Botmonster Tech

You can wire any microcontroller into Home Assistant over MQTT . Publish sensor data to discovery topics and subscribe to command topics. You get full firmware control without ESPHome’s abstraction layer. The trick works on any chip: ESP32, RP2040, STM32, or a Raspberry Pi Pico W. It’s the right pick when your device needs custom protocols, bare-metal timing, or firmware features ESPHome can’t reach.

This post covers when raw MQTT makes sense, the discovery protocol that auto-registers devices, firmware examples on the ESP32 and RP2040, two-way control patterns, and security hardening.

You can self-host a private PyPI registry with pypiserver and a private npm registry with Verdaccio . Both run on a single box or inside Docker containers. You get three wins that public registries cannot match: faster installs from a LAN cache, a safe home for private packages, and cover against outages, typosquatting, and supply chain attacks. Both tools are free, open-source, and take under 30 minutes to set up.

Google’s Gemma 4 is not one model - it is a family of four, each targeting different hardware and different use cases. The smallest runs on a Raspberry Pi. The largest ranks #3 on LMArena across all models, open and closed. All four ship under the Apache 2.0 license, a first for the Gemma family. This guide walks through installing each variant with Ollama (currently at v0.20.2), benchmarks them on real consumer hardware, and helps you decide which one fits your setup.

You can build a private AI search engine modeled on Perplexity . You combine SearXNG with a local language model running through Ollama . Here is the stack. SearXNG pulls results from many search engines at once. A Python scraper fetches and cleans the actual page content. The LLM then turns everything into a cited answer with inline references like [1], [2]. No API keys, no telemetry, no query logging to third-party AI services. A machine with 12 GB VRAM runs the whole pipeline, and most queries come back in 5-15 seconds.

Testcontainers spins up real databases and services as Docker containers inside your test suite. Tests run against production-grade PostgreSQL, Redis, or Kafka instead of flaky mocks. The testcontainers-python v4.14.2 library works with pytest . It automates the container life cycle. You get isolated, reproducible integration tests that catch bugs unit tests miss.

Below: setup with pytest, testing services beyond databases, performance patterns, and CI/CD configuration.

Why Mocks and In-Memory Databases Are Not Enough

Mocking db.execute() only checks if your code calls the function. It does not check if the SQL is valid. It also misses schema errors and type mismatches. You might have passing tests while your queries fail in production.

The most productive developers in 2026 don’t use a single AI tool. They run a three-tier stack. Tier 1 is inline completions for line-by-line speed. Tier 2 is parallel agent sprints that take on feature-sized work. Tier 3 is overnight batch agents that run 30 to 50 improvement cycles while you sleep. GitHub’s research shows AI pair programming makes developers 55% faster, but that gain comes mostly from Tier 1. The real win comes from running all three tiers at once, with clear rules about which task goes where.