System Boundaries: Security, Data Sensitivity & Topology · the finale
You've designed a system, audited its honesty, its provenance, and its evaluation. This last one asks you to draw its boundaries — and then to name the single instinct that has run through the entire course. The grade is in the reasoning, not in finding a flawless system.
Time: ~45–60 min · Due: before the wrap session · Submit: post your write-up in the cohort channel.
Feedback, Evaluation & Honest Metrics
You've designed a system, audited its honesty, and audited its provenance. This time you audit how it evaluates itself — and whether it's quietly fooling itself. The grade is in the reasoning, not in finding a flawless system.
Time: ~45–60 min · Due: before the Module 5 session · Submit: post your write-up in the cohort channel.
Variables store data values. Modern JavaScript uses let and const for variable declarations, replacing the legacy var keyword to ensure proper block-scoping.
const birthYear = 1995; // Cannot be changedlet currentScore = 10; // Can be reassigned
currentScore = 15; // Valid reassignment
Provenance, Preservation & Ground Truth
In Module 1 you designed a system; in Module 2 you audited its honesty. This time you audit its provenance — whether the truth behind its outputs is preserved, traceable, and recoverable. The grade is in the reasoning, not in finding a flawless system.
Time: ~45–60 min · Due: before the Module 4 session · Submit: post your write-up in the cohort channel.
Uncertainty, Calibration & Knowing the Limits
In Module 1 you designed a layered system. This time you interrogate a single layer's honesty — because the whole architecture only works if its confidence estimates can be trusted. The grade is in the reasoning, not in finding a "perfect" system.
Time: ~45–60 min · Due: before the Module 3 session · Submit: post your write-up in the cohort channel.
Layered Architectures & the Division of Labor
In class we named the patterns. Here you apply them to a problem of your own. There's no single right answer — the grade is in the justification.
Time: ~45–60 min · Due: before the Module 2 session · Submit: post your write-up + diagram in the cohort channel.
Attention Is All You Need (Vaswani et al., 2017) — The original 2017 paper. Surprisingly readable; everything else is commentary on this.
The Annotated Transformer — Harvard NLP walks through the paper line-by-line with PyTorch code interleaved. Best companion read.
The Illustrated Transformer — Jay Alammar — Still the best visual explanation on the internet. If a formula feels abstract, Jay has probably drawn it.