Open software

Orionfold Cortex

A second brain on your own desktop. It indexes your notes, grades its own memory against real questions, and refuses any rebuild that makes recall worse. Free, and nothing leaves your machine.

  • Runs local
  • Recall gate
  • Provenance
  • Private
Orionfold Cortex
Language
Python
Run it
fieldkit arena up
Proven on
DGX Spark
License
Free and local

Orionfold Cortex is a second brain for your own notes, running on your own desktop. It grew out of Orionfold Arena, where it started as the cockpit’s memory pane. Point it at a folder of notes and it builds an index: a fast lookup table that lets you ask questions in plain words and get back the right passages, with a citation for each one. All of it runs on the machine under your desk, and none of it leaves.

Why it exists

An index of your notes is the easiest thing to let rot. You build it once, it works, and then the notes grow, a rebuild quietly drops a tenth of them, and nothing tells you. The number that matters, “does a question still find its answer,” stays invisible until the day it fails you. Cortex makes that number something you can see, chart, and gate on one screen.

What you can do

  • Read the health of your memory. One screen shows how much of your corpus is indexed, how much has gone stale, and how many facts carry a stamp saying where they came from. No guessing.
  • Rebuild without a script. One click queues a re-index and a scoring run behind it. You watch both move across a jobs board while the machine does the work.
  • Trust the gate. Every rebuild scores itself against a fixed set of real questions with known answers. If the new index finds fewer of them than the old one did, it is flagged instead of shipped. A worse memory never replaces a better one quietly.
  • Ask with a trust filter. Type a question in plain words and get cited passages back. Each one is tagged by how much you can trust its source, so a number measured on your own machine is never silently mixed up with a claim someone made on the internet.

Jargon, in plain words: an index is a fast lookup table over your notes. Recall is the share of questions where the index finds the right passage. Provenance just means “where a fact came from,” and Cortex stamps it on every piece it stores.

Honest numbers

On its first measured run, over 49 articles split into 313 pieces, Cortex found the right passage in its top five answers 40.91% of the time, and the right note 72.73% of the time, across 44 real questions. Those are the true numbers for the simple baseline it runs today, with no extra ranking step yet. They are printed on the product, not rounded up. The notes have grown since; at the latest rebuild the index holds 328 pieces, every one carrying a stamp that says where it came from.

The first real run also caught a real bug: a missing one-line import that eight unit tests had slept through, because they all used a stand-in instead of the real code. Driving the tool through its own screen found it in minutes. That is the whole idea: a memory you operate, not a black box you hope still works.

Private by design

Your documents, the index, and your questions all stay on your machine. Only aggregate scores are ever shared, and only if you publish them.

Built on fieldkit

Cortex is a thin surface over the fieldkit toolbox: the memory module owns the index and the source stamps, the Arena control plane runs the jobs, and the same test machinery that grades our models grades the index. The parts already existed; Cortex is what you get when you point them at each other and add the one missing piece, a memory that grades itself.

A closer look

The Cortex dashboard after a rebuild, showing 100 percent coverage and a source stamp on every one of its 328 indexed chunks.
After the rebuild, coverage and source stamps are numbers you can read, not guesses.
The query console answering a plain-language question about picking the right shrunk model build, with cited passages, each tagged by the trust level of its source.
Ask in plain words, get cited passages back, each tagged by how much to trust it.
The recall gate after a second scoring run, comparing recall against the last run and showing a promote verdict.
The gate at work. A rebuild is only promoted when its recall holds up.

Install

pip install "fieldkit[arena]"

Use it

# Start the Arena cockpit on your Spark. Cortex is its memory pane.
# Point it at your notes, then rebuild, score, and query from one screen.
fieldkit arena up

Specs

What it is
A memory layer for your own notes that you can drive, inspect, and trust
Coverage you can read
How much of your corpus is indexed, how much is stale, and how many facts carry a source stamp, as numbers on one screen
Rebuild from one screen
One click queues a re-index and a scoring run; you watch both move across a jobs board
The recall gate
Every rebuild is scored against a fixed set of real questions; a rebuild that finds fewer answers is flagged, not shipped
Ask with a trust filter
Plain-language questions come back as cited passages, each tagged by how much you can trust its source
Measured, not promised
Found the right passage in its top five 40.91% of the time, and the right note 72.73% of the time, over 44 real questions
Private
Documents, chunks, and questions stay on your machine; only scores are ever shared
Built on
The fieldkit toolbox (memory, arena, harness, eval)
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