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Code pastes in local storage, shared via Web Sockets

Hacker News

Code pastes in local storage, shared via Web Sockets

I thought it would be great to share code snippets without saving them on a server, so I created http://mem.pe It allows you to paste code and save it in your browser's local storage. When you share the URL of the pasted code, other people will connect to it via web sockets. For example, this http://mem.pe/#sge/5 you will be pulling from my browser. I'll keep it open for a while. If you close the web app, your code paste will not be available for anyone to see, until you open the web app again. This is obviously, not suitable for using at forums and other places where the paste will have to be available for a long time. However, it is great for IRC and chats where you want to send a quick disposable link to a code snippet. Available on Github at https://github.com/naturalist/mempe Any comments would be greatly appreciated.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code, open · 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: io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting
37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
28%28% 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: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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