paul2495

6 ms·

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I got tired of juggling security tools,so I built an AI copilot to do it for me(sydsec.co.uk)
2 points by paul2495 10mo ago|1 comments
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by paul2495 10mo ago
Hey HN, As a security analyst, a typical workflow for me was a frustrating juggling act: run an Nmap scan, manually parse the output, search for CVEs, find an exploit script, set up a listener in Metasploit, and then finally launch the
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by paul2495 10mo ago
Thanksthat makes sense and it never even crossed my mind . FAISS has been great for prototyping but I'm definitely hitting the limits around metadata, updates, and operational overhead. One thing I’m exploring now is Qdrant in embedded
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Ask HN: Scaling local FAISS and LLM RAG system (356k chunks)architectural advice
1 points by paul2495 10mo ago|2 comments
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by paul2495 10mo ago
Really appreciate the detailed feedback—this is exactly what I need to hear. GPU/CPU question: Yes, Syd supports both. It auto-detects CUDA if available and falls back to CPU if not. With GPU (tested on RTX 3060), inference runs at 30-
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by paul2495 10mo ago
Thanks for the links different project though. Those are sandboxing and syscall-monitoring tools, while my Syd is an offline AI assistant built for security workflows (DFIR, pentesting, malware triage, tool-output reasoning, etc.). Complete
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by paul2495 10mo ago
Author here. Happy to answer questions! A bit more context on how Syd works: it uses Dolphin Llama 3 (dolphin-2.9-llama3-8b) running locally via llama-cpp-python. You'll need about 12-14GB RAM when the model is loaded, plus ~8GB disk s
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Syd – An offline-first, AI-augmented workstation for blue teams(sydsec.co.uk)
21 points by paul2495 10mo ago|5 comments