I think it's useful tool, but whenever I have a LLM attempt to develop an entire feature for me, the solution becomes to a pain to maintain (because I don't have the mental model around it or the solution has subtle issues).

Maybe people who are really deep into using AI are using Claude? Perhaps it's way better, I don't know.

The productivity comes from three main areas for me:

- Having the AI coding assistance write unit tests for my changes. This used to be by far my least favorite part of my job of writing software, mostly because instead of solving problems, it was the monotonous process of gathering mock data to generate specific pathways, trying to make sure I'm covering all the cases, and then debugging the tests. AI coding assistance allows me to just have to review the tests to make sure that they cover all the cases I can think of and that there aren't any overtly wrong assumptions

- Research. It has been extraordinarily helpful in giving me insight into how to design some larger systems when I have extremely specific requirements but don't necessarily have the complete experience to architect them myself - I know enough to understand if the system is going to correctly accomplish the requirements, but not to have necessarily come up with architecture as a whole

- Quick test scripts. It has been extremely useful for generating quick SQL data for testing things, along with quick one-off scripts to test things like external provider APIs

On the other hand, I have tried them a number of times in greenfield situations with Python and the web stack and experienced the simultaneous joy and existential dread of others. They can really stand new projects up quick.

As a founder, this leaves me with what I describe as the "generation ship" problem. Is it possible that the architecture we have chosen for my project is so far out of the training data that it would be faster to ditch the project and reimplement it from scratch in a Claude-yolo style? So far, I'm convinced not because the code I've seen in somewhat novel circumstances is fairly mid, but it's hard to shake the thought.

I do find chatting with the models incredibly helpful in all contexts. They are also excellent at configuring services.

Cursor and Claude Code have undoubtedly accelerated certain aspects of my technical execution. In particular, root causing difficult bugs in a complicated codebase has been accelerated through the ability to generate throwaway targeted logging code and just generally having an assistant that can help me navigate and understand complex code.

However, overall I would say that AI coding tools have made my job harder in two other ways:

1. There’s an increased volume of code that requires more thorough review and/or testing or is just generally not in keeping with the overall repo design.

2. The cost is lowered for prototyping ideas so the competitive aspect of deciding what to build or which experiment to run has ramped up. I basically need to think faster and with more clarity to perform the same as I did before because the friction of implementation time has been drastically reduced.

I have a lot of worry that I will end up having to eventually trudge through AI generated nightmares since the major projects at work are implemented in Java and Typescript.

I have very little confidence in the models' abilities to generate good code in these or most languages without a lot of oversight, and even less confidence in many people I see who are happy to hand over all control to them.

In my personal projects, however, I have been able to get what feels like a huge amount of work done very quickly. I just treat the model as an abstracted keyboard-- telling it what to write, or more importantly, what to rewrite and build out, for me, while I revise the design plans or test things myself. It feels like a proper force multiplier.

The main benefit is actually parallelizing the process of creating the code, NOT coming up with any ideas about how the code should be made or really any ideas at all. I instruct them like a real micro-manager giving very specific and narrow tasks all the time.

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.

The suggestions are correct about 40% of the time, so I'm actually surprised when they're right, rather than becoming reliant on them. It saves me maybe 10 minutes a day.

All I can say for sure is that it is absolutely useful, it has improved my quality of life without a doubt. I stick to the principle that it's here to improve my work life balance, not increase output for our owners.

And that it has done, so far. I can accomplish things that would have taken me weeks of stressful and hyperfocused work in just hours.

I use it very carefully, and sparingly, as a helpful tool in my toolbox. I do not let it run every command and look into every system, just focused efforts to generate large amounts of boilerplate code that would require me to have a lot of docs open if I were to do it myself.

I definitely don't let it read or write my e-mails, or write any text. Because I always loved writing, and will never stop loving it.

It's here to stay, because I'm not alone in feeling this way about it. So the staunch AI-deniers are just wasting their time. Just like any other tech, it's going to be used against humans, against the already oppressed.

I definitely recognize that the tech has made some people lose their minds. Managers and product owners are now vibe coding thinking they can replace all their developers. But their code base will rot faster than they think.

We use a mix of agentic and conversational tools, just pick your own and go with it.

For Unity development (our main codebase and source of value) I give current gen tools a C- for effectiveness. For solving confined, well modularisable problems (eg refactor this texture loader; implement support for this material extension) it’s good. For most real day to day problems it’s hopelessly confused by the large codebase full of state, external dependency on chunks of Unity, implicit hardware-dependent behaviours, etc. It has no idea how to work meaningfully with Unity’s scene graph or component model. I tried using MCP to empower it here: on a trivial test project it was fine. In a real project it got completely lost and broke everything after eating 30k tokens and 40 minutes of my time, mostly because it couldn’t understand the various (documented) patterns that straddled code files and scene structure.

For web and API development I give it an A, with just a little room for improvement. In this domain it’s really effective all the way down the logical stack from architectural and deployment decisions all the way down to implementation details and debugging including digging really deep in to package version incompatibilities and figuring out problems in seconds that would take me hours. My one criticism would be the - now familiar - “junior developer” effect where it’ll often run ahead with an over engineered lump of machinery without spotting a simpler more coherent pattern. As long as you keep an eye on it it’s fine.

So in summary: if what you’re doing is all in text, nothing in binary, doesn’t involve geometric or numerical reasoning, and has billions of lines of stack overflow solutions: you’ll be golden. Otherwise it’s still very hit and miss.

I write stuff for free. It's definitely "professional grade," and lots of people use the stuff I ship, but I don't earn anything for it.

I use AI every day, but I don't think that it is in the way that people here use it.

I use it as a "coding partner" (chat interface).

It has accelerated my work 100X. The quality seems OK. I have had to learn to step back, and let the LLM write stuff the way that it wants, but if I do that, and perform minimal changes to what it gives me, the results are great.