NetNerve AI-powered packet analysis that explains .cap in plain English

Hacker News

I built NetNerve! What It Does NetNerve takes your .pcap files and uses AI (LLaMA-3 via Groq) to translate raw network data into actionable security insights. Instead of staring at TCP flags and port numbers, you get plain English explanations of what's actually happening in your network traffic. Tech Stack & Challenges Frontend: Next.js 14 with TypeScript for the interface Backend: FastAPI with Python/Scapy for packet processing AI: LLaMA-3 via Groq API for analysis Architecture: Privacy-first - files processed in memory, never stored Try it: https://netnerve.vercel.app (supports .pcap/.cap files up to 2MB) Looking for feedback from anyone who deals with packet analysis regularly - what would make this more useful for your workflow? Are there specific protocols or attack patterns you'd want better coverage for?

Visit product View on Hacker NewsJun 29, 2025

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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, io · Missing: https docs, excited, just released

31%31% 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 · Strong signals: interface · Missing: plus, platform, intuitive

29%29% 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

15%15% 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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