Data Science Tutorials

Compare stock market data APIs for backtesting, dashboards, screeners, and AI agents, with Python examples covering splits, reliability, and fundamentals.

Learn how to run MiniMax M3 locally on two RTX PRO 6000 GPUs with llama.cpp, test its OpenAI-compatible API and web UI, and connect it to Pi Coding Agent for private, high-speed local coding workflows.

Learn how to use the built-in Claude Code slash commands to manage context, changes, and costs across sessions, and create custom commands to automate repetitive workflows.

Learn how to use Cofounder 2 to turn a rough idea into a structured company with a business plan, brand kit, engineering tasks, marketing campaigns, and sales workflows through specialized AI agents.

How random forest regression works, where it fails, and how to evaluate, tune, and interpret it. Includes a Python implementation and model comparison framework.

Learn how Claude Code routines run your coding agent in the cloud on a schedule or a GitHub event, so PR reviews and audits finish with your laptop closed.

GGUF packages model weights, tokenizer data, and metadata into a single portable file. Learn how to choose the right quantization level and get started with Ollama.

Learn how to accelerate local Gemma 4 31B inference on a single RTX 4090 using DFlash speculative decoding and Flash Attention against a baseline setup.

Learn how to set up Cursor automations with this hands-on tutorial covering PR code review, scheduled cron tasks, tools and MCP configuration.

A complete tutorial on using Python-native tools for async CRUD operations and HTMX interactivity.

Stop your LLM from forgetting context. Learn how to build an AI agent with episodic, semantic, and procedural memory using Spring AI and Oracle Database.

Kernel density estimation is a nonparametric method for estimating the shape of a data distribution without assuming a fixed model. Learn the formula, bandwidth selection, and hands-on implementation in Python and R.