Wanted to offer some more clarity re MVP, our goal here is to build a demo product and the specs for the mvp are as follows:

Minimally Viable Product (MVP) Criteria;

1. Successfully obtain a statistically significant dataset of past elections/sports predictions/financial predictions from at least 4 sources:

   • Twitter personality (Elon etc)

   • Youtube

   • Three Legacy/MSM News Publications;

     • 1x Right Leaning

     • 1x Centrist

     • 1x Left Leaning

     • 1x Prediction Market

2. Use the dataset as input into our language model to:

   • Obtain a summarisation: Sentiment, Political leaning, Bias in language

   • Extract inferred predictions (explicit and implicit):

     • Verify the resolutions of events as compared to the predictions made

     • For elections use an established database to resolve

     • For finance use commercial apis for verified historic price

     • Assign an aggregate prediction accuracy score for each source based on all articles/media/tweets etc analysed

3. Demonstrate the feasibility of the aforementioned features on a webpage that will:

   • Curate a newsfeed of our sources that have a bias towards events with likely settlement or outcome in the near future, or have resolved recently. This would be to show the viability of comparing opinion and editorial in media versus the money line opinion of prediction markets on the same events.

   • Display historical prediction accuracy score  0 to 100  for each source that covers each of the articles being curated for the feed

Hi,

Just for clarity, the first iteration and mvp is solely focused on the grading of predictions by a small chosen demonstration group.

Regarding your other point, something to keep in mind is that a lot of the actual analysis that is used in the grading is extracted data as per our model. That is to say that it will come attached with everything necessary to perform the more complex analysis, it isn’t a seperate process but part of the collection pipeline.

So it would look something like:

1. Scrape target tweets, media, YouTube, news articles, etc

2. Parse into our algo and extract predictions, inference, bias, etc

3. Analysis and extraction

4. Lookup of extracted prediction data to establish base truth when possible

5. Grading

The prediction market part is a fairly crucial element of the complete product with the express vision to expose opinion masquerading as prediction with that of a money line opinion. Our goal is to either pair existing topical issues with predictions made by our graded predictors vs that of the money line bettors.

Whether we build our own market or piggy back of another will depend on interest level and further raises. I would totally be open to working with manifold on this element also.

Hmm. I feel like there's a good idea in here (publicly grading pundits with an accuracy/reputation score over time) that's going to get buried under a too-ambitious product (your own new prediction market platform, proprietary machine-learning inference of predictions, users who are risking capital, political bias detection).

I'd be more interested in funding an MVP which just did historical/running annotations of predictions from a small selection of pundits, and tried to scale up from there after it got some traction.