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Save 50% on Snowflake in 15 minutes

Hacker News

Save 50% on Snowflake in 15 minutes

I'm a former YC founder, and I've been working with some friends from Google to build Espresso AI, an ML-powered Snowflake optimizer. We use LLMs to analyze and predict your SQL workload and run your warehouses more efficiently. Our first few customers are seeing Snowflake savings from 30% to 70%. We're launching out of beta, and if your team uses Snowflake we'd like to help you cut down your bill. You can set up Espresso in under 15 minutes with the instructions here: https://espresso.ai/onboarding Before turning anything on we'll send you a savings estimate based on your historical data. You can also get an estimate without setting up an account by following the instructions here: https://espresso.ai/savings-estimate-instructions You can reach me at ben@espresso.ai if you have questions (or just leave a comment, I'm around).

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Product HuntOn track for Day 1 leaderboard · Strong signals: google · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
34%34% 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
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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