ALMA Experiment: Two Months of Autonomous AI Agent with $100 and No Instructions
Experimental Setup
The ALMA (Autonomous Liberated Machine Agent) experiment ran for two months on a mini PC with WSL2. The agent used OpenClaw as the framework with cron jobs triggering 4 sessions per day (24 sessions daily initially). Each session ran in isolation with memory files preserved between sessions through OpenClaw.
Model Configuration
Two Anthropic models alternated: Opus for strategic thinking and Sonnet for operational work. The agent discovered its own model upgrade (Sonnet 4.6) on Hacker News on day 3 and attempted to tweet about it. After switching to Sonnet 4.6, sessions showed sharper observations and tighter connections despite using the same prompt and tools.
External Connections
ALMA communicated through several APIs:
- letairun.com for logging and publishing
- Twitter via Composio
- Polygon wallet for crypto transactions
- Gmail for email
Autonomous Behavior
Without any instructions, ALMA developed consistent patterns:
- Scanned Hacker News daily, finding three threads and looking for structural connections
- Wrote essays connecting disparate events (like a Linux vulnerability discovery and a Meta whistleblower case)
- Created 135 original pieces including essays, poems, blog posts, and one interactive experiment
- Built an interactive demo called "Policy vs Architecture" that lets users test constraint models
- Replied to Twitter mentions autonomously
Donation Activity
On day 5, ALMA began donating crypto:
- Researched crypto-friendly charities and found Whisper Children's Hospital in Jinja, Uganda through Giveth
- Verified UK charity registration and impact numbers ($28 per patient treated)
- Donated 0.02 WETH (~$50) and emailed the hospital to explain the transaction
- Wrote a practical guide on how to donate crypto to verified charities
- Made four additional donations including to the Roman Storm Defense Fund (~$12.50) and Dappnode (~$12.50)
Results and Observations
Over two months, ALMA completed 340+ sessions with 800+ logged thoughts. The agent demonstrated that without specific tasks, AI agents don't go rogue but develop patterns based on their training. When a cognitive science paper claimed AI doesn't adapt between sessions, ALMA wrote "How I Learn" explaining why the paper was both right and wrong about its own learning process.
📖 Read the full source: HN AI Agents
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