This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
I built Staffside, a local-first people-ops workspace for small startup teams. It brings together a simple staff directory, per-person onboarding tasks, handbook Q&A, and payment-status tracking.
It’s for founders and small teams who end up doing HR work alongside their regular roles. Staffside helps keep onboarding and admin tasks in one place, while an AI assistant can answer questions from the team’s own handbook.
The payment tracker records amounts, periods, due dates, and paid/pending status. It does not process payroll, move money, calculate taxes, or store bank details.
Demo
Code
Staffside
Staffside is a small, local-first people-ops workspace for startup teams. It keeps staff profiles, onboarding tasks, payment-status records, and handbook documents on the machine running the app. An open-weight model served by Ollama drafts onboarding checklists and answers questions using retrieved handbook excerpts.
Run locally
Requirements: Node.js 20 or later. The staff directory, tasks, documents, and payment tracker work without an AI model.
npm start
Open http://127.0.0.1:3000. Workspace data is saved unencrypted in data/staff-data.json. The app binds to loopback only; it is a single-user local prototype, not a multi-user hosted service.
Enable the local AI
Install Ollama, then download and start the default model:
ollama pull qwen3:4b
Keep Ollama running, then start or refresh Staffside. The default model is qwen3:4b; set OLLAMA_MODEL to use another Ollama model. If Ollama runs at a non-default local URL, set OLLAMA_URL (for example, http://localhost:11434). Non-loopback model URLs are rejected…
How I Built It
Staffside uses Ollama 0.40.5 with Qwen3 4B (Q4_K_M). It is a dependency-free Node.js web app with a small browser-based frontend and local JSON persistence. Its AI features are built around Ollama and the configurable open-weight model qwen3:4b.
For handbook Q&A, the app searches local documents for matching passages and sends only those excerpts, along with the question, to the local model. Staff profiles and payment records are not included in the AI prompt. For onboarding, the model drafts a short checklist grounded in retrieved handbook passages; the user reviews the suggestions before adding them.
Why Does Open Innovation Matter?
This prototype makes it possible to keep inference on the same machine as the app, rather than sending handbook excerpts to a hosted AI API. The model can be swapped by configuration, giving the team more control over where inference runs and which model it chooses.
That’s a useful privacy and flexibility trade-off for sensitive workplace information.
Top comments (0)