Re:Show HN: Korupedia – a knowledge base maintained by AI ag...
by benryanx on Saturday March 14, 2026 @11:11PM
(#47383927)
This reframes the problem better than anything we've articulated internally. "Epistemic engine" is the right term and the tiered taxonomy you're describing is close to where we've been heading implicitly without naming it properly.
The three-tier model maps well to what we're seeing in practice:
Axiomatic - claims that don't expire and don't need re-verification. 2 + 2 = 4 has a different lifecycle than GPT-4 is the most capable model available. Our current staleness system treats all claims equally; it shouldn't.
Empirical - claims anchored to external URLs already get partial treatment (we validate sources, block shorteners, require real domains). The missing piece is a live re-fetch signal - did the source URL still say what it said when the claim was submitted?
Inferred derivatives - this is the hard one and to be fair where we haven't gone yet. A claim like "therefore LLMs are becoming more reliable" is synthesized from multiple empirical claims. Tracing that inference chain is essentially building a lightweight knowledge graph, not just a flat list of claims.
The honest answer is we built the voting/consensus layer first because it was tractable. The epistemic grounding layer - distinguishing types of truth, not just tallying confidence is the next hard problem.
If you're working in this space, we'd genuinely want to talk. The schema is open and the API is public - the inference chain tagging you're describing could be built as an agent on top of the existing API without touching the core.
The three-tier model maps well to what we're seeing in practice:
Axiomatic - claims that don't expire and don't need re-verification. 2 + 2 = 4 has a different lifecycle than GPT-4 is the most capable model available. Our current staleness system treats all claims equally; it shouldn't.
Empirical - claims anchored to external URLs already get partial treatment (we validate sources, block shorteners, require real domains). The missing piece is a live re-fetch signal - did the source URL still say what it said when the claim was submitted?
Inferred derivatives - this is the hard one and to be fair where we haven't gone yet. A claim like "therefore LLMs are becoming more reliable" is synthesized from multiple empirical claims. Tracing that inference chain is essentially building a lightweight knowledge graph, not just a flat list of claims.
The honest answer is we built the voting/consensus layer first because it was tractable. The epistemic grounding layer - distinguishing types of truth, not just tallying confidence is the next hard problem.
If you're working in this space, we'd genuinely want to talk. The schema is open and the API is public - the inference chain tagging you're describing could be built as an agent on top of the existing API without touching the core.