AI & ML
A math-light path into running and fine-tuning real AI models — and wiring them to your own tools and data — for engineers, not researchers.
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Practical PyTorch: Running Models — Foundations
Tensors, shapes, the GPU, and building your first model from a single layer up — the math-free groundwork, in Colab.
7 posts
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Practical PyTorch: Running Models — Vision
Convolution and LeNet, ResNet up close, and running a pretrained image classifier end to end — then wrapping it in an app.
5 posts
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Practical PyTorch: Running Models — Language
Words to vectors, attention and the transformer, DistilBERT, Hugging Face pipelines, embeddings, and a semantic search engine.
9 posts
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Practical PyTorch: Running Models — LLMs
Find and run heavyweight models from the Hub, debug runs, and drive a decoder — GPT-2, sampling, chat models, and a chatbot.
6 posts
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Practical PyTorch: Running Models — Audio
Generate speech and music with pretrained models — text-to-speech, neural audio codecs, and MusicGen — in Colab, still without the math.
8 posts
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Practical PyTorch: Training Models
Understanding & fine-tuning — how models learn, and adapting a pretrained model to your own data, still without the heavy math.
9 posts
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Building MCP Servers
The Model Context Protocol from first principles to a production server — tools, resources, prompts, transports, and auth, in Python and TypeScript.
12 posts
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LangGraph from Scratch
Build agentic systems from first principles — LangChain 1.x primitives, the StateGraph, tools and RAG, persistence and human-in-the-loop, up to a multi-agent bookshop support system. Run-verified on langchain 1.3 / langgraph 1.2.
17 posts