It wasn't ok - it's been shown that the data released could individually identify people in releases before the 2010 Census.

Comments URL: https://news.ycombinator.com/item?id=48517377

Points: 870

# Comments: 560

But I assure you that many places will be happy to switch up to Fable when it's available and back to Opus when it's not.

It's a programming tool, not something that will send you out of business if it disappears.

The weights are the useful artifact here. You can modify them, fine tune them and do what you want with them.

Unlike binary software there is nothing limiting that.

It is also useful to have access to the training recipes and to some extent the data. But I'm of the opinion that learning on something is not copyright infringement, so there are many circumstances where distributing the raw training data will not be possible.

For me this is like Open Office: it is open source, and largely inspired by and learned from Microsoft Office. But they don't need to distribute MS Office for Open Office to be Open Source.

In addition there are models that meet the criteria you appear to propose. The AllenAI models are a good example.

Right now there a few people who can run a 1T model at home, even less who can run a 5T model and probably single digits who can run a 10T model.

But if an open source 10T model was available you can be sure people would find new ways to quantize it, new ways to configure hardware and and new ways to think about problems that would make it useful.

1T+ models (Deepseek v4, Kimi K2.6 etc) are available as open weights now, and for ~$5000-$10000 you can run them usefully at home. 2 years ago no on was contemplating that.

$250K to run a 10T model might be possible now. There are many companies that will pay that, and that will push the tools and techniques downwards for the rest of us.

Yes, there is a gap between "taking a stake" and nationalizing one, but..

[1] https://www.reuters.com/business/trump-says-his-team-will-lo...

Anthropic is calling for regulation. For example they endorsed CA SB-53 that even OpenAI and Google thought was too much: https://www.anthropic.com/news/anthropic-is-endorsing-sb-53

They have spoken publicly about how they want open models banned (they call them Chinese models).

They might not want this specific action, but they do want regulation on their own terms. That really is regulatory capture.

> Nobody is doing this intentionally. Have you not paid attention to how quickly idiot stuff gets found out

They don't think is is "idiot stuff" - they are doing it openly and shouting to everyone who will listen! Read Dario's latest essay[1]:

> Many policymakers are showing increased openness to taking action, and it's been encouraging to see our peers come around to the same positions we've been advocating for over the past few years.

[snip]

> Thus, in 2025, Anthropic supported transparency legislation, helping to pass SB 53 in California, RAISE in NY, SB 315 in Illinois (in early 2026), and advocating for a transparency standard at the federal level.

[snip]

> It is time to go beyond transparency to more serious and binding regulation of AI.

> I am grateful to see the Trump administration’s Executive Order move incrementally towards a greater role for government in AI, though Anthropic’s proposal recommends even further action.

> The government should have the power to block or deter deployment of the model if it is determined, in light of third-party assessment, to present unacceptable risks.

I'm not sure why you think they don't want to be "found out"!

It's difficult to predict this administrations actions, but given it included employees that has to be a huge risk for Google, where Deepmind is based in London.

Cohere (Canada) and Mistral (France) are going to get a lot of interest.

It fails all the time - as in it ends up doing something I want to change.

But this doesn't actually matter - if it takes 3 or 4 iterations on something that would have taken me a week it might be a day of human work, but it's still 5 times better than doing it by hand.

I keep https://sql-benchmark.nicklothian.com/#all-data up-to-date with latest releases and try out most that score 24+.

GPT 5.5+ or Opus 4.6+ are the only things I find useful like this. Notably Gemini isn't useful in this way.

A router LLM isn't a MoE.

A MoE is a type of LLM architecture, not lots of different LLMs. They are fundamentally different concepts and it is a fundamental misunderstanding to conflate the two.

This is what the plan review is for.

Usually it will have something like "modify abc.ts to update the widget number in the wnx collection" and I'm "hang on - why does that need to be updated when XYZ" and that subsequent discussion will reveal assumptions that are not shared.

I don't really agree with their point here, but there are plenty of people in the AI community whose views are aligned with Anthropic's. That doesn't make them shills.

It's actually important those views are put forward.

A place like LessWrong has the opposite problem - there is no one there who questions the "safety narrative" so the discussion swings more and more towards the extreme end of that spectrum.

I can sympathize with the argument for the cyber refusals - especially as a temporary measure - especially if Mythos is available to those trying to defend against vulnerabilities.

The LLM development nerfing (and now refusals) is very different though. Anthropic has even said it isn't just for safety reasons:

> Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.

It's at least partially an anti-competitive measure.

The closest analogy is putting measures in a compiler to stop it being able to build other compilers.

Another analogy is priesthoods with secret religious knowledge that "only they are qualified to know".

Maybe I spent 2-4 hours reviewing it, checking things with colleagues etc.

Then I press "go" and maybe an hour later I have a tested system ready for manual review.

It's plans are at least as good as any I've seen. Their weakness is if there are unstated assumptions I have about how things need to be done, so most of my time is now getting those assumptions stated properly and then reviewing.

Why wouldn't I use this? It's the best tool I've used in my 30 years of professional programming.

For example here is Gzip in WASM: https://github.com/ColinTimBarndt/wasm-gzip

I've tried this too, and was disappointed.

Kimi generally benchmarks at "a bit more intelligent than Sonnet Medium" levels[1] and I'd agree broadly with this assessment.

If you have adapted your coding to rely on the agentic style that is doable in Opus 4.7+ then you will find Kimi disappointing.

If you are using it in a more targeted way then it can work well.

[1] https://artificialanalysis.ai/agents/coding-agents?agents=cl...

To be precise - it makes the "won't work on frontier machine learning" refusal the same as the "won't work on cyber security" refusal (instead of the way it previously would work on frontier machine learning problems but give sub-optimal answers without informing the user)