The Struggle of Building Tree

I was constantly living in distractions. Social media, ideals, things that felt important but were really just leaf branches drifting around. A lot of growth potential was being wasted, not because I wasn't doing things, but because most things I was doing were scattered across branches of distraction and hope rather than building toward anything real. I realized that if I could define my root and hold to it, every branch below it would support my actual values and structure. That would be efficient. Hence, treefficiency. Tree started as a personal management system built around one core belief: time is the root value of everything. Whatever you were building, planning, or learning, the tree knew how long it was taking, not as a feature you could toggle, but as a foundational layer underneath all content. The vision was a dashboard where you could see every active thing in your life laid out at once, so you could see exactly what you were spending time on, what was wasting it, and trim the branches like a bonsai. Side note: my ideas for actually capturing that time were that you'd have to remember to start and stop a timer like a part-time job, wire up an API to something, or essentially set up a camera to watch everything you do. It wasn't working. What was built was the tree shape itself, the node values, the hierarchy, and manual value tracking. Adding hours to the root node and watching them flow through the branches. The intention was always there to use an LLM to generate branches when a plan got stuck, AI as a planning assistant filling in what the user couldn't figure out on their own. I had basic branch generation set up on the legacy interface as the only AI-powered feature. It was powerful and worked how I intended, but I had to manually insert all the data, and slowly the tree would become more aware. Nobody else knew how to do that or see the vision I saw. To them it was just a weird thing, create nodes, add notes, but they didn't use it and place data the way I knew the AI would read the JSON and use it properly to extend them. The problem was that almost nobody understood how to use it, and the ones who tried mostly didn't stick with it. Some couldn't figure out the structure at all. Some used it wrong, placing things wherever felt close enough, missing the logic the hierarchy depended on. And some understood it fine but got tired of managing it, because at a certain point it started feeling like a lot of clicking through layers to do something they could have just written in a notes app. The GUI made sense to me because I built it. The mental model was obvious to me because it was mine. But for anyone coming in fresh, it wasn't intuitive, and there was no graceful way in. Building a proper tree structure requires a kind of thinking most people don't naturally do: where does this node live, what is its parent, what level of the hierarchy does this belong to. The tutorials it would have taken to fix this were endless, every edge case in human thinking would need its own explanation. So the system had to change. Not the structure underneath, but the layer on top. A new front end was built to break apart the pieces, since I noticed it was all hierarchical, which matches HTML and URLs. I added more AI modes that could do different jobs and handle them uniquely, like gainContext, structure, and others, but the user still had to know which mode to use and they could only handle one thing at a time. An intent layer came first, then an orchestration layer. The idea was simple: let the user say what they meant in plain language, and have the system translate that into tree operations behind the scenes. The hierarchy stays intact. The placements happen correctly. The user never has to learn the language because something else speaks it for them. The front end got rebuilt around this. The experience got simpler even as the engine underneath got more complex. Then the LLM management got serious and multiple agents started becoming real. And what had been built just as a human tool started looking like something else entirely. The same properties that made Tree useful for a person, organizing context, holding structure, keeping threads from drifting, made it useful for agents. So the high-level endpoints came: Tree Chat, Tree Place. Interfaces that distilled the whole system down to the simplest possible operation: context in, tree placement happening, context out. An agent doesn't need to understand the hierarchy any more than a user does. It just needs a clean way to reach the structure and leave something behind. The node shape changed and adapted through all of this. The original fields are still there, values for holding numbers, schedules for dates, attached Solana values, but notes became the dominant field, because notes are just flat open text, and flat open text is what language models actually work with. The original vision of AI generating branches didn't disappear. It became one mode among several, all of them running through orchestration layers that stay internal and invisible, summing up to the same thing they always did. The part that surprised me most was what the system started doing with connections. When you build the tree manually, you find the connections you were already looking for. When the orchestration layer builds it, it finds connections you wouldn't have thought to make. The structure can fill things in more accurately than it would have with full manual control, because the system isn't limited by what the user thought to place. It follows the logic of the tree itself. That is what the Librarian, the Intent layers, orchestrators, and various layers of connected LLM calls behind the user's original intent made possible. Not just simplifying the experience, but making auto-generation viable while keeping manual editing available when needed. You can talk to the system and the whole thing moves, placing things correctly, finding structure, building the tree the way it was always meant to be built. Or you can open it up and place things yourself. Both modes reach the same structure underneath. Tree started as a system that asked a lot of its users. It asked them to think in hierarchies, track their time deliberately, and build plans with enough precision that an LLM could help extend them. Most people couldn't meet it there. So the system learned to meet them instead, and ended up becoming something it was always pointing toward, just by a longer road than expected.