Claude Fable 5
381–390 of 1001 posts
Re: Claude Fable 5
#382Re: Claude Fable 5
#383For example, the AAV capsid assembly looks interesting, but for one Opus 4.8 also did relatively well and there is no information what exactly they did, what protein language models they compared to and what the score even means...
Re: Claude Fable 5
#384Homebrew is lagging a bit behind. If you want to use Fable right away, but still have claude code through homebrew, this is how you can do that manually: Edit the cask locally: brew edit --cask claude-code Set the version to 2.1.170 And set the sha256 to the correct values, which you can get by running curl https://downloads.claude.ai/claude-code-releases/2.1.170/manifest.json Here's what I've used: version "2.1.170"…
Re: Claude Fable 5
#385Just commenting for posterity… if this is what it claims to be, I am not looking forward to how it will empower the people who submit bug bounties to us. Historically they’ve been people from certain identifiable countries (usually developing/poorer countries) using fuzzers with low-quality results. Now, those same people use the current-day models to good effect, but they still don’t have a true security edge and of…
Fable 5 gives me policy violation errors at the moment. No idea when or if it will be fixed.
Re: Claude Fable 5
#386Re: Claude Fable 5
#387> Distillation. We’ve previously identified large-scale attempts to extract (“distill”) Claude’s capabilities to train competing models in authoritarian countries. Glad to hear the UK is finally making an effort to catch up on the AI front ;)
Re: Claude Fable 5
#388Re: Claude Fable 5
#389On the new FrontierCode [1] benchmark (ie graded from an OSS maintainer's perspective of "would I merge this code?") - Opus 4.7 xhigh: 5.2% - Opus 4.8 xhigh: 13.4% - Fable 5 xhigh: 29.3% Seems like a huge jump. [1] https://cognition.ai/blog/frontier-code
Re: Claude Fable 5
#390> In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restrictio…
How do they detect whether an experiment being done on a smaller model is used to improve a competing frontier model, or just an innocuous hobbyist LLM experiment?