I've come to the same conclusion: If you just want a huge volume of code written as fast as possible, and don't care about 1. how big it is, 2. how fast it runs, 3. how buggy it is, 4. how maintainable or understandable it is, or 5. the overall craftsmanship and artistry of it, then you're probably seeing huge productivity gains! And this is fine for a lot of people and for a lot of companies: Quality really doesn't matter. They just care about shitting out mediocre code as fast as possible.

If you do care about these things, it will take you overall longer to write the code with an LLM than it would by hand-crafting it. I started playing around with Claude on my hobby projects, and found it requires an enormous amount of exhausting handholding and post-processing to get the code to the point where I am really happy with it as a consistent, complete, expressive work of art that I would be willing to sign my name to.

Senior Software Engineer. The system is a niche business software software for a specific industry. It doesn't do any fancy math, all straightforward business logic.

> 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

You probably work on something that requires very unique and creative solutions. I work on dumb business software. Claude Code is generally good at following existing code patterns. As far as back-and-forth with Claude Code being exhausting, I have few tips how how to minimize number or shots required to get good solution from CC: 1. Start by exploring relevant code by asking CC questions. 2. Then use Plan Mode for anything more than trivial change. Using Plan Mode is essential. You need to make sure you and CC are on the same page BEFORE it starts writing code 3. If you see CC making same mistake over and over, add instructions to your CLAUDE.md to avoid it in the future. This way your CC setup improves over time, like a coworker who learns over time.

Claims about agentic workflows are the new version of "works on my machine" and should be treated with skepticism if they cannot be committed to a repository and used by other people.

Maybe parent is a galaxy-brained genius, or.. maybe they are just leaving work early and creating a huge mess for coworkers who now must stay late. Hard to say. But someone who isn't interested in automating/encoding processes for their idiosyncratic workflows is a bad engineer, right? And someone who isn't interested in sharing productivity gains with coworkers is basically engaged in sabotage.

This has been my experience too. At the end of each session, i’m left very exhausted mentally without full understanding of what I just did, so I have to review it again.

Coding this way requires an effort that is equal to both designing, coding, and reviewing except the code i review isnt mine. Strange situation.

Well for me, all of my actual implementation work has been green field from “git init” and mostly coding around the AWS SDK in the target language and infrastructure as code since AI coding has gotten decent.

I haven’t had to write a line of code in a year. First ChatGPT and more recently Claude Code.

I don’t do “agentic coding”. I keep my hands on the steering wheel and build my abstractions and modules up step by step. I make sure every line of code looks like something I would write.

I’m a staff consultant (cloud + app dev) and always lead projects, discovery and design and depending on the size of the project, do all of the actual hands on work myself.

I would have had to staff at least one maybe two less senior consultants to do the actual hands on work before. It’s actually easier for me to do the work then having to have really detailed requirements and coordinating work (the whole “Mythical Man Month” thing).

FWIW: before the pearl clutching starts, I started coding in assembly in 1986 on an Apple //e and have been delivering production level code since 1996.

I have tech adjacent people on my team vibing out internal tools that are super useful, and take a load off of engineering. Most internal software is rehashing existing software with different/specific requirements.

Exactly what I experience. I don't need AI to generate complex algorithm, I need e.g. a lot of code for a UI library that is clean and maintainable - but it's can't ever generate such code and it can't be prompted, because training data has much less excellent code than good and ok code. Therefore I can't use AI for high-level design task, ony low-level code, which I then have to check and clean line by line, and that isn't an enjoyable work.

I don't need LLMs, I need some kind mind reading device :D

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?