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 👇

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 👇

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

Annotate crypto addresses and earn rewards. Southeast Asia CEX Hot Wallet Collection — Indonesia · Thailand · Cambodia. Trade on a local CEX? This one's for you. Submit the exchange hot wallet addresses — 50 points each. app.codatta.io/app/frontier/…

Crypto address metadata is fragmented. Teams duplicate the same research. Centralized providers gate access. Data goes stale right when markets move fastest. Compliance, market analysis, trading — all running on incomplete maps. Codatta is building Crypto Address Annotation — a community-driven database where contributors enrich, verify, and update address data across chains. A shared intelligence layer for the whole crypto ecosystem. Open, current, community-built.

The embodied AI field is starving for data. Sourcing high-quality robotic data is extremely difficult. To understand how the industry is solving this, we need to look at the Data Pyramid, which consists of three main layers: Real Robot Data, Simulation Data, and Internet / Human Video.

3️⃣ Internet / Huan Video Internet and human-centric videos are the most abundant and lowest-cost raw materials available. The Pros: Scale. It helps foundation models build basic physical cognition—understanding how the world works, spatial reasoning, and human intent. The Cons: It lacks force, torque, and tactile feedback. A video shows the result of an action, but not the exact motor signals needed to execute it. The AI knows "what" to do, but not "how" to move its joints to do it. The Trend: Silicon Valley pioneers like Physical Intelligence, Figure AI, and Sunday Robotics are aggressively pivoting here. By combining Reinforcement Learning with crowdsourced, ego-centric (first-person) video collection, they aim to bypass heavy teleoperation. Projects like Apple’s EgoDex and NVIDIA’s EgoScale are exactly about this: extracting high-signal, usable action data from massive, low-cost human videos.