This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
I built Lazy Incident Summarizer, a small local AI tool that turns a long IT incident or ticket description into a concise, actionable summary.
You paste an incident and the tool generates:
- Summary
- Priority
- Key Issue
- Suggested Next Action
The goal is simple: instead of manually reading and condensing repetitive incident descriptions, let a local AI model do the first pass in seconds.
Demo
A short video demo of the application running locally with an incident being summarized.
Demo: [Watch the video demo]
Code
The complete source code is available on GitHub:
https://github.com/SachinNarayanasamy/lazy-incident-summarizer
How I Built It
The project is built with Python, Streamlit, Ollama, and the open-weight Gemma 3 1B model.
The application sends the incident description to a local Ollama instance running Gemma 3 1B. The model is prompted to return four concise sections: Summary, Priority, Key Issue, and Suggested Next Action.
The entire AI processing flow runs locally on my machine rather than relying on a hosted AI API.
Why Does Open Innovation Matter?
Open innovation makes experimentation much more accessible.
For this project, using an open-weight model locally meant I could build and test an AI workflow without depending on a hosted AI API or sending incident text to an external service.
It also makes the project easier to experiment with. The model can be changed, the prompt can be improved, and the application can continue evolving without being tightly coupled to a single closed API.
The Lazy Payoff
Instead of spending a few minutes reading a long incident and manually extracting the important points, I can paste it into the tool and get a structured first-pass summary within seconds.
It's a small automation that removes a repetitive step — exactly the kind of work I would rather let AI handle.
Prize Categories
- Best Use of Gemma
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