Resumine, a smart way to generate custom cover letters

Hacker News

I co-created, with two friends, a friendly website that empowers anybody to generate custom cover letters to any job position. The workflow is simple yet efficient: 1. Copy and paste only once your professional background 2. Copy and paste a job description you would like to generate a cover letter to. 3. Select the theme 3.1. Professional and Formal 3.2. Creative and Innovative 3.3. Enthusiastic and Energetic That's it, you get a tailored cover letter which is stored online and can be edited at any time. We believe in a freemium approach, for which only users with extensive usage would become a customer. It took us +6 months of work as dedicated to this as a side project. We are aware there are similar tools published in the last months, but we didn't want to give up because of this as we believe in competition. We are glad to invite you all to try Resumine out and help us understand your thoughts and where can we focus to build a better service.

Visit product View on Hacker NewsJul 13, 2023

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

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best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: friendly, efficient, users · Missing: plus, platform, intuitive

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

TrustMRRFits verified-revenue profile · Strong signals: month, users, way · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, 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

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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