---
title: "AI Notes — Local inference, no server | Harshith Nayaka L"
description: "Notes app running DeepSeek R1 locally through Ollama: summarisation and keyword extraction with inference that never leaves your own machine."
canonical: "https://harshith-nayaka-l-portfolio.vercel.app/work/ai-notes"
last-updated: "2026-08-22T04:29:48Z"
author: "Harshith Nayaka L"
content-type: "text/markdown"
html-version: "https://harshith-nayaka-l-portfolio.vercel.app/work/ai-notes"
---
# AI Notes — Local inference, no server

> A note-taking app whose AI features run entirely on your own machine, so your notes never leave it.

Case study by Harshith Nayaka L, AI Engineer (Full-Stack), Bengaluru, India.
Canonical page: https://harshith-nayaka-l-portfolio.vercel.app/work/ai-notes

- **Type:** Local-LLM web app
- **Model:** DeepSeek R1 via Ollama
- **Role:** Solo build

## The problem

Almost every AI note app sends your writing to a third-party API. For private notes, that's the opposite of what you want, and it means the app is useless offline.

I wanted to show the alternative most people skip: useful AI features that don't require a cloud call, a subscription, or trusting someone else with your data.

## What I built

AI Notes runs DeepSeek R1 locally through Ollama, called straight from the browser. Summarize, improve writing, expand an idea, generate questions, extract keywords, all of it happens on the user's own machine.

Built with plain HTML, CSS, and JavaScript, no frameworks, with notes stored locally in the browser. Once the model is pulled, the whole thing works offline. The point was to keep it dependency-light and prove local inference is a real option, not a compromise.

## Pipeline

- **Write:** Note in browser (Stored locally)
- **Local model:** Ollama runtime (On the user's machine) → DeepSeek R1 (No external call)
- **Assist:** Summarize / expand / extract (AI actions)
- **Stay private:** Nothing leaves the device (Offline-capable)

## The judgment calls

**Inference stays on the machine**

The model runs through Ollama locally. There's no API endpoint receiving your notes, because there's no external call at all. Privacy isn't a policy promise here, it's the architecture.

**Works offline once set up**

Pull the model once and the app keeps working with no internet. The AI features don't depend on a server being up or a bill being paid.

**Deliberately dependency-light**

Plain HTML, CSS, and JavaScript, no framework, browser storage for the notes. Small enough to understand fully, which was the point.

## What it changed

**Published research:** The approach behind this project was written up and published: "AI-Powered Note-Taking System: A Local Machine Learning Approach DeepSeek R1 Integration", International Journal of Research Trends and Multidisciplinary Research (IJRTMR), Nov-Dec 2025, pp. 178-189 (DOI 10.59256/ijrtmr.20250506023). The paper reports 87% user satisfaction on content summarization with response times of 1.9-3.8 seconds.

**What it demonstrates:** That local and self-hosted inference is a practical choice, and the awareness of when keeping data on-device matters more than convenience.

**Scope:** A focused solo build proving the local-LLM pattern, not a feature-complete notes product.

## Built with

DeepSeek R1, Ollama (local), Vanilla JavaScript, Browser storage

## Links

- [View on GitHub](https://github.com/HarshithNayakaL/AI-Notes-App)
- [Read the paper (DOI)](https://www.doi.org/10.59256/ijrtmr.20250506023)