Pakka? - Plans, with receipts

# devchallenge# weekendchallenge# hf26challenge
Pakka? - Plans, with receiptsAditya Singh Yadav

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend. What I...

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend.

What I Built

A friend recognized the problem behind Pakka?: important plans get buried in busy group chats, tentative suggestions can look final, and later changes are easy to miss. I built a prototype to turn pasted chat text into reviewable plan candidates.

I shared Pakka? with my friend, who already had Ollama installed. He ran it locally on his device, got it working, and genuinely liked the project. That was encouraging: it showed me this idea mattered to someone beyond me.

Pakka? separates confirmed, proposed, unclear, cancelled, and superseded plans. It shows the source lines behind each interpretation, keeps tasks and RSVPs out of calendar events, and requires a person to review an event before export.

Demo

Drive folder

Code

View Pakka? on GitHub.

How I Built It

Pakka? is a Node.js app with a Gemma 4 integration through Ollama. It asks the model for plan candidates with source quotations and line numbers, then checks that cited text appears in the supplied conversation. A person can edit and review candidates before exporting valid, confirmed calendar events. My friend ran it locally using his Ollama setup, and it worked on his device.

The automated test suite passes 13 tests covering evidence checks, dates, cancellations, review requirements, and calendar export. This is a small software test suite, not a broad measure of model accuracy.

Why Does Open Innovation Matter?

Ollama let my friend run Pakka? on his own device instead of having to use a hosted model service. In local mode, chat text goes to the local Ollama instance; the optional hosted mode sends it to Google's Gemini API. That gives people a choice about where inference happens. The local run worked for my friend, though I have not measured model accuracy or compared it with a closed model.