VQV Signal

AI AT WORK WATCH PRACTICAL

Codatta launches Humanbased, a platform for expert-built data for AI agents

According to a post on X by @codatta_io, Codatta has introduced Humanbased, a platform where developers define tasks and rewards. Experts then contribute their knowledge to build the data that AI agents need. The post frames this as a bet that expert human input, not just scraped data, will improve agents. The source snippet does not give pricing, user numbers, or launch dates, so those details are not available here.

Source: Scobleizer News Pulse (X) · x.com Found via Robert Scoble’s Scobleizer News Pulse · a post on X, not independently verified Published 2026-11-13T05:40:00+00:00 Detected 2026-11-13T18:17:19+00:00
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According to a post on X by @codatta_io, Codatta has introduced Humanbased, a platform where developers define tasks and rewards. Experts then contribute their knowledge to build the data that AI agents need. The post frames this as a bet that expert human input, not just scraped data, will improve agents. The source snippet does not give pricing, user numbers, or launch dates, so those details are not available here.

AI-assisted summary based on the listed source.

Codatta introduced Humanbased, a platform where developers define tasks and rewards and experts contribute their knowledge to build the data AI agents need. Why it matters: It is another bet that expert human input, not just scraped data, is what improves agents. Original post on X Section: Launches & New Products

Developers building AI agents may gain a new way to source specialized training data by setting tasks and rewards for human experts. The approach shifts some attention from scraped data toward curated expert input, which could matter for teams whose agents need domain knowledge. Watch for details on how quality is checked, how experts are paid, and whether early users report measurable gains.

Teams using AI at work may want to compare this against current productivity and review workflows.

Signal Strength 90% Technical label WATCH Public Interest 38 Category AI AT WORK Reader Depth PRACTICAL

Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.

Public Interest components
Recognizable Entity Score 0 Practical Impact Score 56 Novelty Interest Score 94 Consequence Score 18 Curiosity Score 16 Shareability Score 35

VQV surfaced this signal because it is recent, relevant to AI Agents, connected to Scobleizer News Pulse (X).