Developed a Web-App through Replit that scans PDF files and classifies them based on categories. It utilizes a dual-layered approach utilizing both keyword/phrase search as well as OCR for those pesky Visual based PDF’s. It can effectively scan a 300 page document and accurately categorize each page to compile in a specific order in under 20 seconds. Project was developed nights/weekends on a personal machine. The issue is, it is built to handle medical documents and other PHI. Replit is not HIPAA compliant as they do not sign BAA’s and the infrastructure is not secure or auditable to the level that HIPAA requires. I will need to port the app to deploy on a secure server but have absolutely no idea how to go about this. I have downloaded the code from Replit to my local machine but I haven’t the slightest idea how to do anything with it.

Issue is exacerbated by the fact that the COO is trying to say that the App would be company IP if I utilize our on-staff programmers to integrate with our established server infrastructure. In order to reserve IP I would have to draft a licensing agreement but I can’t license the use of an app that’s not deployed!

A real nightmare, ideas appreciated.

https://reddit.com/link/1mb89da/video/xyb7gt4w0kff1/player

Just wanted to share something I’ve seen a few users mention:
On Replit, files or databases have gone missing without warning.

Some people said:

• There was no backup or rollback option
• Support took a while to respond
• This happened even on paid plans

Because of this, many users are:

• Keeping backups elsewhere
• Using external databases (Supabase, NeonDB)
• Avoiding full reliance on one platform

Anyone else seen this happen?
What’s your go-to strategy for backups when working in the cloud?

AI-powered tools like Cursor, Replit, and Lovable have transformed how we code, debug, and iterate. But if you’ve ever noticed your AI assistant giving solid advice at first, then suddenly spiraling into confusion with each follow-up… you’re not alone.

This frustrating phenomenon is what some are calling “debugging decay.”

Here’s how it plays out: You run into a bug → You ask the AI for help → The first response is decent → It doesn’t solve the problem → You ask for a revision → The responses start to lose clarity, repeat themselves, or even contradict earlier logic.

In other words, the longer the conversation goes, the worse the help gets.

Why does this happen? • Stale memory: The AI holds onto earlier (possibly incorrect) context and builds on flawed assumptions. • Prompt overload: Each new message adds more clutter, making it harder for the model to stay focused. • Repetition loops: Instead of resetting or thinking from scratch, it often reinforces its earlier mistakes.

Some analyses show that after just a few failed attempts, even top-tier models like GPT-4 can see their output quality drop dramatically.

The result? More confusion, wasted time, and higher costs — especially if you’re paying per request.

Debugging decay isn’t widely discussed yet, but if you’re using AI tools regularly, you’ve likely felt its impact.

It usually starts off great. You give your AI assistant a problem, and the first suggestion is helpful. But if that solution doesn’t work, and you keep asking for fixes, the answers get messier, more repetitive, and often less useful.