An open source Zapier, automations made simple

Hacker News

Howdy HN! I'm Lars, one of the founders @ SVVARM, a platform hoping to bring the power of AI and automation to everyone! Over the last few years, I have witnessed how complicated, convoluted, and costly automation solutions have become. Around 90% of users on Zapier have some form of developer experience, making it very developer oriented. It can often take days and a lot of trial and error just trying to get any automations up and running. SVVARM hopes to alleviate some of the main problems that are core to those looking to automate: - Generating automations with natural language prompting - Exposing these automations with simple to set up triggers (email, message, slack, or API calls) - Connecting to all your apps with a click of a button or a paste of an API key - Enabling your automations with tabular data from spreadsheets or databases - Allowing for customizable nodes that can be written in Python (or using code generated by AI) Right now, SVVARM is only available via our online offering (waitlisted, but free). We hope to bring the engine to open source as soon as possible. Check out the video of our HackerNews Report demo on our Github! Or try sending your resume to svvarmbot@gmail.com with the subject 'Resume' to see one of our automations in action! Let us know what you want to see more of, and if you have any feedback or roasts, please don't hesitate to drop them below! To help push this project along, feel free to join us in Discord.

Visit product View on Hacker NewsSep 18, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, apps, user · Missing: mac, agents, macos

85%85% 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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, soon, users · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, video, users · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, 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 · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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