I’ve been noticing a pattern across different AI builders lately: The bottleneck isn’t always model capability anymore. It’s very specific datasets that either don’t exist publicly or are extremely hard to source properly. Not generic corpora. Not scraped web noise. I mean things like:
- Multi-turn voice conversations with natural interruptions + overlap
- Human tool-use traces for agent training
- Real SaaS workflow screen recordings (not staged demos)
- Emotion-labeled escalation conversations
- Adversarial RAG query sets with hard negatives
- Messy real-world PDFs (scanned, low-res, handwritten, mixed layouts)
- Cross-country company registry data aligned to a consistent schema
- Failure-case corpora instead of polished success examples It feels like a lot of teams end up either:
- Scraping partial substitutes
- Generating synthetic stand-ins
- Or building small internal datasets that don’t scale Curious, what’s the dataset that’s currently blocking your progress? Especially interested in the hard-to-get ones that don’t show up on Hugging Face or Kaggle. submitted by /u/Khade_G
Originally posted by u/Khade_G on r/ArtificialInteligence
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