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CodeRev.app – Code Review as Interview

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CodeRev.app – Code Review as Interview

I've long had a dislike for leetcode interviews on both sides of the coin. Project-based interviews can also be challenging because it can end up filtering out folks who don't want to spend several hours building something in their free time. I met up with a friend last year -- a very senior dev -- who had taken some time off to study leetcode exercises while preparing for FAANG interviews. It struck me that one of the key measures of whether a candidate is a fit for a team is rather synthetic . CodeRev.app is a simple, lightweight tool that helps teams evaluate candidates using code reviews. While programming tends to be a more isolated activity, code reviews tend to be more open ended, collaborative, and reflective of how a candidate communicates, interacts, and provides feedback day-to-day. It may also be a better yardstick for roles that are biased towards reading code rather than writing code (engineering manager, support, QA). More interesting is that as we come to rely on AI generated code in our workflows, testing for the ability to read and evaluate the quality of generated code and whether it is fit-for-purpose becomes more important. Being able to quickly identify security flaws, logical flaws, and domain specific gaps (auditing, logging, etc.) becomes increasingly important. More in depth thoughts here: https://chrlschn.dev/blog/2023/07/interviews-age-of-ai-ditch...

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Product HuntOn track for Day 1 leaderboard · Strong signals: activity, using, code · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, 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 · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, code review, ide · Missing: https docs, excited, just released
66%66% 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
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
34%34% 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
21%21% 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.

Incorrect prediction on native model

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