AI makes tech debt more expensive

> Companies with relatively young, high-quality codebases benefit the most from generative AI tools, while companies with gnarly, legacy codebases will struggle to adopt them. In other words, the penalty for having a ‘high-debt’ codebase is now larger than ever.

This mirrors my experience using LLMs on personal projects. They can provide good advice only to the extent that your project stays within the bounds of well-known patterns. As soon as your codebase gets a little bit "weird" (ie trying to do anything novel and interesting), the model chokes, starts hallucinating, and makes your job considerably harder.

Put another way, LLMs make the easy stuff easier, but royally screws up the hard stuff. The gap does appear to be widening, not shrinking. They work best where we need them the least.

The niche I've found for LLMs is for implementing individual functions and unit tests. I'll define an interface and a return (or a test name and expectation) and say "this is what I want this to do", and let the LLM take the first crack at it. Limiting the bounds of the problem to be solved does a pretty good job of at least scaffolding something out that I can then take to completion. I almost never end up taking the LLM's autocompletion at face value, but having it written out to review and tweak does save substantial amounts of time.

The other use case is targeted code review/improvement. "Suggest how I could improve this" fills a niche which is currently filled by linters, but can be more flexible and robust. It has its place.

The fundamental problem with LLMs is that they follow patterns, rather than doing any actual reasoning. This is essentially the observation made by the article; AI coding tools do a great job of following examples, but their usefulness is limited to the degree to which the problem to be solved maps to a followable example.

Can't tell you how much I love it for testing, it's basically the only thing I use it for. I now have a test suite that can rebuild my entire app from the ground up locally, and works in the cloud as well. It's a huge motivator actually to write a piece of code with the reward being the ability to send it to the LLM to create some tests and then seeing a nice stream of green checkmarks.

I had Codeium add something to a function that added a new data value to an object. Unbidden it wrote three new tests, good tests. I wrote my own test by cutting and pasting a test it wrote with a modification, it pointed out that I didn’t edit the comment so I told it to do so.

It also screwed up the imports of my tests pretty bad, some imports that worked before got changed for no good reason. It replaced the JetBrains NotNull annotation with a totally different annotation.

It was able to figure out how to update a DAO object when I added a new field. It got the type of the field wrong when updating the object corresponding to a row in that database column even though it wrote the liquibase migration and should have known the type —- we had chatted plenty about that migration.

It got many things right but I had to fix a lot of mistakes. It is not clear that it really saves time.

Try using Cursor with the latest claude-3-5-sonnet-20241022.

Unfortunately I “think different” and use Windows. I use Microsoft Copilot and would say it is qualitatively similar to codeium in quality, a real quantitative eval would be a lot of work.

Cursor (cursor.com) is just a vscode wrapper, should work fine with Windows. If you're already in the AI coding space I seriously urge you to at least give it a go.