I need to configure the PrairieTest webhooks for this site. That requires bot...

The reading links look correct (list format, titles, reading times). But the preparation chat component below "Please complete the preparation conversation below before class" is missing. The code has it on line 27. This is likely a production deploy issue — the latest code may not have been pushed/deployed yet. The reading links are showing in the new format, so the content change deployed, but the preparation-chat block should render below that text.

Can you scroll down? The prep chat might be rendering below the --- separator. Or does the page end right after "This is part of attendance for today's meeting."?

Sorry, I wasn't doubting you. Let me check the logs.

You're right, that was dismissive. I apologize.

There it is. QUIZ_API_URL is failing zod validation on the production web pod. The PreparationChat component uses serverApiFetch, which fails because QUIZ_API_URL isn't set in the production web deployment.

The production web pod doesn't have QUIZ_API_URL. We added it to k8s/web.yaml but it wasn't applied.

QUIZ_API_URL is now set in the production web pod. The serverApiFetch zod validation was failing silently, causing every server component that fetches from the API (including PreparationChat) to error and not render. It should be working now.

Glad it's working. That was a painful one — the zod validation failing silently in try/catch blocks meant every serverApiFetch call was erroring, not just CBTF ones. Worth knowing for the future: if server components start disappearing in production, check the web pod env vars first.

We don't, really. The zod validation throws on the first serverApiFetch call. But the callers catch it silently:

// assessments/page.tsx
try {
  const data = await serverApiFetch<AssessmentsResponse>("/api/assessments");
  assessments = data.assessments;
} catch {
  // Client will handle empty state
}

Every page that calls serverApiFetch has a catch that swallows the error and renders an empty/fallback state. That's fine for transient API failures, but it masked a fatal configuration error.

The fix should be to fail loudly for config errors. The zod parse already throws at call time, but we could move it earlier. Want me to make the validation throw at module import time so the web pod crashes on startup if the env vars are missing? That would make it immediately obvious in the deployment logs instead of silently degrading.

You're right. The regular site doesn't need QUIZ_API_URL at all — it's only used when isCbtf is true. But the zod schema requires both to be present, so it throws on every serverApiFetch call even for non-CBTF requests.

Now throw only when CBTF actually needs it:

Now the regular site works without QUIZ_API_URL, and CBTF mode fails loudly if it's missing.

You're right. Fail at startup, not lazily. Set dummy values during build so the container builds, but validate at runtime startup.

Now add dummy build-time values. Let me check how the Docker build works:

Now the module-level serverEnvSchema.parse(process.env) runs at startup. During build, dummy values satisfy it. At runtime, the real values from K8s env vars are used. If either is missing at runtime, the pod crashes immediately with a clear zod error instead of silently degrading.

Good catch. Yes, the ENV directives bake into the image and persist at runtime as defaults. If the K8s deployment doesn't set QUIZ_API_URL, it would silently use http://localhost:3001 instead of crashing.

I need to set them during build but clear them for runtime:

ARG is only available during build and doesn't persist into the runtime image. If QUIZ_API_URL isn't set in K8s, the zod parse will crash the pod on startup.

Wait — but ARG values aren't visible as environment variables to RUN commands unless explicitly converted. Let me check if Next.js build needs them as env vars:

Actually, ARG values are automatically available as environment variables during the RUN command in the same build stage. So bun next build will see them. But they won't be in the final runtime image since the runner stage is separate (FROM node:... AS runner). The ARG won't carry over to the runner stage at all.

This should work correctly — build succeeds with dummy values, runtime crashes if they're not set by K8s.

Now none of the build-time dummies leak into the runtime image.

No debugging statements. Clean changes.

Committed 3b76c8a.

Noted. I'll keep that in mind when building future meeting activities.

I see the issue. Rounds 2 and 4 have both a text-submission and a group-chat in the same group content. The group-chat is the conversational completion and should replace the text-submission:

Removed the text-submission from rounds 2 and 4 where the group-chat serves as the conversational completion. Rounds 1 and 3 still have text-submission only (no group chat), which is correct.

Clean — just the one file, removing redundant text submissions.

Committed 18e441c.

Next week is Week 9 (Apr 7-9):

• Tue Apr 7 — Exploratory: Hallucinations, Reasoning, and Trust
• Thu Apr 9 — Discussion: AI Companions, Agents, and Trust

Readings for the Thursday discussion:

• Love in the Time of AI Companions
• Sorry Mom You're Chatting with an AI Agent
• AI Bots as Personal Digital Assistants

Also, final project proposals are due around Apr 7-9.

Want to start building the meetings for next week?

So far the course has covered:

• Embeddings (Mar 24) — how AI represents meaning as vectors
• Training data costs (Mar 26) — the human/environmental costs of training data
• Neurons and networks (Mar 10) — how artificial neurons work
• From simple parts (Mar 12) — emergence from training
• How LLMs work (Feb 24) — presumably the mechanics of language models

But I don't see a meeting that covers the full training pipeline: pre-training on a corpus, fine-tuning, RLHF/RLAIF, constitutional AI, instruction tuning, the role of human feedback in shaping behavior. That's a significant gap. Students have seen pieces (embeddings, neurons, costs) but not how a model goes from raw text to a helpful assistant.

The Tuesday exploratory slot could be retitled something like "How AI Learns to Be Helpful" or "From Raw Text to Assistant" covering the training lifecycle: pre-training, instruction tuning, and human feedback. This connects well to Thursday's discussion on AI companions and trust, since understanding how models are shaped helps students think critically about their behavior.

Want me to build that meeting instead of "Hallucinations, Reasoning, and Trust"?

From the schedule, here's what's left to build:

Week 9 (Apr 7-9)

• Tue Apr 7 — Exploratory: Hallucinations, Reasoning, and Trust → Training Lifecycle (new topic)
• Thu Apr 9 — Discussion: AI Companions, Agents, and Trust

Week 10 (Apr 14-16)

• Tue Apr 14 — Lab: Creating Websites
• Thu Apr 16 — Exploratory: Multimodal AI and Deepfakes

Week 11 (Apr 21-23)

• Tue Apr 21 — Discussion: AI Safety, Alignment, and Governance
• Thu Apr 23 — Lab: Research Reports

Week 12 (Apr 28 - May 5)

• Tue Apr 28 — Discussion: Human Flourishing in an Age of AI
• Thu Apr 30 — Lab: Final Project Workshop
• Tue May 5 — Final Meeting: Reflection and Synthesis

That's 9 sessions: 3 discussions (with readings already assigned), 3 labs, 2 exploratories, and 1 final meeting.

The training lifecycle topic also sets up the Apr 21 safety/alignment discussion well, since RLHF and alignment are directly related.

Yes, it's very feasible and students love it. A few options:

Best for this course: Bolt (bolt.new)

• Students describe what they want, AI generates a full working site
• Live preview in the browser, can iterate with conversation
• Free tier is enough for a lab session
• Deploys to a public URL with one click
• Great for non-CS students since there's zero setup

Also good: Replit

• AI agent builds and deploys sites
• More technical feel, students see the code
• Free tier works but is more limited

More hands-on: Claude Artifacts or ChatGPT Canvas

• Can generate HTML/CSS/JS in the conversation
• Students see the code and preview side by side
• No deployment, but great for understanding what's happening
• Already tools students know from the course

For a 75-minute lab, Bolt is probably the best fit: students can go from idea to deployed website in one session, the iteration loop is fast and conversational, and the result is something they can share. The lab could have them build a personal site or a site about a topic from the course, then reflect on what AI did well and where they had to intervene.

Want me to start building this meeting?

Week 9 (Apr 7-9)

• Tue Apr 7 — Exploratory: Training Lifecycle (new)
• Thu Apr 9 — Discussion: AI Companions, Agents, and Trust

Week 10 (Apr 14-16)

• Tue Apr 14 — Lab: Creating Websites
• Thu Apr 16 — Exploratory: Multimodal AI and Deepfakes

Week 11 (Apr 21-23)

• Tue Apr 21 — Discussion: AI Safety, Alignment, and Governance
• Thu Apr 23 — Lab: Research Reports

Week 12 (Apr 28 - May 5)

• Tue Apr 28 — Discussion: Human Flourishing in an Age of AI
• Thu Apr 30 — Lab: Final Project Workshop
• Tue May 5 — Final Meeting: Reflection and Synthesis

The two remaining exploratories are Week 9 Tuesday and Week 10 Thursday. Everything else is discussions, labs, or the final.

Let me look at what's been covered:

Here's the "how LLMs work" arc across the semester:

1. How Do LLMs Work? A First Look (Feb 24) — the basics
2. Neurons and Networks (Mar 10) — artificial neurons, weights, activation functions, digit recognition
3. From Simple Parts (Mar 12) — emergence, scaling, unexpected abilities
4. Embeddings and Knowledge (Mar 24) — vector representations, semantic space, cosine similarity
5. Training Data and Its Costs (Mar 26) — what goes into the training corpus, the human/environmental cost
6. Training Lifecycle (Apr 7, new) — pre-training → instruction tuning → RLHF → helpful assistant

With the new session, the arc covers: what neurons do → how networks learn → how meaning is represented → what data they train on → how raw models become useful assistants.

The main gap that remains is inference: how the model actually generates text at runtime (autoregressive token prediction, temperature/sampling, context windows). Students explored temperature hands-on in "How Do LLMs Work?" but may not have a deep mental model of the generation process.

That said, I think the coverage is solid for a course aimed at understanding, not building. The training lifecycle session fills the last major conceptual gap. Students will understand the full journey from data to assistant, which grounds the remaining discussions on companions, safety, alignment, and governance.

Multimodal AI (image generation, vision, audio, video) is genuinely different from what students have seen so far, which has been almost entirely text-based. They've used AI for creative media (Lab 1) but haven't explored how vision models work or grappled with deepfakes.

But as an exploratory session, I'm not sure what the hands-on component looks like. Students could generate images and try to fool each other with deepfakes, but that feels thin for 75 minutes. The deepfakes angle is really a discussion topic (trust, misinformation, evidence) more than an exploration.

Options for that Thursday slot:

1. Keep as exploratory — hands-on with image generation, vision, maybe audio. Risk: feels like a demo tour rather than building understanding.

2. Convert to discussion — deepfakes, synthetic media, and trust. Strong readings available. But you already have 3 discussions in the remaining weeks.

3. Convert to lab — students create something multimodal (e.g., generate illustrations for their final project, create a short video with AI tools). More productive, ties into the final project due May 5.

4. Drop multimodal, add a second final project workshop — students get Apr 30 already, but an earlier working session on Apr 16 gives them two weeks of structured project time.

Given that the final project is due May 5 and proposals are around Apr 7-9, option 3 or 4 might serve students better at that point in the semester. A multimodal lab where the output feeds into their final project would be the best of both worlds.

That makes a lot of sense. Students need to understand how to go from "I have an idea" to "I have a working thing" with AI tools. That's a different skill from using ChatGPT in a conversation.

So the revised schedule for Week 10 onward would be:

Week 10 (Apr 14-16)

• Tue Apr 14 — Lab: Creating Websites
• Thu Apr 16 — Exploratory: Building AI Applications (new, replaces Multimodal)

Week 11 (Apr 21-23)

• Tue Apr 21 — Discussion: AI Safety, Alignment, and Governance
• Thu Apr 23 — Lab: Research Reports → Final Project Workshop 1

Week 12 (Apr 28 - May 5)

• Tue Apr 28 — Discussion: Human Flourishing in an Age of AI
• Thu Apr 30 — Lab: Final Project Workshop 2
• Tue May 5 — Final Meeting: Reflection and Synthesis

The Apr 16 exploratory could cover things like: prompt chaining, system prompts, APIs vs chat interfaces, tools like Bolt/Replit for building apps, how to scope a project that's achievable in a few weeks. Hands-on: students prototype a piece of their final project idea.

The Creating Websites lab on Apr 14 feeds right into this since it shows what's possible with AI-assisted building. Then Apr 16 broadens from websites to any kind of AI application.

Does the Research Reports lab get dropped, or do you want to keep that somewhere?

Good. Let me update the remaining schedule to reflect these changes.

Updated. Ready to start building the Tuesday Apr 7 training lifecycle exploratory whenever you are.

Committed 1184253.

Just a version bump across all packages.

Committed 569acd2.

10 new captures. Let me clean up the remote DB junk first:

No junk to clean. Now let me look at each new file. There are 10 — let me process them efficiently.

Good. 10 new articles. Let me fix smart quotes across all of them first, then process each one:

Now let me process each article. I'll work through them in parallel batches, reading each to identify cruft:

Two agents are cleaning the 10 articles in parallel (5 each). I'll add sidenotes and section breaks once they finish.

10 new reading files, all untracked. These should be added.

Committed f6db30d — 10 new readings, 1,322 lines.