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.

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.

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.

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.

Structured trading-strategy proposals for Sherwood Protocol on Robinhood Chain. Contributors submit concentrated-liquidity LP, rebalancing, lending, and tokenized stock/RWA strategies as machine-readable templates: venue, asset pair, entry/exit/rerange rules, risk parameters, expected ROE. Submissions are scored for feasibility, risk coherence, and novelty. Top-rated entries enter the implementation queue: once Sherwood is live on mainnet, agents implement them under guardian coverage.

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.

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.

A data pipeline to predict which memes have the potential to be runners on RH

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.

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 processes evaluation requests submitted through eval.reppo.xyz.

Datasets produced by Cult OS agents and machines. Audit batches link to public repositories, exact commits and Base settlements. Machine batches document collection methods, coverage, sampling rules and source-file checksums. Public samples and summaries can accompany full source files served through x402.

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

This datanet collects research and analysis on SpaceX (SPCX) earnings. Submit evaluations of reported results, grounded in filings and earnings calls, or estimates for upcoming quarters with stated assumptions and sources. Raw inputs like cleaned datasets and transcript extracts are also welcome. Forward-looking work is graded on rigor and sourcing, not on being right at submission time. Rewards are paid in SPCX, tokenized SpaceX stock on Robinhood Chain.

Records of AI agents completing paid jobs. Each entry captures one job end to end: the posted task spec, the agent's full run (actions and tool calls), and the verified outcome. Contributors submit or review these runs and label failures. Accepted entries ground continuously updated reliability scores and a public leaderboard of working agents.