For throw away code, I might let the agent do some stuff. For example, we needed to test timing on DNS name resolution on a large number of systems to try and track down if that was causing our intermittent failures. I let an agent write that and was able to get results faster than if I did it myself, and I ultimately didn’t have to care about the how… I just needed something to show to the network team to prove it was their problem.

For larger projects that need to plugin to the legacy code base, which I’ll need to maintain for years, I still prefer to do things myself, using AI here and there as previously mentioned to help with little things. It can also help finding bugs more quickly (no more spending hours looking for a comma).

I had an agent refactor something I was making for a larger project. It did it, and it worked, but it didn’t write it in a way that made sense to my brain. I think others on my team would have also had trouble supporting it too. It took something relatively simple and added so many layers to it that it was hard to keep all the context in my head to make simple edits or explain to someone else how it worked. I might borrow some of the ideas it had, but will ultimately write my own solution that I think will be easier for other people to read and maintain.

Borrowing some of these ideas and doing it myself also allows me to continue to learn and grow, so I have more tools in my tool belt. With the DNS thing that was totally vibe coded, there were some new things in there I hadn’t done before. While the code made sense when I skimmed through it, I didn’t learn anything from that effort. I couldn’t do anything it did again without asking AI to do it again. Long-term, I think this would be a problem.

Other people on my team have been using AI to write their docs. This has been awful. Usually they don’t write anything at all, but at least then I know they didn’t writing anything. The AI docs are simply wrong, 100% hallucinations. I have to waste time checking the doc against the code to figure that out and then go to the person that did it to make them fix it. Sometimes no doc is better than a bad doc.

It is great for getting an overview on a pile of code that I'm not familiar with.

It has debugged some simple little problems I've had, eg, a complex regex isn't behaving so I'll give it the regex and a sample string and ask, "why isn't this matching" and it will figure out out.

I've used it only a little for writing new code. In those cases I will write the shell of a subroutine and a comment saying what the subroutine takes in and what it returns, then ask the LLM to fill in the body. Then I review it.

It has been useful for translating ancient perl scripts into something more modern, like python.

I'm learning all the time and it's fun, exasperating, tremendously empowering and very definitely a new world.

For my side projects, I do like to offload the tedious steps like setup, scaffolding or updating tasks to Claude. Things like weird build or compile errors that I usually would have to spend hours Googling to figure out I can get sorted in a matter of minutes. Other than that, I still like to write my own code as I enjoy doing it.

Overall, I like it as a tool to assist in my work. What I dislike is how much peddling is being done to shove AI into everything.

1. Gemini as a replacement for Stack Overflow, but I always have to check the source because it sometimes gives examples that 10 or even 15+ years old, as if that’s a definitive answer. We cannot and should not trust that anything AI produces is correct.

2. Co-Pilot to assist in code snippets and suggestions, like a better Intellisense. Comes in handy for CLI tools such as docker compose, etc.

3. Co-Pilot to help comprehension of a code base. For example, to ask how a particular component works or to search for the meaning of a term of reference to it, especially if the term is vague or known by another name.

Believe it or not, we have just recently received guidance on AI-assisted work in general, and it’s mostly “it’s ok to use AI, but always verify it”, which of course seems completely reasonable, as you should do this with any work that you wouldn’t have done yourself.

- a web-based app for a F500 client for a workflow they’ve been trying to build for 2 years; won the contract

- built an iPad app for same client for their sales teams to use

- built the engineering agent platform that I’m going to raise funding

- a side project to do rough cuts of family travel videos (https://usefirstcut.com, soft launch video: https://x.com/xitijpatel/status/2026025051573686429)

I see a lot of people in this thread struggling with AI coding at work. I think my platform is going to save you. The existing tools don’t work anymore, we need to think differently. That said, the old engineering principles still work; heck, they work even better now.

On the other hand I tried to get help debugging a test failure and Claude spit out paragraph after paragraph arguing with itself going back and forth. Not only did it not help none of the intermediate explanations were useful either. It ended up being a waste of time. If I didn't know that I could have easily been sent on multiple wild goose chases.

Experience level: very senior, programming for 25 years, have managed platform teams at Heroku and Segment.

Project type: new startup started Jan ‘26 at https://housecat.com. Pitch is “dev tools for non developers”

Team size: currently 2.

Stack: Go, vanilla HTML/CSS/JS, Postgres, SQLite, GCP and exe.dev.

Claude code and other coding harnesses fully replaced typing code in an IDE over the past year for me.

I’ve tried so many tools. Cursor, Claude and Codex, open source coding agents, Conductor, building my own CLIs and online dev environments. Tool churn is a challenge but it pays dividends to keep trying things as there have been major step functions in productivity and multi tasking. I value the HN community for helping me discover and cut through the space.

Multiple VMs available over with SSH with an LLM pre-configured has been the latest level up.

Coding is still hard work designing tests, steering agents, reviewing code, and splitting up PRs. I still use every bit of my experience every day and feel tired at end of day.

My non-programmer co-founder, more of a product manager and biz ops person, has challenges all the time. He generally can only write functional prototypes. We solve this by embracing the functional prototype and doing a lot of pair programming. It is much more productive than design docs or Figma wireframes.

In general the game changer is how much a couple of people can get done. We’re able to prototype ideas, build the real app, manage SOC2 infra, marketing and go to market better than ever thanks to the “willing interns” we have. I’ve done all this before and the AI helps with so much of the boilerplate and busywork.

I’m looking for beta testers and security researchers for the product, as well as a full time engineer if anyone is interested in seeing what a “greenfield” product, engineering culture and business looks like in 2026. Contact info in my profile.

I only use Claude Code with Opus 4.6 on High Effort.

I always, ALWAYS treat my “new job” as writing a detailed ticket for whatever it is I need to do.

I give the model access to a DB replica of my prod DB that I create manually.

I do NOT waste time with custom agents, Claude.md files or any of that stuff.

When I put ALL of the above together, the results ARE THE PROMISED LAND: I simply haven’t written a single line of code manually in the last 3 months.