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Code reviews intimidated me, so I built a course on how to master them

Hacker News

Code reviews intimidated me, so I built a course on how to master them

I’m excited to share Master the Code Review: forge a better process, give better reviews, write better code. It’s a video course with 4+ hours of content [1]. I’ve linked a 21-minute sample below [2]. 6 years ago, I began my software engineering career at Amazon Web Services. I struggled with Pull Requests. My authored PRs received many comments, sometimes 50+. They went through many revisions, sometimes 7+. I often shipped late. My reviews of other PRs were surface level and counterproductive. Long story short: I improved. Through trial, error and experience I learned how to be effective in all 3 dimensions of code reviews: process, reviewer, and author. I came to realize that there isn’t much information out there, giving developers actionable guidance to succeed in a code review environment. Doing so takes specific soft skills, social skills, and technical skills. Very few people talk about them. Late 2020, I started writing about code reviews on my Medium, Twitter [3] and LinkedIn. I grew from 0 to almost 17K followers across platforms. The internet held me accountable for ambiguities and inaccuracies of my public writing. I learned a lot, and leveraged both positive and negative feedback to refine my ideas. My writing experience gave me the confidence to build the course. I created it for my younger self, and I hope developers everywhere find it useful. I launched it a month ago. So far there have been more than 650 students. I’ve received positive feedback from senior managers and principal engineers across big tech. Happy to answer questions! [1] Course: https://curtiseinsmann.gumroad.com/l/code-review-course [2] Sample video — Writing effective code review comments: https://app.gumroad.com/s/90c67f7093db4dd0d544187b0a29bccf/vcisZ5CTogObOj95pBKvHQ== [3] Example Twitter thread: https://twitter.com/curtiseinsmann/status/1317149417330364421?s=20&t=q4gYZPN4BhXDrHtcTTiHmA

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
92%92% 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, code review, ide · Missing: https docs, just released, exist
69%69% 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 HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, reviews · Missing: plus, intuitive, host
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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.

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