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Venkat – An inline code snippet execution extension for VS Code

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Venkat – An inline code snippet execution extension for VS Code

We always loved the snipped execution in TextMate that shows the results as a tooltip. Venkat Subramaniam used it in all his talks and demos. Tried to find it as a VS Code extension, but there was nothing. So not knowing how to build one and being TypeScript noobs, we employed GPT-4 to build it late night in a few hours. We called it "Venkat" in his honor. And it turned out pretty well, so we're happy for you to try it out and let us know what you think (or rip it apart).

Visit productView on Hacker NewsMay 17, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
77%77% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, 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
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

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