I'm Om — AI engineer, builder of personal AI systems, incoming MBA student. I used Unabyss as a Claude MCP server during a real working session. This is an account of that experience: what the problem is, how your product lands on it, where the depth is, and where I think it goes.
I work across multiple active projects at any given time. I switch between them constantly. I use AI tools — not as occasional assistants but as the primary surface where I think, draft, and decide. Every meaningful working day involves multiple Claude sessions.
The problem is structural, not incidental. Every session starts at zero. Who I am, what I'm building, what I've decided, what I'm trying to accomplish this week — none of it carries. So every serious session opens with a tax: re-explain yourself, or accept a generic answer that doesn't account for who's asking.
That framing is exactly what made me want to try Unabyss. You're not building a smarter AI. You're building the layer that makes any AI smarter about me. That's the right problem, and almost no one is working on it seriously.
I don't use the web interface. I call Unabyss directly from Claude as an MCP server — meaning your tools are available inside my working sessions the way a file system or search would be. Here's what a real session looks like:
Everything above the error line is the product working. The last line is the only moment it broke — and it broke in a specific way that matters: I had no idea I was spending credits, no visible balance, and no way to check before calling. When Unabyss is wired into a working session and a call silently fails, the session loses its thread with no recovery path. For someone using this as infrastructure rather than a demo, that failure mode is the most important thing to fix.
What I'm trying to build — in my work and in how I use AI — is a system that compounds over time. Not one where I repeat the same context-setup work every session, but one where what I've established, decided, and learned carries forward automatically. Unabyss is the first thing I've found that's genuinely pointed at that problem.
The synthesis that comes back from an agentic_query is meaningfully different from any single-source context. When it pulls from my calendar, my GitHub commits, my Notion databases, and my email threads simultaneously — it produces a picture of where I am that I couldn't assemble manually in under five minutes. That's the value. Not any individual source. The synthesis.
I'm at a transition point — wrapping up a year of AI engineering work, starting a business programme in June, managing several long-horizon technical projects in parallel. The number of things I'm tracking is high. The number of AI sessions I run to manage all of it is also high.
Without persistent context, every session is isolated. I repeat myself, get answers that don't account for what I already know or have decided, and lose the thread between sessions. With persistent context, my AI sessions stop being conversations I start from scratch and start being continuations of something ongoing.
The concrete use case: I work on projects that span months. Coming back to something after two weeks means reconstructing context — re-reading notes, re-establishing where I left off, re-figuring out what the next step is. If Unabyss has been indexing my commits, calendar entries, and Notion updates across that gap, I can ask what happened and get a real answer. That's focus I don't lose. That's time that compounds instead of being spent on setup.
The version of Unabyss I'd describe in two years isn't a context vault you manage. It's a context layer that sits between you and every AI tool you use — so every tool always knows enough without you doing any setup work.
One MCP call at session start. Who I am, what I'm working on, what the integrations know about my current state. This alone justifies the integration — it's a genuine shift from cold to warm in under a second.
Right now context flows in one direction — integrations into Unabyss, Unabyss into sessions. But the decisions I make during a session, the directions I set, the things I figure out — none of that flows back in. Bidirectional accumulation is the difference between a snapshot and a living memory.
Not asking for a full vault sync. A targeted push of specific decision logs and principles would close the biggest remaining gap. Everything else is activity. This is understanding. And it's where the competitive moat is — no one else is indexing the local reasoning layer.
Right now Unabyss is one tool among many. The version that becomes irreplaceable is one where every AI tool I use — not just Claude — pulls from the same context layer. Consistent context across sessions, across tools, across time. That's not a context vault. That's a new category.