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Software Engineering Handbook – A Guide to Engineering, Life, and More

Hacker News

Software Engineering Handbook – A Guide to Engineering, Life, and More

Hey HN, We’re excited to share Software Engineering Handbook – a unique book that goes beyond technical concepts and dives into the life aspects of being a software engineer. From tackling migration challenges to balancing work as a parent, this book is designed to offer practical wisdom for both personal and professional growth. Many of us wish we had mentors to guide us not just through the technical stuff but also through the broader challenges we face in our careers. This book aims to be that guide, answering questions you may not even know you had and providing insights you can apply at every stage of your journey. It's a comprehensive approach to software engineering, covering everything from coding best practices to navigating career and life challenges in the tech industry. Check it out and let us know what you think! https://softwareengineeringhandbook.com/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% 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 NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
62%62% 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 · Missing: mobile apps, ios, entrepreneurs
31%31% 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
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: coding · Missing: mac, agents, macos
18%18% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
12%12% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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