Rivo · 2026 – Ongoing
Most AI tools ask you to describe a task before they can help. Rivo starts from the opposite direction: it reads what you are already working on, keeps that context alive, and lets the model act inside it.
The workspace is built around a single idea — that the hard part of working with AI is not the prompt, it is remembering. Files, decisions, half-finished threads and the reasons behind them all need to survive between sessions.
I designed the interaction model and the underlying context layer, then worked with a small team to ship the first release.
Most agent memory is a transcript — an append-only log of everything that was said. Transcripts are cheap to write and expensive to audit: the decision that matters sits between two paragraphs about lunch.
Rivo keeps the context as a document instead. One Markdown file that describes the work as it stands — the goal, the decisions, the constraints, the open questions, and the things that are no longer true. The conversation is where the document gets written. It is not the document.
A document has properties a log does not. You can read it in one sitting, edit a line without rewriting history, and hand it to someone else — a teammate, or another model — and they can pick the work up.
Because the context is a document, it can be reviewed like one. Six questions, asked over the whole file:
A context that fails these is rarely a memory problem. It is a writing problem — and writing problems are the fixable kind.
The failure modes are predictable, so we surface them next to the document instead of leaving them to be discovered later:
The model does not silently rewrite the document. Every change arrives as a diff: a line added, a line removed, a section collapsed. Each one carries its reason — “this repeats §2”, “the constraint changed”, “this decision was superseded”.
You accept or reject. Accepted edits move into the document; rejected ones become feedback. Nothing rewrites itself while you are not looking.
That is what keeps a long context small. Pruning is a first-class action rather than a cleanup job, so the file gets shorter as the work gets clearer — instead of growing until it is unusable.
A workspace that remembers everything is as useless as one that remembers nothing. The design problem is selection: what should follow you forward, and what should quietly fade.
We treat memory as a ranked, decaying store rather than an append-only log. Items compete for attention based on how recently and how often they were relevant, and anything the user pins stays put.
The result is a surface that stays small.Instead of a chat history that grows without limit, Rivo shows a handful of live threads and keeps the rest a search away.
The next step is making context portable — so that the work you do in one tool can travel to another without being retyped, re-explained or lost.
That means treating context as a format rather than a feature, and building the workspace so it can be embedded anywhere the work happens.