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Software Engineering Handbook – How to Survive Tech

Hacker News

Software Engineering Handbook – How to Survive Tech

Hey HN, We wrote the Software Engineering Handbook because most engineering advice is about technical background. Ours is about surviving everything else. It covers: - Surviving bad managers, layoffs, toxic teams - Navigating promotions, negotiations, career changes - Handling burnout, immigration, parenting, even death - Setting boundaries, managing time, staying sane - Building a life while growing in your a tech career. No tutorials. No buzzwords. Just real-world survival and growth, written by engineers who’ve been through Amazon, Meta, Workday, TripAdvisor, and startups.

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Actual performance

6points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% 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 · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
51%51% 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 · Missing: mobile apps, ios, personal
29%29% 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
20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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