Wrote a Free eBook for Junior iOS Developers

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Ever since I created my first Xcode project back in 2015, I've been maintaining a notebook where I documented everything I was learning. Whether it was a novel code snippet, a particularly challenging bug, or noteworthy insight from a more seasoned engineer - it would all go into the notebook. Over time that notebook became 200+ pages and I've distilled it down into this eBook. It's filled with "practical" tips derived from my day-to-day experiences working professional as an iOS developer. It's totally free for now. I wrote it for a few people I occasionally tutor - hope someone in the community finds it useful as well. If you like it and don't mind dropping a rating, that'd be greatly appreciated! Link: https://gumroad.com/l/fjTSv

Visit product View on Hacker NewsMay 22, 2021

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TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

67%67% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

42%42% 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 · Strong signals: occasional · Missing: plus, platform, intuitive

30%30% 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

20%20% 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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