Why We Built Linubra
Two kinds of tool exist for remembering your own life: the manual notes app that demands constant upkeep, and the passive recorder that captures everything and understands nothing. We built a third thing.
Series
A behind-the-scenes series on building an AI reasoning memory engine from scratch — architecture decisions, technical challenges, and lessons learned.
Two kinds of tool exist for remembering your own life: the manual notes app that demands constant upkeep, and the passive recorder that captures everything and understands nothing. We built a third thing.
Every note-taking system carries a hidden maintenance tax. Most people never add it up. When you do, the number is roughly half a year of focused work every five years — spent not thinking, not creating, not deciding. Spent filing.
A founder's board-meeting stress caused a running injury three months later. Every data point was captured. None of them lived in the same place, so nobody — not the founder, not the physiotherapist, not any of his apps — ever saw the pattern.
The standard 'free in exchange for your data' bargain works fine for a notes app. It doesn't work for a tool that remembers your meetings, your injuries, your finances, and the people you trust. Here's what the architecture looks like when you take that seriously.
Every voice app on the market treats speech as a transcription problem. It isn't. It's a reasoning problem with a convenient input device attached to it.
Everything you said you'd do, in one place. Grouped by when it's due, filtered by who it's for, one tap away from the voice note it came from.