I didn't want to build another AI chatbot
Three months ago I started building HC Real.
At first, I thought the hardest part would be integrating AI models.
I was wrong.
The hardest part wasn't AI.
It was making conversations feel continuous.
Today's AI is incredibly good at answering questions.
But after a few days, every conversation feels like starting over with someone you've just met.
That didn't feel natural to me.
So I started asking myself:
What if an AI could actually grow with its user over time?
Not by storing everything forever.
Not by becoming creepy.
But by remembering what actually matters.
That idea became HC Real.
Over the last three months I've built:
• Local memory stored on the phone
• Voice conversations
• Vision with contextual understanding
• Memory Guardian V2
• Google Play Closed Testing
There were countless bugs, rewrites and moments when I seriously considered starting over.
Looking back, I realized something.
Building AI isn't the difficult part anymore.
Building an AI that feels human, remembers naturally and earns your trust is much harder.
I'm still learning every day, and HC Real is far from finished.
But for the first time, it feels like I'm building something people might actually want to use every day.
I'm curious about your experience.
If you're building AI products...
What has been the hardest problem you've had to solve that wasn't actually AI?
This resonates. The model layer is mostly commoditized now. The product problem is continuity: what to remember, what to forget, and how to surface it without feeling creepy.
Local memory on device is a strong trust signal. Most people will not give that up for a slightly smarter cloud brain if it means their personal history leaves the phone.
One question that helped me when thinking about memory products: what is the smallest memory that still makes day 3 feel different from day 1? If that moment is clear in the first session, retention gets easier to test.
Also curious how Memory Guardian decides what matters versus noise. That filtering layer is probably the real product, more than voice or vision.
If you are in closed testing on Play, what early signal are you watching first: daily opens, conversation length, or people coming back after a few days?
Building voice AI for small businesses and this matches my experience. The AI was maybe 20% of the work. What actually hurt: telephony plumbing (SIP trunks, transfers, callers hanging up over 300ms of silence) and calendar math. Our worst bug had nothing to do with the model: naive datetimes got read as UTC and every booking landed 4 hours early. The other thing nobody warns you about is third-party token expiry — Google OAuth tokens in testing mode die every 7 days, so the "AI" broke weekly for a reason no model card mentions. Your memory point resonates too: remembering a user across sessions without getting creepy is a design problem, not a model problem. The model is the easy dependency now. The edges are the product.
I couldn't agree more.
The deeper I go, the more I realize the LLM itself is only one component. Most of the work is around designing a system that feels reliable, consistent, and genuinely useful over time.
Memory has been one of the biggest challenges. I ended up building a dedicated memory layer that continuously organizes, updates, and cleans stored information instead of simply accumulating conversations. The goal is to make continuity feel natural without becoming intrusive.
It's reassuring to hear someone else had the same experience the model is often the easiest part.
"Every conversation feels like starting over with someone you've just met" — this is exactly the UX problem AI products keep shipping with. The hard part isn't the model, it's continuity across sessions.
I'm seeing the same pattern in a different domain: I built a set of Shopify/KDP guides for beginners. The AI helped me write and format the content, but the real product challenge wasn't generation — it was making the learning path feel continuous across 3 separate PDFs so the user doesn't feel like they're restarting in each file.
Curious how you handle the "memory what matters" piece technically — are you doing semantic summaries of past conversations, or key-value style explicit memory?
That's a great way to describe it, and I think we're solving a very similar UX problem.
I'm intentionally avoiding conversation summaries as the primary memory. Instead, HC Real stores structured facts and events locally, then retrieves only what's actually relevant to the current conversation.
Behind the scenes, there's also a dedicated memory layer that continuously organizes, updates, and cleans stored information. Rather than simply accumulating conversations, it keeps the memory relevant over time by reinforcing important facts, updating changes, and letting temporary information naturally fade.
I'm still refining that approach, but so far it feels much more natural than relying on long conversation summaries.
I like that you're framing the challenge around continuity rather than model capability.
I'll be interested to see what users naturally describe after a few weeks of use. Whether they talk about trust, memory, or simply feeling understood will probably reveal what the product is actually becoming.
Thanks, I really appreciate that perspective.
That's exactly what I'm curious about as well. Right now I have my own assumptions, but I think the real definition of HC Real will come from how people describe it after living with it for a few weeks.
If they naturally say things like "it remembers what's important to me" or "it feels like talking to the same companion every day," I'll know I'm moving in the right direction.
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