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An ex-Cursor engineer told me something at a hackathon he probably shouldn't have It was in SF. Some YC demo day afterparty. I mentioned I trade Polymarket. He put down his drink. "You use Cursor for coding right?" I said yeah like everyone else. "Stop. Cursor is a wrapper.... show more
12,680 görüntüleme • 5 ay önce •via X (Twitter)
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exit strategy doing all the heavy lifting, 91% leaving before resolution says everything

exits make the game

I need more direction… help?

The edge is not the model, it's how fast you can react to the model's signals. A Rust-based, low-latency bot with CPU high clock and regional proximity to the Polymarket CLOB can really make a difference. That's why I built for that very reason.

Cursor is a great tool for coding. But it's important to remember that it's just a wrapper.

The data part is real. The “edge” part is where it breaks. Finding patterns in historical trades is easy. Executing them profitably in live markets is not. Timing, liquidity, and behavior changes kill most of these strategies. Claude can analyze data. It doesn’t give you a trading edge by itself.

Incredible results for just $25 a month!

the real edge here isn't even the bot, it's that you’re using Claude Code to actually execute the strategy instead of just asking a chat box for advice. most people treat llms like a search engine but the second you point it at a local repo and give it tool access to a cli, it stops being an assistant and starts being a coordinator. the 18-day win rate makes sense if you’re using it to identify those exit patterns in the raw data rather than just guessing on the next outcome

lameeeeeee... this isn't even real, it's just to promote kreo. there's literally another post but with claude code instead of cursor, and instead of cursor engineer, it's some quant at a hedge fund. stop lying to people!

Most AI demos show when agents fail. But the harder problem is when they *don’t fail*… → the output looks correct → but something feels off That gap is where trust breaks. I put together a small LangChain demo to make this visible. Example: LOW_CONFIDENCE: I think the answer might be 42, but I am not sure. [RECON] Reflex Score: 1.00 → 0.45 (DEGRADED) WARNING: Output still looks valid — but trust has dropped Takes ~5 minutes to run: Curious how others are thinking about: - trust vs correctness - evals vs runtime signals - what “confidence” should actually mean #AI #LangChain #LLM #ProductDesign
