A site for code reviews

Hacker News

Create a review as you would on pastebin, then any line in a review can be clicked to leave a comment. Comments appear under the line they reference and can be hidden by clicking the red tabs on the left or right. You can optionally toggle all comments by clicking the speech bubble at the top of the review. You don't have to have an account to create a review or leave a comment. I plan on adding multi-file reviews as well as version control access in the next few releases. After that I plan on making some IDE plugins to allow users to send code directly to the site via their IDE. Let me know what you think. (I haven't done a lot of IE testing yet so there could be some issues there) https://codetique.com

Visit product View on Hacker NewsMar 27, 2012

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

74%74% 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.

Hacker NewsStrong engagement from HN community · Strong signals: code review, ide, 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.

AppSumoStrong fit for a featured deal · Strong signals: reviews, users · Missing: plus, platform, intuitive

51%51% 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

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

30%30% 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

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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