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Vibe coding the MIT course catalog

I recently left Microsoft to join MIT's Media Arts and Sciences program. The transition brought an immediate problem: how do you navigate course selection when faced with the "unknown unknowns"? You can easily find courses you already know you want learn, i.e. "known unknowns". But discovering courses you never knew existed, courses that might reshape your thinking entirely, requires different tools altogether. MIT's official course catalog runs on what appears to be a CGI script. The technolog

Vibe Coding the MIT Course Catalog

I recently left Microsoft to join MIT's Media Arts and Sciences program. The transition brought an immediate problem: how do you navigate course selection when faced with the "unknown unknowns"? You can easily find courses you already know you want learn, i.e. "known unknowns". But discovering courses you never knew existed, courses that might reshape your thinking entirely, requires different tools altogether. MIT's official course catalog runs on what appears to be a CGI script. The technolog

Context Rot: How increasing input tokens impacts LLM performance

Recent developments in LLMs show a trend toward longer context windows, with the input token count of the latest models reaching the millions. Because these models achieve near-perfect scores on widely adopted benchmarks like Needle in a Haystack (NIAH) [1], it’s often assumed that their performance is uniform across long-context tasks. However, NIAH is fundamentally a simple retrieval task, in which a known sentence (the “needle”) is placed in a long document of unrelated text (the “haystack”)