AI/ML Pentesting Roadmap (2026 Edition) takes you from zero to practitioner foundations, OWASP LLM Top 10
- LLM fundamentals to Advanced prompt injection, jailbreaks & agentic AI attacks & indirect injection
- MCP exploitation & Agentic AI security (the new frontier) RAG exploitation
- Real tools, CTFs, papers & bug bounty programs
Updated with latest OWASP, MITRE ATLAS, real-world research & offensive tools.
Ideal for bug bounty hunters, pentesters and cybersecurity pros
AI agents are more useful when they can remember, use tools, and run from places you already check.
In this course, Shawn teaches you how to build a Telegram AI agent with Vercel, Cursor, and Composio.
You'll add app integrations, long-term memory, mobile access, and scheduled tasks with Vercel Cron along the way.
https://t.co/HYCwefkP3r
Really liked this SpecterOps write-up.
EDR gives defenders visibility and response options you absolutely want.
But no serious attacker treats EDR as the end of the road. They probe, learn, change their approach, and try again.
SOTA LLMs make that process faster.
EDR is critical. It is not magic.
https://t.co/RZyb8297Qh
MLOps helps you take machine learning models from experimentation to production.
In this course, you'll learn how to use MLflow to manage the entire ML lifecycle.
You'll also learn Databricks and Hugging Face to build reproducible, scalable & observable systems.
https://t.co/5WFsUhjEvW
You can now build & run a complete real electric circuit simulator in full 3D,,,
This is insane:
- Full 3D circuit simulator
- Runs real Arduino code
- Accurate analog & digital simulation
- Blinking LEDs, sensors, motors, LCDs, displays everything and more
Powered by AVR8js & Three.js magic.
Everything runs smoothly right in your browser.
Perfect for learning, prototyping, and teaching electronics.
Real Arduino code running in the browser with full 3D circuits.
- https://t.co/4cWk8IIExQ
Stop learning LLM internals from random one-off tutorials.
LLM Internals is a step-by-step GitHub learning repo for understanding how large language models work under the hood.
It helps you build a cleaner mental model by organizing blogs and videos from tokenization to attention math, Transformer components, training concepts, and inference optimization.
Key features:
• Fundamentals first – starts with LLMs, RAG, MCP, agents, fine-tuning, quantization, tokenization, and BPE
• Attention math – walks through Q/K/V, √dₖ scaling, causal masking, RoPE, and grouped-query attention
• Transformer components – covers the architecture, feed-forward networks, normalization, MoE, and LoRA
• Training concepts – includes backpropagation, cross-entropy loss, RLHF, and reasoning models
• Inference optimization – covers KV cache, paged attention, Flash Attention, speculative decoding, continuous batching, and prompt caching
It’s open-source (Apache License 2.0).
Link in the reply 👇
An Intrusion Detection System acts like a security camera for your network.
And in this in-depth tutorial, @chairahalkar teaches you how to build a real-time IDS with Python.
It'll alert you to any potential cyber attacks and security breaches so you can keep your network safe.
https://t.co/XZA1NViM0M