e.g. a colleague asked Fable to help create an simple app to help calculate the statistics for phase II and III trials. (Ignoring that such things already exist) it passed his request down to Opus, despite only being very marginally, tangentially, somewhat related to biology.

> We retain inputs and outputs for up to 2 years and trust and safety classification scores for up to 7 years if your chat is flagged by our automated trust and safety systems as violating our Usage Policy.

And, since those automated systems apparently have a ludicrous false-positive rate, you should assume that your inputs and outputs are being retained for 2 years even if you are doing nothing that any reasonable person would consider to be problematic.

Oh, and they'll train on that data [1]:

> We will use your chats and coding sessions (including to improve our models) if:

>You choose to allow us to use your chats and coding sessions to improve Claude, learn more here

> Your conversations are flagged for safety review (in which case we may use or analyze them to improve our ability to detect and enforce our Usage Policy, including training models for use by our Safeguards team, consistent with Anthropic’s safety mission)

It appears that the usual controls (including for businesses) to prevent Anthropic from training on your data will not apply.

[0] https://privacy.claude.com/en/articles/7996866-how-long-do-y...

[1] https://privacy.claude.com/en/articles/10023580-is-my-data-u...

It burns through tokens like anything but apparently Claude is much better at prompting Claude than I am.

Would I pay for it? God no. I'm still smarter than I am and it just will not work on my actual problems.

It's a pretty good strategy if they're hoping to fail as a business, I guess.

So you don't know what it should do, you may not even know what you would do, you don't necessarily know what's happening, and can't predict what will happen. How do you align that?

Seems like these overly sensitive filters are responding to this difficulty.

I blogged about it: https://swelljoe.com/post/why-i-had-to-switch-to-gpt/

From my experience, the model itself is very useful when it isn't refusing any of your prompts.

Because it is a rooted tree, only DFS intervals are required to determine ancestry.

You can detect whether a new blocking loop is going to be formed through online dominator maintenance/online cycle detection, etc, during optimization, rather than use a heuristic, if you wanted to.

Not sure it's practically faster, but that's at least the graph-theoretic answer.

In practice, outside of the suggested heuristic, I have to imagine you'd normally throw branch and bound at this, using some lazy-cut for the blocking loops (IE you can keep any of these edges but not all of them) and let it go to town.

The paper (at least, this paper) doesn't compare that to what they did, and i'd be shocked if someone hasn't tried this before, so not sure it's useful.

I'll also say you can get existing AI models to tell you the above, but you have to push them a bit most of the time step by step. Just handing them the whole overall problem, as described, and saying "what are the graph theoretical problems related to this" it sort of gets lost.

Probably because the LLM isn't doing a good job of predicting graph-theoretic words when the language is not graph theoretic, but if you translate it into a graph theoretic language piece by piece, and ask it about that, the prediction becomes better :)

  In flax nnx, what's the idiomatic way to store state on a Module. For example, if I'm handling the carry manually for an nnx.RNN.

  How do I restore one of the orbax checkpoints into NNX from this script?

It's a shame - I like Fable for writing tasks over ChatGPT and I do believe Anthropic is a more ethical outfit than OpenAI. But with the safeguards (and Fable access expiring in a few days) there's no reason to pay for draconian guardrails and harsh rate limits.

For the future of AI, we need to look elsewhere.

Of course it could also be the case that it is just a prompt filter, but Fable sees memories from the authors' prior sessions that cause a rejection. I wonder if the author could control for this is in some way, if Claude lets you run isolated session without memory access.

It's very concerning that we get the nerfed models but you know that somewhere, people with a lot of resources have access to the raw, uncensored, probably more powerful models. The sprint toward AGI looks even more dangerous when you think about who will be gaining access to it first. I do believe the goal is to pull away from the rest of humanity in a near trans-humanistic state. Are we ready for that and how do we counter it?

I had one completely random trip when I was investigating some normal code. As far as I can tell a sub-agent ended up reading a file that tripped Fable during a review, but the whole feature was nowhere near anything secure so I don't know what could have caused it.

I also got completely locked out of Fable when working on parts of a subscription system (stripe subs).

But my experience isn't as bad as some peoples. The above maybe covers 15% of my attempted use cases. For the remaining 85% it has chugged along fine, sometimes in code I assumed would trigger it. It really feels random to me when it actually flags.

Author of the post here wrote Salmon, which is a widely used bioinformatic tool in molecular biology. And the irony is that Anthropic has probably packaged Salmon as a tool in their Claude for Science suite with now remuneration or recognition for the original author.

There has been a lot of recent bs going on in biomed ML with companies publishing without releasing source code, restrictive licenses, etc; which have always given off a whiff of bad citizenship - Ark Institute and Deep Mind I am looking at you - but I feel like this is taking it to a new level.

Leveraging open source bioinformatics code and published methods to take in profit selling into the biotech vertical while restricting access to Fable feels downright cancerous. I think the EA crowd at Anthropic probably has good intentions, but has galaxy brained themselves into becoming bad actors that make Sam Altman and OpenAI look like a paragon of trustworthiness in comparison.

I'm a bioinformatician

Even questions about like my heartrate nunbers while running seem to run into the bio weapon filter

However when it's happy to do the task, its relatively fantastic.

I don't care how capable it is, if it's going to treat me like it's babysitting a terrorist, it can eff off.

Plain and simple.

Typo second paragraph, 4th line. I think you meant "what"

I've had it reject looking at pages served from my local network because it "can't find it with my search tool" and had "ethical concerns about consent for access".

The People's AI Concern Front has gotten the classifier they want, and it's made Claude hilariously useless. I am waiting with bated breath for their next set of revenue numbers. (And happily hand my money to competitors instead)

But it is a huge waste of money for most coding tasks. Opus is still overkill most of the time, too.

It's generally a major downgrade in acting like an assistant.

I don't know what's wrong but it is just bad at multi turn discourse even on a limited amount of content with no MCP or bash calls of any sort.

The thing that makes me mad is how stubbornly confident it is even whets wrong.

I have to tell it many times to actually re read the conversation as it even insists I said something else.

It's like it had a scratchpad where it has some summarized bullet points which it fills of made up content.

I'm so confused. On one side I like to connect it to honeycomb/otel logs and I can see it figures out difficult bugs in the code better than other models.

On some others I feel I'm assisting at a continuos disaster and consistent degradation since Opus 4.6, it's a tragedy.

I'm more and more the assistant to a capable, yet confidently stubborn and wrong LLM.

I would expect them to dial down the sensitivity in a few months when nobody is looking.

The retention schedule behind it:

Deleted conversations: removed from your chat history immediately, but kept on back-end systems for up to 30 days before permanent deletion. Flagged inputs and outputs (Usage Policy violation): retained up to 2 years. Trust-and-safety classification scores (on flagged sessions): retained up to 7 years. API logs: 7 days by default (as of September 14, 2025), extendable to 30 days via a DPA. Zero Data Retention (qualifying enterprise): inputs and outputs aren't stored after the API response returns, though safety classifier results are still retained even here.