Python packaging has needed a reset for years, and uv is the first tool in a long time that feels like a real one. It's fast, opinionated in the right places, and broad enough to replace an entire pile of Python tooling with a single binary that actually makes the day-to-day workflow simpler.
The official uv docs lead with the claim that it's 10-100x faster than pip, and that number doesn't feel like marketing fluff once you use it for real work. uv gets there by doing something the older Python toolchain never pulled together cleanly: it treats dependency resolution, environment management, Python installation, and tool execution as one system.
Most engineering teams still plan work in one tool and ship it in another. Issues live in Jira or Trello, pull requests live in GitHub, and every status change requires a human to copy context across tabs. GitHub Projects removes much of that friction by keeping planning, issue tracking, and delivery close to the code.
Current GitHub Projects is the planning layer on top of issues and pull requests: custom fields, saved views, built-in automations, roadmaps, charts, and API access. It replaces Projects (classic), not the same product with a new coat of paint.
For teams that already live in GitHub, the value is straightforward:
Less context switching between a tracker and the code host
Fewer stale status updates because issue and pull request state can drive project state
One place to plan cross-repository work without losing repository-level detail
A workflow that works for a two-person open source project, an inner source program, or a larger engineering org
Not Projects (classic)
This guide covers the current GitHub Projects experience. If you're starting fresh, use the new Projects workflow, not classic boards.
Mirroring a Git repository sounds simple until you need it to stay current without thinking about it. A one-time copy is easy. The useful version is a mirror that keeps following upstream branches, tags, and rewritten history without becoming another chore on your list.
I use this pattern for repositories I want to preserve, test against, or keep available under my own GitHub namespace. A recent r/github threasd asked about mirroring a public repository into a private one; this is the workflow I use for that and similar cases.
The twist is that I don't put the sync workflow in each destination repository. I keep the automation in a standalone private repository that acts as a mirror controller.
Before getting to GitHub Actions, it helps to understand the manual Git operation the workflow is automating.
A mirror is useful when you want a repository to follow another repository as closely as possible. That is different from a normal fork, where you expect to create your own branches, open pull requests, and maintain local work.
I usually think about mirrors as infrastructure, not collaboration space. They are useful for:
Keeping a personal copy of an upstream project
Preserving access to a dependency you rely on
Testing automation against a repository under your own namespace
Keeping several upstream repositories available from one GitHub account or organization
Avoiding a manual Sync fork habit for repositories that should simply track upstream
That last point is the practical one. If the destination is supposed to reflect upstream, humans should not be the scheduler.
Treat Mirrors as Destructive Targets
A mirror destination should not contain independent work. Mirroring usually involves forced updates to branches and tags, which means local-only changes in the destination can be overwritten. Use dedicated mirror repositories, not repositories where people are actively developing.
If most of the logs from your Go CLI still look like a wall of monochrome timestamps and free-form strings, you're leaving a lot of developer experience on the table. Charm's log package gives you colorful, readable, structured terminal output without forcing you into a heavyweight API or a machine-first JSON mindset from the start.
There's nothing wrong with simple logging. The problem is that simple logging tends to stay simple long after your application stops being simple.
At first, a few log.Println calls feel harmless. Then the output gets noisy. Errors look almost the same as info messages. Request context gets squeezed into ad hoc strings. The one line you need is somewhere in the middle of a hundred identical timestamps, and your terminal gives you very little help separating signal from noise.
That's the gap charmbracelet/log tries to fill. It's a leveled, structured, human-readable logger built for terminals. It looks good out of the box, keeps the API small, and still gives you the structured fields and formatting controls you want for real projects.
Two hours in, what you're actually shipping is a GitHub issue, a JIRA ticket, or a Slack thread: symptoms, hunches, pasted logs, then one more file because someone asked nicely. Each round trip tugs another detail out of your repo while the runnable version still lives mostly on your machine. Then a follow-up makes you spell out the repro steps you sort of skipped the first time, and halfway through your reply the bug stops looking fuzzy. Nobody merged a fix for you. You just finally described it clearly enough to see it yourself.
That loop is about as expensive as debugging gets, and almost none of it is mandatory. The antidote is the minimal reproducible example (MRE): the smallest, most self-contained piece of code that reliably triggers the problem you're trying to explain. Being kind to whoever reads your ticket is a nice side effect. The main payoff is that your own picture of the break gets sharper. Most of the time you'll see the answer yourself before you hit send.
Repositories that ship often, especially to GitHub Pages or similar environments, can rack up a long list of deployments. The UI is fine for spot checks, but it doesn't give you a fast way to clear old rows when you only care about what is current. The REST API deletes one deployment per request, so the practical approach is the same as workflow run cleanup: loop with the GitHub CLI (gh).
This post starts with a loop that deletes deployments whose latest status is inactive, then adds a variant that also removes error and failure without touching rows that still end in success.
I still use RDP when I want a remote desktop on another machine. On Ubuntu 26.04 LTS Desktop (Resolute Raccoon, April 2026) the built-in path is Settings → System → Remote Desktop, matching what Canonical has documented since 24.04.
The steps below walk through the toggles, ufw, and how to connect with a client.
Desktop Sharing: View or control the desktop while you're logged in. Someone joins your live session. Resolution follows your local session, so the client can look a little soft.
Remote Login: Connect when the account isn't in use, closer to a headless workstation login. Resolution follows the client window, so it tends to look sharp. If you're already logged in locally, a remote user may get offered the nuclear option to end your session.
Tip
Generally, I use the Remote Login method.
Select the tab for the path you want.
Turn on the main toggle at the top of that tab.
Tip
You may need to click Unlock and enter your password before the toggle will stick.
For small Go projects, it's easy to tell yourself that gofmt, go mod tidy, and golangci-lint are habits, not policy. That usually works right up until a pull request fails because generated files changed, go.sum drifted, or a formatter was only run on one laptop.
The pattern I keep coming back to is simple: define the validation contract once, usually in a Makefile or Taskfile.yml, then let pre-commit provide fast local feedback and GitHub Actions enforce the same expectations for everyone else. Local hooks keep the loop short. CI keeps the rules shared, visible, and hard to accidentally bypass.
This isn't the only way to validate a Go repository, and it isn't always necessary. But for Go projects with generated code, multiple contributors, or public pull requests, it's a very practical baseline.
Dependency updates are easy to picture one repo at a time. They get political fast when the count is hundreds or thousands: security response, CI minutes, reviewer attention, repo standards, and how much supply-chain theater your org is willing to run.
Most organizations and users will start with Dependabot on GitHub. It's an obvious choice: first-party, no extra service to stand up. Plenty of teams still reach for Renovate when policy, monorepos, or odd manifests stop fitting Dependabot's mold.
Blunt matrix:
Capability
Dependabot
Renovate
GitHub integration
First-party, tight dependency graph and advisory wiring
Strong GitHub support, but you run it as an app, Action, or your own worker
Configuration model
Simple repository-local YAML
Layered policy model with presets and package rules
Custom dependency extraction
Limited to supported ecosystems
Strong, especially with custom regex managers
Monorepo handling
Good for straightforward layouts
Better for large, mixed, policy-heavy monorepos
Pull request grouping
Useful but bounded
Very granular by manager, package, path, dependency type, and update type
Automerge policy
Usually implemented with GitHub Actions and branch protection
First-class Renovate policy, still gated by GitHub rules
Security updates
Best native GitHub Advisory Database integration
More programmable, but integration depends on deployment model
Most terminal emulators ask you to make peace with an awkward tradeoff. You can have something fast but austere, polished but slow, powerful but painfully non-native. Ghostty, created by Mitchell Hashimoto, is interesting because it doesn't merely try to improve one side of that triangle. It tries to make the tradeoff smaller. The result is a terminal that is GPU-accelerated, deeply configurable, standards-aware, and designed to feel like a first-class citizen on the platform it runs on.
At the highest level, Ghostty is a modern terminal emulator for macOS and Linux. That sounds ordinary right up until you look at how it positions itself. The project is explicitly built around three goals:
Fast
Feature-rich
Native
The third goal is where the project gets interesting.
There are plenty of fast terminals. There are plenty of featureful terminals. There are also terminals that feel at home on a specific operating system. Very few try to be genuinely strong in all three categories at once, because that is much harder than slapping a fast renderer on a cross-platform UI toolkit and calling it a day.
Ghostty takes the harder path. On macOS, it uses real Apple frameworks. On Linux, it integrates with GTK4 and the surrounding desktop environment. Underneath those platform-specific shells is a shared Zig core that handles terminal emulation, rendering, and the heavy lifting that makes Ghostty feel so absurdly responsive.
If you spend a good chunk of your day in a shell, an editor, a multiplexer, or a remote session, that combination matters more than it may seem on paper.
