Get GitHub Links from VS Code

Hacker News

Hi HN community, A few friends of mine and I built Tym – a small VS Code extension that makes it easy to share code snippets and get feedback from your teammates. With Tym, you can select a snippet of code and add a question or terminal dump right from the right-click menu. Your teammates can then easily browse your code in a web-based version of VSCode, even if you have uncommitted changes (we make a commit to a hidden git reference in your repo). We made Tym so we don’t have to go to Github and find lines every time we want to share a piece of code. With Tym, you can stay in VS Code and easily share links to your code. Tym is free and all the code is hosted on your Github repository. So only people who have permission to access your repo can see your code snippets. We hope you’ll give it a try and let us know what you think! We’re passionate about improving code collaboration, you can join our discord or follow us on twitter to discuss more! Discord: https://discord.gg/ft8DcsmcjJ Twitter: https://twitter.com/tymcollab

Visit product View on Hacker NewsApr 5, 2022

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

68%68% 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

53%53% 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 · Strong signals: host · Missing: plus, platform, intuitive

49%49% 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

34%34% 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

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

16%16% 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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