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Joined May 2010
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πŸ“‚ ETHICAL HACKING TOOLS ┃ ┣ πŸ“‚ Reconnaissance ┃ ┣ πŸ“‚ Shodan ┃ ┣ πŸ“‚ theHarvester ┃ ┣ πŸ“‚ Maltego ┃ ┣ πŸ“‚ Amass ┃ ┣ πŸ“‚ Subfinder ┃ β”— πŸ“‚ WHOIS ┃ ┣ πŸ“‚ Scanning & Enumeration ┃ ┣ πŸ“‚ Nmap ┃ ┣ πŸ“‚ Masscan ┃ ┣ πŸ“‚ RustScan ┃ ┣ πŸ“‚ enum4linux ┃ ┣ πŸ“‚ SNMPwalk ┃ β”— πŸ“‚ Netcat ┃ ┣ πŸ“‚ Web Application Testing ┃ ┣ πŸ“‚ Burp Suite ┃ ┣ πŸ“‚ OWASP ZAP ┃ ┣ πŸ“‚ Nikto ┃ ┣ πŸ“‚ Gobuster ┃ ┣ πŸ“‚ ffuf ┃ β”— πŸ“‚ Dirsearch ┃ ┣ πŸ“‚ Exploitation ┃ ┣ πŸ“‚ Metasploit ┃ ┣ πŸ“‚ Searchsploit ┃ ┣ πŸ“‚ BeEF ┃ ┣ πŸ“‚ SQLmap ┃ ┣ πŸ“‚ Commix ┃ β”— πŸ“‚ RouterSploit ┃ ┣ πŸ“‚ Credential Attacks ┃ ┣ πŸ“‚ Hashcat ┃ ┣ πŸ“‚ John the Ripper ┃ ┣ πŸ“‚ Hydra ┃ ┣ πŸ“‚ Medusa ┃ ┣ πŸ“‚ CeWL ┃ β”— πŸ“‚ Crunch ┃ ┣ πŸ“‚ Wireless Security ┃ ┣ πŸ“‚ Aircrack-ng ┃ ┣ πŸ“‚ Kismet ┃ ┣ πŸ“‚ Wifite ┃ ┣ πŸ“‚ Bettercap ┃ ┣ πŸ“‚ Reaver ┃ β”— πŸ“‚ hcxdumptool ┃ ┣ πŸ“‚ Network Analysis ┃ ┣ πŸ“‚ Wireshark ┃ ┣ πŸ“‚ tcpdump ┃ ┣ πŸ“‚ Ettercap ┃ ┣ πŸ“‚ Bettercap ┃ ┣ πŸ“‚ TShark ┃ β”— πŸ“‚ Scapy ┃ ┣ πŸ“‚ Active Directory ┃ ┣ πŸ“‚ BloodHound ┃ ┣ πŸ“‚ NetExec ┃ ┣ πŸ“‚ Impacket ┃ ┣ πŸ“‚ Certipy ┃ ┣ πŸ“‚ Rubeus ┃ β”— πŸ“‚ Mimikatz ┃ ┣ πŸ“‚ Cloud Security ┃ ┣ πŸ“‚ ScoutSuite ┃ ┣ πŸ“‚ Prowler ┃ ┣ πŸ“‚ Pacu ┃ ┣ πŸ“‚ Trivy ┃ ┣ πŸ“‚ kube-hunter ┃ β”— πŸ“‚ kube-bench ┃ ┣ πŸ“‚ Reverse Engineering ┃ ┣ πŸ“‚ Ghidra ┃ ┣ πŸ“‚ IDA Free ┃ ┣ πŸ“‚ x64dbg ┃ ┣ πŸ“‚ Radare2 ┃ ┣ πŸ“‚ Cutter ┃ β”— πŸ“‚ Binary Ninja ┃ ┣ πŸ“‚ OSINT ┃ ┣ πŸ“‚ SpiderFoot ┃ ┣ πŸ“‚ Sherlock ┃ ┣ πŸ“‚ Recon-ng ┃ ┣ πŸ“‚ PhoneInfoga ┃ ┣ πŸ“‚ GHunt ┃ β”— πŸ“‚ Holehe ┃ ┣ πŸ“‚ Containers & DevSecOps ┃ ┣ πŸ“‚ Trivy ┃ ┣ πŸ“‚ Dockle ┃ ┣ πŸ“‚ kube-bench ┃ ┣ πŸ“‚ kube-hunter ┃ ┣ πŸ“‚ Docker Bench ┃ β”— πŸ“‚ Checkov ┃ ┣ πŸ“‚ Reporting ┃ ┣ πŸ“‚ Dradis ┃ ┣ πŸ“‚ Ghostwriter ┃ ┣ πŸ“‚ Serpico ┃ ┣ πŸ“‚ CherryTree ┃ ┣ πŸ“‚ Obsidian ┃ β”— πŸ“‚ Markdown ┃ ┣ πŸ“‚ Practice Labs ┃ ┣ πŸ“‚ Kali Linux ┃ ┣ πŸ“‚ Parrot OS ┃ ┣ πŸ“‚ Hack The Box ┃ ┣ πŸ“‚ TryHackMe ┃ ┣ πŸ“‚ PortSwigger Academy ┃ β”— πŸ“‚ VulnHub ┃ β”— πŸ“‚ Security Resources ┣ πŸ“‚ CyberChef ┣ πŸ“‚ Exploit-DB ┣ πŸ“‚ SecLists ┣ πŸ“‚ GTFOBins ┣ πŸ“‚ LOLBAS β”— πŸ“‚ VirusTotal
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Full Claude Course Master Claude to automate repetitive tasks, build apps without coding, create real portfolio projects, and become more valuable at work.

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Is this video antiseptic or simply the truth?
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Stop ignoring ports. You can learn a lot about network activity just by recognizing the service behind the traffic. Start with these 5: 22 β†’ SSH 443 β†’ HTTPS 3389 β†’ RDP 445 β†’ SMB 53 β†’ DNS
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LEARN FOE FREE
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Me when I install Windows 11
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Instead of watching an hour of Netflix, watch this 2 hour hour Stanford lecture will teach you more about how LLMs like ChatGPT and Claude are built than most people working at top AI companies learn in their entire careers.
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CYBERSECURITY TOOLS
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🧠 AI-Powered Red Team β€” 28 Specialized Agents for Offensive Security πŸ€–πŸ”₯ Turn Claude into a full pentesting team. β€’ 28 agents (Recon, AD, Web, Cloud, Mobile) β€’ Auto task routing β†’ correct agent β€’ Real tools support (nmap, sqlmap, nuclei, BloodHound) β€’ Recon β†’ Exploit β†’ Report Link: github.com/0xSteph/pentest-a… #AI #RedTeam #Pentesting #CyberSecurity #Infosec
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Here’s a clean, practical roadmap for a beginner SOC Analyst πŸ‘‡ 1. Foundations (Must not skip) Networking basics (IP, DNS, HTTP, ports, OSI model) Linux basics (file system, permissions, basic commands) Windows fundamentals (Event Viewer, processes, services) 2. Core Security Concepts CIA triad, threat types, attack vectors Malware basics Authentication & authorization Common attacks (phishing, brute force, MITM) 3. SOC Tools Basics SIEM tools (start with Splunk or Elastic) Log analysis (Windows + Linux logs) Basic alert triage 4. Hands-on Practice TryHackMe SOC paths Blue team labs (log analysis, incident detection) Basic packet analysis (Wireshark) 5. Scripting Basics Python (basic automation) Bash (Linux automation) 6. Entry-Level Skills Incident detection & response flow Writing basic incident reports Understanding false positives vs real threats 7. Job Readiness Build a small home lab Practice real SOC scenarios Learn how to explain investigations clearly Prepare for interviews (scenario-based questions) Final mindset shift: SOC is not about knowing everythingβ€”it’s about noticing what others miss.
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Telegram OSINT Intelligence D4rk_Intel-OSINT-Investigative-Toolkit has been updated with Telegram OSINT - a powerful angle for investigations, as the platform hosts vast amounts of public data that isn't always easy to navigate with its native search. #OSINT #Cybersecurity
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Learn Claude Code in 2 Hours- youtube.com/playlist?list=PL…
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The Local LLM Cheat Sheet for your 32GB RAM device I was asked to put together a practical lineup of local models that fit comfortably on a 32GB machine. At this tier, you start getting access to real flagship-class local models, plus a growing number of custom quants. But for most people, these are the core models worth knowing first. Flagship Models Qwen3.5 27B / GGUF / Q6_K_M The best overall 32GB flagship. General chat, writing, research, and agent workflows. Great if you want one model that can handle almost everything well. Qwen3.6-35B-A3B / GGUF / UD-Q4_K_M Best MoE flagship. Stronger for coding, reasoning, and tool use than most smaller generalists. Gemma 4 31B / GGUF / Q6_K_M Dense premium model. Writing, analysis, reasoning, and high-end local chat. Heavier than the MoE options, but excellent when quality matters more than speed. Models for Fast Flagship Use Gemma 4 26B A4B / GGUF / Q6_K_M Great balance of speed and quality for general assistant work, coding, agent tasks, and research. This is one of the best 32GB picks if you want something that feels high-end without dragging. DeepSeek-R1 Distill Qwen 32B / GGUF / Q4_K_M Offline reasoning engine. Best for math, logic, deliberate analysis, and step-by-step problem solving. Mistral Small 24B / GGUF / Q6_K_M Tool-calling specialist. Strong for assistants, chat workflows, local business tasks, and function calling. Available for 24GB machines. Models for Companion Use Qwen3.5 9B / GGUF / Q6_K_M Best sidekick. Fast drafts, search loops, cheap retries, and secondary agent work. Even on a 32GB machine, you still want a smaller model around for support tasks. Llama 3.1 8B / GGUF / Q6_K_M Long-context companion. RAG, doc ingestion, codebase chat, and long prompts. The output quality is not the sharpest anymore, but it is still useful when needing simple tasks fast. From what my community tells me, the best single models are Qwen3.5 27B or Gemma 4 31B. For two models, the strongest general pairing is Qwen3.5 27B + Qwen3.5 9B. If you are more code-heavy, Qwen3.6-35B-A3B + Llama 3.1 8B. Let me know what models you are running on 32GB, and which ones have actually been worth the RAM.
The Local LLM cheat sheet for your 16GB RAM device I pulled together a lineup of small models that can run comfortably on a Mac Mini or personal laptop while still leaving room for context without melting your machine. Models for Daily Use Qwen3.5 9B / GGUF / Q4_K_M Daily driver. General chat, drafting, research, translation. If you're keeping only one, keep this. DeepSeek-R1 Distill Qwen 7B / GGUF / Q4_K_M Reasoning engine. Math, logic, step-by-step problems. Slower, but worth it when you need actual thinking. Models for Specialty Work Qwen2.5 Coder 7B / GGUF / Q4_K_M Code specialist. Completions, refactors, debugging, repo Q&A. Better than a generalist when the task is code. Llama 3.1 8B / GGUF / Q4_K_M Long context worker. RAG, doc chat, codebase Q and A. The output isn't top tier, but the context is strong for its size. Phi-4 Mini Reasoning / GGUF / Q4_K_M Compact thinker. Logic, structured answers, math, and short coding bursts. Smaller context is the catch. Models for Efficiency Gemma 4 E4B / GGUF / Q4_K_M Light all-rounder. Writing, chat, light agents, structured output. Phi-3.5 Mini / GGUF / Q5_K_M Pocket sidekick. Summaries, extraction, background doc chat. Easy to pair with a bigger model. Qwen3.5 2B / GGUF / Q4_K_M Useful for summaries, tagging, rewrites, and lightweight sidekick work. Micro Models Qwen3.5 0.8B / GGUF / Q5_K_M Classification, keyword routing, binary decisions, triage. Gemma 4 E2B-it / GGUF / Q4_K_M Lightweight chat, quick Q and A, summaries, tiny agents. My personal choice for a single model is Qwen3.5 9B For two models use Qwen3.5 9B + Qwen2.5 Coder 7B for code, or Qwen3.5 9B + Phi-3.5 Mini for support tasks. Let me know in the comments your experience with these models, or any I have left out.
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Android Application Penetration Testing, Part 1: The Setup Resource: medium.com/@joelsunny.ch/and…
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Read these books if you want to learn quickly and easily - Linux hackersarise.thinkific.com/c… - Metasploit hackersarise.thinkific.com/c… - Network hackersarise.thinkific.com/c… - Ethical hacking process hackersarise.thinkific.com/c…
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Best Nuts For You Body!!
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SQL cheat sheet - Every JOIN explained:
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