GoKawiilAugust 2026 Huzzah A new experimental way to code with AI
If you’re a software engineer like me, the first few months of 2026 were incredible. Coding agents suddenly became good enough that we no longer needed to manually write code. But if you’re like me, then sometime later you hit a wall. The honeymoon period ended, and the novelty wore off. No more dopamine hits.
It’s August, and I feel utterly fatigued. To be honest, I’m sick to death of writing longform English to describe every change I want to my codebase. However, I also don’t want to go back to writing all my code manually. There was real tedium in that practice that I’d prefer to avoid for…well, the rest of my life.
And yet, I sense that I need to have better insight and control over what my code is doing. I want to know that my output is high quality, reliable software. I want to feel good about myself as a professional. So I’m trying to find a way to have my cake and eat it too.
My problem with coding agents is that
There’s no reliable record of human intent. Prompts are discarded, and the code may or may not have been generated by AI. We’ve lost the central authority that expresses what the human wants out of the machine, and I think it’s important to contend with that fact. AI chats are imperative, step-by-step instructions that describe changes to the application, not the application itself. This means instructions are often repeated, and thus consume tokens, many times over the course of development. This is inefficient. Much of natural language exists for social reasons, not informational. The average sentence is scarce in real information. Writing in this manner, to a machine, is cumbersome.
To address these problems, I’m building an experimental editor. I’m calling it Huzzah, and it poses an alternative paradigm for working with LLMs.
With coding agents, prompts are (a) longform, (b) imperative, and (c) transient. With Huzzah, prompts are (a) pseudocode, (b) declarative, and (c) persistent.
It’s easier if I just show you.
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