Lindy, build your own AI employees with no-code

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I'm a big fan of Chris Dixon's idea that "what the smartest people do on the weekends is what everyone else will do during the week in ten years." One such thing rn is "creating AI agents." Most devs I know have a few agents lying around. But you shouldn't have to be an engineer to use agents — and even engineers don't want to code every time they need a new agent. So we built a no-code platform to make it 100x easier to create those: * you give a prompt * select amongst 3,000+ API connectors we built (no need to fiddle with OpenAPI specs) * add event-based triggers that'll wake up your Lindy (e.g. a new Gmail message, a calendar event starting etc) You can even get your Lindies to work together in "teams" — that's like OOP for AI agents, where you can create "utility Lindies" re-used across your entire agent base. And of course, we have a "Lindy Store" to publish / install Lindies other people created — monetization is coming soon. I have a bunch of these Lindies already that I use every day to: * Transcribe meetings and take notes in different formats for team syncs, interviews, user chats etc * Schedule meetings when I cc a Lindy to an email chain (using the LindyMail trigger) * Create flash cards based on content I send it * File away links I send it and retrieve them later * Summarize Youtube videos / podcasts / web articles and log them * Help me keep my life on track — make sure my activities are aligned with my priorities, keep me accountable on workout / meditation schedule etc * Log my mood in a Google Sheets and help me detect patterns in what puts me in a good / bad mood And at work obviously, Lindies take care of all our customer support, help with recruiting, data analysis, etc… Excited to hear everyone's thoughts & feedback!

Visit product View on Hacker NewsFeb 15, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, google · Missing: mac, macos, cursor

97%97% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist

44%44% 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 · Missing: plus, intuitive, reviews

37%37% 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

27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, smart · Missing: web3, crypto, cryptocurrency

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

Correct prediction on native model

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