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- 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>
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title, description, section, order
| title | description | section | order |
|---|---|---|---|
| Autoresearch | Start an autonomous experiment loop that iteratively optimizes toward a goal. | Workflows | 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
From the REPL:
/autoresearch Optimize prompt engineering strategies for math reasoning on GSM8K
From the CLI:
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:
- Hypothesis -- The agent proposes a hypothesis or modification based on current results
- Experiment -- It designs and executes an experiment to test the hypothesis
- Analysis -- Results are analyzed and compared against prior iterations
- 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:
/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.