Labour Quest, a Simple Resume Builder

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Hi, I am working on a simple resume builder call Labour.Quest ( https://labour.quest ). Features you can host your resume on a custom URL like https://labour.quest/<username> My resume for example is located at https://labour.quest/asindu if you want to see a sample of what it may look like. I was tired of hosting my resume on Google Docs for many reasons, that's why I built this service. There are currently three themes, "Academic", "Millennial" & "Gen Z". The goal is to allow users to create resume using themes they "vibe" with. So I plan to create a "theme store" like that of Tumblr where anyone can upload, buy & sell themes. [1] I also plan to add analytics and job boards the coming months. Please help me with any feedback (positive or negative). That I may use to improve the project. If you are interested in working with me in any way, I am happy to receive suggestions or offers in my inbox, hi@asindu.xyz [1]: https://www.tumblr.com/themes/

Visit product View on Hacker NewsApr 29, 2024

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

80%80% 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: google, user, inbox · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: host, builder, 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, google, users · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

14%14% 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.

Correct prediction on native model

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