Especially with junior engineers it is helpful to ask them to provide a Loom video or other proof that they have verified a feature or bug works as intended. I have tried setting up Claude Code with playwright to verify it's work, but so far am not very satisfied with the results. Any tools that are helpful with this end to end testing for web apps using Claude Code and other AI assistants? Feel free to share your product if it is relevant.
The AI coding trap
31–40 of 424 posts
Re: The AI coding trap
#32It's a fine post, but two canards in here: First, skilled engineers using LLMs to code also think and discuss and stare off into space before the source code starts getting laid down. In fact: I do a lot, lot more thinking and balancing different designs and getting a macro sense of where I'm going, because that's usually what it takes to get an LLM agent to build something decent. But now that pondering and planning…
> figuring how to get good product out of them
What have you figured out so far, apart from explicit up-front design?
Re: The AI coding trap
#33Earlier quoted context omitted.
DO NOT WRITE ANY CODE YET. haha I always do that. I think it's a good way to have some control and understand what it is doing before the regurgitation. I don't like to write code but I love the problem solving/logic/integrations part.
I'm surprised (or maybe just ignorant) that Claude doesn't have an explicit setting for this, because it definitely tends to jump the gun a lot.
Plan mode (shift-tab twice) might be what you want.
Re: The AI coding trap
#34I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless. The plan-build-test-reflect loop is equally important when using an LLM to generate code, as anyone who's seriously used the tech knows: if you yolo your way through a build without thought, it will collapse in on itself quickly. But if you DO apply that loop, you get to spend much more ti…
> I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless.
The article sort of goes sideways with this idea but pointing out that AI coding robs you a deep understanding of the code it produces is a valid and important criticism of AI coding.
A software engineer's primary job isn't producing code, but producing a functional software system. Most important to that is the extremely hard to convey "mental model" of how the code works and an expertise of the domain it works in. Code is a derived asset of this mental model. And you will never know code as well as a reader and you would have as the author for anything larger than a very small project.
There are other consequences of not building this mental model of a piece of software. Reasoning at the level of syntax is proving to have limits that LLM-based coding agents are having trouble scaling beyond.
Re: The AI coding trap
#35Earlier quoted context omitted.
DO NOT WRITE ANY CODE YET. haha I always do that. I think it's a good way to have some control and understand what it is doing before the regurgitation. I don't like to write code but I love the problem solving/logic/integrations part.
I'm surprised (or maybe just ignorant) that Claude doesn't have an explicit setting for this, because it definitely tends to jump the gun a lot.
Is that not exactly plan mode?
Re: The AI coding trap
#36I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless. The plan-build-test-reflect loop is equally important when using an LLM to generate code, as anyone who's seriously used the tech knows: if you yolo your way through a build without thought, it will collapse in on itself quickly. But if you DO apply that loop, you get to spend much more ti…
It's "anti-AI" from the perspective of an investor or engineering manager who assumes that 10x coding speed should 10x productivity in their organization. As a staff IC, I find it a realistic take on where AI actually sits in my workflow and how it relates to juniors.
Re: The AI coding trap
#37> While the LLMs get to blast through all the fun, easy work at lightning speed, we are then left with all the thankless tasks: testing to ensure existing functionality isn’t broken, clearing out duplicated code, writing documentation, handling deployment and infrastructure, etc. I’ve found LLMs just as useful for the "thankless" layers (e.g. tests, docs, deployment). The real failure mode is letting AI flood the rep…
That's my experience as well. The LLM is great for what I consider scaffolding. I can describe the architecture I want, some guidelines in CLAUDE.md, then let it write a bunch of stubbed out code. It saves me a ton of time typing.
It's also great for things that aren't creative, like 'implement a unit test framework using google test and cmake, but don't actually write the tests yet'. That type of thing saves me hours and hours. It's something I rarely do, so it's not like I just start editing my cmake and test files, I'd be looking up documentation, and a lot of code that is necessary, but takes a lot of time.
With LLMs, I usually get what I want quickly. If it's not what I want, a bit of time reviewing what it did and where it went wrong usually tells me what I need to give it a better prompt.
Re: The AI coding trap
#38It's a fine post, but two canards in here: First, skilled engineers using LLMs to code also think and discuss and stare off into space before the source code starts getting laid down. In fact: I do a lot, lot more thinking and balancing different designs and getting a macro sense of where I'm going, because that's usually what it takes to get an LLM agent to build something decent. But now that pondering and planning…
>First, skilled engineers using LLMs to code also think and discuss and stare off into space before the source code starts getting laid down
Yes, and the thinking time is a significant part of overall software delivery, which is why accelerating the coding part doesn't dramatically change overall productivity or labor requirements.
Re: The AI coding trap
#39I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless. The plan-build-test-reflect loop is equally important when using an LLM to generate code, as anyone who's seriously used the tech knows: if you yolo your way through a build without thought, it will collapse in on itself quickly. But if you DO apply that loop, you get to spend much more ti…
> I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless. The article sort of goes sideways with this idea but pointing out that AI coding robs you a deep understanding of the code it produces is a valid and important criticism of AI coding. A software engineer's primary job isn't producing code, but producing a functional software system. Most…
Not really, its more a case of "potentially can" rather than "will". This dynamic has always been there with the whole junior, senior dev. split, its not a new problem. You 100% can use it without losing this, in an ideal world you can even go so far as to not worry about the understanding for parts that are inconsequential.
Re: The AI coding trap
#40I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless. The plan-build-test-reflect loop is equally important when using an LLM to generate code, as anyone who's seriously used the tech knows: if you yolo your way through a build without thought, it will collapse in on itself quickly. But if you DO apply that loop, you get to spend much more ti…
> I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless.
Here's mine, I use Cline occasionally to help me code but more and more I find myself just coding by hand. The reason is pretty simple which is with these AI tools you for the most part replace writing code with writing a prompt.
I look at it like this, if writing the prompt, and the inference time is less than what it would take me to write the code by hand I usually go the AI route. But this is usually for refactoring tasks where I consider the main bottleneck to be the speed at which my fingers can type.
For virtually all other problems it goes something like this: I can do X task in 10 minutes if i code it manually or I can prompt AI to do it and by the time I finish crafting the prompt and execute, it takes me about 8 minutes. Yes that's a savings of 2 minutes on that task and that's all fine and good assuming that the AI didn't make a mistake, if I have to go back and re-prompt or manually fix something, then all of a sudden the time it took me to complete that task is now 10-12 minutes with AI. Here the best case scenario is I just spent some AI credits for zero time savings and worse case is I spent AI credits AND the task was slower in the end.
With all sorts of tasks I now find myself making this calculation and for the most part, I find that doing it by hand is just the "safer" option, both in terms of code output but also in terms of time spent on the task.