Upload test cases and get automated Playwright tests back
Replaces manual Playwright scripting, but Claude-generated tests and GitHub Copilot already cover this.
Shorthand writing for doctors. Write shortly and later convert into structured case documentations.
Parses zero-punctuation medical shorthand into SOAP notes faster than voice AI for noisy hospitals.
Doctors, medical residents, clinical students
Nuance DAX · Ambience · TextExpander
It is a simple shorthand, command- and token-based domain-specific language that may help doctors and clinicians write structured clinical case notes and histories faster.
It will convert these short notes into SOAP notes (currently available), and later into Markdown, JSON, and PDF.
I do not intend to make it a clinical decision support tool, as that would impose many restrictions.
I am positioning it as a note-taking tool for doctors, medical students, and residents.
I understand that ambient AI tools are now available for faster case documentation, but they may not be usable everywhere, especially in busy and noisy hospital environments.
I am using AI agents to write most of the code, while I supervise the app architecture, editor UI, and the system for easily adding plugins.
Regarding plugins, I plan to implement them as additional commands and tokens specific to departments—for example, obstetrics and gynecology-specific commands, and ICU-specific commands.
I have implemented the parsing engine in Go, and the front end is built using React + Vite for the web application.
It is still incomplete and highly bloated with AI slop (yes I accept that) but once I make the satisfying version. I am willing to do the trimming later on.
You can take a look at the code here : https://github.com/ppnpm/clinlang
I am open to suggestions, feedback and criticism. I know I might get backlash over AI use but I am still learning GO. (I am from JavaScript background)
Replaces manual Playwright scripting, but Claude-generated tests and GitHub Copilot already cover this.
Excel-to-test automation when Testim and Mabl already dominate this space.
Multi-language literal generator when quicktype already handles JSON-to-code conversion.
Intermediate representation layer for AI instructions could reduce tokens, but product-market fit remains unproven.
Append-only lineage prevents LLM outputs from collapsing structure—but unclear if it ships or works.
Forces your draft to survive adversarial critique before a single word is written.