Learning Go Pills

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Learning Go Pills

I am committed to preparing at least two "Learning Go Pills" per week to allow anyone who is passionate and intrigued to approach this powerful, fun, versatile and simple programming language. What the heck is a "Learning Go Pill" ? ...well a "pill" is a presentation with few straight and juicy slides! Here the new content, enjoy! Day #7 - Maps https://speakerdeck.com/lucasepe/maps-in-go Previous "pills" (https://speakerdeck.com/lucasepe): Day #1 - Type System Overview Day #2 - Constants and Variables Declaration Day #3 - Structs Day #4 - Pointers Day #5 - Arrays Day #6 - Slices

Visit product View on Hacker NewsMay 30, 2020

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Did not reach leaderboard

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

65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

38%38% 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: arr · Missing: mrr, revenue, profit

16%16% 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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