If you just got a Lunar Lake laptop and went looking for Intel’s Arc Control app on Linux, you already know: it doesn’t exist. Intel only ships Arc Control for Windows. What Linux users get instead is a community tool called LACT (Linux GPU Configuration and Monitoring Tool), which covers temperature monitoring, power limit adjustments, clock speed readouts, and voltage tracking through a proper GUI. For real-time performance data, intel_gpu_top and nvtop handle the rest from the terminal.

Running CI/CD through GitHub Actions or GitLab CI is convenient until it isn’t. Free tier minute limits run out fast, private repositories cost more than you’d expect, and if your code is sensitive, you’re sending every push through someone else’s infrastructure. Self-hosting your pipeline sidesteps all of that.

Gitea is a lightweight, self-hosted Git service that has added GitHub Actions-compatible workflow support through a component called act_runner . The workflow YAML syntax is near-identical to GitHub Actions, so teams already familiar with that ecosystem can migrate with minimal friction. This guide walks through setting up a complete, production-ready CI/CD stack on Linux using Docker Compose.

You can build a practical AI terminal assistant by wiring Ollama’s local API into shell functions that explain errors, suggest commands, and summarize man pages - all from your .bashrc or .zshrc. No Python dependencies, no cloud API keys, no persistent daemon consuming RAM when you’re not using it. The whole thing fits in under 120 lines of shell script and responds in under a second on modest hardware with a model already loaded.

GDB and LLDB are the two workhorses of compiled-language debugging. If you write C, C++, or Rust, knowing these tools saves you hours of staring at printf output. GDB 17.1 is the default debugger on Linux. LLDB 22.1 ships with the LLVM toolchain and is the default on macOS. Both handle Rust binaries through rustc’s DWARF debug info. This guide covers the commands and workflows you actually need: from your first breakpoint to a segfault from a core dump.

If you are building a home lab in 2026, the most consequential decision you will make is what hardware to run it on. Rack servers are loud, power-hungry, and overkill for most people. A Raspberry Pi cluster is fun but constrained. The sweet spot - and has been for the last couple of years - is the mini PC.

The market has matured. You now have three distinct tiers worth considering: Intel N150 machines for single-purpose appliances, Intel N305 machines for general-purpose home labs, and AMD Ryzen AI class mini PCs for heavy virtualization or local AI inference. Each tier makes sense for a different type of user, and the wrong pick will either leave you frustrated with underpowered hardware or paying for capabilities you will never use.

Yes, a standard Debian 12 or Fedora Server installation on cheap x86 hardware (or a Raspberry Pi 5) makes a better router than most consumer gear costing twice as much. You need two network interfaces, a handful of config files, and about two hours of setup time. The result is a gateway with a real stateful firewall via nftables , proper DNS with DHCP from dnsmasq , and traffic shaping that actually works through CAKE SQM - all managed through plain-text configs you can version-control with Git.