Presbot – ML Powered LinkedIn for Chatbots

Hacker News

Hello HN, I just launched presbot.com Presbot makes it really easy to create a professional grade chatbot. There’s no code to write, workflows to build, is office appropriate, has community features (likes, connections, private messages), ability to track views, chat transcripts and more, plus is totally free. You can personalize the look and feel of your chatbot, train it with prescribed questions, add your own questions and share its link on your resume. It can answer questions on your behalf when you’re not reachable or busy. Think of it as a super scaled down interactive version of LinkedIn. Acting as a job search agent is one of the many applications of Presbot. For example, and citing real bots from the app, there's a bot representing a community (Basic Attention Token Indonesia Community - presbot.com/21-bat-indonesia), someone's virtual assistant (presbot.com/18-juanmtj) or even a insurance broker (presbot.com/16-hooly). The core intent of all these bots is to provide information. They are designed to assist and not replace or impersonate the end user. My chatbot: presbot.com/3-mohapsat/ Please let me know if you need any help creating your chatbot, questions on the tech, have any feedback or suggestions to improve. Reach us at hello@presbot.com Satya [Founder and Curator of Presbot] p.s. I love making new professional acquaintances. Please connect with me on linkedin.com/in/satyamadhav if you'd like to talk about Presbot, technology, entrepreneurship or gaming.

Visit product View on Hacker NewsFeb 4, 2020

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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: personal, entrepreneurs · Missing: mobile apps, ios, apps

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

12%12% 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 · Missing: web3, crypto, cryptocurrency

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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