- cross-posted to:
- pulse_of_truth@infosec.pub
- cross-posted to:
- pulse_of_truth@infosec.pub
cross-posted from: https://infosec.pub/post/47200357
One critic called the move “petulance beyond measure.”
cross-posted from: https://infosec.pub/post/47200357
One critic called the move “petulance beyond measure.”
What a terrible article. That’s not what vibe coding means.
Yeah, that’s really dishonest framing. The whole point of vibe coding is not reading the code but trusting in its correctness based on vibes. That’s fine for low-risk internal programs, but just a downright terrible strategy for anything else, even if you have an independent test suite. Those tests may pass, but the implementation itself will be an unreadable mess
At least it outputs an unreadable mess in 20 mins, rather than 2 weeks
Guess I am a puritan for not wanting garbage that burns the planet in my everyday life I guess.
Removed by mod
How about the smaller open source models? Is the impact the same? I’m also wondering how much DeepSeek v4 changes this since the inference costs are several times lower than before. I’m sure there’s still a lot of negative effects, but I’m wondering if the needle has moved at all.
Until the datasets to train the models are curated and paid for, there won’t be an ethical LLM.
I haven’t looked into smaller models, but I’d wager that training the models is still power intensive.
And finally, how the LLM are used currently make them a net negative.
I understand the lack of ethics and I agree that their current mode of use is definitely a net negative, but was wondering more about the impact on the environment specifically.
It still takes a lot of energy to train the local models, only to get a bullshit generator.
Is there a better definition (I understand the articles one is kind of shitty)? Personally I do query the bot for various reasons but I’m not delegating complex problem solving to it, obviously.
I would say accepting AI code without review, without having to understand any of the code.