I\u2019ve been noticing something across different AI builders lately\u2026 the bottleneck isn\u2019t always models anymore. It\u2019s very specific datasets that either don\u2019t exist publicly or are extremely hard to source properly.<\/p>\n
Not generic corpora. Not scraped noise.<\/p>\n
I mean things like:<\/p>\n
\ud83d\udd39 Raw \/ Hard-to-Source Training Data<\/strong><\/p>\n – Licensed call-center audio across accents + background noise<\/p>\n – Multi-turn voice conversations with natural interruptions + overlap<\/p>\n – Real SaaS screen recordings of task workflows (not synthetic demos)<\/p>\n – Human tool-use traces for agent training<\/p>\n – Multilingual customer support transcripts (text + audio)<\/p>\n – Messy real-world PDFs (scanned, low-res, handwritten, mixed layouts)<\/p>\n – Before\/after product image sets with structured annotations<\/p>\n – Multimodal datasets (aligned image + text + audio)<\/p>\n \u2e3b<\/p>\n \ud83d\udd39 Structured Evaluation \/ Stress-Test Data<\/strong><\/p>\n – Multi-turn negotiation transcripts labeled by concession behavior<\/p>\n – Adversarial RAG query sets with hard negatives<\/p>\n – Failure-case corpora instead of success examples<\/p>\n – Emotion-labeled escalation conversations<\/p>\n – Edge-case extraction documents across schema drift<\/p>\n – Voice interruption + drift stress sets<\/p>\n – Hard-negative entity disambiguation corpora<\/p>\n \u2e3b<\/p>\n It feels like a lot of teams end up either:<\/p>\n – Scraping partial substitutes<\/p>\n – Generating synthetic stand-ins<\/p>\n – Or manually collecting small internal samples that don\u2019t scale<\/p>\n Curious, what\u2019s the dataset you wish existed right now?<\/p>\n Especially interested in the \u201chard-to-get\u201d ones that are blocking progress.<\/p>\n<\/div>\n submitted by \/u\/Khade_G <\/a>
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