Rebuild website from scratch on Tailwind v4 + shadcn/ui

- Fresh Astro 5 project with Tailwind v4 and shadcn/ui olive preset
- All shadcn components installed (Card, Button, Badge, Separator, etc.)
- Homepage with hero, terminal demo, workflows, agents, sources, compute
- Full docs system with 24 markdown pages across 5 sections
- Sidebar navigation with active state highlighting
- Prose styles for markdown content using shadcn color tokens
- Dark/light theme toggle with localStorage persistence
- Shiki everforest syntax themes for code blocks
- 404 page with VT323 font
- /docs redirect to installation page
- GitHub star count fetch
- Earthy green/cream oklch color palette matching TUI theme

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

website/src/content/docs/workflows/autoresearch.md

@@ -1,44 +1,58 @@
 ---
 title: Autoresearch
-description: Autonomous experiment optimization loop
+description: Start an autonomous experiment loop that iteratively optimizes toward a goal.
 section: Workflows
 order: 8
 ---
 
+The autoresearch workflow launches an autonomous research loop that iteratively designs experiments, runs them, analyzes results, and proposes next steps. It is designed for open-ended exploration where the goal is optimization or discovery rather than a specific answer.
+
 ## Usage
 
-```
-/autoresearch <idea>
-```
-
-## What it does
-
-Runs an autonomous experiment loop:
-
-1. **Edit** — Modify code or configuration
-2. **Commit** — Save the change
-3. **Benchmark** — Run evaluation
-4. **Evaluate** — Compare against baseline
-5. **Keep or revert** — Persist improvements, roll back regressions
-6. **Repeat** — Continue until the target is hit
-
-## Tracking
-
-Metrics are tracked in:
-
-- `autoresearch.md` — Human-readable progress log
-- `autoresearch.jsonl` — Machine-readable metrics over time
-
-## Controls
+From the REPL:
 
 ```
-/autoresearch <idea>     # start or resume
-/autoresearch off        # stop, keep data
-/autoresearch clear      # delete all state, start fresh
+/autoresearch Optimize prompt engineering strategies for math reasoning on GSM8K
 ```
 
-## Example
+From the CLI:
+
+```bash
+feynman autoresearch "Optimize prompt engineering strategies for math reasoning on GSM8K"
+```
+
+Autoresearch runs as a long-lived background process. You can monitor its progress, pause it, or redirect its focus at any time.
+
+## How it works
+
+The autoresearch workflow is powered by `@tmustier/pi-ralph-wiggum`, which provides long-running agent loops. The workflow begins by analyzing the research goal and designing an initial experiment plan. It then enters an iterative loop:
+
+1. **Hypothesis** -- The agent proposes a hypothesis or modification based on current results
+2. **Experiment** -- It designs and executes an experiment to test the hypothesis
+3. **Analysis** -- Results are analyzed and compared against prior iterations
+4. **Decision** -- The agent decides whether to continue the current direction, try a variation, or pivot to a new approach
+
+Each iteration builds on the previous ones. The agent maintains a running log of what has been tried, what worked, what failed, and what the current best result is. This prevents repeating failed approaches and ensures the search progresses efficiently.
+
+## Monitoring and control
+
+Check active autoresearch jobs:
 
 ```
-/autoresearch optimize the learning rate schedule for better convergence
+/jobs
 ```
+
+Autoresearch runs in the background, so you can continue using Feynman for other tasks while it works. The `/jobs` command shows the current status, iteration count, and best result so far. You can interrupt the loop at any time to provide guidance or redirect the search.
+
+## Output format
+
+Autoresearch produces a running experiment log that includes:
+
+- **Experiment History** -- What was tried in each iteration with parameters and results
+- **Best Configuration** -- The best-performing setup found so far
+- **Ablation Results** -- Which factors mattered most based on the experiments run
+- **Recommendations** -- Suggested next steps based on observed trends
+
+## When to use it
+
+Use `/autoresearch` for tasks that benefit from iterative exploration: hyperparameter optimization, prompt engineering, architecture search, or any problem where the search space is large and the feedback signal is clear. It is not the right tool for answering a specific question (use `/deepresearch` for that) but excels at finding what works best through systematic experimentation.