VS Code GoLang Productivity Extension

Hacker News

Let me introduce the Tooltitude VS Code extension. Our extension augments the official GoLang extension with a set of power tools which make you a more productive developer. What do we provide: - Inspections - checks which look at your code and warn you about potential problems in your code. Here’re some highlights - Highlighting of deprecated symbol usages in the editor (gopls doesn’t highlight them at the moment of writing) - Variable shadowing detection - Use of interface{} instead of any - Redundant parenthesis - Code actions - code changes activated by Ctrl+. (Win, Linux), or Cmd+. (on Mac). - Handle error: https://twitter.com/tooltitude/status/1620448875311165442 - Apply DeMorgan laws: https://twitter.com/tooltitude/status/1620814134240747520 - Iterate over collection: https://twitter.com/tooltitude/status/1621146816736800768 - Invert If: https://twitter.com/tooltitude/status/1623336440930107397 - Overall we currently have more than 20 code actions, and working on adding more What’s next: - Install and try in VS Code right now: https://marketplace.visualstudio.com/items?itemName=tooltitu... - Read more about our features: https://www.tooltitude.com/ We are looking for feedback from users, so if there’re any features, inspections or code actions you re interested it, feel free to comment here, write to support@tooltitude.com, or write to us on twitter: https://twitter.com/tooltitude

Visit product View on Hacker NewsFeb 14, 2023

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3points

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Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, user, visual · Missing: agents, macos, agent

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive

36%36% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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