an outcome and expose this refinement as part of the prediction.

News Data Sources: https://www.gdeltproject.org/ https://credibilitycoalition.org https://data.worldbank.org/

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