A modern laptop GPU can run a capable language model and read text off a screenshot in the time it takes you to switch windows. That power sits idle while you pay a monthly subscription to a note-taking app that stores your life on someone else's server. Off Grid AI Desktop is a free, open-source app that turns your own machine into a second brain that builds itself.
Free, open-source, runs offline. No account, no API key, no data leaving your machine.
Day lays out your meetings, suggested actions, and to-dos in one place.
The problem with note-taking apps
A second brain is only as good as what you put into it. The catch is that putting things into it is work, and work is the thing you skip when you are busy. So the notes go stale, the system rots, and you are back to forgetting what you read last Tuesday.
The fix is to stop relying on manual capture. Off Grid AI Desktop watches your work, with your explicit permission, and writes the notes for you. You decide when it is on. It does the recording, reading, and summarizing. Your second brain fills itself.
What You Need
This is the heaviest of the local AI features, since it runs a chat model continuously. Give it room.
Minimum
- macOS: Apple Silicon (M1), 16 GB RAM, macOS 13+
- Windows: NVIDIA GPU (CUDA) or a GPU with Vulkan, 16 GB RAM, Windows 10+
- About 10 GB free disk for the app and a small model
Recommended
- macOS: M2 Pro or newer, 24 GB or more unified memory
- Windows: NVIDIA GPU with 8 GB+ VRAM, 32 GB RAM
- A fast SSD, since the memory database grows over time
The chat model is a quantized GGUF file. A larger model distills cleaner observations, so more memory pays off here.
How the capture-to-memory loop works
This is the part nothing else does the same way. The loop has five stages, and every one runs on your machine.
- Capture. You turn on screen capture, per device, opt-in. A visible recording indicator stays on the whole time, so you always know it is running. Nothing is captured silently.
- OCR. Each captured frame is read for text on-device. The pixels become words.
- Distill. The local LLM reads that text and writes short observations, then pulls out the people, projects, companies, and concepts it mentions.
- Store. Observations and entities go into a local database on your disk. No cloud, no sync to a vendor.
- Reflect. You browse the result through several views, described below.
Capture is always something you switch on, with a light showing while it runs. It is never the default and never hidden.
What Off Grid AI Desktop Can Do
Once the loop is running, your second brain shows up in five places. Each one is a different angle on the same captured memory.
Day. A journal of your day, written for you in time blocks. Glance back and see what you actually worked on, not what you meant to.
Entities. A private CRM for everything, not just people. Projects, companies, and concepts each get a record, with merge, hide, and hierarchy controls and synthesis summaries the model writes from your captured context. The colleague you talked to last week is a record. So is the project you keep half-remembering.
Replay. A scrubbable movie of your day. Drag through time and see what was on screen, the way you would scrub a video.
Reflect. Trends across your Day and Week. Where your focus went, how much you context-switched, what pulled your attention. Patterns you cannot see from inside a busy day.
Actions. Action items the model detects in your communication, gathered for you to review. They are never sent on your behalf. You decide what to do with each one.
Everything in these views came from frames you chose to capture, read and summarized on your hardware.
How Hardware Acceleration Works
Running a chat model continuously is what makes the hardware matter.
On macOS, the model runs on Apple Silicon with Metal acceleration, and unified memory lets the CPU and GPU share one pool. That is why an M-series machine can keep a model resident and distill in the background without grinding.
On Windows, acceleration comes from CUDA on NVIDIA GPUs or Vulkan on a wider range of cards, with a CPU fallback for machines without a compatible GPU. More VRAM means a larger, sharper model.
The models are quantized GGUF files, compressed so they fit in consumer RAM or VRAM. Quantization, plus on-device OCR, is what makes a self-building memory practical on a desktop you already own.
Tips for a Cleaner Memory
A few habits keep your second brain useful instead of noisy.
Capture in sessions, not all day. Turn it on for deep work and off for breaks. You get richer observations from focused time and less clutter from idle browsing.
Tidy your entities now and then. Merge duplicates, hide the noise, set a hierarchy. A few minutes of cleanup makes the synthesis summaries far more accurate.
Pick a model that fits your RAM with headroom. If distillation lags, drop to a smaller quantization. The loop should feel like it is keeping up, not falling behind.
Review Actions on a schedule rather than reacting to each one. They are a queue you control, not an inbox firing at you.
Privacy: Stronger Than Cloud Note-Taking
A cloud note-taking app stores your work on its servers, indexes it, and ties it to your account. A second brain like this, built on captured screen content, would be a serious thing to hand to a vendor. So nothing here does.
Off Grid AI Desktop keeps every frame, observation, and entity on your disk. The app is AGPL-3.0 open source, so you can read exactly what it captures and where it stores it. No telemetry, no account, no upload. Capture only runs when you turn it on, with a visible indicator the whole time. Pull the network cable and your second brain keeps working.
Getting Started
- Open the GitHub repo and download the latest release for macOS or Windows, or clone and build from source.
- Install and launch Off Grid AI Desktop.
- Download a chat model from inside the app.
- Turn on screen capture, grant the OS permission, and confirm the recording indicator is showing.
- Work for a while, then open Day, Entities, Replay, and Reflect to see what was built.
No sign-up, no key, no cloud account.
What's Coming
- Cross-device sync so memory from your laptop and desktop join up
- Unified search across Day, Entities, and captured observations
- More capture sources beyond the screen
- Richer Reflect trends over longer time spans
Reflect shows where your attention actually went across the day.
Replay is a scrubbable movie of your day, captured on-device.
Entities: a private CRM for the people, projects, and topics in your work.
FAQ
Q: Is it really free?
Yes. The app is free and open source under AGPL-3.0. The capture-to-memory loop is part of the open core.
Q: Does it work offline?
Yes. OCR, distillation, and storage all run on your machine, so it works with no network.
Q: Is the screen capture always on?
No. It is opt-in, per device, and only runs when you turn it on, with a visible recording indicator the whole time.
Q: How much RAM do I need?
16 GB is the floor because a chat model runs continuously. 24 GB or more lets you run a larger model for cleaner observations.
Q: Does it work on Windows as well as Mac?
Yes. macOS uses Metal and unified memory, Windows uses CUDA or Vulkan, and both fall back to CPU.
Q: Is any of my captured data uploaded?
No. There is no server. Frames, observations, and entities stay on your disk, and Actions are never sent on your behalf.
Build a second brain that builds itself, and keep every frame of it on your own machine.
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