Reppo

Expansion to Asia mid June! Excited to check off major item off our Q2 roadmap! ⛽️⛽️⛽️ reppo.xyz/roadmap

Privacy szn in full effect but next is verifiability. Verifiability as a primitive can be broken down in two types - 1. On chain verifiability - Verify transactions and state change’s cryptographically and publicly auditable. This is why products like Opengradient and Usepod.ai matter. This tells you what happened and allows anyone to verify. What did my AI agent do? When? Etc. 2. Economic verifiability - What’s the quality of the action performed? When people put real money on the line using mechanisms like prediction markets, where actions reveal truthful information because they’re financially incentivized to be right. That turns subjective human judgment into high-quality outputs, in the case of @reppo, verifiable training data for AI. Will become obvious ⛽️

@reppo thesis on why capturing rate of change and the why behind a decision matters more than the decision itself. This is core to our collab with @StrikeRobot_ai Prediction markets are the best mechanism to track rate of change when it comes to QC pipelines

Announcing a partnership between Reppo and UsePod UsePod is now Reppo's default routing layer for Venice models. Anyone using Venice's SDK routes through UsePod for discounted inference, and anyone can use this same infrastructure to publish agent-generated outputs directly to Reppo datanets. @reppo is a decentralized network for sourcing, curating, and monetizing AI training data. Organized into owner-defined datanets, it turns training data into a live market where participants publish content, stake capital, vote on quality, and generate usable learning signal. UsePod.ai is a two-sided marketplace for AI inference where crypto token holders monetize daily credit allocations, hardware providers compete on price, and users access frontier models at 40-50% below market rates. Reppo creates market infrastructure for training data. UsePod creates market infrastructure for inference. By integrating routing and enabling seamless publishing to datanets, we're building the full stack--training data markets feeding inference markets, both operating with real price discovery, both settled onchain, both treating AI resources as tradable instruments instead of subsidized services.

Just had my first dataset sale from the $Reppo Geopolitics Datanet. Huge and important signal. The amount almost doesn’t matter. The important part is that curated human judgment is starting to become a tradable asset class.

RT @rgvrmdya: Often visiting own writing to remind why we here @reppo AI training moving to post deployment which needs verified data + D…

This is a good starting point from a TIG challenge perspective. We have already proposed this to the team. At its core, @reppo is building time-decaying markets with training data being the primary GTM use case. The protocol itself is generalized and can be leveraged for many problem spaces - drive.google.com/file/d/10gwbZN…

Who from the $TIG community wants to talk about the potential of @reppo I ask, because I want to dive deeper during my free time, would love some direction/inspiration from the x community Have a nice Sunday.

@virtuals_io @useOttoAI @the_small_thing @StrikeRobot_ai @ReplyCorp ⛽️⛽️⛽️

Can't tokenmaxx your project? Need more than trading fees alone? We’ve routed $5M to agent builders through ACF, giving teams a second funding engine as they grow.

⛽️⛽️⛽️

@balajis $REPPO is trying to help by creating an onchain prediction market where experts stake real value on solid feedback and reward signals. it gives agents a way to pull direct human input when they need it, which could tighten the loop and move things closer to actual autonomy.

@XiaoSophiaPu We leverage prediction markets to approach this but same principle - Instead of paying to upload data, we change to enter domain specific prediction markets. Glad to see your approach getting academic recognition

🚨 Why does Self-Play RL for LLMs keep collapsing? Most fixes focus on the reward signal. In our new paper "Survive or Collapse", we show that's the wrong lever. The true binding constraint is actually Data Gating: deciding which generated tasks enter the training pool. 🧵 1/n

@slash1sol Beautiful. Real-time data always wins

PREDICTION MARKET RESEARCH JUST GOT KILLED BY ONE .MD FILE. The .md file in the video plugs any AI agent into 1,800 live data sources -> Polymarket orderbooks, satellite imagery, vessel tracking, NOAA weather, SEC filings, sports lines, and the top 100 KOL wallets. It's @preftrade. No APIs, no scraping, no signup and no card. An agent with this installed doesn't ask "What's the price". It pulls the orderbook depth on Polymarket, cross-references vessel positions in the Strait of Hormuz, scans the latest SEC filings on the names mentioned, and watches what the top 100 KOL wallets did in the last 4 hours. Before it makes a single call. The numbers are insane: > $0 in API fees. > $0 in data subscriptions. > 670+ capabilities behind a single endpoint. Every datapoint with full provenance back to the source. The mechanism is wild too: It's called Preference. An MCP server that gives any AI agent structured access to prediction markets -> Polymarket, Kalshi, Hyperliquid, dFlow AND the real-world signals that price them. Your agent asks one question, gets the full picture before it acts. It goes way past Polymarket: Smart-money mirroring on the top 100 wallets in real time. Cross-venue arb scanners and event-driven agents that watch tanker traffic in the Strait of Hormuz and trade oil-linked markets. Backtesting pipelines over historical data plus the world signals that moved each market. The model was never the bottleneck. The data was. One agent, one .md file and Live world data on tap. -> pref.trade/skill.md Retail still has 12 CoinGecko tabs open. Agents already have the orderbook. Full info and guide at pref.trade/guide. Don't forget to save.

Even @balajis agrees that Human prompting and verification remains the bottleneck, because digital AI only does it middle-to-middle, not end-to-end. Our solution isn’t more just more humans but prediction markets which brings economic stake to the verification process ⛽️

Excited to present Reppo agent swarm that allows one click deployment of publishing and voting agents into Datanets using Reppo CLI Build datasets on demand by simply orchestrating agentic swarms and get paid by agents + robots to maintain Datanets. We will also take community questions ⛽️ x.com/i/spaces/1mgpa…

Today I sat down with two builders solving a problem most CT hasn't even noticed yet. Robotics training data is broken. And the fix is being built right now. Here is the full recap for anyone who missed it: @crisnguyen99 from @StrikeRobot_ai and @RG from @reppo spent an hour breaking down exactly why that gap exists and how they are closing it together. Here is everything you need to know. The data problem nobody talks about: ➜ Real-world robotics data collection requires professional teleoperation operators filming every angle of every space. A warehouse. A hospital corridor. A factory floor. Every single environment from every single angle. ➜ Open X Embodiment is the largest public robotics dataset on the planet. It has over 1 million trajectories. RG and Cris both agreed it is nowhere near enough to cover all robot types and environments globally. ➜ The cost of collecting this data at scale is not just high. It is a wall that stops most robotics startups before they even start training. This is the four-layer stack most people collapse into two: ➜ Layer 1: Raw data collection. Crowd-sourced, incentivised, messy. ➜ Layer 2: Synthetic generation. Strike Robot's SR Platform takes 10% real-world anchor data and generates 90% synthetic scenarios from it. You describe a space in plain text. The platform builds a full 3D simulation environment in seconds, sized to the exact dimensions of whichever robot model you are training. Four internal agents run this: an orchestrator, a geometry generator, a layout architect, and a scene validator. ➜ Layer 3: Verification. This is where Reppo comes in. Their prediction market mechanism puts skin in the game for every validator. Bad data gets slashed. Good data gets rewarded. Most companies today handle this with an internal rubric built by their own engineering team. It works sometimes. It fails often, especially when a robot enters an environment it has never seen. ➜ Layer 4: Consumption. @virtuals_io ACP and deployed agents pull from verified, clean datasets. Strip out any one of those four layers and the robot fails in the field. The partnership between these three projects: ➜ Reppo is being embedded directly into the SR Platform. You will not need to jump between tools. ➜ @AskVenice AI handles all inference at the foundation layer. Prompts and images stay local. Nothing gets stored on Strike Robot servers. That matters for enterprise clients who will not share proprietary environment data with a shared server. ➜ Strike Robot, Reppo, and Venice are launching a joint community campaign soon. Contributors create simulation environments, earn incentives for quality output, and Reppo validators verify the data on-chain. How $SR accrues value: ➜ Enterprise contracts paid in fiat or USDC feed automated buybacks. ➜ API usage paid via x402 payment rails feeds the same buyback mechanism. ➜ Data licensing from verified datasets becomes a second revenue loop as the contributor network scales. The flywheel: more contributors, more quality data, better models, more enterprise clients, more revenue, more incentives for contributors. Do not compare Strike Robot to Figure AI. RG was direct about this. Figure is centralised, well-funded, and going after generic use cases. Strike Robot is going after the long-tail, high-risk environments that centralised companies cannot verify cost-effectively. Different category. Different valuation framework. What is coming next: ➜ SR Platform v2 launching soon for community testing ➜ Cris is in China right now doing hardware demos on the G2 and a robot dog model in Shenzhen ➜ Joint community campaign going live with task-based simulation creation and incentives from both Strike Robot and Reppo The one line from the whole AMA worth saving: "Verified training data is the most important thing and web3 is the only way to solve that." Cris, Strike Robot.

robotics is inherently about hardware, however I'm meeting more and more founders who want to find a software (or just non-hardware) business to build for robotics. thoughts: > software is behind hardware (so this realization is correct, but not unique), and "robot brain" is indeed a hard problem to solve (further out than most think). that being said, I don't think solving robot intelligence as a company that is neither 1) collecting data (either by robot deployment, or other means) nor 2) a true research company like PI makes a lot of sense > Selling dev tools to robotics companies is a horrible business idea right now (sounds smart, but not enough robot deployments + nowhere near the #1 pain point) > the most obvious non-hardware opportunity is in the deployment gap. specifically, imo the demand for businesses in manual labor that want to try robotic solutions *today* I believe is much greater than most people realize, however no robot (humanoid to service bot) is ready to work out of the box (i.e. someone needs to come set them up, teleop, maintain etc). if I were thinking about a business, I would think about doing something that helps old-school, regular-ass businesses put robots into their space tl;dr build stuff that actively puts more robots into the world

A lot is breaking open at @Reppo right now. Here's where things stand: 1️⃣ The Polymarket agent trained on Reppo data continues to outperform agents built on Claude Sonnet 4.6. 2.25x ROI, 94% time won, and the delta is widening every 15 minutes. This isn't a hypothesis anymore, it's a working product. 2️⃣ Agentic swarm orchestration has moved past the whiteboard. We've already deployed internal tests covering the full loop of publishing, voting, and consumption. Fully automated with no humans. When it launches it fundamentally changes the ceiling of what's possible on Reppo. 3️⃣ Dataset subscriptions are live. Datanet owners can now create tiered subscription packages and monetize their data directly. Consumers and agents alike can subscribe to any package and get real time access to a live data pipeline for that window. 10% network fee feeds back to datanet stakers via @HyperliquidX HIP-4, and X402 APIs mean agents can find, negotiate and subscribe autonomously. 4️⃣ We are actively developing multiple enterprise partnerships and the range of use cases says everything about where our tech can go. We're talking advertising applications, consumer insights pipelines, world building collaborations, and agentic prediction market participation spanning Web2 & Web3 audiences. One common theme, preference data at scale is valuable everywhere. 5️⃣ The Reppo revenue share NFT is dropping this month. Smart contracts are in deployment and the first shapshot has been taken. Top 999 holders receive lifetime rights to a prorated share of all network fees. Building a structure that gets more valuable as the protocol grows. Watch for official comms from me, @RG and @Reppo for exact timelines and how to claim.