181–190 of 212 posts
There is a lot of language floating around what effectively groups of text files put together in different configurations, or selected reliably.
I ran their tool with an otherwise empty CLAUDE.md, and ran `claude /context`, which showed 3.1k tokens used by this approach (1.6% of the opus context window, bit more than the default system prompt. 8.3% is system tools).
Otherwise it's an interesting finding. The nudge seems like the real winner here, but potential further lines of inquiry that would be really illuminating: 1. How do these approaches scale with model size? 2. How are they impacted by multiple such clauses/blocks? Ie maybe 10 `IMPORTANT` rules dilute their efficacy 3. Can we get best of both worlds with specialist agents / how effective are hierarchical routing approaches really? (idk if it'd make sense for vercel specifically to focus on this though)
The key finding is that "compression" of doc pointers works. It's barely readable to humans, but directly and efficiently relevant to LLM's (direct reference -> referent, without language verbiage). This suggests some (compressed) index format that is always loaded into context will replace heuristics around agents.md/claude.md/skills.md. So I would bet this year we get some normalization of both the indexes and the…
If I remind it, it will be go, "oh yes, ok, sure." then do it, but the whole point is that I want to optimize my time with the agent.
I will have to look into this this weekend. Antigravity is my current favorite agentic IDE and I have been having problems getting it to explicitly follow my agent.md settings. If I remind it, it will be go, "oh yes, ok, sure." then do it, but the whole point is that I want to optimize my time with the agent.
Models are not AGI. They are text generators forced to generate text in a way useful to trigger a harness that will produce effects, like editing files or calling tools. So the model won’t “understand” that you have a skill and use it. The generation of the text that would trigger the skill usage is made via Reinforcement Learning with human generated examples and usage traces. So why don’t the model use skills all t…
How do you know? What if AGI can be implemented as a reasonably small set of logic rules, which implement what we call "epistemology" and "informal reasoning"? And this set of rules is just being run in a loop, producing better and better models of reality. It might even include RL, for what we know.
And what if LLMs already know all these rules? So they are AGI-complete without us knowing.
To borrow from Dennett, we understand LLMs from the physical stance (they are neural networks) and the design stance (they predict next token of language), but do we understand them from an intentional stance, i.e. what rules they employ when they running chain-of-thought for example?
Earlier quoted context omitted.
> No it's more than that - they didn't just put the skills instructions directly in AGENTS.md, they put the whole index for the docs (the skill in this case being a docs lookup) in there, so there's nothing to 'do', the skill output is already in context (or at least pointers to it, the index, if not the actual file contents) not just the front matter. The point remains: That is still just down to how you compose the…
However you compose the context for the skill, the model has to generate output like 'use skill docslookup(blah)' vs. just 'according to the docs in context' (or even 'read file blah.txt mentioned in context') which training can affect.
Of course training can affect it, but the point is that there is nothing about skills that need to be different to just sending all the skill files as part of the context, because that is a valid way of implementing skills, though it looses the primary benefit of skills, namely the ability to have more documentation of how to do things than fits in context.
Other options that also do not require the main model to know what to include ranges from direct string matching (e.g. against /) via embeddings, to passing a question to a smaller model (e.g "are any of these description relevant to this prompt: ...").