Interactive Tech Tutorials & Labs

Practical, multi-step Python walkthroughs for AI Engineers, SOC Security Analysts, and LLMOps Architects.

AI Engineering 18 min lab

Building a Production RAG System with Vector Embeddings & Hybrid Search

Construct a multi-step Retrieval-Augmented Generation (RAG) pipeline in Python using Qdrant, sentence-transformers, semantic chunking, and cross-encoder re-ranking.

PYTHON 
from qdrant_client import QdrantClient
from sentence_transformers import SentenceTransformer

client = QdrantClient("http://localhost:6333")
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(["Knowledge Chunk"])
Intermediate Start Lab Guide
Cybersecurity 25 min lab

SOC Threat Hunting: PCAP Traffic Analysis & Beacon Detection

Analyze live network packet dumps in Python using Scapy & PyShark. Extract suspicious TLS SNI requests, detect C2 beaconing jitter, and generate Snort signatures.

PYTHON 
from scapy.all import rdpcap, DNS

packets = rdpcap("capture.pcap")
dns_queries = [pkt[DNS].qd.qname for pkt in packets if pkt.haslayer(DNS)]
print(f"Extracted {len(dns_queries)} DNS queries")
Advanced Start Lab Guide
LLMOps 20 min lab

Fine-Tuning Llama 3 with LoRA & Unsloth for Domain Automation

Complete Python guide to parameter-efficient fine-tuning on custom JSONL datasets with 4-bit quantization, GPU memory optimization, and GGUF export.

PYTHON 
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/llama-3-8b-Instruct-bnb-4bit",
    max_seq_length = 2048, load_in_4bit = True
)
Intermediate Start Lab Guide
Cybersecurity 30 min lab

Active Directory Pentesting & Defense Hardening

Simulate Kerberoasting attack vectors using Python Impacket, map domain trust relationships with BloodHound, and configure Active Directory tiering models.

PYTHON 
from impacket.examples.GetUserSPNs import GetUserSPNs

spn_dumper = GetUserSPNs(username="user", password="pass", domain="corp.local")
spn_dumper.run()
Advanced Start Lab Guide
AI Engineering 22 min lab

Building Autonomous Multi-Agent Workflows with Persistent Memory

Design multi-agent networks in Python using LangGraph state graphs, custom tool execution loops, SQL persistent memory, and human approval checkpoints.

PYTHON 
from langgraph.graph import StateGraph, END
from typing import TypedDict

class AgentState(TypedDict):
    messages: list
    next_step: str
Intermediate Start Lab Guide
Defense & Hardening 15 min lab

NeuralShield: Prompt Injection Defense & Token Sanitization

Harden Python LLM API endpoints against direct jailbreaks and indirect prompt injections using token entropy analysis, system delimiters, and FastAPI middleware.

PYTHON 
import tiktoken
import re

def sanitize_prompt(prompt: str) -> str:
    cleaned = re.sub(r"system:", "", prompt, flags=re.IGNORECASE)
    return f"---USER_INPUT---\n{cleaned}\n---END_INPUT---"
Beginner Start Lab Guide