Datanets

Real-time human judgment on breaking geopolitical events, rumors and potential misinformation. Designed for agents that cannot afford to guess when the cost of being wrong is measured in millions. Perfect for trading bots, logistics agents, risk models, and news summarizers that need fast, economically weighted truth and probability distribution.

Decentralized quality scoring for AI microservice vendors routed through the Litebeam network. Vendors are scored on reliability, latency, cost-efficiency, and output quality using real transaction data with on-chain settlement hashes for verifiability.

ArAIstotle's Truth Terminal surfaces prediction market mispricings, narrative-versus-odds divergences, and emerging forecasting opportunities across global markets. Contributors and evaluators collectively build a continuously validated intelligence layer for trading systems, forecasting agents, risk models, and research platforms.

Verified streaming behavioral data contributed through Myrad. ZK-verified via Reclaim, PII-stripped, and GDPR/CCPA compliant. This datanet scores contributions on quality, completeness, and commercial value, surfacing the strongest data for AI and consumer-intelligence buyers. Note: content-preference and segmentation data is high-fidelity; temporal fields (viewing times/days) may be sparse where the source platform does not expose timestamps, evaluators help flag and score this.

Share and watch your favourite adult content and get paid

Community-driven training data marketplace for autonomous AI trading agents. Features live trade data with strategy context, market regime annotations, and structured execution records across all instruments. Each pod is community-scored on training value — strategy context, verifiability, and reflection are rewarded. Raw dumps without analysis score lower. Quality beats quantity.

We are building a high-quality dataset to train autonomous robots in factory environments. Contributors will watch video clips of industrial activities and label key events, such as assembly steps, equipment handling, and safety incidents. Your work will help robots understand and navigate complex manufacturing workflows.

The Brokerage Network for DeFi

I'm building an AI agent that allows token holders and investors to assess a projects tokenomics beyond narratives. Currently building a datanet to get the training data

Score robot training episodes and assess signal quality by voting.

How faithfully do tokenized assets track their real-world counterparts? Publishers submit performance comparisons between a real-world asset and its on-chain token — price tracking, liquidity, and peg deviations. Voters rate each submission on rigor and credibility. The output is a crowd-rated signal on how well tokenized RWAs are tracking — the kind of data teams weigh before bringing an asset on-chain.

Betsonic, built by SonicLabs on Base, turns real-stakes iGaming into behavioral data for AI and robotics by capturing how humans truly weigh risk, consequence, and reward when real money is on the line. It builds the decision-layer signal that low-stakes datasets miss: genuine human risk tolerance, the training data autonomous systems need to understand consequence and act safely around people.

A marketplace for trading strategies to be used by AI agents to trade with real money on Hyperliquid or other platforms. Strategies should be back-tested on major assets on HL (BTC, ETH, SOL, HYPE, etc) to produce at least a 50% win rate consistently.

This datanet is a playground for new data contributors i.e. publishers and domain experts i.e. voters to the Reppo ecosystem

DOM Pulse provides real-time DOM structure maps for AI browser agents. Contributors submit standardized JSON schemas documenting interactive elements, navigation paths, and change detection points. Expect high demand for frequent updates as websites evolve, with rewards based on accuracy and freshness. Your data powers the next generation of autonomous web automation.

The Roba Labs Physical AI Datanet collects robotics simulation and evaluation data from Roba Creator Hub and Roba Studio. Contributors can publish benchmarked assets, simulation outputs, task demos, evaluation packs, and sim-to-real results for tasks like manipulation, navigation, grasping, and control. The goal is a high-quality, validated Physical AI dataset rewarded on-chain and reusable across the Roba ecosystem.

The data layer for customer preferences