I am fully blind, I have read braille all my life, and over the last couple of months I built a production web platform for generating 3D-printable braille objects. I think the interesting part for this subreddit is not the launch itself, but the AI workflow behind it. The system now includes:
- a browser-based generator
- backend geometry/layout logic
- multilingual braille support
- printer-fit validation
- quote generation
- Stripe checkout and webhooks
- order persistence
- admin fulfillment flow
- customer email notifications From a stack perspective, it is a real-world application rather than a demo:
- FastAPI backend
- frontend JS/HTML/CSS
- OpenSCAD-based geometry/export pipeline
- Liblouis-based braille support
- Stripe Checkout + webhook flow
- SQLite order persistence
- SMTP notifications
- Linux production deployment behind Nginx The reason I think this is relevant here is that AI coding tools were not just used for isolated snippets. They were used continuously across the lifecycle of an evolving production codebase. The main ways AI helped were:
- speeding up implementation of repetitive backend/frontend plumbing
- accelerating refactors as the product scope changed
- helping reason through validation models and API surface changes
- generating and revising test scaffolding
- tracing deployment and integration bugs
- tightening documentation and operational runbooks
- making it much faster to try multiple implementation paths before choosing one But the important part is what AI did not replace. It did not replace:
- braille knowledge
- accessibility judgment
- product prioritization
- architectural tradeoffs
- testing discipline
- deployment verification
- lived experience of the problem That distinction matters a lot. This was not a case of “AI made an app for me.” It was a case of using AI coding tools to compress the execution loop in a domain where the problem knowledge is highly specific and experience-driven. That matters especially in accessibility work. A recurring problem in accessibility software is that:
- the people who understand the real problem best often do not have large engineering teams
- the people with engineering resources often do not have lived experience of the actual friction AI seems particularly powerful when it helps narrow that gap. In my case, it let me spend more energy on the parts that required me:
- what braille problems were worth solving
- what output should count as usable
- what workflows would actually work for blind users
- what tradeoffs were acceptable
- how the system should behave in real production use while reducing the cost of the implementation-heavy parts around that. A few things I learned from using AI this way:
- it is strongest when the problem is already well understood by the human driving it
- it is far less useful when product thinking is vague
- it can accelerate coding significantly without reducing the need for verification
- it is especially good at helping maintain momentum across a large number of small engineering tasks
- for accessibility-oriented software, lived experience plus AI assistance is a very strong combination For me, the broader takeaway is not “AI replaces developers.” It is that AI can materially expand the building power of people with strong domain expertise, including disabled builders who understand a problem from the inside and can now move from idea to deployed software much faster. That feels like a more grounded and useful AI story than most of the generic hype. Here are a few images of a business card created by the generator, and printed on my 3d printer 😄 https://preview.redd.it/5m93ygcchj2h1.jpg?width=4032&format=pjpg&auto=webp&s=9c200e9eeac23566ab53d6d10959c83e186e73fd https://preview.redd.it/60n7jhcchj2h1.jpg?width=4032&format=pjpg&auto=webp&s=d2d7c1e11ee80ce137d602f6cb26b6a4221bfcf5 submitted by /u/Mrblindguardian
Originally posted by u/Mrblindguardian on r/ArtificialInteligence
You must log in or # to comment.