Hands-on experience with AI, self-hosting, Linux, and the developer tools I actually use
If you host your own code on a Gitea instance, you’ve likely felt the friction of cloning new projects. Opening the web UI, searching for a repo, clicking the “SSH/HTTP” button, and then jumping back to your terminal is a workflow that belongs in 2010.
If you want to “walk through” your repositories and pick what to clone directly from your terminal, here are the best tools for the job.
In 2026, the web has returned to its roots: speed, simplicity, and security. Static Site Generators (SSGs) are now the top pick for bloggers. You can focus on content and skip the worry about database holes or slow load times. These tools turn plain Markdown (.md) files into fast static HTML, so your blog is quick, SEO-friendly, and easy to host. Once it’s live, you can speed up repeat visits with a service worker
that serves pages instantly from the browser cache.
Small Language Models, sub-4B parameter models built to run on local hardware, now handle most of the edge AI work that used to need the cloud. Phi-4 , Gemma 3 , and Llama 3.2-1B run offline on Raspberry Pi boards, phones, and industrial PLCs. The economics, latency, and privacy story all point the same way: edge first.
In 2023, “small” meant under 13B parameters. Today, three tiers matter for edge work.
Willow gives you sub-second local voice control for Home Assistant without sending your audio to the cloud. With an ESP32-S3 Box, you can build a private smart speaker that matches the speed of commercial assistants. Every spoken word stays inside your own network. This guide walks through the full setup: hardware, server deployment, firmware flashing, pipeline config, and the fixes for the most common problems.
Say “Hey Alexa” or “OK Google” and an audio clip travels from your home to a data center. There it gets transcribed by a third-party model, passes through an intent classifier, triggers an action, and returns a response. The whole trip usually takes under two seconds. That pipeline is impressive engineering. It is also a steady stream of your household’s spoken data flowing to Amazon and Google servers, where it is logged, reviewed by contractors, and used to train future models.
Cursor wins for most coders in 2026. If you write code daily and you’re not using it, you’re leaving real speed on the table. GitHub Copilot in VS Code still wins in specific cases. What decides it isn’t the model. It’s how deep the tool reads your code, and the agent loop around it.
“Agentic” gets slapped on every AI coding tool with a chat box, so it helps to be precise. The capability ladder runs from tab completion at the bottom, to inline chat for single-block edits, to multi-file edit suggestions, and at the top, a real agent loop. That top loop reads your project index, edits across ten or twenty files, runs your linter and tests, reads the errors, fixes them, and keeps going until everything is green. That top tier is where Cursor and Copilot diverge most.
If your Linux dev workflow still leans on one or more VirtualBox VMs, you’re not doing anything wrong. VirtualBox has been the default pick for isolated dev setups for years: clean snapshots, clear network modes, and a full guest OS that acts just like a separate machine.
But in 2026, most app work doesn’t need full hardware emulation. It needs fast startup, easy sharing, the same deps each time, and low cost. That’s where Docker Desktop
and docker compose
shine.
