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This is great. Asking questions of library code is a big pattern of mine too. Here's an example I saw on twitter. Asking an LLM to document a protocol from the

This is great. Asking questions of library code is a big pattern of mine too. Here's an example I saw on twitter. Asking an LLM to document a protocol from the codebase: https://ampcode.com/threads/T-f02e59f8-e474-493d-9558-11fddf823672?q=tmux https://ampcode.com/threads/T-f02e59f8-e474-493d-9558-11fddf... Do you think you will be able to capture any of this extra value? I think I'm faster at coding, but the overall corporate project timeline feels about the same. I feel more relaxed and confident that the work can be done. Not sure how to get a raise out of this.

loandbehold 10mo ago
For me, as a remote developer, it means I'm able to finish my work in 1 hour instead of 8 hours. So I'm able to capture "extra value" in the form of time. In our team everyone uses GitHub Copilot and I use Claude Code. My teammates' productivity increased slightly but my productivity increased a lot. This is because 1. Claude Code is just a better coding agent 2. I invested time to get good at agentic coding. Eventually Copilot will catch up and management will realize that now 1 developer can do what previously would take a whole team.
- overfeed 10mo ago
  I'm really curious on what your role is, and which industry are you in? I'm awed by these productivity gains others report, but I feel like AI helps in such a small part of my job (implementing specific changes as I direct). Agentic workflows for me results in bloated code, which is fine when I'm willing to hand over an subsystem to the agent, such as a frontend on a side project and have it vibe code the entire thing. Trying to get clean code erases all/most of my productivity gains, and doesn't spark joy. 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. 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.
  - saxenaabhi 10mo ago
    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. ``` 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?
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    - bccdee 10mo ago
      I don't know if that example is real, but if it is, that's exactly the reason I find AI tools irritating. You do not need six different ways to handle the connection being down, and if you do, you should really factor that out into a connection management layer. One of my big issues with LLM coding assistants is that they make it easy to write lots & lots of code. Meanwhile, code is a liability, and you should want less of it.
      - 10mo ago
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loandbehold 10mo ago

For me, as a remote developer, it means I'm able to finish my work in 1 hour instead of 8 hours. So I'm able to capture "extra value" in the form of time. In our team everyone uses GitHub Copilot and I use Claude Code. My teammates' productivity increased slightly but my productivity increased a lot. This is because 1. Claude Code is just a better coding agent 2. I invested time to get good at agentic coding. Eventually Copilot will catch up and management will realize that now 1 developer can do what previously would take a whole team.

overfeed 10mo ago
I'm really curious on what your role is, and which industry are you in? I'm awed by these productivity gains others report, but I feel like AI helps in such a small part of my job (implementing specific changes as I direct). Agentic workflows for me results in bloated code, which is fine when I'm willing to hand over an subsystem to the agent, such as a frontend on a side project and have it vibe code the entire thing. Trying to get clean code erases all/most of my productivity gains, and doesn't spark joy. 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. 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.
- saxenaabhi 10mo ago
  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. ``` 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?
  - deleted 10mo ago
    [deleted]
  - bccdee 10mo ago
    I don't know if that example is real, but if it is, that's exactly the reason I find AI tools irritating. You do not need six different ways to handle the connection being down, and if you do, you should really factor that out into a connection management layer. One of my big issues with LLM coding assistants is that they make it easy to write lots & lots of code. Meanwhile, code is a liability, and you should want less of it.
    - 10mo ago
deleted 10mo ago
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