That’s the role of the commit title, with tags if you follow https://www.conventionalcommits.org/ standard.

99% of commit description will never be read. Only the title is usefull when you scroll to a git log.

But, for the remaining 1%, it means you ended up doing a git blame, git bisect or any funky stuff like this to troubleshot a bug. And when you find the commit that introduced the regression, written by some dude that leaved the company 5 years ago and see a description like “Add the whatever method in whatever class that do the stuff explained by the doc written in the file”… Well, thanks bro. But why did you do this !!!

A commit message is not supposed to be a summary of the code. It is supposed to gives information that you cannot deduct from the diff. Like:

• Is there an attached ticket ?
• Does it produce side effects ?
• Is it a quick and dirty fix just before the release ?
• What are the consequence of the modification ?
• Why you choose this approach instead of another ?
• Why are you not able to add regression test ? etc …

The first guy at Cursor that create the feature to generate commit message from diff is just a fucking moron that don’t understand the role of a commit message.

I’m still convince that polish notation was the biggest mistakes of Lisp. At some point, the guy who create Lisp language decide to get rid of 2 centuries of mathematic convention just because of a technical limitation that last for maybe 2 decades.

Oh, and no one to talked about (1- x) ?

Interesting point of view. You got my upvote 😉 I think many people took it 1st degree, so the many downvote you have.

I’m personnaly not a big fan of natural language programing. Natural language is too ambiguous and subject of interpretation. So you have to give very long description of what you want to achieve. I often do the parallel with Mathematics. If you read Euclid’s_Elements’s definition for point, line, etc…, wording is extremly wordy and indigestible. But moderne formalism is much more concise and precise. Now with LLM and vibe coding, I have the impression that programming is evolving backward 😟

The role of a developper has never been about writing code. The role of a developper is to formalize a problem. In a certain way, job of developper didn’t changed a lot since the last 50 years. My grandpa use to be developper on punch cards, and he had the sames problems than us today. How to translate accounting department request into series of instruction, how to define boundaries, etc…

I don’t know if the last part “What do these software developers actually do all day? ;->” is ironique or not.

Anyway, from my experience, industrial software developpers generate around 10 to 100 loc/day. For mobile game or shitty web stuff, it can go up to a few hundreds. For DO178A certified aeronautic code, this is closer to several days per loc 🤪. To know what’s going on when manager try to increase this metric, just have a look to Boeing.

Here is my “Tuesday evening 15min before leaving work analysis”.

Before COVID, IT market was stable. After COVID, there was WAY too many IT offers. Then, market collapse supposedly because of AI:

(Source: https://fred.stlouisfed.org/series/IHLIDXUSTPSOFTDEVE)

In blue line, actual curve. In green line, what should have been IT market without COVID and AI.

For IT market to get back to equilibrium, red area should be equal to blue area. If I do some patchwork, I ended up with this:

So my conclusion is that it indeed slowly starting to recover and should be back to normal ~mid 2027.

Time to go home, bye 😁

I use AI for small, atomic, stuff that don’t bring any intellectual value to spend time for.

Like “Typescrit. Find smallest element in an array”. “Python. Simulate keboard event to avoid computer going to sleep mode”. Or copy/past error message because I missed an import and I just want to know which one.

I also use it sometime for well identified algorithm that could be interesting but are not the core of the problem. Like “C#. Clustering algorithm to group points together in a point cloud”.

The generated code is catastrophic in term of performances/memory, but it’s good enough 80% of the time.

But eveytime I tried to use AI for higher level stuff, or that require several interdependant concepts, it ended up into hallucination pit.

• I have this problem
• Cool ! Use solution A !
• Doesn’t work
• My Bad, use solution B !
• Doesn’t exist
• Indeed ! For this problem you should apply method A which will work !
• (-_-)’