AI acceleration makes human-in-the-loop more important, not less. The bottleneck shifts from generation to verification: can humans understand, trust, and responsibly act on what the system produces?
Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor.
It’s happening faster than we thought, and the implications deserve greater attention. https://t.co/OVVPJO7VQx
Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor.
It’s happening faster than we thought, and the implications deserve greater attention. https://t.co/OVVPJO7VQx
@antonosika there will always be people who wants to delegate more about decisions made behind a platform, but also people who prefer transparency around it, not necessarily always inspecting but presence. this types of selection has to be designed well so that user trusts remain in the loop
Claude Opus 4.7 is a horrible model. Its performance is significantly worse than the Claude Code experience with 4.6. It shows terribly narrow-minded behavior, poor intent prediction, and often fails to align with the user’s actual intent unless explicitly corrected.
Instead, it relies too much on its own phrasing, such as “verified,” and similar expressions. The model also tends to over-prioritize verification, but the verification often goes sideways, burning more tokens without properly completing the task.
Am I the only person who thinks it is worse than 4.7? I committed few hundreds of commits over the weekend, it a lot more narrow minded (aka aligned) but I spend more effort to steer it towards correct. Bug fix back and forth.
I agree majority thoughts here. However, this is not surprising and we all know structure limits the growth and people cognition. Transformation means a complete rethink of a known structure as well. The fundamental assumption is: will the core stablize?
@diptanu I think the choice of where to run harness is different by whether builder owns the harness implementation. The core problem is where do you draw the boundary of harnesses. This is tight to the model capability which is forever shifting based on the data and training.