Hands-on experience with AI, self-hosting, Linux, and the developer tools I actually use

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Hands-on experience with AI, self-hosting, Linux, and the developer tools I actually use

A desktop compute box on a workbench linked to a home outweighs a stack of monthly cloud-bill coins on a balance scale

n8n and Ollama Local AI: $0/Month, Honest Hardware Math

Running private n8n and Ollama AI automations at home costs $0/month in software, but the hardware bill is real. The honest anchor: a used 64GB Mac Studio near EUR1,995 can replace a $90 to $125 monthly cloud bill, yet local tool-calling stays broken until you raise Ollama’s default num_ctx from 2048 to 8192.

Key Takeaways

  • “$0/month” covers software only. The hardware and electricity are still real costs.
  • Dockerized n8n reaches Ollama at host.docker.internal:11434, never localhost.
  • Ollama’s 2048 context default cuts off tool results. Raise it to 8192.
  • qwen2.5:14b is the most reliable local model for the AI Agent node.
  • Once set up, a local n8n stack runs for months without babysitting.

What is the n8n and Ollama local AI stack?

Ollama is the local engine that runs language models on your own machine. It serves them over port 11434, so anything on your network can send prompts to it. The same engine powers other local builds, like an Ollama-driven terminal assistant wired into shell scripts. n8n is the workflow orchestrator. It has over 400 integrations and dedicated AI nodes, so you can chain a model into real automations.

Smart Thermostat Under $30: DIY with ESP32, No Subscription

Smart Thermostat Under $30: DIY with ESP32, No Subscription

A fully local smart thermostat can be built from a 5 dollar ESP32 , a BME280 temperature sensor, and a small relay module. ESPHome ’s built-in thermostat climate component runs the control loop directly on the microcontroller, while Home Assistant handles schedules, presence detection, and the dashboard. Total parts cost is under 30 dollars, nothing talks to a cloud, and because the heating logic lives on the ESP32 itself, the thermostat keeps working even if your Home Assistant server is rebooting or your internet is down.

Build a CLI Dashboard with Go and Bubble Tea

Build a CLI Dashboard with Go and Bubble Tea

The Charmbracelet Bubble Tea framework lets you build live terminal dashboards in Go using the Model-Update-View pattern from Elm. Pair it with Lip Gloss for styling and Bubbles for ready-made widgets. You get live panels, key navigation, and flex layouts. It all ships as one binary with zero runtime dependencies.

Terminal dashboards fill a niche that classic CLIs and web apps both miss. Think of a monitor that runs over SSH on a headless box. Think of a database explorer that starts in milliseconds with no browser. Think of a log viewer your ops team can reach with no auth layer to set up. These are the use cases where TUI dashboards shine. Bubble Tea now sits at v2 with over 41,000 GitHub stars and more than 18,000 apps built on it. It has become the go-to framework for this kind of work in Go.

Three Docker management tools shown as a multi-server console, an industrial control panel, and a small single-host unit on a workbench

Komodo vs Portainer vs Dockge: A 2026 Homelab Decision Guide

Pick Komodo for Git-driven deploys across many Docker servers from one screen. Choose Portainer if you run Kubernetes, which Komodo does not support. Pick Dockge for a single lightweight host. Komodo added a dedicated Docker Swarm resource in 2026, closing what used to be the single most-cited reason people held off, a complaint that once drew 168 votes on Reddit.

Key Takeaways

  • Komodo wins on Git-driven deploys across many servers from one screen.
  • Portainer stays ahead for Kubernetes and mature production tooling.
  • Dockge is the lightest pick if you run a single host.
  • Komodo now manages Docker Swarm; Kubernetes is the remaining orchestration gap.
  • Komodo’s default VPS setup is insecure until you lock the agent port.

What is Komodo and what problem does it solve?

Komodo is an open-source tool that builds and deploys Docker software across many servers from one place. It is licensed under GPL-3.0 and written in Rust and TypeScript. The project lives at moghtech/komodo and was renamed from “Monitor” before the rebrand.

Hailo-8 vs Google Coral TPU for Frigate NVR: Which Edge AI Accelerator Wins in 2026

Hailo-8 vs Google Coral TPU for Frigate NVR: Which Edge AI Accelerator Wins in 2026

The Hailo-8 (26 TOPS) is the clear winner for any Frigate build beyond four cameras, and the Hailo-8L (13 TOPS) has taken over as the sweet spot for mid-tier setups of six to ten cameras. The Google Coral Edge TPU (4 TOPS) is still a defensible pick for ultra-budget one-to-three-camera Raspberry Pi builds where an M.2 slot or spare USB port is already sitting idle, but the Hailo-8L usually beats it on price per TOPS even in that range. Reach for Coral when the only goal is stopping Frigate from melting a Pi’s CPU. Reach for Hailo-8 when there is headroom to grow into YOLOv8, higher resolutions, and future model upgrades.

Robotic open-weight coding models compete on a podium while one shakes hands with an architect robot over a blueprint, with cost scales in front.

The Chinese Open-Weight Coding Stack in 2026: Is Kimi K2.7 Real?

The Chinese open-weight coding stack leads several benchmarks in 2026, but the rankings disagree. Kimi K2.7-Code just landed, yet auditors call it more honest than capable, not better than K2.6. No single model wins outright, so the smart play is a hybrid: plan with Claude, code with Kimi for about $39 a month.

Key Takeaways

  • No single Chinese model wins; the leader depends on your task and budget.
  • Kimi K2.7-Code looks more honest than K2.6, not clearly smarter.
  • Benchmark lists and real-usage data disagree on who leads.
  • Kimi K2.6 burns about twice the thinking tokens of K2.5.
  • Most heavy users plan with Claude and code with Kimi to cut cost.

What is the Chinese open-weight coding stack in 2026?

The Chinese open-weight coding stack is the group of open-license models built mainly by Chinese labs for agentic software work. The roster includes Kimi K2.6 and the new K2.7-Code from Moonshot, GLM 5.1 from z.ai, Qwen3-Coder-Next from Alibaba, DeepSeek V4-Pro and V4-Flash, MiniMax M3, and Xiaomi’s MiMo V2.5. All ship under Apache, MIT, or near-equivalent open terms.