Every release of these companies makes me angry because I know they take advantage of all the people who release content to the public. They just consume and take the profit. In addition to that Anthropic has shown that they don't care about our privacy AT ALL.
Claude Skills
191–200 of 443 posts
Re: Claude Skills
#192Earlier quoted context omitted.
Why are you asking them to cite something for that statement? Are you questioning whether it's the foundation for intelligence or whether LLMS understand goals and consequences?
Yes, I'm questioning if that's the foundation of intelligence. Says who?
Richard Sutton. He won a Turing Award. Why ask your question above when you can just watch the YouTube link I posted?
Re: Claude Skills
#193The way this is headed - I also see a burgeoning class of tools emerging. MCP servers, Skill managers, Sub-Agent builders. Feels like the patterns and protocols need more explainability to how they synthesize into a practical dev (extension) toolkit which is useful across multiple surfaces e.g. chat vs coding vs media gen.
Re: Claude Skills
#194It's an interesting idea (among many) to try to address the problem of LLMs getting off task, but I notice that there's no evaluation in the blog post. Like, ok cool, you've added "skills," but is there any evidence that they're useful or are we just grasping at straws here?
Re: Claude Skills
#195Earlier quoted context omitted.
You forgot mcp-everything! Yes, it's a mess, and there will be a lot of churn, you're not wrong, but there are foundational concepts underneath it all that you can learn and then it's easy to fit insert-new-feature into your mental model. (Or you can just ignore the new features, and roll your own tools. Some people here do that with a lot of success.) The foundational mental model to get the hang of is really just:…
> Call it what you want, you can write it in 100 lines of Python. I encourage every programmer I talk to who is remotely curious about LLMs to try that. It is a lightbulb moment. Definitely want to try this out. Any resources / etc. on getting started?
This is the classic blog post, by Thorsten Ball, from way back in the AI Stone Age (April this year): https://ampcode.com/how-to-build-an-agent
It uses Go, which is more verbose than Python would be, so he takes 300 lines to do it. Also, his edit_file tool could be a lot simpler (I just make my minimal agent "edit" files by overwriting the entire existing file).
I keep meaning to write a similar blog post with Python, as I think it makes it even clearer how simple the stripped-down essence of a coding agent can be. There is magic, but it all lives in the LLM, not the agent software.
Re: Claude Skills
#196While not generally a bad idea, I find it amusing that they are reinventing shared libraries where the code format is...English. So the obvious next step is "precompiling" skills to a form that is better for Claude internally.
...which would be great if the (likely binary) format of that was used internally, but something tells me an architectural screwup will lead to leaking the binaries and we'll have a dependency on a dumb inscrutable binary format to carry forward...
Re: Claude Skills
#197AGI nowhere near
I know I'm replying to a shitpost. But I had a realisation, and I'm probably not the only one. If you can manage to keep structuring slightly intelligent tools so that they compound, seems like AGI is achievable. That's why the thing everyone is after right now is new ways to make those slight intelligences keep compounding. Just like repeated multiplication of 1.001 grows indefinitely.
But how often can you repeat the multiplication when the repetitions are unsustainable?
Re: Claude Skills
#198Earlier quoted context omitted.
> 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. 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). Mor…
This is an uninformed take. Much of the improvement in performance of LLM based models has been through RLHF and other RL techniques.
> This is an uninformed take.
You may disagree with this take but its not uninformed. Many LLMs use self‑supervised pretraining followed by RL‑based fine‑tuning but that's essentially it - it's fine tuning.
Re: Claude Skills
#199At term (and not even far term) - LLMs will be able to churn up their own "skills" using their sandbox code environments - and possibly recycle them through context on a per-user basis.
While I like the flexibility of deploying your own skills to claude for use org-wide, this really feels like what MCP should be for that use case, or what built-in analysis sandbox should be.
We haven't even gone mainstream with MCP and there are already 10 stand-ins doing roughly the same thing with a different twist.
I would have honestly preferred they called this embedded MCP instead of 'skills'.
Re: Claude Skills
#200Earlier quoted context omitted.
There's so much white space - this is the cost of a brand new technology. Similar issues with figuring out what cloud tools to use, or what python libraries are most relevant. This is also why not everyone is an early adopter. There are mental costs involved in staying on top of everything.
> This is also why not everyone is an early adopter. Usually, there are relatively few adopters of a new technology. But with LLMs, it's quite the opposite: there was a huge number of early adopters. Some got extremely excited and run hundreds of agents all the time, some got burned and went back to the good old ways of doing things, whereas the majority is just using LLMs from time to time for various tasks, bigger…
There's early adoption from individuals. Much less from enterprises. (They're buying site licenses, but not re-engineering their company processes)