Smarter Interviews (Collaborative Editing with Replays)

Hacker News

I love Visual Studio Code and I use it everyday. Visual Studio Code uses Monaco as it’s code editor. Monaco is a really impressive code editor with some amazing features. One of the best features is really how fast it feels. As i started to experiment with the editor more, I realized how amazing it would be if someone used Monaco as the base of a new collaborative editor. Maybe, I should do it. Thinking about this problem a bit more, if I was going to build a collaborative editor it would need to have the following feature-set: Reliable editing. No centralized datastore for the documents created. The documents should belong to the user who created them. Easy access controls. (I don’t want to share my document with the whole world, but only a few users). Presence support (Knowing who else is looking at the document in real time). Instant Replay (Being able to replay the entire session as it happened would be a differentiating feature). App: http://app.interview-smart.com/index.html#about More info at: https://rahulrav.svbtle.com/smarter-interviews

Visit product View on Hacker NewsFeb 22, 2018

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4points

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

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% 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: users · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time, smart · 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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