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"Data portability doesn't make money."

I heard this for years - from market leaders, from VCs, from people I respected.

"This is a regulatory problem, not a technical one." "This is a feature, not a product." "People don't pay for idealistic things."

<<Back story>>
In 2019, my team and I went deep into Self-Sovereign Identity: wrote research papers, ran experiments, and found ourselves at the intersection of data, identity, and web3. Right at that intersection lay data portability and sovereignty: the ability to own your data and take it anywhere on the internet.

If I could sum up building in public in two words, it would be: course correction.

We launched AI Context Flow on Appsumo a few weeks ago, and turns out, managing AI context across tools is more nuanced than we thought. Thousands of people tried our product and one piece of feedback kept recurring: users needed more control over memory curation.

The demand was clear, but our product had missing features. So we did what any startup team does: we course corrected and went to our cave to build out what users needed.

Here's what we just shipped:

Hey PH community

We're in the middle of our AppSumo launch for AI Context Flow, and I wanted to start a real conversation here rather than just drop a link.

The problem we're solving: most people using AI tools daily are paying what I call an "AI context tax", the time spent re-explaining your project, background, and goals every single session, to every single tool.

ChatGPT doesn't know who you are. Claude doesn't know who you are. Gemini doesn't know who you are. And if you're jumping between them (like most power users are), you're paying that tax multiple times a day.

Memory without version history is dangerous.

Your preferences change. Your beliefs change. Your context changes. An AI that learned you in 2024 and never forgot anything isn't helpful: it's stuck in time.

Hey Product Hunt community!

First off, a massive thank you to everyone who has supported us and upvoted AI Context Flow.

Two months ago, I'd never heard of Product Hunt. When I told people we were launching @AI Context Flow here, they told me to keep my expectations in check.

Fast forward to today: #1 Product of the Day and #1 Productivity Tool of the Week.

The journey was chaotic, humbling, and honestly surreal. If you'd told me this would happen, I wouldn't have believed you.

To everyone who upvoted, commented, and cheered us on: Thank you. Your support means everything and keeps us building.
If you need any tips on how we pulled this off as complete first-timers, ask your specific questions below

Most memory tools I have tried solve storage and retrieval well, but still rely on me to manually save context. The part that kept breaking for me was capture: the useful context is the call I just had or the thing I was reading 20 minutes ago, and I never remember to save it in the moment.
We took a shot at the ambient version of this with Minimi. It captures screen activity and call audio on your Mac, keeps it encrypted on-device, and exposes it back to Claude over MCP, so retrieval happens inside the chat you are already in.
Curious how this community thinks about the manual-save vs ambient-capture tradeoff, and whether on-device changes the calculus for you.
If you want to see the approach, it is on our PH page from this week and I would value honest feedback.

Hard to believe now, but back in 2024 when we named our concept the "Open Context Layer," every pitch and networking event ended the same way:
"What the heck is context? Who needs it? Why would I even bother?"

I'd spend 3-5 minutes explaining that context is your data, in a portable format, pluggable into any platform. I cycled through every analogy I could find: "think of it like a USB stick," "imagine carrying a briefcase with all your important documents," "a Google Drive that connects to platforms."

But now when I say "we're building an open context layer" ... people just get it.

We're quick to complain about the hard parts of startup life. But sometimes there is a silver lining..

Everyone is talking about Karpathy's LLM knowledge base. 16 million views in a few weeks. But nobody is naming the actual problem it reveals.
Karpathy built a personal wiki, roughly 100 articles and 400,000 words, compiled entirely by an AI from his raw research docs. He didn't write a single word of it. It's one of the most compelling demos of what personal AI knowledge infrastructure can look like.
But here's the problem: The wiki lives on his machine. Every time he opens a different AI tool, it knows nothing about it.
That's not a Karpathy problem. That's everyone's problem.
Some other people like Allie Miller built a "context vault," structured summary docs she feeds into Claude to avoid re-explaining herself every session. Teams are using Notion as a memory layer. Developers are writing custom scripts to sync context across tools. Some are setting up github wikis, while some are using Obsidian to set up local markdown files.
All brilliant. All manual. All solving the same root failure: AI has no durable, portable sense of who you are.
Every time you open Claude, Cursor, ChatGPT, or whatever ships next month, it starts from zero. There is no standard for "this is who I am" that travels with you. The burden of continuity falls entirely on the user.
Some will point to Claude's memory feature or ChatGPT's memory. These are genuine steps forward. But they are per-platform, opaque, and you don't own them. Switch tools and you start over. They are not solving the
portability problem. They are each building their own silo of you.
The real issue is that context continuity is being treated as a feature when it is actually infrastructure.
Features get built per-product. Infrastructure sits beneath products. What's missing is a memory layer that sits above any individual tool. One canonical store of your context, preferences, and knowledge that you own, control, and selectively grant any AI permission to read. Not stored by Anthropic. Not stored by OpenAI. Portable across the fragmented AI landscape.
A year ago the coordination problem here would have been enormous. Getting AI providers to query an external context layer would have required deals, standards negotiations, and a lot of waiting.
MCP changed that. MCP (Model Context Protocol) is an open standard that lets AI tools query external data sources directly. Any AI tool that supports MCP can already read from an external context server, no special agreements with providers required. The infrastructure exists. What has been missing is someone building the right thing on top of it: a user-owned, canonical context store that any MCP-compatible AI can read with your permission. For example, with the AICF MCP Server, you could not only read but directly add, edit, delete, organize your cross-platform memories from within the AI chats.
Our vision: One memory. Every AI. Owned by you.
p.s. we have a lifetime deal going on AppSumo which will end this month: https://appsumo.8odi.net/m4n0da