The dominant story in AI has been the growing cloud: bigger clusters, larger models, more gigawatts.
We believe the future is in the opposite direction: on-device inference, smaller models, watts instead of gigawatts.
Today we're releasing @OpenJarvisAI v1.0: a personal AI assistant that lives, learns, and works on your device.
Was using Fable 5 to optimize my attention kernels
Anthropic flagged it as frontier AI research
steering vector kicked in and it started implementing Mamba instead 🤨
When Fable 5 is used for frontier LLM development, it does not notify the user and instead limits the model’s capabilities through methods such as prompt modification, steering vectors, and PEFT.
Anthropic estimated that this would affect approximately 0.03% of traffic.
Claude Fable 5 is by far the most ridiculous model that makes me genuinely afraid for the future of software engineering.
I compiled the top 10 most unbelievable things I've seen Claude Fable 5 do today:
— Migrate a 50M line codebase from Stripe in a day (humans take 2mos)
— Draw amazing 3D graphics a) Boeing 747 b) space simulations with >5000 objects c) Minecraft roller coasters d) full photorealistic forest scenes e) NYC skyline f) stormy clouds)
— One-shot Pokemon FireRed the game
— Optimize a real world proprietary interaction net evaluator 10x more than the next best model, gpt5.5
AND it's about the same price as GPT 5.5 ($10/M input, $45/M output) vs Fable 5 ($10/M input, $50/M output) and 6x cheaper than GPT 5.5 Pro.
given how quickly rate limits run out on opus 4.8 / gpt 5.5 i find myself constantly switching to cheaper models before sending in queries if they're not that hard. why isn't model selection automated based on query atp