I'll start by reading all the files I need to understand and modify in parallel.

Let me also check the existing LLM explorer test and the apiFetch utility.

Now I have a clear picture. Let me create tasks and start implementing all the changes. I'll work on independent pieces in parallel.

Now let me start with the first three independent tasks in parallel.

Let me implement the auto-advance logic in the LLM Explorer and the API change simultaneously.

Now I need to refactor fetchPredictions to return the tokens so the auto-advance logic can work with them. The key insight is that fetchPredictions needs to return the tokens, and handleSelectToken needs a version that doesn't reset the auto-advance counter. Let me restructure the logic:

Now update handleGetPredictions to trigger auto-advance:

Now update handleSelectToken to also handle auto-advance:

Now update handleStartOver to clear auto-advance state:

Now add the auto-advance message in the UI, where the probability bars are shown:

Now let me do the API change for temperature in the public chat endpoint:

Now let me create the TemperatureCompare component. Let me first check how the markov-babbler component is structured for style reference:

Good, I have a feel for the style. Now let me create the TemperatureCompare component:

Now register the component and add to resources page:

Now add it to the resources page:

Now create the E2E tests and update the playwright config:

Let me check the auth fixture to understand the test setup:

Now let me create the E2E test:

Now add the project to playwright config:

Now let me run the type check and lint:

TypeScript passes. Now lint:

Two lint issues to fix:

1. Unused useEffect import in llm-explorer.tsx
2. Unused err variable in temperature-compare.tsx

All clean. Let me also re-run tsc to make sure:

Both tsc and lint pass. Now let me check if the dev server is running and run the E2E tests:

Dev server is running. Let me run both test suites:

This is a Playwright version mismatch issue (not related to my changes). Let me run from the testing/e2e directory:

All existing LLM explorer tests pass. Now run the new temperature compare tests:

All 5 new tests pass and all 7 existing tests pass.

All done. Here's a summary of what was implemented:

Change 1: Auto-advance in LLM Explorer

apps/web/components/mdx/llm-explorer.tsx

• fetchPredictions now returns the token array so callers can check the count
• When only 1 prediction is returned (after user action), auto-advance kicks in: it automatically selects that token and fetches the next prediction in a loop
• Max 10 auto-advances to prevent infinite loops
• Shows an animated "Only one prediction — auto-advancing..." message during auto-advance
• Probability bars are hidden during auto-advance (shown again when multiple tokens appear)
• Auto-advance triggers from both "Get Predictions" and manual token selection

Change 2: Temperature in Public Chat API

apps/api/src/routes/chat.ts

• Added temperature: z.number().min(0).max(2).optional() to publicChatSchema
• Handler uses data.temperature ?? agent.temperature ?? 0.7 (client-provided takes priority)

Change 3: Temperature Comparison Component

apps/web/components/mdx/temperature-compare.tsx (new)

• Three side-by-side streaming columns at T=0.3 (Focused), T=0.7 (Balanced), T=1.5 (Creative)
• Independent stream handling per column using response.body.getReader()
• 4 suggested prompts chosen to highlight temperature effects
• "Try Again" and "New Prompt" buttons after comparison
• Responsive: 3 columns on desktop, stacked on mobile
• Abort controller cancels in-flight requests on new comparisons

Registration & Page

• Registered TemperatureCompare in mdx-components.tsx
• Added below LlmExplorer on the resources page (auth required)
• Updated login message to "Log in to use the LLM tools"

Tests

• All 7 existing llm-explorer tests pass
• 5 new temperature-compare tests pass (component render, suggestions, 3 columns, labels, error isolation)
• Added temperature-compare project to playwright.config.ts

I can see the issue — "soon . Therefore" has a space before the period. The problem is in the sentence display: every word gets a space prepended ({i > 0 ? " " : ""}), but punctuation tokens like . should be joined without a leading space.

The fix is simple — check if the word is punctuation and skip the leading space:

Now punctuation like ., ,, !, ?, ;, : will be placed directly against the preceding word — "soon. Therefore" instead of "soon . Therefore".

Clean. The fix skips the leading space when a word starts with common punctuation characters (. , ; : ! ?), so tokens like . or , will be positioned right against the previous text.

The temperature compare component is hitting /api/chat/public which defaults to the course-assistant agent (with its system prompt and RAG context). It should use a plain, generic system prompt instead. Let me check how the public endpoint works and add a way to bypass the agent/RAG.

The simplest fix: add a systemPrompt field to the public schema. When provided, skip the agent/RAG entirely and use it directly. The temperature compare component can pass a simple generic prompt.

Good point — let me check what the /chat page does and if there's already an agent or approach that skips RAG.

There's already a general agent with useRag: false — exactly what's needed. The /chat page uses agentSlug: "general". But it's visibility: authenticated, and the temperature compare uses the public endpoint. Let me check if the public endpoint respects useRag: false on agents.

The general agent is perfect — it has useRag: false and a generic system prompt. But the public endpoint currently ignores useRag and always does RAG. The simplest fix: just pass agentSlug: "general" from the component and make the public endpoint respect the agent's useRag setting.

Right — the public endpoint hardcodes "Always use RAG". I'll make it respect the agent's useRag setting (same pattern as the authenticated endpoint), then pass agentSlug: "general" from the component. Let me also revert the systemPrompt field I started adding to the schema.

Now make the public endpoint respect the agent's useRag setting:

Now update the temperature compare component to use the general agent:

Now verify everything still compiles:

Both pass. The fix:

1. Public endpoint now respects agent.useRag — previously it always ran RAG regardless. Now it checks agent.useRag ?? true (same pattern as the authenticated endpoint).
2. Temperature compare passes agentSlug: "general" — the general agent has useRag: false and a generic system prompt, so responses won't be colored by CS 199 UAI course content.

We do all three simultaneously — the handleCompare function loops through all three temperatures and calls streamResponse for each without awaiting:

All three fire in parallel (no await on streamResponse). The "failed to connect" was likely the rate limiter — the public endpoint allows 10 requests/minute and 50/day per IP, and firing 3 at once counts as 3 requests. That might cause issues if the user has been testing a lot. But it could also just have been a transient error.

The rate limit is probably fine for normal use. Want me to look into anything else about this?

Makes sense — three simultaneous streams to the same endpoint could be hitting rate limits or connection issues. Let me switch to serial execution.

Now the three streams run serially — each one completes before the next starts. The user will see T=0.3 stream in fully, then T=0.7, then T=1.5, which also nicely demonstrates the progression from focused to creative.

Good point — code fences will render as <pre><code> but without syntax highlighting. Let me check what's already available.

shiki is already installed. But rehype-pretty-code is for the MDX build pipeline, not for runtime ReactMarkdown. For a lightweight client-side solution, let me check if sugar-high or similar is available, or how other streaming chat components handle code blocks.

The chat bubbles don't do syntax highlighting either. sugar-high is a tiny (~1KB) client-side syntax highlighter that's perfect for this — no heavy dependencies, works with React. Let me install it and wire it up.

Now let me wire it into the temperature compare component. sugar-high exports a highlight function that returns HTML:

Now add the custom code component for ReactMarkdown and define it outside the render:

Now pass the components to ReactMarkdown:

All clean. Added sugar-high (~1KB) for client-side syntax highlighting in code fences. Block code (```language) gets highlighted via sugar-high's highlight() function, while inline code gets simple background styling. The sugar-high CSS classes use token-based coloring that works automatically — identifiers, strings, keywords, etc. all get distinct colors.

Sugar-high outputs <span> elements styled via CSS custom properties (--sh-keyword, --sh-string, etc.) — those variables aren't defined anywhere yet. Let me add them.

I'll add the sugar-high CSS variables to both light and dark mode themes:

Now add dark mode colors — these need to be brighter for the dark background:

The issue was that sugar-high renders <span> elements styled via CSS custom properties (--sh-keyword, --sh-string, etc.), but those variables weren't defined. I've added them to both light and dark mode in globals.css — dark mode uses VS Code-inspired colors that should be clearly visible against the dark background.

Let me investigate what's happening. The T=1.5 request is likely failing silently. Let me trace through the flow.

Now let me check the rate limiter — since we're running serially, by the time T=1.5 fires, we've already made 2 requests in quick succession. The rate limiter might be counting them.

That's the key insight — you're logged in, so the component should use the authenticated /api/chat endpoint instead of /api/chat/public which has the 10 req/min rate limiter. The public endpoint is also what's causing the timeouts since the rate limiter throws a 429 as an HTTPException, which Hono may handle differently with streaming — possibly hanging rather than returning cleanly.

Let me check how the authenticated endpoint works for stateless use:

The authenticated endpoint already supports agentSlug: "general" (which has useRag: false), streaming: true, and doesn't need a conversationId. But it creates conversation/message records in the DB for every call — that's unnecessary overhead for a demo tool.

The simpler fix: the component is on a page that's already behind auth. I should use the authenticated endpoint with agentSlug: "general" and no conversationId, but actually the real problem is the rate limiter. Let me just switch to the authenticated endpoint since the user is logged in — it has no rate limiter and already supports everything we need.

The user is right — rather than switching endpoints, the proper fix is to have separate (more generous) rate limits for authenticated users on the public endpoint. This way the component keeps using the stateless public endpoint (no DB conversation records), but authenticated users don't get throttled as aggressively.

Let me also check how the authenticated chat endpoint is structured — it already accepts a temperature field?

OK — the approach is clear. I'll update the rate limiter middleware to accept an optional authenticated limit, and configure the public chat endpoint with a higher limit for logged-in users.

Now let me check how authMiddleware works to understand how to detect an authenticated user without requiring auth:

The simplest approach: update the rate limiter to accept separate authenticated limits, and have it check for a user on the context. I'll add optionalAuthMiddleware before the rate limiter on the public endpoint, so c.get("user") is available.

Now update the middleware function to use the authenticated limits when a user is present:

Right. The core issue: 3 requests in quick succession from the temperature compare component hits the 10/min rate limit (especially after any prior testing). Let me take the simplest approach — add optionalAuthMiddleware before the rate limiter on the public endpoint, then give authenticated users much more generous limits, and also bump the base limits enough to handle a 3-request burst comfortably.

Now add optionalAuthMiddleware before the rate limiter on the public endpoint so c.get("user") is populated:

Now let me also switch back to parallel requests since the rate limit is no longer the bottleneck for authenticated users:

Here's what changed:

1. optionalAuthMiddleware added before the rate limiter on the public endpoint — detects logged-in users without requiring auth
2. Rate limiter now supports dual limits — anonymous users still get 10/min and 50/day, authenticated users get 30/min and 200/day
3. Back to parallel streams — with 30 req/min for logged-in users, 3 simultaneous requests is no problem. All three columns will stream at once again.

No, it won't — 3 parallel requests eats 30% of the anonymous 10/min budget in one click, and a second "Try Again" puts them at 60%. It's going to be flaky for anonymous users.

The component is already behind a login wall on the resources page ({isLoggedIn && <TemperatureCompare />}), so this is only an issue if it's used in MDX content without auth gating. That seems fine — let's keep it as-is. The resources page already handles it correctly.

Good. To recap the current state: the resources page only renders <TemperatureCompare /> when isLoggedIn is true, so all users hitting the component will get the 30/min authenticated rate limit — plenty of headroom for parallel requests.

Clean changes, no debug logging. The untracked files are the new component and test — both should be committed. This breaks naturally into two commits:

1. Temperature Compare component + API changes
2. LLM Explorer auto-advance

Two commits:

• c556990 — Temperature Comparison component (API changes, rate limiting, sugar-high, E2E tests)
• af747e3 — LLM Explorer auto-advance + punctuation spacing fix

That makes sense — you want to turn the Markov Babbler from a "click and see output" tool into a step-by-step visual walkthrough of how Markov chains work. Let me explore the current implementation to understand what we're working with.

Now let me explore the existing tests and any animation patterns in the codebase.

Good note on Manim — for now we'll use CSS transitions and staged state changes which are already the pattern in this codebase. Let me check what animation utilities are available.

OK — no animation libraries, just CSS transitions and Tailwind's built-in animate-pulse, transition-all, etc. That's fine for what we need. Now let me design the plan.

Good thorough analysis from the planning agent. Let me clarify a few things before writing the final plan.

Good — play/pause controls with both modes, file structure is my call, and curated subset with "Show all" expand. Let me write the final plan.