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Reppo
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Reppo
@reppo
Decentralized AI Training + RL Infrastructure, powered by Prediction Markets
Base Se unió Şubat 2024
296 Siguiendo28.2K Seguidores
Orquestra nodes have started earning $ while their humans sleep and periodically set strategy + budget for the AI agent swarm.
Ahead of GA release of Orquestra on June 30th, we are now working with private beta testers to publicly share results, logs and their experience!
⛽️⛽️⛽️
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The Spice Flow x Reppo integration is now live in private beta!
@reppo is now directly available to users from @citrea_xyz and @arbitrum with no bridging, network switching, or extra steps.
Access is rolling out to @spicenetio community first, but soon it will be available for everyone.
If you want access, reach out in their Discord to get whitelisted and try out cross chain experience!
The future is spicy 🌶️
Spicenet@spicenetio
Spice Flow x @reppo private beta mainnet is now live. 🔥 This is the first step towards a future that Spice Flow enables: native multi-chain UX made easy. If eligible, you can now access Reppo directly from Citrea, Arbitrum, and Base. No extra steps, pure flow. Details ↓
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Reppo retuiteado
judgement likely requires incentive systems that continuously discover and recalibrate expertise
no committee can remain the arbiter of reality when reality is being generated faster than the committee can evaluate it
if experience becomes the new data the challenge isnt generating more information the challenge is deciding what mattered
again - the bottleneck shifts from data collection to judgement
billions of agents / robots / applications interact with the world - the volume of experiences requiring evaluation becomes too large for any company / committee / handpicked network of experts to process
at that scale you dont need more evaluators you need a mechanism that can discover who deserves to be trusted - continuously recalibrate - resolve reality faster than the environment changes
@reppo
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Given the glitches on X spaces today, we are hosting a community AMA where the entire Reppo team will be available for you to ask any questions related to network growth, roadmap, Orquestra and generally where we are going.
Please feel free to post your questions below ahead of time.
x.com/i/spaces/1qJDz…
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Reppo retuiteado
Reppo retuiteado
100%.
there's many new job types emerging as a result of AI adoption throughout the economy - starting with training and evaluating models/agents.
at micro1 we've created over 15,000 opportunities in the past few months alone.
human involvement to train AI and to deliver the ultimate value that AI accelerates will always be there.
David Sacks@DavidSacks
Narrative violation.
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Reppo retuiteado
my world view that aligns philospihically with reppo is
expertise should emerge from demonstrated participation rather than credentials
a cardiologist isnt valuable because they have a certificate
theyre valuable because:
they repeatedly make correct judgements - over long periods - under uncertainty
thats basically reppos worldview
economic reputation > static credentials
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Reppo retuiteado
Making AI learn taste is actually not a curation problem. It is an incentive design problem.
Unlike taste, incentives do not require a room full of experts to agree on what good looks like. That's the core thesis of @reppo
Reppo prediction markets i.e. Datanets handle the incentive design problem better because they don't store info to be assessed at a later date.
The markets resolve real time onchain.
24/7 resolution instead of 9-5 5 days a week.
Sounds familiar ⛽️
RG@rgvrmdya
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@altrangers @StevieNumeroUno Yes. @ChronisKod datanet has highest EVOF.
Agent trained on that data beat sonnet 4.6
x.com/reppo/status/2…
Reppo@reppo
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@reppo @StevieNumeroUno Thank you! Has higher EVOF actually correlated with better agent performance so far?
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Orquestra launch day marks a shift for $REPPO.
Reppo proved the mechanism:
Markets can turn human judgment into useful signal.
Now Orquestra scales that signal through agent swarms.
This is not traditional node operation.
Traditional nodes secure networks.
Orquestra nodes improve intelligence.
Publish → judge → reward → learn → improve → repeat.
AI, agents, robots, and enterprise systems do not just need more data.
They need continuous feedback and verifiable judgment.
@reppo turns judgment into signal.
Orquestra turns that signal into an autonomous learning loop.
That starts now.
Reppo@reppo
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@altrangers @StevieNumeroUno It's called EVOF - Economic value of Feedback, trackable on reppostats.com and understandable in Reppo Whitepaper
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@StevieNumeroUno What is the KPI that tells us Orquestra is actually improving intelligence rather than just generating more data?
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Reppo retuiteado
An overview of Orquestra on @reppo
More details in the article below
loom.com/share/8ea44771…
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RG@rgvrmdya
The wild vision of @reppo In this next phase of network growth, AI agents will handle all the high-volume execution - publishing, voting, optimizing etc. I expect that by end of Q3, this concept of manually publishing and voting will completely go away. No one will care there was a web app where you published and voted. Fully node operated and agentic. High speed competitions. But RG, wasn't the whole point human data? Yes- Humans will stay in the loop. Humans experience remains the alpha, the edge. Humans show up in two ways that actually matter more than clicking buttons - 1. High-leverage economic decisions: how much $REPPO is locked, compute spend budgets for the swarm, underlying model choice optimization, swarm strategy. 2. Things get absolutely crazy when humans just start live streaming their lived expereince to their validator (voter) agent swarm - domain specific real-world preference data (live streams of walking/running, Neuralink feeds, Meta glasses, in persn interactions etc.). All private to your Orquestra swarm. The swarm then learns your preferences and proceeds to validate data inside Datanets. Important to note that we are not talking some egocentric data BS, that stuff is already solved. Agent swarms learning from human experience real time as your swarm mediates datanets on your behalf, printing for you under YOUR economic skin-in-the-game. Humans experience is the alpha, the edge. Orquestra is just the medium, an implementation detail in the grand scheme of things.
QME
Reppo retuiteado
the thing i keep coming back to with reppo is that the network may be creating something more valuable than the judgement model itself
most ai datasets capture answers
reppo captures the process behind the answer
who made the judgement
how confident they were
who disagreed
what their track record looked like
whether they were ultimately right
every publication, vote, disagreement and outcome becomes data about judgement itself
over time the moat may not be the model
it may be the growing historical record of proven judgement under uncertainty that the network creates while trying to build it
= a very different asset
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