So

Solving 1 DS/Algo problem a day.

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Solving 1 DS/Algo problem a day.

Hi Hackers. I have been solving Data Structures/Algorithms problem as part of my interview preparation. I thought of sharing this here because this resource would be helpful for people just like me who are also in the process of preparation. I have provided links to the questions in my programs itself where it is necessary. Link: https://github.com/sunilkumarc/100 The name of the repository is 100. This started as a project to solve 1 question each day for 100 days. But it is really difficult to manage between my work, Bangalore traffic and my side projects like this one. I have been trying my best to keep this project going. I have already solved around 50 questions. 50 more to go!

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best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
45%45% 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
44%44% 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
39%39% 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
24%24% 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
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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