ChronoLens
Cultural research workspace that labels every AI claim by how far you should trust it. Built solo in about five hours at the OpenAI Codex Hackathon, Sydney.
The problem
A researcher asking about a 14th-century trade route gets back a fluent paragraph with no way to tell which sentence came out of an archive and which one the model inferred. The fluency is the problem: it makes an inference and a citation look identical.
What I built
One query fans out to ten analysis surfaces and six live archive APIs, producing a knowledge graph, geographic transmission flows, image analysis with bounding boxes, and PDF/PowerPoint export. Next.js 14, TypeScript, GPT-4.1.
One decision
Every claim carries its epistemic status. Output is labelled fact, context, hypothesis, or needs-expert-review, and the label travels with the claim all the way into the exported deck. It makes the system look less confident than a plain summary would, which is the point: a researcher can act on a fact and go verify a hypothesis, but a paragraph that blends the two can't be checked at all.