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Show HN: Agent framework that generates its own topology and evolves at runtime
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Re: Show HN: Agent framework that generates its own topology and evolves at runtime
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Re: Show HN: Agent framework that generates its own topology and evolves at runtime
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Re: Show HN: Agent framework that generates its own topology and evolves at runtime
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Re: Show HN: Agent framework that generates its own topology and evolves at runtime
#25It forces you to write code that is "strategy-aware" rather than just "procedural." It’s a massive shift from standard DAGs where one failure kills the whole run. Really interesting to see how the community reacts to this "stochastic" approach to automation.
Re: Show HN: Agent framework that generates its own topology and evolves at runtime
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Re: Show HN: Agent framework that generates its own topology and evolves at runtime
#27The comments on this post that congratulate/engage with OP all seem to be from hn accounts created in the past three months that have only ever commented on this post, so it seems like there is some astro-turfing going on here.
Re: Show HN: Agent framework that generates its own topology and evolves at runtime
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Re: Show HN: Agent framework that generates its own topology and evolves at runtime
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Re: Show HN: Agent framework that generates its own topology and evolves at runtime
#303. What, or who, is the judge of correctness (accuracy); regardless of the many solutions run in parallel. If I optimize for max accuracy how close can I get to 100% matemathically and how much would that cost?
I’m working on an open source project that treats this as a consensus problem instead of a single model accuracy problem.
You define a policy (majority, weighted vote, quorum), set the confidence level you want, and run enough independent inferences to reach it. Cost is visible because reliability just becomes a function of compute.
The question shifts from “is this output correct?” to “how much certainty do we need, and what are we willing to pay for it?”
Still early, but the goal is to make accuracy and cost explicit and tunable.