Context Copy – VS Code extension for quickly copying context

Hacker News

Show HN: Context Copy - VS Code extension for quickly copying context I've developed a VS Code extension called "Context Copy" that allows developers to swiftly copy multiple files with their full context, formatted as markdown. It's designed to streamline the process of sharing code snippets with AI assistants (like ChatGPT or Claude) or in documentation. Key features: - Select multiple files in VS Code Explorer - Right-click to copy full context as markdown - Automatic language detection for syntax highlighting - Includes file paths for better context preservation - Copies directly to clipboard for instant sharing Use cases: - Providing comprehensive code context to AI assistants for more accurate responses - Creating detailed code documentation with proper formatting and file structure - Generating quick, well-formatted code snippets for Stack Overflow answers or GitHub issues It's open source and available on https://github.com/collinc777/context-copy . I'm keen to hear your thoughts and suggestions for making it even more useful! VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=collinc7...

Visit product View on Hacker NewsJul 21, 2024

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

90%90% 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

65%65% 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: open source, 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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% 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.

Incorrect prediction on native model

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