Story
What I learned by putting GitHub Copilot behind a MitM proxy
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Overview
GitHub Copilot's network traffic and memory utilization were analyzed using a MITM proxy. The findings reveal insights into how context is utilized as a product within the AI tool.
Summary from lighthousenewsletter.com.
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lighthousenewsletter.com
What I learned by putting GitHub Copilot behind a MitM proxy
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Community reactions
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Hacker News · lighthousenewsletter.com
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Historical count: 5 · 5 saved in sample · Historical sample
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Based on 5 sampled comments from this thread. Analyzed 2026-08-11.
- Praise: Using eBPF for data interception is a game changer; it captures everything from telemetry to prompts effortlessly, revealing the hidden data collection practices of tools like Copilot.
- Praise: The deep dive into Copilot's workings sheds light on its model and capability discovery, which is a fascinating peek into how AI tools operate behind the scenes.
- Praise: Discovering that Copilot can pull context from unrelated files is both intriguing and concerning, highlighting its potential for smarter coding assistance.
- Concern: Copilot's episodic memory is a major letdown; it loses context during the coding process, which hampers its effectiveness in multi-step tasks.
- Concern: Users are frustrated with Copilot's performance in Java coding, feeling it's akin to a less advanced version of ChatGPT, which raises doubts about its overall capabilities.
- Concern: The absence of a rule for handling .env files is shocking for a tool integrated with GitHub; it seems like a glaring oversight that compromises security.