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Hello, Open World
1_RTO
1.1 Red Teaming 101
1.1.1 OW64
Recon
1. External
2. Internal_NoCreds
3. Authenticated_Pivot
4. C2_SOCKS
6. Low_Footprint_Alternatives
7. Blue_Team_Detection
8. Jump_Server_Quickstart
opensource
Windows AD Recon — GOAD-Light Lab
Windows Tradecraft
Active Directory
1. External Initial Access
2a. Credential Harvesting
2b. Gaining Access Without Credentials
3. Entering a Domain-Joined Machine
4. Enumerating a Domain-Joined Machine
5a. Privilege Escalation
5b. Establishing Persistence
6. Enumerating Forest and Trust Relationships
7. Pivoting to Other Machines on the Network
8. Owning a Domain Admin
10. LDAP
11. Powerview
Shared Drive
Case Studies
CVE-2016-6563
OW64 Windows Tradecraft — Detailed Notes
0 README
0.1 SecureHomelabs
0.2 Red Team Workspace Cheatsheet
1.1a Linux Deep Dive P1
1.1b Linux Deep Dive P1b
1.1c Linux Deep Dive P2
1.3_macOS_Post_Exploitation
1. Linux
2.1 Windows-ad-attacks
2. Windows AD
3. Web
4.1_Pivoting_and_Tunneling
4. Networking
5. Cloud
6.1 EDR Evasion
6.2 Syscall Evasion Deep Dive
6. EDR
7. C2
8. Reporting
1.1 Red Teaming 101 — Index
1.2 Web
1.2.2 Attack Web
Web_Fingerprint_Script
Web_URL_Fingerprinting
1.3 Clouds
1.3.1 Learn Cloud
0.1.1. General
0.1.1.1. Clouds
0.1.1.2. Clouds testing Scoping
0.1.1.3. Associate roles and services
0.1.2 Azure
0.1.2.2. Azure Services
Azure Cosmo
Azure Function Apps
Azure Kubernetes
Azure Logic App
Azure Monitor
Azure Repos
Azure Services
1.3.2 Attack Cloud
0.2.2. AWS
skills
skills
Tips
AWS Access Key to Web
0.2.3. Azure
skills
skills
1.4 Tunneling
ligolo-ng-guide
SSH Tunneling
1.5 Syntaxes
Chisel
SCP to move file
SMB Download
xfreerdp
1.6 Kubernetes
Kubernetes-PenTest
1_RTO — Index
2_AI
2.1 Learn AI
01-foundations
AI Foundations — Index
AI, Machine Learning, Deep Learning, and Generative AI
Data, Training, Validation, and Inference
Neural-Network Basics
Transformers and Attention
Tokens, Embeddings, and Context
Generation, Decoding, and Hallucinations
Model Families and Multimodality
02-using-ai
Using AI Effectively — Index
Frame the Job and Success Criteria
Prompt Anatomy
Context Engineering for Users
Verification, Citations, and Abstention
Model Selection, Cost, and Privacy
Repeatable User Workflows
03-harness-engineering
AI Harness Engineering — Index
What Is an AI Harness?
AI Harness Reference Architecture
Instruction and Context Assembly
Model Adapters and Routing
Tools and Structured Outputs
State, Memory, and Checkpoints
Permissions, Sandboxing, and Human Approval
Retries, Idempotency, and Recovery
Observability, Cost, and Run Records
Harness Contract and Lifecycle
04-agents-and-tools
91-existing-agentic-ai-note
Agentic AI
Agents and Tools — Index
Agents Versus Workflows
AI Workflow Patterns
Planning Loops and State Graphs
Tool Use and the Model Context Protocol
Multi-Agent Systems
Human-in-the-Loop Control
Skills and Capability Packages
05-knowledge-systems
90-existing-rag-build-notes
0. Retrieval-Augmented Generation
1. Text Extraction
2. Text Chunking
3. Embedding Chunks and Storing in Vector DB
4. Querying and Retrieving Relevant Context
5. RAG interface
6. main
7. Ollama
AI Knowledge Systems — Index
RAG Reference Pipeline
Ingestion, Chunking, and Metadata
Retrieval, Hybrid Search, and Reranking
Grounding, Citations, and RAG Evaluation
Memory, RAG, CAG, and Fine-Tuning
Graph and Structured Retrieval
06-building-and-deployment
90-existing-langchain-langgraph
LangChain and LangGraph
Building and Deploying AI — Index
API-Hosted Versus Local Models
AI Frameworks Without Lock-In
Fine-Tuning, PEFT, and Distillation
Inference, Quantization, and Serving
Multimodal Application Pipelines
AI Production Release Lifecycle
07-evaluation-and-operations
AI Evaluation and Operations — Index
Evaluation Layers and Metrics
Evaluation Datasets, Rubrics, and Edge Cases
Deterministic Checks, Model Judges, and Humans
Online Monitoring and Drift
Experiments, Versioning, and Regression Gates
AI Incidents and Change Management
08-security-privacy-governance
90-existing-ai-red-team-notes
2.4.1. Test Logging Template
2.4.2. AI Red Teaming
2.4.3. LLM Questioning Playbook
2.4.4. Top Commenting Patterns
AI Security, Privacy, and Governance — Index
Threat-Model the Complete AI System
Prompt Injection and Untrusted Content
Data Privacy and Sensitive Information
Model, Data, and Software Supply Chain
Excessive Agency and Tool Risk
Govern, Map, Measure, and Manage AI Risk
09-automation
90-existing-n8n
CLAUDE_DESKTOP_N8N_MCP_SETUP
researcher
AI Automation — Index
Event-Driven AI Automation
Scheduled AI Research and Reporting
n8n AI Workflow Safety and Operations
10-labs
02-local-rag-cas-existing
rag_backend
0. Readme
1. Dockerfile
2. requirements.txt
3. main.py
4. doc_processor.py
5a. vectorizer.py
5b. ollama_embed.py
6. api.py
6a. config.py
6b. env
uploader.py - CLI TOOL
docker-compose.yml
Practical AI Labs — Index
Lab 1 — Prompt and Verification
Lab 2 — Local RAG
Lab 3 — Read-Only Tool Agent
Lab 4 — Production Harness Capstone
11-reference
AI Reference — Index
AI Authoritative Source Ledger
Dumpster AI Research Map
AI Glossary
agentctl Research Audit — Learn AI Curriculum
Learn AI — Curriculum Index
2_AI — Index
3_Platform Internals
3.2 Windows OS
6.1 Learn Windows
1. Windows Basic
7. Platform Invoke .net specific
Platform Internals — Index
4_Domain Playbooks
Drones
01-drone-landscape
02-radio-and-frequency-guide
03-safe-learning-path
LiteWing ESP32 Drone — From Box to First Flight
LiteWing First-Flight Runbook
LiteWing ML Autonomy — Phased Project Plan
RadioMaster Boxer ELRS — bench-only setup reference
RadioMaster Boxer — EdgeTX and internal ELRS firmware upgrade bench guide
11-wifi-fpv-drone-101-target-to-attack
_moc
sources-and-fact-check
linux
Linux Domain Playbook — Index
01-linux-internal-device-scan
02-linux-log-system
Mobility
01-methodology
Mobility Research Guideline — Initial Entry Vectors (Equipment & Site Focus)
02-theory
Mobility Security Assessment Threat Model
Mobility — The 5G Core, End to End
03-phases
02-ue-android-sim
04-open5gs_lab
00_index
01_4g_lte_fundamentals
02_5g_nsa_architecture
03_5g_sa_architecture
04_lab_4g_epc_docker
05_lab_5g_nsa_docker
06_lab_5g_sa_docker
07_kubernetes_deployment
08_threat_model_4g
09_threat_model_5g
10_threat_model_k8s_telecom
11_real_world_ss7_signaling
12_real_world_sim_identity
13_real_world_sms_malware
14_real_world_apt_mobile_ops
15_real_world_attack_matrix
16_android_cell_analysis
17_test_plan_4g_5g_holistic
18_test_plan_mobility_site_to_core
05-hardware
Mobile Security Research Lab — Equipment Guide
Pixel 9 — Mobile Security Research Setup
06-toolkit
MBX-02 & MBX-08 — App Interception Toolkit
07-test-plans
Master Blackbox UE-to-Node Test Plan
09-evidence
Telecom Fraud Evidence Matrix
Scattered Spider to Telecom Fraud Evidence Matrix
12-incidents
01-proxy-cleanup-2026-05-22
02-proxy-cleanup-script
13-apt-threat-intelligence
Mobility APT Threat Intelligence — Index
Documented APT and Telecom Intrusion Campaigns
Mobility Attack Surfaces and Inspection Files
Local Open5GS Lab APT Exposure Map
Mobility APT Defensive Validation Backlog
Mobility APT Research Source Ledger
Mobility Domain Playbook — Index
windows
Choosing a DLL for Read-only Inspection
Windows Playbook — Index
Windows 11 Security Engineering — Map of Content
Module 1 — Kernel and Privilege Rings
Module 2 — User Mode and Syscalls
Module 3 — DLLs, Loaders, and PE
Module 4 — Filesystem, Folders, and Artifacts
Module 5 — Processes, Threads, and Handles
Module 6 — Applications, Services, and Autoruns
Module 7 — Security Engineer Map
Lab Workbook — Windows 11 (192.168.50.114)
Windows Defender — Architecture and Rulesets (Deep Dive)
Lab Baseline — Windows 11 (192.168.50.114)
Module 9 — Evidence, Event Logs, and Artifacts
Module 10 — LSASS, Identity, and Authentication
Lab Tools Inventory — Windows 11 (.114)
Module 11 — Sysmon First Hunt Workbook
AD Domain Lab Setup — TESTER.LAB
Module 26 — c2_rust Win11 Implant Hunt Correlation
30-windows-internal-device-scan
Domain Playbooks — Index
5_Projects
Phase 1 — Mobility (M1–M6)
999. Stuff
Learn Programming
Learn Python
uv
Publish Stuff
Use Gemini with Obsidian
9999. Projects with Kids
Make Water
Start a Fire
Published — Index
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Building and Deploying AI — Index
#ai
#building
#deployment
#index
Building and Deploying AI
#
Lesson
1
API-hosted versus local models
2
Frameworks without lock-in
3
Fine-tuning, PEFT, and distillation
4
Inference, quantization, and serving
5
Multimodal application pipelines
6
Production release lifecycle
Preserved framework note:
LangChain and LangGraph
.