GLAMMBOX

THE TRAIL

ZERO TO A REAL AGENT. SEVEN STOPS.

This is the walk Pat did with a friend, in order, the same morning: pick a model, install it, understand what it can and can’t see, put an agent in an editor, unlock it on purpose, teach it with real prompts, then — for anyone who wants to go all the way — build the actual memory machine underneath. Do this, see that. Each station ends with a pack: download it and give it to your AI.

Updated 5 September 2026

1

STATION 1 · THE DOOR

Choose your model

Each stop below ends with a file you can download. Hand that file to your own AI — Claude, Gemini, or any agent installed on your machine — and it contains the procedure, step by step, for the agent to carry out.

Two doors are enough to start: ChatGPT (from OpenAI) or Claude (from Anthropic). Both have a real free tier and a real paid tier. Pick one — you can always try the other door later.

DO THIS

Open chatgpt.com or claude.ai in your browser and sign up with an email.

WHAT YOU WILL SEE

A chat box appears. Ask it one real question: that first answer shows concretely what the free tier includes.

Sign up here — the official doors, nowhere else:

We do not print a dollar figure here — prices change. Read today’s number off the official page yourself, every time.

Download the file for this step
2

STATION 2 · COMPUTER AND PHONE

Install it

Same account, two apps: the PC app for real work, the phone app so it is with you the rest of the day. Both stores, first login done by you.

DO THIS

Install the official desktop app, then find the same app in the App Store or Google Play and confirm the publisher before installing.

WHAT YOU WILL SEE

Sign in on both with the same account. The same conversation shows up on the PC and the phone — one account, two doors.

Download the file for this step
3

STATION 3 · THE BOUNDARY

First contact

A plain chat window sees only what you type or upload into it — not your desktop, not your other apps, not your files. That changes the moment you give it a folder. Understanding that line is the whole point of this station.

DO THIS

Ask it: “What can you see about me and this computer right now?” Then make one folder — your “home folder” — for it to work in later.

WHAT YOU WILL SEE

The honest answer: nothing outside the conversation. One folder is the only door you are about to open, and you decide when.

One folder, on purpose — never the whole Documents drive, never Downloads, never the home directory “to be safe.” A scoped agent is a safer agent, not a slower one.

Download the file for this step
4

STATION 4 · THE AGENT IN THE EDITOR

VS Code

A chat answers. An agent inside an editor can work on files you deliberately open. Start with one small, reversible task; it may help with later setup only after you review its work and approve each permission.

DO THIS

Install VS Code, add Claude Code first (or Codex if you signed up with ChatGPT), sign in yourself, and open your one home folder.

WHAT YOU WILL SEE

You should see the proposed edit in that one folder. Read the diff and confirm the tool’s permission setting before allowing it to save anything.

Download the file for this step
5

STATION 5 · THE KEYS

Set the permissions

Two modes exist, and the safe one is the default for a reason. Ask-before-write means the agent proposes and waits. Full access means it can write, delete, and run commands without asking — that is a key, not a convenience toggle.

DO THIS

Open the agent’s settings and confirm it reads “ask before write” or “ask for approval” — not “full access” or “allow all.”

WHAT YOU WILL SEE

Every change now shows up as a proposal you approve or reject — nothing saves itself while you are not looking.

“PC access” for an agent scoped to one folder means exactly that: it can read and write inside that folder and run commands the session allows. It does not hand over your passwords, your other apps, your camera, or your whole disk — scope is the safety.

Remote control — an agent working while you’re away — multiplies what ask-before-write protects, because nobody is at the keyboard to catch a mistake live. Keep the safe default on for any remote session until you have a specific, reviewed reason to change it.

Download the file for this step
6

STATION 6 · THE FIRST MEMORY

Give it a memory: build it from three written instructions

No database yet — just files and three prompts. A MEMORY.md index, a folder of daily notes, and a bootstrap prompt that makes the model read its own memory before it answers.

DO THIS

Make a memory/ folder with MEMORY.md and a memory/daily/ subfolder. Paste the bootstrap prompt below at the start of your next session.

WHAT YOU WILL SEE

The model opens MEMORY.md before answering, and says plainly when nothing relevant exists yet instead of guessing.

Copy-paste this at the start of a new session:

Before we start, read memory/MEMORY.md and today's file in memory/daily/
if it exists. Treat MEMORY.md as an index, not the full truth — if you need
detail, open the file it points to. Do not invent facts that are not in
these files. If nothing relevant exists yet, say so plainly and we will
start one.

Copy-paste this at the end of a work session:

Write today's entry in memory/daily/YYYY-MM-DD.md: what we did, what we
decided, and one open question for next time. Keep it factual. Then add
one line to memory/MEMORY.md only if today produced a fact worth finding
again later.

The full pack adds a third prompt — for correcting the model when it gets a fact wrong — and the exact file-creation steps for an agent.

Download the file for this step
7

STATION 7 · THE BRAIN, STEP BY STEP

The brain — GLAMMBRAIN

GLAMMBRAIN is a set of local programs that makes a chosen folder of text files searchable after a chat ends. The files stay readable. The v4 feed builds one search aid for meaning and another for exact words; its search command combines matching passages and returns the filename behind each one. Separate jobs maintain the optional relationship map, summaries, journals, and snapshots. It is a real installation with ongoing refreshes, not a switch inside the AI model.

A question comes in — what happens next

  1. First, you choose a configured folder of supported text files. A chat becomes a durable file only when it is written there — directly or by the session-summary job. The feed then makes that file searchable.
  2. The feed splits long files into overlapping passages. It puts a numerical copy in Qdrant for meaning search and the actual words in BM25 for exact search.
  3. A person or coding agent passes the question to unified-search.py. The v4 command checks those two indexes; it does not check the separate Neo4j graph.
  4. The command merges both result lists. If the optional reranker is enabled and reachable, it reads the candidates again and adjusts their order.
  5. The search returns evidence passages and source filenames. It does not write the final prose answer; the person or AI using the search decides what to say from that evidence.
READABLE FILES
The notes and other text you choose are the durable record. A person can open, correct, move, or remove them.
QDRANT · MEANING
Keeps numerical copies of passages with their source paths so different wording can still lead to a related idea.
BM25 · EXACT WORDS
Keeps the actual words from those passages so names, dates, codes, and quoted phrases remain findable.
NEO4J · OPTIONAL MAP
Separate jobs build file, folder, reference, and optional entity relationships. The normal v4 search does not consult this map.
NIGHT FILES
Session summaries and dream journals are written as Markdown that a later feed can make searchable. Proposals stay in a separate JSON file. If safe apply explicitly accepts an eligible deterministic patch, it appends that patch to a Markdown memory file the feed can index.

You do not need any of what follows to finish the trail: the six steps above already leave you with a working agent. If you want to install the GLAMMBRAIN memory on your own machine, the full technical procedure is one click away.

Open the full technical installation — download, checksum, Mac and Linux/WSL setup
MAC INSTALLATION · GUIDED BETA SAME V4 ARCHIVE · NOT ONE-CLICK

Installing GLAMMBRAIN on a Mac? Start here.

Use the same v4 archive for the core stack. Install Docker Desktop and Ollama as native Mac apps, then give the Mac guide to a coding agent. The archive’s Linux installer does not create macOS background jobs: the agent must use launchd or leave scheduling manual. On this path, the installation has not yet been tested from start to finish on a new Mac.

Starting point: macOS 14 or newer · Apple silicon recommended · 20 GB free recommended.

Download the Mac installation guide

Platform status: The verified installer path is Linux with systemd, or WSL2 with systemd and Docker integration. macOS is the guided-beta path above: same core package, native Docker Desktop and Ollama, with launchd or manual scheduling. Native Windows without WSL is experimental. The package requires Python 3.12 or newer and recommends 20 GB of free disk.

Stated plainly: the archive starts EMPTY. It contains no owner documents, sessions, indexed passages, graph data, or model weights. Installation downloads dependencies, container images, and models. The package code uses the MIT license; downloaded model weights keep their upstream licenses. Verify the SHA-256 before extracting or using its contents.

Download GLAMMBRAIN — full package (v4)

File
glammbrain-20260827-full-v4.tar.gz
Size
5.4 MB (5,417,952 bytes)
SHA-256
c7fde89a22ba289891041c25718891921312f107d0b1a9e7184f6147299c2723

What's inside:

  • The full local cockpit and its brain engine: feed and search code, memory and dream scripts, and optional graph and maintenance jobs.
  • The installer, Docker Compose file, service and timer templates, pinned Python requirements, and verify-brain.sh for checking the result.
  • The model manifest and puller. Model weights are not bundled; the package docs estimate roughly 12 GB for the models and caches, before Python packages and container images.
  • AGENT-PACK.md — the step-by-step installation and verification work order for a coding agent with shell access to the target machine.

The background jobs, grouped by purpose:

  1. Bring in changed text and turn configured conversation files into readable daily summaries.
  2. Build and refresh the optional document relationship map and any configured domain views.
  3. Check and repair the derived stores, audit aged isolated graph nodes, reduce confidence for knowledge that has not been accessed recently while protecting strongly connected records, and create Qdrant snapshots.
  4. Write a source-based nightly digest and proposals, then a weekly journal that connects recent grounded entries. These jobs write files; they do not retrain model weights.

What “learning overnight” means: the scheduled dream reads recently indexed passages and writes a dated digest plus proposals. The default scheduled run does not apply those proposals. Safe apply is a separate, explicit option for corroborated, low-risk deterministic patches.

Time and space: the archive is 5.4 MB. A full installation downloads roughly 12 GB of models and caches, plus Python packages and container images. The docs recommend 20 GB free. Installation time varies with the machine and download speed.

Quick start — two lines:

tar xzf glammbrain-20260827-full-v4.tar.gz && cd cockpit-20260827
cat brain-engine/AGENT-PACK.md

Then give that file to a coding agent with shell access to the target machine. On macOS, give the agent the Mac installation guide above first; it overrides the Linux/systemd steps. The agent must report what it actually observes.

How to verify: after installation, run bash brain-engine/infra/verify-brain.sh. Its receipt reports the infrastructure, collections, services, timers, and last-run layer states; a required FAIL means the install is not complete.

This station also has three short packs: the plain map, the standalone install work order, and the Mac guided-beta override. After downloading the archive, its own brain-engine/AGENT-PACK.md is authoritative for Linux/WSL; Mac users must keep the Mac override beside it.