Hirune — Understand Your Workday. Keep the Evidence. Own Your Data.

What if you could understand your computer activity at the end of the day without manually reconstructing everything you did?

We switch between applications, write code, browse the web, research information and work on different tasks throughout the day. But remembering exactly what happened can be difficult.
That's why I built Hirune, a privacy-first AI-powered desktop activity reporting application.

What I Built
Hirune transforms recorded desktop activity into structured, understandable daily reports.

It records active application names and window titles at regular intervals, organizes them into sessions and uses AI to generate activity summaries.

Core features:

1. Activity tracking: Records active application names and window titles.
2. Session organization: Groups recorded activity into meaningful sessions.
3. Session review: Allows users to review sessions, edit labels and add personal notes.
4. AI-powered reporting: Generates summaries from recorded activity using Gemma 4.
5. Evidence-backed AI claims: AI-generated statements include references to supporting activity records.
6. Human verification: Users can confirm, reject or edit AI-generated claims.
7. CSV export: Allows users to export their activity records.

Demo
Here's Hirune in action:

The demonstration covers activity tracking, session review, AI-generated reporting, evidence references, claim verification and CSV export.

Code
GitHub repository:
https://github.com/JASWANT-KUMAR-NAYAK/Hirune

The repository includes the application source code, setup instructions, documentation and tests.

How I Built It

Hirune is built using:

1. Python: Activity tracking, session processing, report generation and backend logic.
2. SQLite: Local activity storage.
3. HTML, CSS and JavaScript: Dashboard and user interactions.
4. Gemma 4 (31B): AI-powered activity summarization.
5. Ollama Cloud: Access to the AI model through an OpenAI-compatible API.
6. python-dotenv: Environment-based configuration for AI credentials.

The application follows a straightforward workflow:

1. Capture active application information.
2. Store activity records locally.
3. Organize records into sessions.
4. Generate reports from recorded evidence.
5. Use AI to produce interpretations.
6. Allow users to inspect and verify AI-generated statements.

I also focused on handling AI responses carefully, including parsing structured JSON and validating evidence references.

Why Does Open Innovation Matter?

Open innovation makes it possible to build useful applications using open-weight AI models and accessible developer tools.

For Hirune, Gemma 4 provides the language-model capabilities needed to turn activity records into readable summaries.

Using an open-weight model gives the project flexibility in model selection and deployment options rather than tying its AI layer to a single proprietary model.

Hirune currently uses Ollama Cloud for inference. The application also keeps its activity-tracking and reporting logic separate from the AI provider.

This separation makes future experimentation with different models and inference environments easier.

Privacy by Design

Activity tracking should not require invasive monitoring.
Hirune does not capture:

1. Screenshots
2. Keystrokes
3. Clipboard contents
4. Audio
5. URLs Activity records are stored locally. Window titles can contain sensitive information, so they should be treated carefully.

When Ollama Cloud is enabled, relevant application names, sampled window titles and report context are sent to the cloud provider for AI summarization.

The Lazy Payoff

Instead of manually remembering and reconstructing a workday, users can review an organized activity report.

Hirune aims to reduce the effort involved in understanding recorded computer activity while keeping users in control of the final interpretation.

It is not a productivity score or proof of attention. Recorded activity provides context, and AI-generated interpretations remain subject to human review.

*What's Next?
*
There is plenty of room to improve Hirune.

Some ideas for future versions include:

1. AI Daily Insights.
2. Visual activity timelines.
3. Daily and weekly comparisons.
4. More detailed evidence-backed recommendations.
5. Additional privacy controls.

For this submission, I focused on getting the core tracking, reporting, evidence review and export workflow working reliably.

Hirune

Understand your workday. Keep the evidence. Own your data.

Thanks for reading!