Cool paper showing how effective tuning a system prompt for an agent can be.
This is a dev post classified by Jev as Other (a free resource), kept by the Dev Radar because it carries real work, not commentary.
Cool paper showing how effective tuning a system prompt for an agent can be. Recommended paper if you tune agent harnesses. This paper presents EvolveTrade, which treats a trading agent's system prompt as its policy. After each trading interval, a separate Policy Agent reads the decision traces and the realized returns and rewrites the prompt. The backbone model stays frozen. Across several market regimes and two backbone models, the evolved agent beats fixed-prompt baselines on Sharpe ratio and cumulative return in most settings. The rewritten prompts also led the agent to run more code-
Posted by DAIR.AI (132.7k followers) 1 h ago · 10 likes · 1.9k views · view the original post on X. Kept by the Dev Radar as Other. Tools mentioned: academy.dair.ai.
More dev work like this
- Meet Tolly, your upcoming AI trading companion, developed in partnership with… — @TollyLabs
- Holy Sh*t. This is insane. — @kimmonismus
- New Opus 5.5 looking pretty fire tbh! — @mr_r0b0t
- And btw: rumors were not true. Haiku 5.5 is also being released in the coming weeks.… — @kimmonismus
- Cactus Needle x MicroDuck: watch our 29MB action model drive @pollenrobotics' MicroDuck. — @cactuscompute
- Claude Opus 5.5 takes the top spot on the Artificial Analysis Intelligence Index, along… — @ArtificialAnlys
- Deploying physical AI on wearables and phones requires real-time perception inside… — @RekaAILabs
- 🚨 Opus 5.5 benchmarks look crazy — @LuminaBench
Every post is read and classified by Jev (TypeSafe): what it is, which market it belongs to, and whether the link is a real tool. 21.2k posts from 5k X accounts over the last 21 days, 2.4k tools, 12 markets. Collected every 5 minutes, fully re-ranked every hour — last update 2026-09-22 17:37 UTC. Full method.