That sounds like Léon – The URL Cleaner, which I use on a daily basis.

This quote from the article very much sums up my own experience of Claude:

In my recent experience at least, these improvements mean you can generate good quality code, with the right guardrails in place. However without them (or when it ignores them, which is another matter) the output still trends towards the same issues: long functions, heavy nesting of conditional logic, unnecessary comments, repeated logic – code that is far more complex than it needs to be.

AI coding tools definitely helpful with boilerplate code but they still require a lot of supervision. I am interested to see if these tools can be used to tackle tech debt, as often the argument for not addressing tech debt is a lack of time, or if they would just contribute it to it, even with thorough instructions and guardrails.

Thank you! When I stumble across the Neovim posts, I try to share them here if I think someone will find them useful.

The way the author described programming in 2025 did make me chuckle, and I do think he makes some excellent points in the process.

It’s 2025. We write JavaScript with types now. It runs not just in a browser, but on Linux. It has a dependency manager, and in true JavaScript style, there’s a central repository which anyone can push anything to. Nowadays it’s mostly used to inject Bitcoin miners or ransomware onto unsuspecting servers, but you might find a useful utility to pad a string if you need it.

In order to test our application, we build it regularly. On a modern computer, with approximately 16 cores, each running at 3 GHz, TypeScript only takes a few seconds to compile and run.

As the author notes, it is very impressive what generative AI can produce these days.

The frontier of what the LLMs can do has moved since the last time I tried to vibe-code something. I didn’t expect to have a working interpreter the same day I dreamt of a new programming language. It now seems possible.

However, as they point out, there’s definitely downsides to this approach.

The downside of vibe coding the whole interpreter is that I have zero knowledge of the code. I only interacted with the agent by telling it to implement a thing and write tests for it, and I only really reviewed the tests. I reckon this would be an issue in the future when I want to manually make some change in the actual code, because I have no familiarity with it.

What about developers who are required to use AI as part of their job?

I stumbled upon this article as I was having issues with my LSP setup for TypeScript projects. However, in my case, it appears the bug is in the plug-in nvim-lspconfig.

I’m not an architect, but I do dislike how much of development work has AWS wrangling, dealing with the architectural hoops that are mentioned in the article

Things are getting easier. Many of the JavaScript runtimes support TypeScript out of the box now.

Oh, it’s not my own blog, I just stumbled upon it, and wanted to share the post.

Back in the day, I used CakePHP to build websites, and it had a tool that could “bake” all the boilerplate code.

You could use a snippet engine or templates with your editor, but unless you get a lot of reuse out of them, it’s probably easier and quicker to use an LLM for the boilerplate.

I also make use of ‘⚠’ to mark significant/blocking comments and bullet points. Other labels, like or similar to conventional comment prefixes, like “thought:” or “note:”, can indicate other priorities and significance of comments.

Thank you for introducing me to conventional comments! I hadn’t heard of them before, and I can see how they’d be really useful, particularly in a neurodiverse team.

How does one measure code quality? I’m a big advocate of linting, and have used rules including cyclomatic complexity, but is that, or tools such as SonarQube, an effective measure of quality? You can code that passes those checks, but what if it doesn’t address the acceptance criteria - is it still quality code then?

Exactly but generative AI has exacerbated the problem

What is new is the scale of the problem being created as lightning-speed code generators spew reams of unread code into millions of projects

My understanding is that an example of a hypothesis, is that users want a feature. The experiment is putting that feature in front of users, or performing user research, which which then allows you to validate if a hypothesis is true or not.

I am intrigued. Could you elaborate on this with some examples?

To clarify, when you say freelancer, do you mean on a part-time basis? Or do you mean having a contract and working on a full-time basis for that client? Also, where are you based? I’m happy to give advice as a UK based contractor, who does a tiny bit of freelance work