Asian labs generated synthetic datasets from UBS labs but also innovated with technology. Now it is harder to get the thinking traces AND Anthropic is recorded to poison it as well.
Thus Asian labs will have to generate their own data sets, which with the huuuuge usage boom from deepseek, mimo, kimi, etc, they will be able to.
I'm not sure how it might be with Fable in practice, but we are already not that far away from AI costing as much as a full-time professional, faster in some ways but considerably less independent.
Perhaps not that close to US salaries, but those are inflated to hell. Worldwide senior engineers and scientists have salaries just about an order of magnitude away from AI subscriptions that you can use most of the day every day.
Do I remember correctly when techcrunch was charging $10k per month for a square banner on its website, 2005? And that was considered the top, for a tech blog. Even then they posted slop.
I am not saying vibe coding is the issue. The issue is that a typical developer might be working on a lot more projects that run concurrently then they used to. And because of the various nature of the project the risk is significantly increased.
Scale this across the workforce and you not just doubled the problem.
You can vibecode docs and tests also but I'm truly not seeing more of those.
In the end it can just be a culture thing. A dev who was going to write docs and tests before is going to have a LLM generate docs and tests today. Same with safe practices and defensive coding. The machine does whatever you want from it, for most that's "just get the job done I don't care". So that's the output.
If I vibe code a project, that involves docs and tests as well. Obviously I do not, at any point, do anything blindly and there are some iterations for everything. I always double-check, and I do not use "agents", I do everything manually. I always check what the LLM is thinking, in real-time. I might be old school, but that allows me to write code that is not a pile of shit. :P I am still conscious about quality.
I think that the numerical example you gave appears to be wrong unless you intended 1% rather than 0.01%.
In any case, fair enough. The concern is that organizations will build processes around AI where many people do not review outputs carefully. I do not disagree with this.
I also agree that my particular workflow is anecdotal and does not work at scale.
Yes my bad I even checked it in the calculator but then typed in .01 again but added % again. I meant to do it to serve as an example of how bad humans are at thing.... right...
You can also fork everything and maintain local versions that you much more easily resolve conflicts with upstream with AI and get the best of both worlds while you work through the backlog of internally reimplementing all dependencies, which even with AI will take a long time.
And then load up MusicBrainz Picard to tag it after-the-fact; I've found some cases, usually on more obscure music, where Youtube metadata was close to right but wrong on some details. But it had enough details to allow Picard to find the correct album, and then the file(s) was/were tagged correctly.
Have you even used deepseek pro/flash? Yes, it is astroturfed to the maxx. There is a reason for that. The performance/price ratio beats anything available today.
You misused the term 'astroturfed.' If the performance/price is that good than it'll be spreaded by word of mouth and no need to astroturfed to the death.
... and I believe which is happening. I've been advocating for DeepSeek V4 Pro and no one paid me. It's almost too good to be true.
"Don't you understand? I'm on team deepseek! It doesn't matter what's written about it. Heck it doesn't even matter if it's all lies - it supports my team and here's why I love my team."
"You're on the team against me so I oppose everything you say".
Again it's the same problem - what you're doing. I'm not on "team OpenAI". I'm also not on "team deepseek". I'm commenting on how so much of the population is literally unable to see the world unless it is filtered through some "team" lens that they are for or against.
Judge the material based on what's in the material. Not as it boosting or hurting your "team".
The material in this article is crap judge it as crap and say so regardless of your team.
But here you look at my saying something negative about a post that is pro "team deepseek" so the only conclusion you're able to make is that I must be for the other team.
It's the inability to think critically that is astounding me here. So many opinion's people have now is now just "is it for team or against my team". They are unable to even think of anything else.
I wrote that entire post and you even said you couldn't understand it unless you put it through a lens of being for or against a team...
> Your area again making the same mistake as before.
> You are making the most passionate defense of team openai
At no point did I mention Openai, referr to openai or imply anything about openai (just mentioned your reference). Nothing I'm saying weighs in on any form of discussion or debate between Deepseek & Open Models vs OpenAI.
The fact that you are unable to separate those two is your failing, not mine. Your argument is the equivalent of the following:
A: Deepseek ran into a burning building last week and saved 10,000 orphans from a fire.
Me: No Deepseek did not save 10,000 orphans from a burning building last week. Regardless of what you think of Deepseek it didn't save 10,000 orphans. It's an LLM in a computer, not a humanoid robot - if you look at that for 2 seconds you see that claim is nonsense.
You: By attacking those supporting Deekseek you have declared yourself for team OpenAI and are clearly an OpenAI supporter!
Me: Saying deepseek didn't save 10k orphans has nothing to do with OpenAI. It is a lie saying that deepseek saved 10k lives. It's an LLM chat bot. Regardless of how anyone feels about deepseek - discuss it on it's merits not on bs.
You: See! You keep defending OpenAI you open AI shill! Stop passionately defending OpenAI!
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