Launch HN: Rivet (YC W23) – Scale and Orchestrate Agents on S3
The supplied GitHub README page describes Rivet, an orchestrator for agentic workloads built on the actor model, where each Rivet Actor is a long-lived process with in-memory state.
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- Primary sourceGitHub
- Discovery linkHacker News
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The supplied GitHub README page describes Rivet, an orchestrator for agentic workloads built on the actor model, where each Rivet Actor is a long-lived process with in-memory state. The page claims 4.99 ms p99 cold starts, 72 KB RAM per Actor, and scaling to billions of Actors, with the tagline 'Built on S3 at SSD speeds.' The text is truncated before the architecture is explained. The full article was not checked beyond this README excerpt.
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Attribution, source and conditionsSource-stated points from the stored brief. Claim-level assessments have not been recorded.
Performance figures are self-reported in the project README and were not independently checked; the excerpt is truncated.
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Questions worth asking
Open questions for further reportingWhat remains unconfirmed?
Performance figures are self-reported in the project README and were not independently checked; the excerpt is truncated.
What material was available for this brief?
The recorded brief basis is fetched source page. These points are AINewsIQ paraphrases, not quotations or independent confirmations.
Rivet describes itself as an orchestrator for agentic workloads built on the actor model.
Source: GitHub ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseEach Rivet Actor is described as a long-lived process with in-memory state.
Source: GitHub ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseThe README claims 4.99 ms p99 cold starts and 72 KB RAM per Actor.
Source: GitHub ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLimits from the stored brief
Performance figures are self-reported in the project README and were not independently checked; the excerpt is truncated.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
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Brief history · 1 retained version
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- Version 1 · published
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Why this may matter
Agent workloads need many lightweight, stateful processes. If the stated performance figures hold, an actor-based orchestrator could matter for teams running large numbers of agents.
Limits and open questions
Performance figures are self-reported in the project README and were not independently checked; the excerpt is truncated.
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