Warning: this blog is an experiment. The idea for a blog came from a real conversation I had with somebody. The content is researched, debated, refined, “hardened” by a Gemini skill I created (using Gemini 4 Argon). Then Claude Opus…
Source: https://securityboulevard.com/2026/11/i-dont-need-a-siem-i-have-ollama-yes-you-do. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
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Try itUnderstanding how malicious actors infiltrate networks is the first step to building resilient infrastructure. To help developers and aspiring security analysts learn the fundamentals of offensive sec
pwn.college is a free, hands-on education platform for learning core cybersecurity and hacking skills, maintained by a team at Arizona State University. Learners progress through belt ranks, starting with a 'white belt' and earning belts through practice. Hackers continue beyond the platform to 'brown belt' and eventually 'black belt' status, which must happen outside the dojo. The site powers much of ASU's cybersecurity curriculum and asks users not to post writeups or walkthrough videos of challenge solutions. It points learners toward CTFtime.org for Capture The Flag events and a GitHub list of wargames for further practice. The underlying infrastructure is open source and accepts pull requests, though some modules remain closed-source.
Run an LLM locally with Ollama. See how much memory you need, where LM Studio and llama.cpp fit, and how to call the model from code.
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PacketHound is a free outbound firewall for Windows. The first time an app tries to connect to the internet, it asks you: Allow or Block.
How DeepSeek Harness's Super-agent works: a Router, isolated Workers, event triggers and a Watchdog let one AI agent run many tasks. Diagrams inside.
The Model Context Protocol (MCP) helps connect LLMs to external tools, databases, and APIs. But giving AI agents hands and feet to take real-world actions introduces an entirely new attack surface. We
Large language models remain far less efficient than biological brains at learning, representing knowledge, and planning, the post argues. A child hears roughly one token per second, reaching a billion tokens only after 31 years, while GPT-4-class models reportedly use four orders of magnitude more data. The Phi model series, trained on 10 trillion tokens, still trails GPT-4 along most axes. A Portia spider plans prey-catching behavior with a brain a million times smaller than a human's, and squirrels recall cached food after months, suggesting that existing models may be far less representationally and memory efficient.
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