u/GearFar5131

For years, I had the same problem. I’d see a good video on YouTube, then something interesting on Facebook, then a Reddit post I wanted to read properly later. Every time I told myself, “I’ll come back to this.”

I never did.

My system was chaos. Sometimes I’d send links to myself on WhatsApp, sometimes save inside apps, sometimes Notes, sometimes bookmarks. Then when I finally had time, I’d just sit there like: “Where did I even save that thing?” and give up.

I build apps all the time, bigger, sometimes complex ones. So one day I thought, “This should be easy. Just save links and open them later.” A quick mini project. I also assumed there were already tons of apps doing this anyway.

Before building, I asked a few friends how they handle links. Same story: WhatsApp, saved posts, open tabs, forgetting most of them. Basically… the same mess.

So I built something small. Simple. No overthinking.

When I shared it with them, their reaction was interesting. They were happy.. but not like, “Wow you built this?” More like, “You introduced us to a solution.” I don’t even think they know I built it.

Then I got curious about what everyone else around the world is using. Turns out there was a popular app (I think it was Pcket) that died. Tools like Raindrop. io came along, but they feel more like power-user bookmark managers, folders, tags, organization. Great, but not what I wanted.

I just wanted this:
save something → come back later → watch or read it.

No friction. One place. Done.

That small “lazy weekend project” is now something I use every day now, and a few people around me use it too.

If you’re like us, saving links everywhere, forgetting them, or never going back, this might help:
https://play.google.com/store/apps/details?id=com.contenthub.app

Would genuinely love feedback... does this solve your problem? Anything you’d improve? Any bugs?

NB: “Share to” feature is coming in the next update.

I’ve been thinking about this a lot lately, and honestly… I don’t think most companies have a real answer.

Everyone is using AI now:

• devs debugging with ChatGPT
• support teams pasting customer issues
• analysts uploading reports
• even internal tools calling LLM APIs directly

But if you look closely at what’s being sent…

It’s not just “text”.

It’s:

• customer emails, phone numbers, addresses
• API keys and internal tokens
• database connection strings
• payment details
• sometimes even full identity info

And all of that is being sent to external models.

The uncomfortable part:

Most teams rely on:

• “don’t paste sensitive data” policies
• trust in the model provider
• or nothing at all

But in reality:

• people will paste real data (especially under pressure)
• logs, retries, and debugging can store that data
• models can echo or transform it in weird ways
• prompt injection can literally try to extract secrets

Simple example:

A developer debugging might paste something like:

>

That’s it.
Now your credentials just left your system.

So what’s the actual solution?

This is where I got stuck.

Because telling people “don’t do it” doesn’t work.

You need something that works even when people make mistakes.

What we’re experimenting with:

We started building a proxy layer in front of LLMs that:

• detects sensitive data before it leaves your system
• replaces it with tokens
• sends only safe data to the model
• then reconstructs responses safely
• and blocks anything suspicious coming back

So from the user’s perspective:

>

But under the hood:

>

The tricky part:

Now we’re dealing with questions like:

• Should the system remember sensitive data across sessions?
• If a user asks “what was the card number again?”, do you allow it?
• How do you stop the model from hallucinating fake sensitive data?
• Where do you draw the line between usability and security?

Why I’m posting:

I feel like this problem is way bigger than people admit, but not many are talking about it seriously.

If you’re working in:

• engineering
• security
• AI/ML
• or building internal tools

How are you handling this?

Actual solutions, not policies.

We’re building something around this (OpenAI-compatible proxy with detection + tokenization), but I’m more interested in whether people think this approach makes sense, or if we’re missing something obvious.
Sample Video Demo of Aegis: https://youtu.be/IFhf3k-Tjf8