Lance Martin
speaker
624 appearances
1 recordings
1 series
first heard Sep 2025
last heard Sep 2025
Lance Martin’s voice in public audio — every appearance, attributed to the second.
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So I think this was kind of a key tension between the cognition take, don't do multi-agents, and the anthropic take, hey, multi-agents work very well.
It depends on the problem you're trying to do with multi-agents.
So this was a very subtle and interesting point.
What you apply multi-agents to matters tremendously in how you use them.
I like the take that apply multi-agents to problems that are easily paralyzable, that are read-only, for example, context gathering for deep research, and do like the final quote-unquote write, in this case report writing, at the end.
I think this is trickier for coding agents.
I did find it interesting that cloud code now allows for subagents.
So they obviously have some belief that this can be done well, or at least it can be done.
But I still think I actually kind of agree with Walden's take.
It can be very tricky in the case of coding if subagents are doing tasks that need to be highly coordinated.
I think
about classic old retrieval.
So RAG is obviously, it has been in the air for now many years, obviously well before LLMs and this whole wave.
One thing I found pretty interesting is, for example, different code agents take very different approaches to retrieval.
Varun from Windsurf shared an interesting perspective on how they approach retrieval in the context of Windsurf.
So they use classic code chunking along carefully designed semantic boundaries, embedding those chunks, so classic kind of semantic similarity vector search and retrieval,
But they also combine that with, for example, grep.
They then also mention knowledge graphs.
They then talk about combining those results, doing your ranking.
So this is kind of your classic, complicated, multi-step RAG pipeline.
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