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I built Skilltrick.ai, a web extension to help pass coding interviews

Hacker News

I built Skilltrick.ai, a web extension to help pass coding interviews

Hi HN, I’ve built Skilltrick, a browser extension designed to help people pass online technical tests, QCMs, and coding challenges on platforms like Leetcode, CoderPad, CodinGame, HackerRank, TestGorilla, and others. The extension works by analyzing the content of the test page in real-time, then offering contextual suggestions or guidance — either via LLM-powered responses or custom hints. It’s particularly useful for: Developers preparing for technical interviews Career switchers taking online assessments Students practicing for exams or certifications Skilltrick was born out of a very personal experience. A few months ago, I had to shut down my company following a serious surgery that nearly cost me the use of my legs. During my recovery, I started job hunting again — and I was shocked at how dehumanizing and mechanical the recruitment process had become, especially with platforms like CoderPad or Leetcode. So I decided to build something that would make this phase more bearable, a tool to help understand questions faster, reduce pressure, and give candidates a fairer shot without grinding endless edge-case algorithms. https://skilltrick.ai Currently works best with CoderPad and Leetcode. Still improving it — feedback, criticism, or interesting test cases are all welcome. Happy to answer any questions or share details on the tech stack (Svelte + browser APIs + LLM + DOM parsing engine). Thanks! Jérémy

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Actual performance

2points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
92%92% 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: context, coding, code · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, 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
40%40% 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 · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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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