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CanSnippetCE Easily manage your repetitive code, phrases and paragraphs

Hacker News

CanSnippetCE Easily manage your repetitive code, phrases and paragraphs

Hi HN, I am really happy to announce the new version of canSnippetCE (v2.0.0) today. canSnippet Community Edition is a web-based snippet management tool. It allows you to save and share all the precious pieces of code and text that you often refer to. The whole story started during my PhD when I often needed to get the list of all hexamer sequences (4096 sequences of 6 letters) as a Python array. Instead of writing the function every time in different project or creating limited access functions I was simply copy-pasting these few lines of code to all the scripts that needed this information. I then decided to implement a code snippet manager to make my life easier. Today, I use canSnippet for anything from a single hexstring for colors in CSS to complete functions to repetitive text for emails. canSnippet CE was born in 2013, followed by a little desktop brother in 2017 ( https://cansnippet.com ) and a completely new version 2.0.0 coming out today. The main advantage of the desktop version is the ability to use keyboard shortcuts to have your snippets always in your hands. canSnippet CE is a complete content management system for your repetitive code and text. The main features of canSnippet are as follow: - easily share your snippets with a unique URL - multiple user accounts - syntax highliting - tag support - copy the snippet to your clipboard with a button - rating system - multiple themes You can follow the whole project via: - http://cansnippet.org/ - Github: https://github.com/ademcan/canSnippet - Twitter: https://twitter.com/cansnippet Feel free to share and let me know your feedback. canSnippetly yours :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
75%75% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
48%48% 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
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% 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.

Correct prediction on native model

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