The plan indicates all work has already been implemented and committed across 5 commits:

• fe66d17 — Add student status page with participation and assessment results
• c7e6214 — Add E2E seed endpoint and tests for status page
• 5d5a520 — Bump glob to 13.0.6 and unist-util-visit to 5.1.0
• be8ee03 — Retry embeddings API on 429 with exponential backoff
• 35eee69 — Hide own status for instructors on /status page

There's nothing left to implement — the plan is fully complete. Is there something else you'd like me to work on?

All 23 files are just version bumps from 2026.2.25 → 2026.2.26. No debugging statements or unintended changes. This looks like a clean version bump commit.

Committed: 097ccfb — Bump version to 2026.2.26 (23 package.json files, all 2026.2.25 → 2026.2.26).

Let me explore the current course content to understand what's been covered and what's planned.

Good exploration. Here's what I found:

Current State

7 meetings completed so far:

1. Jan 22 — Welcome & AI Perspectives (orientation)
2. Jan 27 — AI Scavenger Hunt Part 1 (lab)
3. Jan 29 — AI Scavenger Hunt Part 2 (lab)
4. Feb 3 — Assessments and Agents (discussion/exploration)
5. Feb 5 — Creative Media Lab (lab)
6. Feb 10 — The Medium is the Message / McLuhan (discussion, guest speaker)
7. Feb 17 — AlphaGo Documentary Discussion (discussion)

Course arc — the syllabus has four blocks:

• Foundations (Weeks 1-4): What is AI? History and deep learning revolution ← done
• Technical Understanding (Weeks 5-8): How AI works — language, knowledge, data, infrastructure ← entering now
• Capabilities & Limitations (Weeks 9-11)
• Societal Impact (Weeks 12-14)

Week 5 in weeks.md is titled "How AI 'understands' language" with pre-work being the Andrej Karpathy on Dwarkesh Podcast (~2 hours) plus a companion guide. Discussion themes include "knowledge" vs "cognition", prediction vs understanding, AGI timelines.

Meeting type tally so far: 3 labs, 3 discussions, 1 orientation. No explanatory sessions yet.

Six Meetings Before Break

If I'm counting right, that's roughly:

1. Feb 24 (Tue) — tomorrow
2. Feb 26 (Thu)
3. Mar 3 (Tue)
4. Mar 5 (Thu)
5. Mar 10 (Tue)
6. Mar 12 (Thu)

The weeks.md plan suggests Weeks 5-8 cover: language → knowledge → data → infrastructure — which maps nicely to these 6 meetings (3 weeks × 2 meetings each).

What would you like to clarify? Happy to adjust the options or discuss further before we nail down a direction.

Great ideas. Let me share my thoughts on feasibility and approach.

On tokenization: I'd skip it for this meeting. It's a fascinating rabbit hole but tangential to the core insight you're building toward. If students understand next-word prediction and attention, tokenization is a detail that can come later (or in a lab where they play with OpenAI's tokenizer tool).

On the "reveal context" exercise: This is the strongest idea and very doable for tomorrow. Conceptually: show a sentence with all words masked except the last one, ask students to predict the next word. Then reveal one more word of context. Then another. Each time predictions get better. This directly builds intuition for why attention over the full context matters — and it's low-tech enough that you could even do it on paper/slides, though an interactive component would be nicer.

On the next-word babbler: Love the concept and the philosophical framing ("aren't we all?"), but building a tool students can train on arbitrary text is probably not a tomorrow thing. That feels like a great Thursday lab activity — give it two more days of development time.

On the probability visualization: There are good external resources (e.g., showing the probability distribution over next tokens), but a custom one tuned to your course would land better. Also probably a Thursday deliverable.

My suggestion for the 6 meetings:

For tomorrow specifically, I'm thinking a 75-minute session structured roughly as:

1. Hook (~10 min): Students try to predict next words in sentences — on paper, low-tech, sets the frame
2. The big idea (~15 min): LLMs are next-word predictors. Walk through the concept with examples. Probabilities.
3. Interactive: context matters (~20 min): The "reveal more context" exercise — could be a simple interactive MDX component where students tap to reveal words and update their predictions
4. Attention at a high level (~15 min): Why looking at ALL the words matters, not just the last few. Simple examples where a word 20 tokens back completely changes the prediction.
5. "Sophisticated babblers" (~15 min): Discussion/reflection. Are LLMs just babblers? Are humans? What's the difference, if any?

All-in on interactivity, and babbler tomorrow — got it. That's ambitious but very doable. Let me explore the existing component patterns to understand what building blocks we have.

Love the inductive framing. That changes the session design significantly — students discover the concepts before being told what they mean. The interactive tools become exploration stations, not demonstrations.

Let me read one meeting file to nail the exact MDX structure, then write the plan.

That's a really interesting middle ground. Students become the researchers, and you become the curator. The session would look something like:

1. "What do you want to know?" — collect questions
2. Pairs research specific questions — using whatever they can find (articles, videos, visualizations, ChatGPT itself)
3. Pairs share what they found — present to class or to another pair
4. You review and correct — fill gaps, redirect misconceptions, connect the dots

This has a nice advantage: you see what resources students naturally gravitate to (and what misconceptions those resources create), which informs how you teach the rest of the technical block.

The interactive tools (babbler, context predictor) could still exist but play a different role — either as one of the "stations" you point students toward, or as something you deploy in a later meeting once students have the conceptual scaffolding from their own research.

What would you like to clarify?

Good question. Let me think about what's both pedagogically strong and buildable by tomorrow.

The babbler — yes, this is very doable. A Markov chain bigram model is maybe 50 lines of JS. Students paste text, it "learns," it generates. We add a nice visualization of the probability distribution at each step. Strong concept, tight scope.

For next-word prediction visualization, here are some ideas ranked by feasibility for tomorrow:

1. "Guess the next word" quiz — We show a sentence fragment, students pick from 4-5 options (one correct, others plausible). After they pick, we reveal the actual probability distribution — "here's what the model thought." This is basically a multiple-choice component with a reveal. Simple to build, highly interactive, and it directly teaches that LLMs assign probabilities to words, not certainties. We could pre-author 8-10 good examples that illustrate different phenomena (long-range dependencies, common phrases, ambiguity).

2. Temperature explorer — Same sentence fragment, but students drag a slider from temperature 0 to 2 and watch the probability bars shift from "always picks the top word" to "chaos." This is a nice visual that teaches a practical concept they'll use with every AI tool. Could be a second mode on the same component as #1.

3. Collaborative sentence builder (your idea) — Students vote on the next word round by round. This is the most ambitious — it needs real-time voting, a shared state, and pacing. Doable with the existing WebSocket infrastructure but risky for tomorrow.

4. Context window demo — Same sentence shown with 1 word of context, then 3, then 10, then 50. At each level, show how the probability distribution changes. This teaches attention/context without explaining the mechanism. Could be pre-computed examples rather than live inference.

My recommendation: the babbler + the "guess the next word" quiz with probability reveal. Both are self-contained client components, no API calls needed, and they teach complementary concepts — the babbler shows how generation works from training data, the quiz shows how prediction works from context. Together they cover the two big ideas.

What would you like to clarify?

Yes — and there's an even better approach than 10 separate calls. The OpenAI API has a logprobs parameter. With logprobs: true and top_logprobs: 5, a single API call returns the actual probability distribution over the top next tokens. One call, not ten.

So the component could work like this:

1. Show a sentence fragment (pre-authored or student-typed)
2. One API call with max_tokens: 1, logprobs: true, top_logprobs: 5
3. Display a bar chart of the top 5 next words with their actual probabilities
4. Student picks which word to continue with (or clicks "sample" to let the model choose)
5. Repeat — the sentence grows one word at a time

This is extremely cheap. Each step is one API call returning one token. A full 20-word sentence costs 20 calls, each with a tiny prompt. With GPT-4o-mini that's fractions of a cent per student.

You could also add a temperature slider — make the same call at temperature 0.2 vs 1.0 vs 1.5 and watch the probability bars flatten out. That teaches temperature intuitively.

The architecture would be:

• New API endpoint (Hono route) that proxies to Azure OpenAI with max_tokens: 1, logprobs: true
• Client component with the bar chart and step-through UI
• Rate limiting per session to prevent abuse

The existing packages/llm already has Azure OpenAI wired up. We'd just need a thin endpoint.

This pairs beautifully with the babbler: the babbler shows the simple version (bigram frequencies from raw text), then this component shows the real version (what an actual LLM does). Same concept, vastly different sophistication. Students can see the through-line.

Let me explore the API structure and LLM package to write a solid implementation plan.

Good, I have everything I need. Let me write the plan.