Learn. Build. Advance.
Three tracks, one path. Start with the fundamentals, build something real, then get ready for the interview. Every guide is written to be followed on your own machine.
Foundations
Build practical skills with step-by-step tutorials covering Python, machine learning and generative AI — how the models work before you start wiring them together.
Systems & Tooling
Learn modern data engineering, cloud platforms, Databricks and scalable systems through clear technical guides you can actually reproduce.
Interviews & Certification
Prepare for interviews and certifications with focused roadmaps, practice questions and real-world system design problems.
Latest tutorials
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Claude Opus 5.5 vs GPT-6 Astra: 7 Powerful Comparisons
Updated September 23, 2026 Claude Opus 5.5 arrived on September 22, 2026, and Anthropic is making an unusually aggressive claim for the…
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nanoMoE: Implementing Mixture-of-Experts LLMs from Scratch in PyTorch
Table of Contents Conclusion FAQs Primary MoE References Introduction Research on large language models (LLMs) has advanced at an extraordinary pace over…
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LLM as a Judge: Langfuse Setup, Best Practices & MT-Bench Lessons
LLM as a judge is one of the most practical ways to evaluate open-ended AI outputs when exact-match tests are too narrow.…
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DeepSeek MLA: 4 Attention Methods Compared
DeepSeek MLA: 4 Attention Methods Compared DeepSeek MLA is the focus of this practical guide to MHA, GQA and MQA because its…
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Multi-Head Latent Attention (MLA) Explained: DeepSeek Architecture, KV Cache, Math & PyTorch Code
Multi-Head Latent Attention (MLA): How DeepSeek Reduces the KV Cache Run a transformer with a short prompt and the attention mechanism may…
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Google OKF is a Knowledge Layer for AI Agents
I have recently been evaluating Google’s Open Knowledge Format (OKF). My initial assessment is that the framework is intentionally minimalist. Author: Ravi…
How to work through these
- Read the guide end to end once before you touch any code
- Run it locally — every tutorial is written to work on consumer hardware
- Break it on purpose, then fix it. That is where the understanding comes from
- Write up what you built. It doubles as portfolio and interview material