[retrochameleon]

I was skeptical that LLMs could be the right path to AGI, but then I kept being impressed by how much further we could take it by expanding upon the way we use it, the harnesses we use with LLMs, and better context engineering.

When I see how LLMs are capable of essentially prompt and context engineering for themselves, it makes me think they won't need human guidance forever.

When it comes to simple fact-based tasks that have a concrete methodology, it is no surprise to me that LLMs aren't the right tool, and I believe it's a failure of the harness to not recognize those types of tasks and handle them with a more concretely functioning tool instead of relying on statistical probabilities in the LLM "brain" to spit out the correct number to a math problem.

In the same sense that LLMs can use "skills" when necessary, it should have tools or possibly even specialized "brains" for it to pass of certain types of tasks to.

I'm starting to feel that our first form of AGI is not going to be a single brain but an elaborate system of harnesses, multiple LLM models, skills, domain and task specialized subsystems it passes tasks off to, etc. Whether we get there with current LLM technology before some other evolution in AI is the question, to me.

[djhn]

This sounds a lot like ignoring the Bitter Lesson, and expending a lot of effort rebuilding slightly better Expert Systems.

[retrochameleon]

Thanks for introducing me to those concepts.

If I take the Bitter Lesson into account, I would frame this as needing more focus on enabling a general intelligence to use tools more effectively and when appropriate to essentially stop making mistakes.

A basic example being a calculator. The AI needs to recognize its default thinking pattern doesn't work well for math calculations, so it delegates it to the available calculator tool / skill / MCP instead. An LLM should not be relying on LLM prediction to give a mathematical resultant figure, ever. It should come from a deterministic tool. If anything, the LLM may interpret the problem and convert it into starting math figures to use for calculation.

If we can enable AI systems to learn and apply that for themselves, and even develop their own deterministic tooling and sense of what tool to use for what job, that starts to sound promising to me.

Skills feel like a conceptual stepping stone to the next useful abstraction.