Huzzah wants your intent in a file, not a chat log. The prototype hasn't got there yet.

Daniel Vaughn has published Huzzah, an experimental AI coding editor built around persistent, developer-written pseudocode, with its source public on GitHub ^[1]. The bet is that human-authored intent can stay a durable software artifact instead of disappearing inside coding-agent chats ^[2], and the current public prototype is considerably narrower than that bet: it sends a full pseudocode input to OpenAI and displays read-only generated code, with no reconciliation, review states or accept-or-reject controls ^[3].

The diagnosis is the strongest part. Chat records a sequence of requested changes, while source code becomes the lasting artifact, so the original intent ends up scattered across discarded sessions, repeated instructions and implementation details written by a model ^[4]. Vaughn says Huzzah grew from frustration at spending increasing time describing changes in longform English even as agents became capable enough to do the implementation ^[5]. His article contrasts agent prompts, which he calls "longform, imperative, and transient", with a workflow he describes as "pseudocode, declarative, and persistent" ^[6]. Vaughn positions the tool as an alternative interface to large language models, not a replacement for coding agents ^[7]. He identifies himself as a design engineer and Head of UX at Dreadnode, an AI security company whose team page lists him as Head of UX/UI, with roughly 12 to 15 years of web experience ^[8].

The mechanics are deliberately loose. A developer writes a specification in a file ending in .hz, and Vaughn prescribes no grammar: it can be terse, verbose or organised around whatever concepts suit the author ^[9]. In his demonstration, a six-line fizz buzz spec generates an implementation; changing fizz_buzz() to fizz_buzz(n) and loop 100 to loop n is meant to produce a variable-iteration version ^[10]. Vaughn's article says Huzzah captures the pseudocode diff, uses it as the model prompt and regenerates the affected source ^[11]. Shopping-cart and todo-list examples describe data structures and operations such as adding an item, calculating a checkout total and toggling a task's completion state, and Vaughn argues a language-agnostic specification could eventually support several languages or execution environments ^[12].

That diff step is the load-bearing claim, and it is the one the public build does not demonstrate: full-document submission and read-only output is a different mechanism from diff-guided regeneration of affected source, and without review states there is nothing to reconcile a spec edit against code a model has already changed ^[13]. The repository describes the project as an experimental interface for editing persistent pseudocode and synchronising it with an AI-generated implementation, running locally on Node.js 22.19 or newer against a model provider supported by the Pi coding-agent framework ^[14]. Documented providers include Anthropic, OpenAI, Google, Azure OpenAI, Amazon Bedrock, Ollama, LM Studio and vLLM, which is broader plumbing than the demonstration exercises ^[15]. The supplied materials establish no production deployments, usage metrics or hosted service ^[16].

Watch for three things: whether pseudocode diffs actually drive regeneration rather than full resubmission, whether accept-or-reject controls arrive so the two artifacts can diverge and be merged, and whether one .hz file ever produces implementations in more than one language ^[11][3][12]. Vaughn has circled this boundary before with Matry, a design-tooling project described in an earlier profile as an attempt to build a programming language for designers ^[17].