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Retrieval-augmented LLM debate opponent on DebateSum dataset
Hacker NewsRetrieval-augmented LLM debate opponent on DebateSum dataset
There is a dataset in the argument mining field called DebateSum. Using our AI infrastructure product suite, we built a semantic search UX for it at https://search.arguflow.ai/ and then connected it to a LLM via retrieval augmentation for a really high quality debate opponent.
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Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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Incorrect prediction on native model
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