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Trayce – Network tab for Docker containers

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Trayce – Network tab for Docker containers

Trayce ( https://github.com/evanrolfe/trayce_gui ) is an open source desktop application which monitors HTTP(S) traffic to Docker containers on your machine. It uses EBPF to achieve zero-configuration sniffing of TLS-encrypted traffic. As a backend developer I wanted something which was similar to Wireshark or the Chrome network tab, but which intercepted requests & responses to my containers for debugging in a local dev environment. Wireshark is a great tool but it seems more geared towards lower level networking tasks. When I'm developing APIs or microservices I dont care about packets, I'm only concerned with HTTP requests and their responses. I also didn't want to have to configure a pre-shared master key to intercept TLS, I wanted it to work out-of-the-box. Trayce is in beta phase so feedback is very welcome, bug reports too. The frontend GUI is written in Python with the QT framework. The TrayceAgent which is what does the intercepting of traffic is written in Go and EBPF.

Visit productView on Hacker NewsJul 29, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, dock · Missing: agents, macos, cursor
80%80% 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: open source, io · Missing: https docs, excited, just released
66%66% 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 · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, 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
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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