A website to practice for job interviews

Hacker News

A little backstory- When I first arrived in Canada as an international student, I met a guy who created a product called InterviewPal. I was fascinated and eager to use it, but soon discovered it was exclusive to University of Alberta students. This made me frustrated so I decided to take on the challenge of building a better alternative- something more accessible and affordable. After I announced Zilta’s waitlist on LinkedIn, Aryan blocked me, which bummed me out for a bit. But it only fueled my drive to create something much better than his overpriced product. My goal was to offer a solution that students and recent grads could actually afford and benefit from. And that’s how Zilta came into existance. I also wanted to give indie hacking a shot and took this endeavor so you can say I hit two birds with one stone haha --- Zilta was launched in June but I wasn't able to work on it extensively because of the workload from college and a part-time job I do. Now that the semester is approaching its end, I'm getting more time for it. Frankly speaking, I don't really know where to take this product to. Right now I am a little confused if I should focuss on marketing and getting more users to try it out and then add more features to it or the opposite. On the other hand, I've also listed this startup to sell for a few bucks because marketing is so hard. What I want deep down? I want this product to grow to 1-2K MRR and then sell it. The problem? I am not sure if I'm heading in the right direction. I'd like you all to give it a try and roast this product. I'm open to receving constructive critism and any advice that can help me succeed in this journey. Should I work on marketing, adding more features or selling this?

Visit product View on Hacker NewsNov 27, 2024

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Actual performance

2points

Did not reach leaderboard

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

95%95% 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: user, open · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

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

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

38%38% 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 · Strong signals: exclusive, soon, users · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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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