cloudmanager
Joined 1 year ago
Joined 1 year ago
Exit permanently. Device fingerprinted. Email hashed. Payment method flagged. All future signup attempts are blocked.
How it works: Triple verification on every signup attempt. If any identifier matches an exit record, registration is rejected. The ban is permanent.
Technical details: - FingerprintJS for device tracking - SHA-256 email hashing - Stripe payment fingerprinting - Indexed database lookups for instant ban checks
Free 7-day trial. Then $9 for permanent exit or $99 for ceremonial exit with physical artifact (US only).
The service functions as specified. Once you exit, you cannot return.
It runs four analysis layers: permission audit, prompt injection detection, code analysis via TypeScript AST, and cross-reference checks for permission mismatches.
Zero config, zero API keys, one command: npx acidtest scan ./my-skill
I built a small system for myself about a year ago: a set of simple ASCII wireframe patterns plus some workflow instructions that I load into an LLM. The goal is to give both me and the model a shared visual language so we can reason about flows, screens, and constraints early, without pixels or long prose.
As a concrete example, I used this workflow to think through an iOS app concept called Still Human — a small app designed to add a pause before opening AI tools. The result is this spec: https://gist.githubusercontent.com/currentlycurrently/2dfbaf...
It’s a full handoff-style document: ASCII wireframes, user flows, edge cases, data model, and implementation notes. Rough, but coherent enough to build from.
I eventually packaged the workflow itself as AsciiKit. It’s just text files (no signup), meant to stay low-fi and disposable. This is pretty niche, and I’m not convinced it’s for everyone (anyone?), but it’s changed how I handle early-stage ideation with LLMs.
Curious whether others feel this same gap between “idea” and “ready to code,” or if this feels like overengineering.
Happy to answer questions.