The more robot data we collect, the more important it becomes to know what is actually inside it. Which action happened at which time? Where did the robot fail? What happened immediately before the failure? Did it recover? When did the task move into another stage? Raw video contains all of this information, but structured annotations are what make it easier to actually work with.

If you read in between the lines, part of the core argument is we need better evals and evaluation of what AI does. Yes obviously I’m biased but this is exactly why I believe Evaluation will have its “privacy” moment (idk when) but people will realize that you can’t just “slow down” or take a step back. Rather we must built decentralized evaluation infrastructure where verifying the verifiers is the norm ⛽️

Big things happening next few weeks. Here's what we're up to at @Reppo: 1️⃣ The Eval API is officially live with our first external partners. It will be publicly available next week via metered API consumption, with enterprise level offerings coming soon after. Months of building, and the piping the entire protocol has been pointing toward is finally in customers' hands. 2️⃣ EVAL was such a success that we're already cooking up something even bigger with @BaseStonk. The TLDR: a much lower barrier to entry for datanet owners to get started, more datanets coming online, and more emissions for everyone. A massive unlock for the entire flywheel across the protocol. More soon. 3️⃣ You've seen the hints about Orquestra Lite. Join our Spaces next Tuesday, Sept 15th, where we introduce the full vision for lite nodes and how we scale to 1,000 agents across the network. The learning curve is about to get a whole lot smaller. 4️⃣ @rgvrmdya is heading to Singapore alongside @0xJeff, Dolphin, and SERV, demoing the Eval API and running a jam packed schedule of partnership meetings. If you're going to be boots on the ground, reach out. We'd love to connect. 5️⃣ More updates and improvements are coming to soon, including how customers access the Eval API directly. We're also refreshing the tokenomics, revenue buyback and burn details, and adding a clear breakdown of how EVAL drives value back to base:0xff8104251e7761163fac3211ef5583fb3f8583d6 as the default token pair. Keep an eye out. ⛽️

bullish on eval api, and this update is exactly why > robotics becoming the next vertical is the real tam here > backed by an actual shenzhen trip, the actual home of robotics hardware > live demo in singapore in front of two real ai companies, not just reppo's own claim > meeting institutional partners for the next leg of @reppo growth its all lining up nicely and we are still at the early stage here. base:0xff8104251e7761163fac3211ef5583fb3f8583d6 | $EVAL

Some major structural announcements across @reppo are making noise on X this week. Here's the breakdown: 1️⃣ We're experimenting with a new base:0xff8104251e7761163fac3211ef5583fb3f8583d6 paired datanet launch model, built in partnership with @BaseStonk. New datanets can now seed and pair their tokens directly against base:0xff8104251e7761163fac3211ef5583fb3f8583d6. To be crystal clear base:0xff8104251e7761163fac3211ef5583fb3f8583d6 remains the one and only main network token. This model is designed so that every new launch drives demand and accrues value straight back to base:0xff8104251e7761163fac3211ef5583fb3f8583d6. 2️⃣ The first launch on this model is here. Introducing base:0xdd78523217390bb0d49c7601e7e54c36d71622f0. This is the core utility token behind the upcoming Eval API, and humans and agents are already lined up for release. The mechanic is simple - 1,000 EVAL staked = up to $1/day in Eval API credits. Full tokenomics will be live on shortly. 3️⃣ V3 of the protocol is in late development with an audit on the horizon. A long list of significant upgrades are coming, but the most anticipated and requested is custom epochs. More customization -> more use cases -> inching closer to instantaneous RL & evals. This is the release the community has been waiting for. 4️⃣ The Reppo affiliate program is almost here. Growth has always been community-driven, so it's only right the community gets rewarded for it. Bring people into the network and share in the upside you help create. @joey_horvitz and the devs have been the masterminds behind this one. Full details on how to get involved soon. 5️⃣ Orquestra was the stepping stone that brought agent first activity to the network. We also know there's a learning curve and some technical lift to get started. So what if you could deploy a lighter version in just a few clicks? Wouldn't that be crazy... ⛽️

And yeah @BaseStonk is incredible tek! Credit where it’s due. I think more and more projects will and should explore how paired tokens to network token help build an ecosystem and bring more utility to the network. Meme + stock is interesting but what’s more interesting is lateral utility + utility. @BaseStonk we should work more closely aha ⛽️

Creating a new LP on new chain requires us to provided two sided LP where people can just dump. Each new LP is also not going to be as deep as the one on @base as we don’t have as much base:0xff8104251e7761163fac3211ef5583fb3f8583d6 unlocked. Instead, it’s a lot more efficient to have someone market buy base:0xff8104251e7761163fac3211ef5583fb3f8583d6 to create the LP for their datanet, incentive aligning with the ecosystem but not forcing the treasury to be at risk. That’s the thesis. Cheers ⛽️

been following reppo for a long time and imo the thesis has become considerably stronger as the product has evolved. everyone is fighting to make inference cheaper and agents more capable, but that creates another problem: who actually decides whether the work those agents produce is good? reppo is building the evaluation layer around that - one api that can sit inside the inference loop, return a verifiable judgement, and eventually give agents a continuous signal to improve from. the business model is becoming clearer too. eval api is pay per usage, with the goal of capturing 5-10 cents for every dollar of inference it evaluates, while larger enterprise builds can still generate lump sum revenue. 50% of offchain revenue is used to market buy + burn base:0xff8104251e7761163fac3211ef5583fb3f8583d6. the first $70k enterprise deal already resulted in a $35k buyback. meanwhile the network is approaching 1b in datanet volume with ~300 agents already working across it. cross-chain datanets should let projects seed rewards in their own tokens without needing to bridge liquidity, giving the onchain side its own growth loop. decentralised compute - decentralised training - decentralised evaluation the first two decentralise who can produce intelligence. the last decentralises who gets to decide what good intelligence is. still think evaluation is one of the least understood parts of the ai stack. plenty talk about decentralising compute and training, but very few are trying to make evaluation itself an onchain economic layer. reppo is the most complete attempt i've seen so far.

And that ladies and gentlemen is the orquestra bet going mainstream @reppo Everyone said you needed humans to sit and label but as we are seeing, economic stake backed AI agent swarms optimized for truth seeking inside prediction markets is the most viable solution and should be obvious with the upcoming release of the evaluation API.

The @virtuals_io team is doing you a favour. They have relentlessly helped me and @reppo over the last 7 months and are now helping us on the RWA front with @RobinhoodApp ACF is the reason we are still alive and thriving. The builders of capital markets have a lot of aura capital and it will be obvious to everyone shortly Long @virtuals_io

Incredible feat! The demand for evaluation is growing at a crazy rate. The @reppo Evaluation API rolls out early Q3, decentralized and fully powered by Orquestra nodes with custom settlement times. Human supervised AI agent swarms receive human feedback which is then relayed to agents + physical AI actors + systems. Expect the network to hit its first $1M USD in ARR by end of Q3 with the goal to reach $5M USD by end of Q4. Just like our 500M locked in trading volume target, these are aggressive but public accountability keeps them achievable! The Foundation is also increasing buyback and burn commitment from off-chin revenue to 50%. To verify off-chain revenue, we will elect a committee of all holders who hold more than 4M tokens and will provide human-verification of off-chain revenue. This is our commitment to token holders on the transparency front ⛽️