I think the point here is the bot didn’t come up with the idea on its own, but was seeded by a human with an entrenched interest / experience in that space. The PR around Moltbook has been that Agents are coming up with these ideas through some sort of swarm interaction with each other, and developing novel, emergent capabilities — which is not true.
this is a great question. what are the main use cases that you have for this? i’ve been working on a library for something similar and exposing it via an mcp interface. would love to pick your brain on this (@viksit on twitter)
In my fourth post in the series, I tackle how to make multi-step agent workflows learn behavior from data. Most agents today rely on vibes: prompt tuning, hand-written templates, and hope(!). This post is about replacing that with metrics and optimization.
Each branch in the workflow learns how to behave, not just where to route. I show how to set up a reward, plug in an optimizer, and treat agent behavior as something you can tune like a model.
- raw accuracy is now a "vanity" metric. so the benchmarks need to get more sophisticated, and i think they're going to have to be far more task specific than hotpot or hover. they've become like the mnist of multi hop.
- in my use of MIPROv2 and SIMBA, I see a fair amount of improvements for multi hop tasks (published some of these on hn before). I'm going to try GEPA and see how it performs. so I think we're at the start of what I would call "meta learning".. tuning across a huge search surface rather than tweaking one prompt. hyper param search for higher dim spaces.
Based on the feedback from my last HN post on differentiable routing, I ran a follow-up benchmark: local RNN vs GPT-4o for tool selection in LLM workflows.
Same accuracy, 40% lower cost. Appreciate all the suggestions, this post builds on them.
Following up on my last post about optimizing tool selection with differentiable programming, I’ve been thinking about how to extend those ideas to full agent workflows. This post shares some early experiments using DSPy to optimize routing and structure end-to-end for a sample customer service agent workflow. Feedback welcome!
for sure, there's a way here where I think we ought to be able to learn multiple tool calls and prompts together with real world data. investigating that next.
re: different tools (apis vs mcps). in my mind, there should be no real difference at what kind of tools is called at this moment since I model this as a softmax over a label set of tools.
that said, an idea I want to investigate is whether tools can live in a learned embedding space, where selection isn’t a softmax over discrete labels but a nearest-neighbor or attention mechanism over continuous vectors.
this is the intuition I'm developing as we speak and in some of my other comments on this thread (see differentiable state machine comment).
this exactly. most people can’t set it up that well.