Fable 5.1 is a thoughtful collaborator, thinking hard about my requests, proactively patching my blindspots, and verifying the work's correct without being asked. And with cache reads now costing 75% less, to $0.25/M tokens, huge savings for long-running, agentic tasks!
Been using Astra for the last few weeks and it’s so good and proactive. There’s a bunch of PRs on vitest, tsx or SwiftPM where Astra debugged OC and ended up finding and patching issues in upstream dependencies.
when AIs are extremely extremely capable my view is that those AIs will be harder to align than current systems. So for current systems, we have this feedback loop where we basically like we create an AI. We do some evaluations on it. We see that it has some kind of messed up behavior that we can kind of quickly understand. Then we like can like go look in training and be like, "Oh, the these training environments led to this problematic behavior. Let's like tweak that training data. Let's introduce some additional training data to like correct this other issue and then move forward from there." But in a regime where the AIs are extremely situationally aware, very very very very capable and um you know uh we don't necessarily understand what they're doing, this feedback loop breaks down.
One exception is that Grammarly was a different type of product in that you install it and you don't proactively open it every day. So that was kind of interesting to me because I assume that you should always just focus on existing user retention. But for a product like Grammarly, it's actually the activation, installation, aha moment that's really really critical and will carry the user for a very very long time.
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