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PostgresML integrates Hugging Face to bring SOTA models into the db

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PostgresML integrates Hugging Face to bring SOTA models into the db

Hello folks, It's been a few weeks, so we thought it might be time for another update and this one is very exciting indeed. We've added automatic integration with Hugging Face transformers! By running just a single SQL command, anyone is now able to deploy any of the state-of-the-art models into their Postgres DB for real time inference & tuning. The list of models is long, but here it is anyway: translation, sentiment analysis, summarization, question answering and text generation. Let us know what you think! Montana & Lev

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, single · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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