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Python & AI learning guides for students
Beginner-friendly articles on Python, machine learning basics, RAG, and AI tools—written for students and self-learners. Each post links to sources and our live online tutoring programs.
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Live Python Tutor vs Recorded Courses: What Works for Beginners
Compare live 1:1 Python tutoring with Udemy, Coursera, and YouTube: when recorded courses help, when you need a mentor, and how students in India and the US can choose wisely.
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Google AI Updates (March 2026): What Matters for Students & Educators
Google AI updates for education summarized: what changed across products and what it means for studying, teaching, and responsible classroom use.
Read moreLLM Fit (2026): How to Choose the Right Model for Your Task
Model selection framework for 2026: match tasks to model strengths, optimize accuracy-cost-latency, run evals, and decide between prompting, RAG, or fine-tuning.
Read moreAutoResearch by Andrej Karpathy (2026): How Self-Improving Research Agents Run
AutoResearch-style agent loop explained: experiment planning, safe code changes, short-run training cycles, evaluation gates, and how students can practice responsibly.
Read moreLLM Inference Optimization (2026): Speed, Cost, KV Cache, Quantization & Batching
LLM inference optimization playbook: KV cache, batching, quantization, speculative decoding, and when distillation beats brute-force scaling on cost and speed.
Read moreLLM Evaluation in 2026: Benchmarks, Evals, Regression Tests & Red-Teaming Basics
LLM evaluation guide for builders: benchmarks, custom eval suites, regression tests, human review loops, and safety checks for production reliability.
Read moreModel Context Protocol (MCP) in 2026: Why It Became Agentic Infrastructure
MCP in 2026 explained simply: how standard tool connections work, why adoption accelerated, how it differs from ad-hoc plugins, and what to learn first.
Read moreAgentic AI in 2026: Planning, Tool Use & Multi-Step Workflows Explained
From chatbots to agents: clear overview of planning, tool use, API calls, delegation, and the skills students and developers need for agentic systems.
Read moreRAG Flow Diagram (2026): How Retrieval‑Augmented Generation Works End‑to‑End
Visual RAG system walkthrough from ingestion to evaluation: chunking, embeddings, hybrid retrieval, re-ranking, prompt assembly, citations, and quality checks.
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