Comparing data annotation platforms [D]

<!-- SC_OFF --><div class="md"><p>doing infrastructure evaluation for inference workloads and running into the same problem everywhere: every platform publishes p50 cold start

<!-- SC_OFF --><div class="md"><p>Scale AI and similar services charge a lot for annotation. MTurk is cheap but the quality is horrible for anything requiring real domain

<!-- SC_OFF --><div class="md"><p>Just went through a Stanford paper that tracked 51 actual AI deployments - not surveys or sentiment polls, real production systems across 41

<!-- SC_OFF --><div class="md"><p>Built a third-party quality rating system for ML datasets. Multi-oracle (7 scorers across 5 algorithm families), conformal prediction intervals

<!-- SC_OFF --><div class="md"><p>Public datasets on HF or Kaggle can sometimes be too generic, wrong domain, wrong schema, outdated, or just not enough volume to generalize

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I’ve built a fairly flexible RAG pipeline that was initially designed to handle any type of document (PDFs, reports, mixed

<p>Every AI agent has the same problem. You ask it to read a webpage and it comes back with a 403, or worse, 5000 tokens of navigation bars and cookie banners.</p> <p>I spent the

<!-- SC_OFF --><div class="md"><p>I am trying to understand the current consensus and legal landscape regarding how public data and developer contributions are being utilized by