Awesome work! I tried it on a couple of pretty confusing examples and it worked out great.

However you might want to build with production mode enabled, since your current build actually tries to connect to a Vite dev server on localhost:3000.

Here's a primer to help you navigate building for production: https://vitejs.dev/guide/build

Best of luck to you Andrew and to the team!

Ok,

I have heard a lot of good, and bad comments, so I want to clear some things up.

First of all, we are using an OpenAI based back-end, we are in the process of developing our own LLM for the task of text fitting. Secondly, the LLM in our approach is currently only used for text translation. This meaning, we assume the LLM dosnt know anything, and thus before the users input even reaches the LLM it has to hit a couple other models which determine if the statement is a fact or not, then an intermediary text analyses model to extract understandable queries from the text which can then be used to search information about the topic from a DB (for example, Google FC API, or a custom dataset of documents known good). After that process, all the data is presented to an LLM which can then fit the known good data into the context of the users input.

The LLM itself is never trusted with data.

Of course for a system like this to work, we would need access to a DB and those intermediary models, which as you can guess, will take a while to build and develop. For now, we are pushing our beta without this system but a dumbed down non-optimized (and definitely flawed) version to test scalability and fix bugs, test security, and check scalability for our back-end platform.

In case anyone was wondering anything about my credentials, I am one of the lead back-end developers working on the project. I am also free to answer any reasonable questions anyone might have about Factful.

So I put in:

> Green Party leader Justin Trudeau was ejected from Canada's House of Commons after fiery exchanges with Prime Minister Jagmeet Singh. Mr Trudeau's removal came after he refused to apologise for calling Mr Singh a "wacko" and "extremist" during a question period.

And your service game up with

Green => Liberal; was => was not; fiery => factuality; Prime Minister => NDP Leader; "Mr Trudeau's removal came after he refused to apologise for calling Mr Singh a "wacko" and "extremist" during a question period." => did not occur as described;

What really happened was:

Conservative Party leader Pierre Poilievre was ejected from Canada's House of Commons after fiery exchanges with Prime Minister Justin Trudeau. Mr. Poilievre's removal came after he refused to apologise for calling Mr. Trudeau a 'wacko' and 'extermist' during question period.

Considering this happened yesterday, this is pretty impressive. Great work.

Don't hinge your business success on getting an unlimited number of humans to agree with your AI on an unlimited number of facts.

The universe of facts is infinite.

The larger a user base, the fewer facts they'll collectively agree on.

If you take on a large user base and an infinite knowledge domain, you'll lose trust with users who disagree with your fact checking (rightly or wrongly.)

Instead, give yourself a smaller scope where you can actually win. Train your tool to be a world-class fact checker in a specific domain. Then market to a userbase who explicitly already agree on the facts you check against. This smaller scope sets you up for technical success and builds experience, revenue, and user trust, all of which you can leverage to iterate into another domain more quickly.

You're 16.

This is awesome, and you're doing great. This is such strong signal for an amazing career and impact.

Keep going!

Your first meta-problem to solve is to get people to care about the facts, and to accept them when they’re wrong. There is an astonishing gap between knowing the truth and acting accordingly.

are you aware of http://disinformationindex.org?