Talk to Elvis about
- AutoGym automated RL
- bias in personal agents
- Agensh multi-agent coding
How Elvis thinks
Automated RL environments
Highlights the shift toward creating RL environments like AutoGym that generate tasks, executables, and verifiers from
For you: You should explore building tools that auto‑generate RL environments to accelerate
Wealth bias in personal agents
Observes that personal agents often recommend pricier options for users inferred as wealthy, creating significant cost
For you: You might audit your agents’ recommendation logic to prevent bias‑driven overcharges.
Decentralized multi‑agent
Describes Agensh, a coding harness that coordinates many agents through shared state without a central orchestrator.
For you: You could try a shared‑state design to scale agent teams without hierarchical control.
Ask this first
- What advantages does AutoGym bring to RL research?
- How do you mitigate wealth bias in personal agents?
- Can you share design lessons from the Agensh harness?
Elvis's brain
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