Li

Lie on your resume – Applie-AI

Hacker News

Lie on your resume – Applie-AI

The first time I tried to get a part time job I was surprised by the difficulty in getting a yes. But it was inevitably because my resume was empty. Then one day I had the though: "why don't I just lie, who would know". It didn't work instantly as I was too ambitious and when they asked for references, I lost everything. But then I took a step back and decided to say I had worked at a niche charity shop near me and boom, got the first job I applied to. I thought, I have to let other people know this is the best method, as employers are far more unfair than this. Any feedback on the site or service, even the landing page would be greatly appreciated, thank you!

Visit product View on Hacker NewsJul 27, 2025

Actual performance

1points

3comments

Did not reach leaderboard

Launch Intel predictions

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

67%67% 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.

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

47%47% 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 · Missing: mobile apps, ios, personal

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% 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

21%21% 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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