AI generalizes well. It specializes poorly — until you give it the right human knowledge.
Codatta contributors improve AI across four layers:
→ Fine-Tuning: real-world examples that teach models to think like specialists
→ RAG: verified data that keeps AI grounded instead of
Understanding how robots perceive and interact with everyday controls requires datasets that capture both geometry and function at a fine-grained level.
Codatta’s Appliance Knobs dataset on @huggingface is designed specifically for this challenge, providing high-quality,
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
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
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
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
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
Meet more judges of Bitget AI Hackathon S1!
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Luki Song (@ChainbaseHQ )
Vlad
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
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
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
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
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 👇
