Clear, practical guides for developers who want to build real things. AI tools, Python, Docker, Git — explained step by step, with code you can actually use.
Every command and code block is run in a clean environment before publication. Version numbers are explicit.
Written and edited by a working developer. We do not publish raw AI output.
Comparisons include what each tool does badly, not just marketing points.
Guides are reviewed quarterly and updated when tools, APIs, or pricing change.
Write tools your agent actually calls correctly: outcome-based naming, disambiguating descriptions, small typed schemas, recoverable errors, and a pytest selection suite.
Give agents real memory: conversation buffers, sliding windows, SQLite checkpoints, and vector-backed long-term recall — with token budget math.
Five guardrail layers that hold up: input separation, filtering, least-privilege tools, Pydantic output validation, approval gates, and a pytest regression suite.
Train a custom 7B model on one consumer GPU: 4-bit base setup, LoRA hyperparameters, TRL training, adapter merge, GGUF export.
Trace every LLM call: span attributes, nested agent loops and RAG, per-user cost attribution, and backend choices.
Build a local Git hook that writes Conventional Commit messages from your staged diff. No API keys needed.
Write your first unit tests, use fixtures, parametrize inputs, and avoid common testing mistakes.
Build your first REST API in 30 minutes — from setup to deployment with real code examples.
Build a working AI app with LLMs, chains, and prompts — complete Python code included.
Install Ollama, pull Llama and Mistral, run AI locally. Full setup + Python integration.
Containerize your first app in 15 minutes. Build, run, and manage containers with simple examples.
From zero to your first pull request. The 8 commands you will use every day.
Tips, tricks, agent mode, chat commands — the daily workflow that saves hours.
Basic types, Optional, TypedDict, and static checking with mypy — all with real examples.
Master list, dict, and set comprehensions. Common mistakes and when not to use them.
5 free hosting platforms compared: Railway, Render, Fly.io, Vercel, PythonAnywhere.
Write concurrent code for API calls, file I/O, and AI workflows — real examples you can run today.
Connect your tools and APIs to Claude, GPT, and any MCP-compatible AI client with one standard protocol.
The exact extensions, interpreter, and debugger config I use — with real screenshots of every step.
Stop print()-debugging. Breakpoints, stepping, and post-mortem debugging — hands-on with real sessions.
Take a real FastAPI service from code to running container, with screenshots of every step.
Ollama + Open WebUI: a ChatGPT-like assistant that never leaves your machine. With setup screenshots.
Index documents, retrieve relevant chunks, and generate grounded answers with an LLM. Full Python tutorial.
Build a working AI agent from scratch — tool calling, decision making, and multi-step tasks. No framework required.
Five prompt patterns that handle 90% of production LLM work: system prompts, few-shot, chain-of-thought, structured output, and tool calling. With real Python code.
Install, authenticate, and use Anthropic's terminal AI to read files, write code, run commands, and ship faster. Step-by-step with real examples.
Build a multi-agent AI graph with state, tools, conditional routing, and persistent memory. Full Python tutorial with working code.
The built-ins worth memorizing (/clear, /compact, /cost), custom commands with $ARGUMENTS, and team-shared command libraries. Real examples.
Text classification, summarization, image classification, and speech recognition in 10 lines of Python. Real models, working code.
Send tokens to the browser the instant they arrive. SSE vs WebSocket, OpenAI/Anthropic/Ollama code, fetch reader, and the 5 buffering gotchas.
Stop parsing broken JSON. Typed, validated Python objects from OpenAI, Anthropic, instructor, and Ollama — plus the retry pattern that makes it reliable.
Golden datasets, deterministic checks, embedding similarity, LLM-as-judge rubrics, RAG groundedness, and a CI setup that does not burn your API budget.
Stop re-paying for the same system prompt. Anthropic cache breakpoints, OpenAI automatic caching, cache-aware Python wrappers, and Redis response caching.
In-depth comparison of the two leading AI coding tools. Features, pricing, performance.
venv vs uv vs Poetry vs conda. Speed benchmarks and clear decision guide.
Honest comparison. Speed, features, learning curve. No fanboy wars.
API testing tools compared: SaaS, open-source, Git-native. Which fits your workflow?
Built-in BackgroundTasks vs distributed Celery — code examples, benchmarks, and a decision framework for picking the right async task tool.
Extensions that actually boost productivity. AI, formatting, Git, Docker, remote dev.
Code generation, debugging, testing, docs — tools every developer should know.
Hand-picked exercises for beginners. Each with detailed solution and explanation.
50 essential commands every developer needs. File ops, networking, processes, text.
Solutions for the most common Python import error. Virtual env, pip, PATH — covered.
SSL errors, permission denied, pip not found, dependency conflicts — all solved.
Identify, understand, and resolve merge conflicts. VS Code + CLI + prevention.