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Every problem and solution in Beyond Cracking the Coding Interview

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Every problem and solution in Beyond Cracking the Coding Interview

Hey HN, I'm Aline, founder of interviewing.io and one of the authors of Beyond Cracking the Coding Interview (the official sequel to CTCI). We just compiled every problem (and solution) in the book and made them available for free. There are ~230 problems in total. Some of them are classics like n-queens, but almost all are new and not found in the original CTCI. You can read through the problems and solutions, or you work them with our AI Interviewer, which is also free. I'd recommend doing AI Interviewer before you read the solutions, but you can do it in whichever order you like. (When you first get into AI Interviewer, you can configure which topics you want problems on, and at what difficulty level, and you can add topics and change difficulty levels as you go.) Here's the link: https://start.interviewing.io/beyond-ctci/all-problems/techn... (You'll have to create an account if you don't already have one, but there's nothing else you need to do to access all the things.)

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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: io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, coding · Missing: mac, agents, macos
42%42% 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
34%34% 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
26%26% 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
13%13% 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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