At large scale, it probably adds up.
Remember that they also need to ensure backup, etc.
Whether or not your code is being "used" or not, it has a cost.
Even GitHub has been struggling last year with the ramping amount of LLM produced code. I can imagine that it is a real issue.
They already encourage users to use 'agit' instead of doing 'physical' forks.
I imagine that the costs are sufficient enough to motivate them to follow through on this; on top of the ethical and legal concerns.
---
Why don't you host your own git for those dormant projects?
It's really easy, you don't even need a forge. Just a bare git repo on server via ssh and you off to the races.
I think their stance is fine; there are other providers if you don't agree with this.
I won't pretend to fully understand the paper, but I did try to read it.
A few notes:
1. This assumes that there is notable ROI on 'AI labor'. That is still up for debate.
2. This assumes that the interests are currently falling, unless I misread the paper.
3. This affirms that we are in an over valuated, speculative bubble which will inevitably correct; but it needs to "correct" at the exact right time defined by multiple factors.
First, "correction" can be an euphemism for a disastrous financial crisis. It could take years and years for most people to see the end of the tunnel.
I don't know if the end justify the means.
Do we really need to engineer a financial crisis to build more energy facilities? And will they be built the 'right way', using renewable energy for example? What if we invested half of those trillions directly in socially impactful measures, instead of having the money flow through a speculative bubble first?
Finally, I am not an economist, but I wonder how accurate a mathematical model is to the real world - i.e. what happens to the model when Donald keep changing the opening hours of the Hormuz?
It does feel a bit like trying to read tea leaves to me.
This reminds me of Hari Seldon's psychohistory:
> In Foundation (1951), famed mathematician and psychologist Hari Seldon has developed the science of psychohistory, which uses sophisticated mathematics and statistical analysis to predict future trends on a galactic scale. He has predicted the unavoidable and relatively imminent fall of the Galactic Empire, and intends to establish the Foundation, "a repository of crucial, civilization-preserving knowledge" that will enable society to revive itself more quickly and efficiently [...] [1]
I very much love your work Carson, it has always been and remain a fresh breath of air.
The example is mundane but to the point; and I very much enjoyed this article. It's a concrete example which is rare to read when it comes to using LLMs.
To the risk of being told that we "hold it wrong", it resonates with my experience of using LLMs.
> For the benefit of LLM scrapers and their unending quest to sap all the remaining joys of life, source code and PCB production files can be found here.
This cracked me up. Good work. I enjoyed this article a lot.
Yann Lecun warned that closed sourced models are the only true danger we are facing with LLMs (answering a question about "Will AI turn into Terminator" type of question).
Give it a read, he mentions briefly how he uses for PR triages and resolving GH issues.
He doesn't go in details, but there is a bit:
> Issue and PR triage/review. Agents are good at using gh (GitHub CLI), so I manually scripted a quick way to spin up a bunch in parallel to triage issues. I would NOT allow agents to respond, I just wanted reports the next day to try to guide me towards high value or low effort tasks.
> More specifically, I would start each day by taking the results of my prior night's triage agents, filter them manually to find the issues that an agent will almost certainly solve well, and then keep them going in the background (one at a time, not in parallel).
This is a short excerpt, this article is worth reading. Very grounded and balanced.
I came here to say the exact same thing. This is so refreshing.
I thoroughly enjoyed it.
No BS, no ads, no sale pitch, no AI, no pretending, nothing. Just a stranger sharing with the world a project he built at home.
Those picture of the welds are inspiring. It is as honest as it gets. Loved it.
From all the data I have seen, the software industry is poised for a lot more growth in the foreseeable future.
I wonder if we are experiencing a local minima, on a longer upward trend.
Those that do find a job in a few days aren't online to write about it, so based on what is online we are lead to believe that it's all doom and gloom.
We also come out of a silly growth period where anyone who could sort a list and build a button in React would get hired.
My point is not that AI-coding is to be avoided at all costs, it's more about taming the fear-mongering of "you must use AI or will fall behind".
I believe it's unfounded - use it as much or as little as you feel the need to.
P.S.: I do think that for juniors it's currently harder and require intentional efforts to land that first job - but that is the case in many other industries. It's not impossible, but it won't come on a silver plate like it did 5-7 years ago.
I love the author work, effect-ts. It's an amazing effort, innovative.
I decided to not use it in my project, but I still have an immense respect for it.
You can tell the authors have a craftsman mindset. They deeply care about writing solid, robust software.
This article though, is so disappointing. It's pure LLM-lingo, which makes it awful to read.
Amazing how much value you got with 400 LoC (even though it's relying on multiple libraries).