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Projects sprawl quicker than a runaway cattle stampede, and release trains keep missing the station. Huckleberry harnesses Copilot Chat into a diligent foreman, slicing specs into bite-sized tickets so your team can scale from small posse to full tech rodeo without dropping the reins.
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.
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.
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.