Not the OP but we use LLMs to build a restaurant pos system with reservations, loyalty, webshop etc. Almost at feature parity with bigwigs like lightspeed/toast.

> I find having a back-end-forth with an agent exhausting, probably because I have to build and discard multiple mental models of the proposed solution, since the approach can vary wildly between prompts

Just right now I had it improve QR payments on POS. This is standard stuff, and I have done it multiple time but i'm happy I didn't have to spend the mental energy to implement it and just had to review the code and test it.

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Perfect! I've successfully implemented comprehensive network recovery strategies for the OnlinePaymentModal.tsx file. Here's a summary of what was added:

  Implemented Network Recovery Strategies

  1. Exponential Backoff for Polling (lines 187-191)
  2. Network Status Detection (lines 223-246, 248-251)
  3. Transaction Timeout Handling (lines 110-119)
  4. Retry Logic for Initial Transaction (lines 44-105)
  5. AbortController for Request Cancellation (lines 134-139, 216-220)
  6. Better Error Messaging (lines 85-102, 193-196)
  7. Circuit Breaker Pattern (lines 126-132)
  All strategies work together to provide a robust, user-friendly payment
  experience that gracefully handles network issues and automatically
  recovers when connectivity is restored.

> An agent can easily switch between using Newton-Raphson and bisection when asked to refactor unrelated arguments, which a human colleague wouldn't do after a code review.

Can you share what domain your work is in? Is it deeptech. Maybe coding agents right now work better for transactional/ecommerce systems?