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Upplai – AI Feedback on Resumes and Cover Letters

Hacker News

Upplai – AI Feedback on Resumes and Cover Letters

Hey HN, I built Upplai to help job seekers get quick, AI-driven feedback on their resumes and cover letters. The goal is to improve the chances of getting past Applicant Tracking Systems (ATS) and human recruiters. If you're job hunting, you can try it here: https://you.uppl.ai/login?invitation=ycombinator . Would love to hear your thoughts— does it actually help? What’s missing? Why I Built This: I founded Upplai because I’ve experienced firsthand how broken the hiring process is from both sides of the table. As a hiring manager, I was overwhelmed by the sheer volume of applications, forcing me to rely on technology to shortlist candidates. As a candidate, I faced the frustration of endless application cycles, automated rejections, and often, no response at all. One experience particularly stood out: I applied for a role I was well-qualified for on a company website, only to receive an automated rejection within a few hours. Yet, later that same week, a recruiter from the company reached out to me via LinkedIn inviting me to apply for the role. That disconnect revealed a fundamental flaw in the job application process. Digging deeper, I began to understand the intricacies of Applicant Tracking Systems (ATS). I had a beautifully designed resume, which made my resume stand out but the ATS couldn’t parse its complex formatting. As a result, it deemed me to be unqualified for the role. I also discovered how ATS algorithms rank resumes based on keyword matching. It became clear that it wasn't a lack of skills that was hindering my job search, but rather a lack of the right language and formatting. And I knew that I wasn't alone. Upplai is my attempt to make the job application process less painful. Let me know what you think!

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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: new · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% 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
35%35% 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 · Missing: plus, platform, intuitive
31%31% 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
15%15% 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.

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