* The codebase consists of many modules with their own repo each

* The test back end has a lot of gotchas, borked settings or broken ongoing work by the back-end team

* The code is incredibly layered

I’m spending up to 10% of my time writing actual code. The rest is overhead like multi-repo PR’s, debugging, talking to people etcetera.

Once I found the issue, the code is the easy part and explaining it all to the LLM is more work.

Assistive coding tools need to get a lot better

I found the Microsoft Copilot to be reasonably good when given a complete context with extremely limited scope such as being provided a WSDL for a SOAP service and asked to write functions that make calls and then writing unit tests for the whole thing. This had a right way and a wrong way of doing things and it did it almost perfectly.

However, if you give it any problem that requires imagination with n+1 ways of being done it flounders and produces mostly garbage.

Compared to the Microsoft Copilot I found the Github Copilot to feel lobotomised! It failed on the aforementioned WSDL task and where Microsoft's could be asked "what inconsistencies can you see in this WSDL" and catch all of them, Github's was unable to answer beyond pointing out a spelling mistake I had already made it aware of.

I have personally tinkered with Claude, and its quite impressive.

My colleagues have had similar experiences, with some uninstalling the AI tooling out of frustration at how "useless" it is. Others, like myself, have begun using it for the grunt work; mostly as "inteligent boilerplate generator."

Claude Code i will admit i find occasionally useful, but the flood of overly verbose and lackingly meaningful "AI Summaries" I'm being forced to waste time reading is really grating on me. Copilot PR summaries turning a 20-line PR into a fifty-line unhelpful essay is driving me insane.

Also I can't imagine how being handed a bunch of autogenerated terraform and ansible code would help me. Maybe 10% of my time is spent actually writing the code, the rest is running it (ansible is slow), troubleshooting incidents, discussing how to solve them & how to implement new stuff, etc.

If someone works in a devops position where AI is more of a threat, I'd like to hear more about it.

The division I worked in was demanding that developers use AI at least once a week and they were tracking people's usage. The boss nagged about it every day.

I had no problem meeting the requirement but did find it's contributions to be very hit or miss as to usefulness.

I suppose us engineers familiar with the product had just a bit more context than the investors issuing a blanket statement to their portfolios to use AI.

To be honest. I think it's pretty cool tech (I mostly use copilot with either Claude Sonnet 3.7 or 4, or otherwise GTP 4.1). Agent mode is cool. I use it every day and it has helped me work faster, do better work by it preemptively catering for things that might have otherwise taken many iterations of releases to discover, so yeah, I think AI is pretty good for software developers overall. It's a great tool to have. Is it going to do my work and leave me redundant? Not any time soon. I think the company I work for will fail in their enforced AI efforts, spend a gazillion dollars and will go quietly back to outsourcing overseas when the dust settles. I feel sad for the junior devs though as they are basically vibe coding their way through Jira tickets atm. I am a graybeard, 30+ years in the industry.

I'm certainly seeing the benefits. A lot of tasks are faster with AI. Even some quite fiddly bits of log-diving and finding subtle bugs can be done by AI, which would have taken me considerably longer.

I'm still finding that overall architecture needs to be done by me, though. Once you make the task big enough, AI goes off the rails and makes some really odd stuff.

The development and project teams I primarily work with are all encouraged to identify suitable use cases for GenAI. Most development teams have already started trials with AI assisted coding but reported a relatively low adoption rate of 5–10%.

Is this similar to companies forcing TDD or extreme programming or pair programming on their employees? Some manager hoping to get more productivity by imposing a tool or technique?

The client I work at, through them, has made some tools available but no-one is using them for anything.

We are allowed to use AI for coding, with the understanding that we are responsible for the code generated by the AI, not legally obviously, but functionally.

I'm kinda worried about how the massive usage of AI coding tool will affect the understanding of large codebases and complex systems, but to be totally honest I'm really impressed by Claude Code and how it can write Terraform/Helm/Ruby based on the company's idioms (and I'm talking about a repository with 250k+ lines of HCL!).

What are best practices? What tools are genuinely helpful, such as automatic reviews in a build street, or sentiment analysis in commit messages?

Everybody focuses on programming, but the real value in is project management imho.

Lately it's taken over code reviews, for myself and when I review other people's code. Extremely helpful. It's made software development fun again.

I've heard of companies that are shoehorning it into everything, I feel this is many companies just playing the game to get better valuations.

Permitted for development with the explicit caveat that code is always the responsibility of the people connected to the pull request.

Our company is positioned right at the edge of the wave for this though so it's understandable.

I've already communicated that I don't want to see nor hear the "but AI generated it this way" opinions. But other than that, I can see the potential, and I'm using it as well. Just not for generation of production code, rather to test assumptions, maybe initial implementations, to make things faster, but in the end I'm always reimplementing it anyway.

Also, to be completely honest, AI does better code reviews than most of my coworkers.

I recently did for the first time. Spent 15 minutes writing a long prompt to implement a ticket. A repeated pattern of code, 5 classes + config per topic that deeply interact with each other and it did the job perfectly.

It convinced me that the current code monkey jobs, which are >90%, >95%? of software engineering jobs, will disappear within 10 years.

We‘ll only need senior/staff/architect level code reviewers and prompt engineers.

When the last generation that manually wrote code dies out, all people will do is prompting.

Just like assembler became a niche, just like C became a niche, high level languages will become a niche.

If you still don‘t believe, you haven‘t tried the advanced tools that can modify a whole project, are too incompetent to properly prompt or indeed work in one of the rare, arcane frontier- state-of-the-art niches where AI can‘t help.