Join our livestream with Marina Wyss, Senior Applied Scientist at Twitch, as we discuss how to break into AI Engineering in 2026.

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Why should you join?

Many people interested in AI Engineering are asking the same questions:

❓ Where do I start?
🤔 Do I need deep math first?
🧠 Should I focus on ML, LLMs, RAG, or AI agents?
🧭 How do I avoid wasting time learning the wrong things?
🚀 How do I go from learning to becoming hireable?

If you’re interested in AI Engineering but unsure how to approach it, this livestream is for you.

What you’ll learn

✦ What AI Engineering really is
✦ Where beginners should start
✦ What skills and topics actually matter
✦ Common mistakes to avoid
✦ Self-study vs bootcamp vs MSc
✦ How to think about becoming hireable in AI
✦ Practical advice from someone already working in the field

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🔖 Boosting AI agents for cybersecurity tasks

A huge collection of skills for AI agents focused on cybersecurity has been posted on GitHub.

It includes more than 750 skills: malware detection, certificate analysis, phishing detection, security audits, penetration testing, and many other tasks.

⛓️ Link to GitHub
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I often see people say that it's impossible to enter the IT field without expensive courses.

However, there's a huge amount of high-quality materials available for free:

📚 Computer Science
https://github.com/ossu/computer-science

📚 Data Structures & Algorithms
https://github.com/jwasham/coding-interview-university

📚 System Design
https://github.com/donnemartin/system-design-primer

📚 Web Development
https://github.com/TheOdinProject/curriculum

📚 Frontend / Backend / DevOps / Cloud
https://github.com/kamranahmedse/developer-roadmap

📚 Data Engineering
https://github.com/DataTalksClub/data-engineering-zoomcamp

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https://github.com/microsoft/ML-For-Beginners

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https://github.com/DataTalksClub/mlops-zoomcamp

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https://github.com/OWASP/CheatSheetSeries

📚 Linux
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If you have internet and a bit of free time, you can learn computer science, algorithms, system design, DevOps, clouds, security, and machine learning for free.

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Google has published a free guide on scaling AI models and working with GPUs. 🚀

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📘 How to Think About GPUs
https://jax-ml.github.io/scaling-book/gpus/

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A large collection of materials on LLM Systems,

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10 GitHub repositories that are worth checking out for an AI engineer 🤖

1. Hands-On AI Engineering 🛠️

A collection of AI applications and agent systems with practical use cases of LLM.

👉 https://github.com/Sumanth077/Hands-On-AI-Engineering

2. Hands-On Large Language Models 📘

Full code from the book Hands-On Large Language Models: from basics to fine-tuning.

👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

3. AI Agents for Beginners 🎓

A free course from Microsoft with 11 lessons on creating AI agents.

👉 https://github.com/microsoft/ai-agents-for-beginners

4. GenAI Agents 🤖

A large collection of tutorials and implementations of agent systems.

👉 https://github.com/NirDiamant/GenAI_Agents

5. Made With ML 🚀

About the development, deployment, and support of production-ready ML systems.

👉 https://github.com/GokuMohandas/Made-With-ML

6. Learn Harness Engineering ⚙️

A practical course on Harness Engineering for AI agents.

👉 https://github.com/walkinglabs/learn-harness-engineering

7. AutoResearch 🔬

Autonomous cycles of ML experiments from Andrej Karpathy.

👉 https://github.com/karpathy/autoresearch

8. Designing Machine Learning Systems 📚

Notes and materials from Chip Huyen's book.

👉 https://github.com/chiphuyen/dmls-book

9. Awesome LLM Inference ⚡

A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.

👉 https://github.com/xlite-dev/Awesome-LLM-Inference

10. LLM Course 🗺️

A practical course on LLM with a roadmap and Colab notebooks.

👉 https://github.com/mlabonne/llm-course

#AI #MachineLearning #LLM #DataScience #Tech #GitHub

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🔖 Cheat sheets on Transformers and LLMs from Stanford

We found an excellent set of materials from the Stanford CME-295 course on large language models.

They cover tokenization, the self-attention mechanism, prompting, fine-tuning, LLM-as-a-judge, RAG, AI agents, and reasoning models.

⛓️ Download here
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