Lexicon

Live captions break on the words a lecture is actually about: names and course-specific terms the recogniser has never seen. A student who can't hear the room has no way to tell a caption is wrong. With Lexicon, you paste the course glossary and start a session. Your microphone streams to AssemblyAI's real-time recogniser with those terms sent as keyterm boosts, and large, high-contrast captions appear with speaker labels. During the lecture you can type a question. The answer comes only from what has been captioned so far, with the exact caption lines it relied on. Click the citation to jump back to that moment. The server checks every quoted passage word for word against the captions. If the check fails, or the captions don't cover it, you get "not found", never an answer from general knowledge. When you press Stop, you get a summary and key terms defined as this lecture used them, each tied to caption lines, plus a report of glossary terms that never appeared as written. Everything exports as Markdown. Measured, not claimed: on a recording full of unfamiliar surnames, 4 of 10 terms appeared as supplied only with boosting ("n-k Dirac" became "Nkemdirim"). On standard linear-algebra vocabulary boosting made no difference, because the recogniser already got those words right. A 20-minute test run finished with no crash and no reconnects. Stack: React client, Hono proxy that keeps both API keys off the browser, AssemblyAI streaming, OpenAI Structured Outputs. No database: everything is session-scoped.