RustNet, a network monitoring TUI with process identification

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Hi HN! I built RustNet, a Terminal UI based network monitor written in Rust that shows real-time connections with process identification and protocol detection. What may make it interesting: • Deep packet inspection for HTTP, HTTPS/TLS (with SNI), DNS, and QUIC protocol detection • Process identification using eBPF on Linux (experimental) and PKTAP on macOS which does also catch short-lived processes that polling procfs or lsof would miss • Multi-threaded packet processing with lock-free data structures for the UI • Cross-platform (Linux, macOS, Windows but process identification so far only on Linux/macOS) The eBPF implementation was a bit more tricky to implement than using PKTAP, but it was very interesting to learn about how to hook into tcp_connect, udp_sendmsg, etc. in order to catch process info before connections disappear. I built this as a lightweight Wireshark alternative for quick TUI based network inspection with process identification. Install: cargo build --release, run with sudo or set capabilities. Homebrew tap also available. Would love feedback on the project and any ideas for additional protocol detection or any other suggestions. Thanks

Visit product View on Hacker NewsSep 19, 2025

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, using · Missing: agents, agent, cursor

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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.

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

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

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

36%36% 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

13%13% 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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