Python

AppDaemon 4.5.14 is a Python runtime that runs next to Home Assistant . It lets you write rules as full Python classes. You get state machines, scheduling, outside API calls, and logic that YAML can’t handle. Install it as a Home Assistant add-on, drop a Python file in the apps folder, define a class that inherits from hass.Hass, and use callbacks like listen_state() and run_daily() to drive multi-step flows, saved values, and live data.

You can catch domain-specific anti-patterns that ESLint , Ruff , or golangci-lint miss by writing custom linter rules that parse your code into an Abstract Syntax Tree (AST), walk the tree to match specific node patterns, and report violations with auto-fix suggestions. The process is the same regardless of language: parse source into a tree, define the pattern you want to catch, walk the tree to find matches, and emit diagnostics. In JavaScript/TypeScript, this means writing an ESLint plugin with a visitor-pattern rule. In Python, you write a flake8 plugin using the ast module or a Ruff plugin in Rust. In Go, you use the go/ast and go/analysis packages.

Promptfoo is an open-source CLI tool that runs your test cases against one or more LLM providers at once. You write a YAML file with prompts, test cases, and checks, then run promptfoo eval to get a report with pass/fail rates, regressions, and side-by-side comparisons. It scores results three ways: simple text checks, LLM-as-judge grading, or your own scoring code. The point is to catch prompt regressions, broken model upgrades, and quality drops before users see them.

You can build a working static site generator in about 100 lines of Python. The result reads Markdown files from a content directory, parses their YAML front matter, converts the Markdown to HTML, wraps everything in Jinja2 templates, and writes the output to a public/ folder ready to be served by any web server. It is the same fundamental pipeline that powers tools like Hugo , Jekyll , and Eleventy - just stripped down to the essentials so you can see exactly how the pieces fit together.

Redis Streams give you a light, self-hosted option versus Apache Kafka for event-driven data pipelines. You get append-only log semantics, consumer groups with ack tracking, and sub-millisecond latency on a single Redis 7.4+ instance. Producers XADD events to a stream. Consumer groups read with XREADGROUP in Python via redis-py . Manual XACK calls plus a pending entry list (PEL) give you at-least-once processing.

What follows covers stream basics, consumer groups with failure recovery, a full producer and consumer pipeline with a dead-letter queue, and the ops practices to keep Redis Streams healthy in production.

Pydantic v3 shipped in late 2025. It has a new Rust-backed core and a fresh model system. With FastAPI 0.115+, you get auto request checks, fast JSON output, and OpenAPI 3.1 docs. No manual schema work. Data errors get caught at the API edge. Client SDKs come from the live spec. The check overhead that used to be a bottleneck is now mostly gone.

This guide walks through what changed in v3, how to lay out a production project, the validation patterns to know, and what deployment looks like when you care about speed.