> you might be considering low-effort what’s actually an attempt at simplifying - which is arguably higher effort
I'm not saying that simplifying complex topics is low-effort, good simplification can obviously require a lot of work and I fully agree here.
What I meant is more that some of these tests feel methodologically sloppy, they are too shallow, miss important technical context, do not control for enough variables etc, yet the conclusions are sometimes presented lets just say... too strongly, as I don't want to be too harsh.
> He created Django, what do you mean he's not an engineer?
I specifically said that he is not an ML engineer (emphasis on ML), so I'm not sure what Python web frameworks have to do with anything.
> Also 'low-effort??' his posts are extremely in-depth, clearly very thought through with a significant amount of time and energy
And yes, low effort. Pelican was low effort, his Fable test was low effort, his HN filter etc. Read the discussion in the comments under the Fable test, it's not just my opinion. There was also another example a few months ago. You can search for it, I don't keep track of these things.
I discussed this with him directly after he called himself an "ML expert" in comments.
This is a classic case of the Gell Mann amnesia effect. I read ML papers and work with ML, but to people outside the industry, his writing can look "extremely in-depth" even though it really isn't. People I work with have the same opinion.
> clearly very thought through with a significant amount of time and energy. Additionally he does perform multifaceted checks across LLMs in many of his other blog posts.
I have never seen an article by him about any model that I would describe that way.
And the most revealing sign that he is not an expert is the type of questions he asks and the mistakes he sometimes makes in the comments here. They show why he is not capable of doing any technically in depth evaluation (at least with his current knowledge level).
If you actually want to learn something as a layperson, read articles written by ML PhDs like Sebastian Raschka or watch Stephen from Welch Labs etc. that are directed at general audience.
He is not an ML researcher or engineer, he is a passionate AI enthusiast blogger. He mostly does SVGs and other low effort checks (sometimes with major flaws, as people have pointed out a few times in the HN comments).
Properly evaluating the model across all fronts requires a deep understanding of LLMs, how they work, the trade offs behind new architectures and the relevant research papers. It also takes a lot of time to build a proper evaluation framework so basically you can't just vibe code that if you want something that is solid.
> Meanwhile, on June 12, two days after Anthropic sent the letter, the Commerce Department imposed controversial restrictions on Anthropic's latest Mythos and Fable AI models because officials feared they could be deployed by military intelligence users in China and other countries of concern.
So that was the real reason for the Fable restriction? Because Anthropic wrote a letter to the US government saying that China was distilling Fable?
Do we have any estimates on how much the price might increase? I was waiting for the MacBook Pro with the M6 Max and 128 GB of RAM, especially since there are rumors that it will come with a design refresh
It doesn't for me. I use Fable to make plans, then give them to GPT 5.5 to review, and it always finds flaws and edge cases that Fable misses (some are really critical). It was the same with Opus 4.8. I'll admit it finds a bit fewer issues now, but Fable feels more like an incremental improvement than a major generation ahead.
Probably. This is an 8-12 trillion-parameter model, which is why it costs so much, that is also a major reason, besides RL and synthetic data, why it suddenly gained these new capabilities. They claim it was not fine-tuned or trained specifically for cybersecurity, but is instead a general purpose model.
> now the EU, instead of making it easy for companies to innovate, spends billion on trying to catch up to the US. not even catching up. getting to where the US clouds are today.
What's your alternative? The US has behemoths with trillions of dollars in market cap, more than GDPs of most countries in EU. What kind of innovation in context of cloud do you think would allow anyone to compete with them? Who would risk their own money and pour billions into challenging them?
No, they wouldn't, and they don't have it because they have chosen not to. There is something called an escalation ladder: you do not threaten to leave or kill your partner just because she spilled milk on your floor. That is the same reason Russia did not use nukes, and why other nuclear armed countries involved in conflicts have also avoided using them. The same logic applies here. Another example is that the US could bomb the Kharg island containing Iran's oil infrastructure, but that would be a major escalation. Iran would then have no reason to show restraint and could bomb the oil infrastructure of the Gulf states, creating a worldwide crisis.
Well, there are multiple token proposals processed in parallel, from which only one is picked, seems like branching to me. The only difference is that in case of CPU there is always only one possible branch that is correct.
I'm not saying that simplifying complex topics is low-effort, good simplification can obviously require a lot of work and I fully agree here.
What I meant is more that some of these tests feel methodologically sloppy, they are too shallow, miss important technical context, do not control for enough variables etc, yet the conclusions are sometimes presented lets just say... too strongly, as I don't want to be too harsh.