The lazy payoff
We all have that folder. The Downloads folder. The Documents dumping ground. The logs directory that's grown to ten thousand lines. You know you should tidy it, sort it, review it — but it's boring, it's repetitive, and it always feels like it can wait until tomorrow.
GhostShift AI is built for exactly that procrastination. It's a local-first automation studio that scans, classifies, analyzes, and archives the files and logs already on your machine — but only after you approve each step. It does the work so you don't have to.
GhostSort watches a folder you register, proposes where each file should go, shows you a preview, and moves only what you explicitly approve. Fingerprint-verified undo means if you change your mind, it goes back exactly where it came from.
GhostDev reads your log files, groups errors deterministically, and (if you have a local model) adds a clearly labelled interpretation — without sending your logs anywhere.
GhostCapture records an allowlisted sequence of file actions (no screen recording, no browser snooping), lets you edit and reorder the steps, then replays them as a dry run or live run whenever you want.
GhostBrain drafts new workflows from a plain-language prompt using a local open-weight model — and nothing executes until you say so.
It's slow and steady by design: AI suggests, deterministic code validates, you authorize, the engine executes, history records.
Why open innovation matters here
GhostShift runs entirely on your laptop. There is no account, no cloud control plane, no telemetry, and no hosted model provider. The optional AI layer uses Ollama with an open-weight model (default: gemma2:2b), which means:
- No internet required. The deterministic pipeline — scan, classify, hash duplicates, analyze logs, guard moves — works completely offline. The model is only needed for interpretation and workflow drafting.
- Your data never leaves your machine. Filenames, log signatures, and file contents stay in a local SQLite database and local reports. Nothing is uploaded, scanned by a third party, or used to train someone else's model.
- Set it up once, never pay again. Local inference costs nothing per token. There's no subscription, no API credit burn, and no rate limit from a hosted provider.
- Swap the model, fine-tune it, change the behavior. Because the model talks to a standard Ollama endpoint, you can point GhostShift at any local model you prefer, swap in a fine-tuned version, or run it CPU-only. The AI is never an authority — it can't name a destination path, add a workflow action, or run a command. All it can do is suggest, and the application code decides whether to act.
The stack
| Layer | Choice |
|---|---|
| Backend | Java 25, Micronaut 5.2, Gradle 9.8, Netty |
| Frontend | Vue 3 + TypeScript + Vite, Pinia, custom CSS design system |
| Database | SQLite (WAL mode, direct JDBC) |
| AI runtime | Ollama (optional, local open-weight models) |
| Packaging | Docker Compose (loopback-only ports) |
The UI binds to 127.0.0.1:3000, the API to 127.0.0.1:8080, and both are loopback-only by default. An optional bearer token can lock the API down further.
Try it
# Clone
git clone https://github.com/Senthil455/GhostShift-AI.git
cd GhostShift-AI
# Configure
cp .env.example .env
# Edit .env if you want an API token or a different Ollama model
# Run with Docker
docker compose up --build
# UI: http://127.0.0.1:3000
# API: http://127.0.0.1:8080
If you have Ollama running locally:
ollama serve
ollama pull gemma2:2b
Then open GhostBrain in the UI and click Test model. If Ollama isn't available, GhostShift still works — every deterministic feature runs without it, and AI-specific panels show an honest notice instead of inventing a result.
Honest limitations
- No browser or screen recording. GhostCapture records reviewed file actions only.
- No file deletion. Duplicate detection reports exact matches; it does not remove them.
- Docker images are packaged but were not runtime-tested in the build environment.
- Single-user local design — SQLite and an in-process worker are for one person on one machine.
Build it your way
GhostShift is open source because the automation that touches your files should be inspectable, auditable, and yours to modify. If you want a sloth-speed automation studio that conserves your energy and keeps your data under your control, this is the repo to fork, extend, and run on your own hardware.
Built for the Hacktoberfest Open-Source AI Challenge: Week 3 — "Slow and Steady."
Top comments (0)