Notes on memory, evaluation and local-first design, written as we build rather than after. Short, and dated so you can tell what's changed since.
Real exports from ChatGPT (1,463 conversations), Claude (223) and Grok (309) compared by structure and by how much of each file is conversation text.
Export ChatGPT data from Settings, then read it correctly: conversations are a tree, not a transcript. Findings from a real 1,463 conversation, 916 MB export.
Delete a saved memory, delete its chat, then run a two-query check. Includes a timed delete test: recalled, forgotten, 0 hits seven seconds later.
Seven AI-memory prompts sent to ChatGPT on October 26, 2026: every cited source was the vendor's own domain, none were independent publishers.
Three ways to build an AI second brain, compared on who captures a thought, who keeps it current and what the assistant can do with it.
Repository files, Claude team features and encrypted shared partitions compared on what a leaver keeps and what they can no longer see.
Compare ChatGPT memory and Claude memory by what you can take out, in what format, and whether the other assistant can use it.
The ChatGPT desktop app, a tunnel and a relay compared on who runs the hop and whether ChatGPT can save or only read.
The three readers a saved memory can have, which ModelBrain modes close each, and what stays open.
Four memory stores, compared by who writes them, how far they reach and which other app can read them.
A three-layout test on Claude Code 2.1.301: when AGENTS.md loads, when it is ignored, and how to share one file.
Count the tools that can change or delete a note, whether a read-only switch exists, and which servers need Obsidian open.
A one-minute test for whether an Obsidian tool needs the app open, and which options pass.
Text leaves at two moments, save and recall. Recall always reaches your AI; save time is where memory tools differ. Tested against ModelBrain and Mem0.
Obsidian has no built-in AI assistant. Three ways to add one to your vault, and what each one sends where.
Each AI app keeps its own memory. Here is the setup that gives them one shared memory on your own computer, and what it does not do yet.
Why an accuracy number means little without its scoring method: our 70% strict and 86% lenient results, and the 79% weak spot, published together.
Six seeds, three arms, a full 285-row evaluation, and a pass/fail bar written down before we saw a single number.
Six real MCP memory servers, compared on storage, provenance tagging, and whether an account is required.
Eight real local-first examples, Obsidian to Huly, and where an AI assistant's memory usually falls outside the pattern.
The September five-call surface (remember, recall, forget, expand, connector_sync) and why it was kept small. The server now exposes fourteen tools; see the docs.
Source evidence, stated assertion, or model-generated summary, kept distinct instead of one opaque blob.
It comes with device sync; the only thing metered is the space it uses, at the same $0.05/GB as everything else we store.
The free tier has no login for a seat count to attach to, so we bill machines, not people.
Undo versus deletion, and why we test them as separate cases.
Why the evaluation came before most of the features it measures.
Where the line is between local-first and merely local-by-default.
The tradeoff behind the decision, and what protects the local tier instead.
A checklist for evaluating recall claims, including ours, before you rely on one.