I don’t usually share what I build. Most of it lives on Codeberg or Github as a glorified off-site backup. 3-2-1 and all that. If I nuke my drive, I can pull it back down. That’s the whole point for me.
A while back I mentioned Pimaps here, a few people asked to see it, so I posted it. Got dunked on for AI slop despite explicitly marking which parts were AI-assisted (documentation and review- flagged with AIP). Similar story with Cliparr. I get it - Lemmy hates AI in any capacity.
What does the community actually expect from devs when sharing a project? I’m not assuming everyone here is a professional dev - though I’d guess the overlap is significant - so I’m wondering where the bar sits.
Polish? Full handwritten codebase? Something else?
How much of the sausage making do you actually want to see?
In this post and your linked post you come off as quite confrontational while exaggerating the dissent and underplaying the support. It’s a great way to get comments that strongly disagree with you. In all of that commotion, it sounds like you’ve missed the point: You’re regretting the AI disclosure, when really people were voicing their opinions in hopes that you’d regret/rethink the AI use.
Personally, I really like the AI disclosures. I like seeing how the sausage is made and what it’s made with. If the sausage is stuffed with hot dog “meat” I want to know. I’ll also avoid it like I avoid junk food, because that’s what it is. LLM-generated code is the junk food of programming. It’s a waste of resources, it’s bad for your (and everyone’s) health, and it can’t be trusted.
Some people really like junk food and hot dogs, and that’s fine. We just need to be clear that that’s not healthy and encourage healthier habits like eating fruits and vegetables (or at least real meat). People like ignoring the externalities of junk food and LLMs; both are responsible for a lot of pollution and societal harm (increased healthcare costs, dark patterns, misinformation, etc.). That’s enough reason for some people to completely remove them from their lives and be understandably vocal about how others should too.
The occasional sweet treat isn’t bad, but making sweet treats at home is always better. At home, I have more control over what ingredients I use and where I buy them from. Though I know I’ll never be able to use zero resources to make a good cinnamon roll, I can at least make the most optimal choices for myself. Ask yourself this: what value does text I generate provide? Could someone else, given the same information, generate the same text? Could someone else write functionality similar text without boiling the oceans and massive copyright infringement? I think any LLM-generated content that anyone could reasonably generate and use themselves is worthless and a net negative impact on the world. If it’s LLM-generated content that can’t be immediately/easily used, then you’re at least providing some real value by doing some work to make it usable.
AI disclosure doesn’t protect you against other people’s opinions. Some people will buy and consume the product, some people will try out the product and decide it’s not for them, some people will discover the product is made very unethically and decry it publicly in hopes that others won’t enable the ethical violations.
My intention isn’t to be confrontational, though I am genuinely perplexed, and that’s probably what you’re reading.
I don’t believe highlighting the AI tribalism is exaggerated. Technology is literally one of the top communities on Lemmy - one rarely (never?) sees positive comments about AI there. Hell, FuckAI is literally an top and active community here.
On regret: why should anyone “regret” using a tool? If we need to take moral consideration into tool use… well…should we regret using mobile phones? I would argue they are just as reprehensible.
If a dev used a local model to spelunk the codebase and write the documentation, would that sit better? Why? I’m not sure the line is where people think it is.
Meanwhile…
Anyway:
I think you’re finally getting to the realization: doing things without considering the moral or ethical implications is inherently, uhhh, immoral and unethical. Do you do more good than bad with your smartphone? Does it use thousands of watts (hours) every time you use it? Do you replace your phone every year with the new model, maximizing your environmental impact with the manufacturing? What do you think makes using a smartphone just as bad as using an LLM?
Look, I get it: considering all of the consequences of your actions all of the time is tiring. I don’t like doing it either. Clearly companies don’t do it at all (at least not for external consequences). Look where that has got us. Apathy is not an option.
Local models are better in many ways, yes. The local user has control over how much power they use (for the query, not on training), other resource usage (water, electrical power sources), what model (and by extent the training data it uses), and exactly what documentation they get out (depending on the query/ies). The best option is still obviously to write the documentation yourself, as the subject-matter expert.
Hang on though; you’re glossing over a lot of salient context.
See: https://aussie.zone/post/37281785/25338365
That’s neither here nor there. Let’s talk turkey.
On an individual level I actually think it’s worse. A lot worse.
Smartphone (Ericsson LCA): 57 kg CO₂e (Carbon Dioxide Equivalent) total over 3-year lifetime. Manufacturing dominates. https://www.ericsson.com/en/reports-and-papers/research-papers/life-cycle-assessment-of-a-smartphone
Smartphone (Carbon Trust): ~80% of footprint is production, embedded before you even turn it on. https://www.carbontrust.com/news-and-insights/insights/circular-economy-and-net-zero-how-can-carbon-footprinting-reinvent-the-mobile-phone-market/
Meanwhile - LLM inference: 0.3–0.4 Wh per query (short), up to ~17 Wh for long reasoning runs. https://arxiv.org/html/2505.09598v1
Even at 500 AI-assisted coding sessions (heavy runs at that), I’m looking at 3–4 kg CO₂e. That’s 4–6% of a single smartphone’s manufacturing emissions.
So no, they’re not “just as bad.” Individual smartphone ownership is orders of magnitude worse environmentally than individual, occasional LLM use for a hobby project. And people typically replace their mobile phones every 2-3 years . Which means every ~3 yrs your phone adds ~60 kg CO₂e.
A good primer on the topic: https://blog.andymasley.com/p/a-cheat-sheet-for-conversations-about
You’ve ignored all of the other impacts of LLMs while comparing different lifecycles.
If you’re going to include the manufacturing of smartphones, you must also include the “manufacturing” of LLMs. That requires everything from the construction of datacentres and the manufacturing of computers which go into them to the less “real” copious scraping, training, and shutdown that is required.
Here’s some other numbers that I found for a mix of ChatGPT 3.5 and 4 (since the stats for newer models are hard to find):
That puts the total per-card emissions around 3280 kg CO2e and the total usage around 32 800 tonnes CO2e just for one model. Per your smartphone metrics, that’s about equivalent to 560K people’s smartphone use. If you use a smartphone that is more carefully constructed, you can get that up to 1.13M. So for the same atmospheric pollution as 0.5-1 million people buying a phone, we made one model run for 3 years. Now consider there are multiple models and they usually get replaced more often than once every 3 years.
Most people rely on a smartphone these days, very few people need an LLM. Phones are significantly less polluting than an LLM datacentre so you’d be better off using your phone.
Now for other concerns, including the rest of the environmental devastation, which smartphones and most other products do not have. Most of these are still unknown or hard to quantify because the tech industry has long since abandoned open development of new products. I’m just going to list the problems because no one wants to read anymore paragraphs from me:
The only real positive of the tech industry shenanigans is that prices have gone up, which is discouraging people from buying these significantly environmentally impactful products and instead extending the lifespan of existing hardware.
why are you comparing manufacturing to inference? compare it to training.