81–90 of 443 posts
Subagents, plugins, skills, hooks, mcp servers, output styles, memory, extended thinking... seems like a bunch of stuff you can configure in Claude Code that overlap in a lot of areas. Wish they could figure out a way to simplify things.
I'm perplexed why they would use such a silly example in their demo video (rotating an image of a dog upside down and cropping). Surely they can find more compelling examples of where these skills could be used?
Interesting. For Claude Code, this seems to have generous overlap with existing practice of having markdown "guides" listed for access in the CLAUDE.md. Maybe skills can simply make managing such guides more organized and declarative.
I'm perplexed why they would use such a silly example in their demo video (rotating an image of a dog upside down and cropping). Surely they can find more compelling examples of where these skills could be used?
I've been emulating this in claude code by manually @tagging markdown files containing guides for common tasks in our repository. Nice to see that this step is now automatic as well.
I feel like a danger with this sort of thing is that the capability of the system to use the right skill is limited by the little blurb you give about what the skill is for. Contrast with the way a human learns skills - as we gain experience with a skill, we get better at understanding when it's the right tool for the job. But Claude is always starting from ground zero and skimming your descriptions.
Of course OpenAI and Anthropic want to be able to reuse the same servers/memory for multiple users, otherwise it would be too expensive.
Could we have "personal" single-tenant setups? Where the LLM incorporates every previous conversation?
I feel like a danger with this sort of thing is that the capability of the system to use the right skill is limited by the little blurb you give about what the skill is for. Contrast with the way a human learns skills - as we gain experience with a skill, we get better at understanding when it's the right tool for the job. But Claude is always starting from ground zero and skimming your descriptions.
Which is precisely why Richard Sutton doesn't think LLMs will evolve to AGI[0]. LLMs are based on mimicry, not experience, so it's more likely (according to Sutton) that AGI will be based on some form of RL (reinforcement learning) and not neural networks (LLMs).
More specifically, LLMs don't have goals and consequences of actions, which is the foundation for intelligence. So, to your point, the idea of a "skill" is more akin to a reference manual, than it is a skill building exercise that can be applied to developing an instrument, task, solution, etc.