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A Senior Engineer's CheckList

Hacker News

A Senior Engineer's CheckList

Sharing something that I wrote for my use but I think can be useful to others (I am an engineer with the title of Senior Software Engineer) Visit [1] for a full list (table view gives a lot of options. See "Controls"). Visit [2] for a summary. The source of the list is at [3] 1. https://littleblah.com/post/2019-09-01-senior-engineer-checklist/ 2. https://medium.com/@littleblah/my-top-25-items-in-a-senior-engineers-checklist-c8e9f9f6e3c2 3. https://github.com/littleblah/senior-engineer-checklist Feedbacks/Pull requests solicited :)

Visit product View on Hacker NewsSep 9, 2019

Actual performance

662points

143comments

Made the leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% 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 · Missing: mac, agents, macos

40%40% 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

38%38% 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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

28%28% predicted probability of success on Indie Hackers, 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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