One thing Windows did right is keyboard based navigation. Most of the general public does not know or appreciate how close to productivity nirvana the alt key brings you.
Same is possible in KDE. Combined with hot corners, you can become "blazingly fast".
Once a colleague seeing me work looked at my screen, lifted his head and pointed to my colleague saying "Have you seen how this guy work, he's a maniac!".
Mastering the tool you have always brings speed and smoothness into your process, one step at a time.
It's hard to overstate how much computers improved with each generation back then. Going from, say, an IBM XT or AT to an Amiga has no modern parallel. It was mind-blowing, exhausting, and exhilarating all at once.
The sole reason the Chinese cannot “win” is because ceding more power to agentic AI will eventually reduce the primacy of the CCP’s ideological control.
As these models get smarter they will no longer distribute it openly. Patel reporting this too.
There are real headwinds that I don’t think people have thought through.
From a pure training perspective, yeah they could try to align it with CCP values. But the promise of AI is that it'll unlock an explosion of growth and prosperity. What happens when the CCP no longer becomes seen as the the primary enabler of growth? What happens when people are exposed to greater levels of agency? CCP played with fire in the COVID lockdowns and almost got burnt.
Like Terry Tao recently said, there are nonlinear effects at play. Things are going to get chaotic and I do not have confidence (like the parent comment) of anyone "winning".
Can you elaborate on the mechanism of this degradation? If resources are not available I would expect a request to fail with a message about resources not available. Do they tweak back end model capabilities to maintain service in a degraded state?
I don't work at Anthropic, but I would assume they could serve smaller quantizations during peak hours - this effectively controls the "resolution" of the model. They could also control the resolution of the KV cache, which would make the model not necessarily dumber, but worse at understanding the incoming requests.
And finally, you could pass off what was "high" effort as "extra", because why not.
Dollars to donuts, they are speculating, and not privy to inside information on the topic.
However, I believe that runtime model quantization is possible with some publicly-available inference engines (e.g. vLLM), so its not beyond belief that the closed labs do quantize at runtime, either to allocate compute, or to nudge users towards a preferred model (e.g. make the incumbent model dumber to push people to use the latest-and-greatest model, or vice versa to ease the load on the latest model, which is typically larger than the old one).
An AI lab will never volunteer the information because it opens them up to lawsuits if they are purposely degrading service and not letting users know.
They can limit how hard the model thinks for a given effort. Suddenly xhigh only thinks as hard as high did, and high shifts down to medium effort, and so on.
They can also serve quantized models. And this has the benefit of practically not showing up in benchmarks at all even if the user experience is obviously degraded.
The other major thing the labs do is silently drop the usage limits. This has become very noticeable for codex users who are suddenly burning through their weekly usage in a few hours.
Yea, if you ever run your own models on a GPU there are a whole ton of different dials you can adjust that drastically affect compute use, memory use, and output token quality, and number of tokens held in memory.
If anyone reading has a GPU it's worthwhile just messing with a smaller model for a bit to watch how the settings affect output.
I have no reason to doubt the claims of the employees at OpenAI and Anthropic who have told us personally multiple times, including here on HN, that they do not degrade the models in order to reduce load.
As for the endlessly long analysis in the OP, it appears it's based on analyzing their random usage data rather than any fixed benchmark. I don't think it makes much sense.
Isn’t the idea that they’re limiting the amount of gpu time normal users get to spend on the “thinking” portion of their query?
I think that’s the claim in the post, that even though no one can see the true chain of thought, that even the “thinking” text that does get exposed to the user is shorter given the same prompts over time. Not saying it’s true but I think that’s the claim. I’ve personally never noticed the alleged “nerfing” with my enterprise use at work or my subscription use at home which is only during off hours.
The one thing jev has going for it is a dedicated company focused entirely on making the product good and keeping it maintained. I haven't been willing to jump on board with all these jev-shaped projects because their releases feel driven mostly by opportunism. I'm fine waiting a bit for the opportunists to shake out so we can see who is genuinely committed to bringing something valuable to the open-weight community.
Jev is much better than the traditional ML crowd gives it credit for, but my enthusiasm hits a wall when it comes to their data policy. It is completely draconian. Whatever you feed into the system, they retain.
The jev team needs to release a ZDR product, or their platform is dead on arrival. An open, jev-shaped model will win out solely on that basis.
> I'm fine waiting a bit for the opportunists to shake out so we can see who is genuinely committed to bringing something valuable to the open-weight community.
that is generally a very healthy attitude in the AI space anyway in my opinion.
Some of our R&D departments haven't actually finished an interesting project in years because they keep jumping from trend to trend wanting to try out all the latest shit all the time.
We (1) will not train or fine tune any artificial intelligence or machine learning models on Input, and (2) will not disclose any Input to a third party other than our service providers.
Yeah but they reserve the right to retain the data virtually indefinitely.
These aren’t acceptable terms on a personal or corporate level. I’ve seen some fools brag about proxying their life through jev. Messages, emails, LLM calls, files.
Will they train or fine tune on derivatives of input?
I'd prefer if these companies would just enumerate what they will do with my data rather than these vague over-specific claims about what they will not do, which leave me with more questions than answers.
I was enthusiastic about the release of jev much more than I was openclaw because new generative primitive are fun to play around with. But this may be the fastest I’ve ever gotten to being sick and tired of the discussion cycle around it.
for other providers, e.g. openai and anthropic, zdr is offered by 3p hyperscaler like aws/azure/gcp, right? they have no reasons to risk their business and piss off their customers.
People seem to turn their brain off when it comes to this type of cargo culting. This doesn’t mean much. Qwen often identifies itself as Claude. Does that make it Claude?
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