Comment by mattmaroon

No, it is incredibly streamlined because it tailored specifically to achieve this modernization.

The paid program can do it because it can accept these files as an input, and then you can use the general toolset to work towards the same goal. But the program is clunky an convoluted as hell.

To give an example, imagine you had tens of thousands of pictures of people posing, and you needed to change everyone's eye color based on the shirt color they were wearing.

You can do this in Photoshop, but it's a tedious process and you don't need all $250/mo of Photoshop to do it.

Instead make a program that auto grabs the shirt color, auto zooms in on the pupils, shows a side window of where the object detection is registering, and tees up the human worker to quickly shade in the pupils.

Dramatically faster, dramatically cheaper, tuned exactly for the specific task you need to do.

It's not just a joke, it's a meta-joke! To address the substance of your comment, it's probably an opportunity cost thing. Programmers on staff were likely engaged in what was at least perceived as higher value work, and replacing the $250/mo subscription didn't clear the bar for cost/benefit.

Now with Claude, it's easy to make a quick and dirty tool to do this without derailing other efforts, so it gets done.

The problem with this reasoning is it requires assuming that companies do things for no reason.

However possible it was to do this work in the past, it is now much easier to do it. When something is easier it happens more often.

No one is arguing it was impossible to do before. There's a lot of complexity and management attention and testing and programmer costs involved in building something in house such that you need a very obvious ROI before you attempt it especially since in house efforts can fail.

Exploring a codebase tells you WHAT it's doing, but not WHY. In older codebases you'll often find weird sections of code that solved a problem that may or may not still exist. Like maybe there was an import process that always left three carriage returns at the end of each record, so now you got some funky "lets remove up to three carriage returns" function that probably isn't needed. But are you 100% sure it's not needed?

Same story with data models, let's say you have the same data (customer contact details) in slightly different formats in 5 different data models. Which one is correct? Why are the others different?

Ultimately someone has to solve this mystery and that often means pulling people together from different parts of the business, so they can eventually reach consensus on how to move forward.

> creating documentation

How is an AI supposed to create documentation, except the most useless box-ticking kind? It only sees the existing implementation, so the best it can do is describe what you can already see (maybe with some stupid guesses added in).

IMHO, if you're going to use AI to "write documentation," that's disposable text and not for distribution. Let the next guy generate his own, and he'll be under no illusions about where the text he's reading came from.

If you're going to write documentation to distribute, you had better type out words from your own damn mind based on your own damn understanding with your own damn hands. Sure, use an LLM to help understand something, but if you personally don't understand, you're in no position to document anything.

I don't find this surprising. Code and data models encode the results of accumulated business decisions, but nothing about the decision making process or rationale. Most of the time, this information is stored only in people's heads, so any automated tool is necessary blind.

Please don't feed people LLM generated docs

Thanks for the link.

So, basically you made a replacement for webflow for your use case in 10 days, right?

I’m not sure the world needed yet another CMS

Just like coding the AI can reach out to a human for clarification on what to do.

It's not that AI is magically going to do it, it's that the human running the migration now has better tools to generate code that does account for those one-off edge cases.

I was interviewing with a company that has done ETL migration, interop and management tools for the healthcare space, and is just dipping their toes in the "Could AI do this for us or help us?"

Their initial answer/efforts seem to be a qualified but very qualified "Possibly" (hah).

They talked of pattern matching and recognition being a very strong point, but yeah, the edge cases tripping things up, whether corrupt data or something very obscure.

Somewhat like the study of MRIs and CTs of people who had no cancer diagnosis but would later go on to develop cancer (i.e. they were sick enough that imaging and testing was being ordered but there were no/insufficient markers for a radiologist/oncologist to make the diagnosis, but in short order they did develop those markers). AI was very good at analyzing the data set and with high accuracy saying "this person likely went on to have cancer", but couldn't tell you why or what it found.