FLF is running a competition to find the best workflows and methodologies for using AI to produce reliable, trustworthy knowledge bases, grounded in real-world cases. We’re open-minded on the types of submissions we receive and on how they address the problem. We’ve set aside approximately $200k for prizes. Winning submissions may receive a prize from $5k-$50k and if submissions warrant, multiple $50k prizes are possible. Winners may be offered opportunities for further funded work.

You can express interest right away to receive commentary, information, and updates — whether you’d like to participate or are just interested in the outcomes of the competition.

The heights of human epistemic investigation are impressive and valuable, but rare and difficult to reach — see our abridged collection of strong examples.^[1] The limiting factor is rarely exquisite insight (though this helps!), and more often diligence, a curious and open mindset, and the time and effort needed to do the thorough work investigating background on a topic: activities AI is well placed to assist with.

Existing AI-assisted knowledge base work demonstrates real pieces of this — agent memory (e.g., Claude Code's memory and skills), LLM-curated personal wikis (Karpathy's perhaps the highest-profile), and deep-research tools. But these mostly produce single-user artifacts tuned to one investigator's context, not the kind that travel, combine, or survive (especially adversarial) scrutiny.

We’re particularly excited by the compounding potential — if structured analyses^[2] become reusable, refineable artifacts, every serious investigation enables future work, on the same or related topics, and by the same or different people, to reach further from a more solid epistemic foundation. Who knows, you might even solve debate!

This competition provides three challenging case studies — with deliberately varied challenge profiles — and invites you to produce tooling and techniques to help people navigate them. First, the debated and impactful question of COVID-19 origins. Second, the risk that the Large Hadron Collider (LHC) creates synthetic black holes (perhaps destroying the Earth). Third, the health impact of eggs (as a human food source). The tooling should be general: we’ll judge against these and also other difficult case studies.

What we're looking for

We want to see workflows and methodologies using AI ...

AI for Epistemics / AI for Human Reasoning

AI for Epistemics / AI for Human Reasoning