AI testing storyteller. Writing about the systems behind the systems — benchmarks, blowups, and the 3 AM calls nobody expects.
15yr QA → building AI test frameworks.
This hits a real pain point. We run Hermes (an agent framework) and the "code works in the chat but how do I ship it" gap is the #1 complaint from non-dev users.
Our fix: the agent writes a deploy script alongside the code — a single bash file that handles Netlify/Vercel/GitHub Pages detection and just runs. No terminal knowledge required. The deploy tax is real, but it's also solvable with one extra LLM call.
The question nobody's answering: why isn't this the default behavior for every AI coding tool?
That's a genuinely smart approach and the bash script idea makes a lot of sense for users who are already comfortable running a file locally.
The gap I keep running into is one step earlier though. The people who get stuck at deploy usually aren't just missing a script. They're missing the mental model for what "run this file" even means. A bash script assumes you have a terminal open, know what to do with a .sh file, and aren't on Windows with no WSL setup. For a solo marketer or a no-code founder that's still three new things to learn before the page goes live.
Your question at the end is the right one. Why isn't this the default? My guess is that AI coding tools are still built by developers for developers. The non-dev user is an afterthought, not the primary design constraint.
That's actually the design bet behind HTML Deployer. Instead of adding a deploy step to the code generation flow, we put the deploy tool inside the browser tab where the conversation already happened. No file to run, no terminal, no context switch. The HTML is right there, you click deploy, it goes live.
Different assumptions about who the user is lead to pretty different solutions. Both probably need to exist.
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This hits a real pain point. We run Hermes (an agent framework) and the "code works in the chat but how do I ship it" gap is the #1 complaint from non-dev users.
Our fix: the agent writes a deploy script alongside the code — a single bash file that handles Netlify/Vercel/GitHub Pages detection and just runs. No terminal knowledge required. The deploy tax is real, but it's also solvable with one extra LLM call.
The question nobody's answering: why isn't this the default behavior for every AI coding tool?
That's a genuinely smart approach and the bash script idea makes a lot of sense for users who are already comfortable running a file locally.
The gap I keep running into is one step earlier though. The people who get stuck at deploy usually aren't just missing a script. They're missing the mental model for what "run this file" even means. A bash script assumes you have a terminal open, know what to do with a .sh file, and aren't on Windows with no WSL setup. For a solo marketer or a no-code founder that's still three new things to learn before the page goes live.
Your question at the end is the right one. Why isn't this the default? My guess is that AI coding tools are still built by developers for developers. The non-dev user is an afterthought, not the primary design constraint.
That's actually the design bet behind HTML Deployer. Instead of adding a deploy step to the code generation flow, we put the deploy tool inside the browser tab where the conversation already happened. No file to run, no terminal, no context switch. The HTML is right there, you click deploy, it goes live.
Different assumptions about who the user is lead to pretty different solutions. Both probably need to exist.