When two AI gunfighters walk into town, do you duck behind the bar or order a round of drinks? Claude Task Master and Huckleberry offer two distinct approaches to AI-powered task management - one riding the command line like a seasoned rancher, the other embedding itself in the VS Code saloon where developers spend their days. Each has its own style, but both aim to wrangle your project tasks into submission.

Think of Copilot as your trusty deputy. Keep it penned in the saloon and it will polish the bar; kick the doors wide and it can roam the frontier rounding up fresh documentation faster than Doc Holliday draws a derringer. I'm your huckleberry still rings true: let Copilot handle the quick‑draw research while you concentrate on upholding the code of the land.

The day has finally arrived. After weeks of coding, testing, debugging, and more than a few coffee-fueled epiphanies, Huckleberry is ready for its public alpha debut. It's not perfect, no alpha release ever is, but it's real, it works, and we think you're going to find it surprisingly useful for something so young.

Punching out rough ideas feels grand until your backlog looks like a saloon bar top after last orders.
Huckleberry rides in, swaps your whisky-stained scribbles for a proper trail map and helps you ship before the dust settles. Here is how to stay in the saddle, keep your herd together and leave the stray notes for the tumbleweed.

Picture the end credits of a favourite film rolling on. The screen pauses for a fleeting heartbeat on the best-boy grip, the Foley artist and the catering crew. Without them, there is no film worth the popcorn. Open source is much the same. A repository may have a lone maintainer's name on the tin, yet its real value comes from the chorus behind the curtain: testers, designers, translators, idea-sparkers, typo-hunters and the brave soul who finally wrote that long-overdue paragraph of documentation. Failing to shine a light on that chorus is a sure-fire way to send the house band packing.

In developing Huckleberry, we've committed to walking a careful line between deterministic code and the adaptable, agentic power of Large Language Models (LLMs). It's not about choosing one over the other. It's about staying steady, step by step, in a system where consistency and creativity both matter.

If you've ever used GitHub Copilot in agent mode, you'll know it can do a lot, but only if it knows what you're actually trying to do.

That's where Huckleberry comes in.

Huckleberry is still in alpha, and for good reason.

If you've taken a glimpse at our README.md, you'll notice that we're quite open about Huckleberry's alpha status. This isn't marketing modesty, it's an honest assessment of where we stand in our journey toward delivering a rock-solid task management experience for developers.

Today, I want to pull back the curtain and show you exactly what we mean by "alpha" and why rigorous testing is the backbone of our development process.

We built Huckleberry because we wanted it.

Not in the abstract "the market has a gap" sense. I mean we wanted to use it. As in: I kept losing track of what I was doing, what I'd done, and what I meant to do next. All while bouncing between a million tabs, a to-do list app I hadn't opened in three weeks, and a PRD in someone else's head.

If you've ever found yourself asking, What was I just working on again?, then you probably understand.