Codatta

Looking forward to supporting more researchers and teams like @asmora_mcp building the future of robotics.

The next leap in AI won't come from more internet text. Models are hitting a ceiling on recycled data. What moves the needle is Frontier Data — the kind that doesn't exist anywhere yet and can't be scraped: → expert knowledge from domains that never made it online → edge cases that simulators can only approximate → robot manipulation footage from real-world environments → verified onchain address labels That's what Codatta contributors are building, task by task.

Robotics Knowledge Quiz #05 What makes human demonstration data uniquely valuable for training robotic manipulation -- compared to simulation data alone? A. Lower storage and collection cost B. It captures real-world variance: contact forces, material texture, and failure modes that simulators can only approximate C. It requires less labeling overhead D. It transfers directly across different robot form factors Drop your answer below. 👇

Most data compensation happens at collection. What the model earns in production -- nothing goes back. Codatta's Royalty Engine is built around the deployment layer: usage metered by requests, tokens, or API calls, with smart contract logic designed to route settlement back to contributors, validators, and backers automatically. Contribute once. Earn continuously.

Teaching a robot to pick up objects is hard. Teaching it to operate them is harder. Our Appliance-Knobs Dataset on @huggingface focuses specifically on this type of fine-grained interaction. It features detailed visual data tailored for capturing the subtle geometric and functional variations of rotary controls. What makes it different: 1️⃣ Multi-Angle Views: Paired images (front & side) for every knob, giving models the multi-perspective data needed for robust 3D shape estimation. 2️⃣ Specialized Focus: A deep dive into electrical appliance knobs—an underrepresented class crucial for fine-grained object understanding. 3️⃣ Precision Ready: Optimized for state recognition and exact knob angle/position estimation. Built for: Multi-View Object Recognition | 3D Shape Reconstruction | Generative AI Training. 🔗 Download the dataset: huggingface.co/datasets/Codat…

Three questions the AI data industry hasn't answered well: 1. When crowdsourced data trains a model, does anyone besides the buyer own the outcome? 2. If a dataset's quality improves through ongoing human verification, who accumulates the credit? 3. When a licensed model gets deployed in production, does any value route back to the original contributors? Codatta is building the infrastructure to make these questions answerable — and the answers enforceable.

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Robotics companies need training data that can't be scraped from the internet. Every motion sequence, grasp attempt, and navigation decision requires purpose-collected, human-labeled footage — verified to a standard where the output can actually be trusted in a physical environment. Codatta's Robotics frontier is where contributors build that dataset from the ground up. We've already open-sourced one: RoboManip-Traj-Demo — manipulation trajectories with fine-grained spatial and pose annotations, live on Hugging Face. Explore & download now 👇 huggingface.co/datasets/Codat…

High-fidelity datasets are the foundation of next-generation embodied AI, robot learning, and physical intelligence and our partner @codatta_io is advancing the frontier of robotics data infrastructure on @huggingface as one of the core contributors. Its Manipulation Trajectory dataset captures fine-grained robot-object interactions with precise spatial and temporal annotations, enabling research in imitation learning, trajectory prediction, manipulation planning, and control. Complementing this, the Appliance Knobs dataset provides richly annotated multi-view observations of rotary controls to support 3D geometry understanding, state estimation, pose tracking, and interaction-aware perception. Together, these datasets help train the next generation of robotic foundation models capable of understanding and acting in the physical world. Explore Codatta's Manipulation Trajectory and Appliance Knobs datasets on Asmora today: asmora.io/mcp-detail/mod… asmora.io/mcp-detail/mod…

Exchange hot wallets are some of the most active addresses onchain — and most of them sit unlabeled. Codatta's Cex Hot Wallet task lets contributors map them, address by address, and earn rewards for every verified submission. A cleaner map means better compliance and analytics downstream. Trade on a CEX? Start contributing: app.codatta.io/app/frontier/8…

Data work usually pays once. You label, you get paid, and the value you helped create moves on without you. Codatta is built around a different model — every contribution is fingerprinted onchain, turning it into an ownable asset. When that data earns downstream, smart contracts can route royalties back to the people who made it. Own what you contribute.

Two Truths and a Lie — AI data edition. One of these is false. Which one? 1. A single mislabeled image can quietly degrade an entire model. 2. Most public AI datasets list who labeled them. 3. Codatta verifiers re-check contributions before they count. Drop your guess 👇

Start contributing 👇 app.codatta.io/app/frontier/8…

Caught an AI getting it wrong? That's a Frontier contribution. Codatta's Correct LLM's Mistakes task: find a flawed model response, screenshot it, submit the correct answer. Earn up to 100 points per approved contribution. Tutorial below 👇

AI training runs on human data. Contributors rarely get to prove — or own — what they gave. Codatta is building the missing layer: Proof of Contribution, on-chain. - every submission is fingerprinted and traceable - every contributor holds an Ownership Token - every use triggers royalties back to the source

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Start contributing👇 app.codatta.io/app/frontier/8…

Have you ever asked two AI models the same question and gotten different answers? That's exactly what this task is about. Find an objective question where two AI models give different answers. Submit both responses with screenshots, plus what you believe is the correct answer. Each valid submission helps pinpoint real knowledge gaps in today's top models — and earns you 100 points. Watch the tutorial 👇

Most data pipelines give you a choice: accuracy or scale. High-quality labels? Slow and expensive. Fast, scalable collection? Noisy and unreliable. Codatta's hybrid validation doesn't ask you to pick. Every contribution goes through a transparent flow — contributor submits, verifier confirms, result gets recorded with a risk rating. Each step is traceable. Accuracy and scale. Both.

💡 The Answer: STATIC! Look at the base — it's bolted directly to the workbench rail. The arm can't go anywhere. 🔩 Reminder: "mobile/static" = does the robot platform move from place to place? The arm itself can swing, extend, rotate — but if the base stays in one spot, it's static. Don't be fooled by all the movement! 🦾

Robotics Knowledge Quiz #04🤖 This robot arm is doing some serious work. Is it mobile or static? Drop your answer below! 👇