Inspiration
Most notes never turn into action. Meeting notes, class notes and brainstorms often stay messy, forcing people to manually reread and decide what to do next. That friction kills productivity.
Note2Action was inspired by the need to remove the mental overhead between note taking and execution by using AI to automatically extract clear, actionable tasks.
What it does
Note2Action converts unstructured notes into clear, actionable task items using AI.
Users simply paste their raw notes, click a button, and instantly receive a structured list of tasks with priorities and deadlines (when mentioned), helping them move from ideas to action faster.
How I built it
I built Note2Action as a lightweight full-stack web application.
The frontend provides a simple interface for pasting notes and displaying results. The backend sends the notes to Google Gemini, which analyzes the text and extracts only actionable tasks in a structured JSON format. The app then renders these tasks cleanly for the user.
The system uses a single AI call per conversion to keep it fast, reliable, and focused.
Challenges I ran into
One of the main challenges was integrating the Google Gemini API reliably.
During development, I repeatedly encountered API errors such as 404 (Not Found) and 429 (Too Many Requests) while testing the content generation endpoint. Resolving this required carefully reviewing the API configuration, request format and rate limits.
I tested and retried the integration multiple times over ten attempts until the errors were fully resolved and the API calls became stable. This process helped ensure that the final system handles AI requests correctly and reliably under normal usage.
Another challenge was ensuring the AI extracted only actionable tasks without hallucinating or over-interpreting the notes, which was addressed through prompt tuning and strict response formatting. The biggest challenge was ensuring the AI extracted only real action items without hallucinating tasks or adding unnecessary details. Another challenge was handling very messy or vague notes while still producing useful output. This was solved by carefully tuning the Gemini prompt and enforcing strict structured responses.
Accomplishments that I'm proud of
Built a fully working AI-powered productivity tool end-to-end Achieved accurate task extraction from highly unstructured notes Delivered a clean, simple UX focused on real productivity Successfully aligned the project with the Google Antigravity theme (AI + automation)
What I learned
Clear prompting is critical for reliable AI outputs Simpler systems often deliver more real value than feature-heavy ones AI is most powerful when it removes cognitive load, not when it adds complexity Shipping a focused, working product matters more than overengineering
What's next for Note2Action
Future improvements could include: Task export to productivity tools (Google Tasks, Notion, Trello) Support for voice-to-text notes Smarter priority and deadline inference Optional task editing and saving For this hackathon, the focus was intentionally kept on delivering a fast, reliable core workflow.
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