DEV Community: Mayur P

PrioritiAI — Transforming Task Paralysis into Action with Open-Source Local AI

Mayur P — Tue, 13 Oct 2026 02:49:55 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

PrioritiAI is a privacy-first, open-source AI task management and scheduling web application that cuts through decision paralysis for students, freelancers, and small business owners.

The Problem It Solves

Traditional todo apps function as static checklists: they collect tasks, but leave users overwhelmed by the hardest question: "What should I work on right now, and why?" When users face 15+ competing responsibilities, they experience decision fatigue and fall into procrastination or default to low-impact tasks. Meanwhile, closed cloud AI tools demand that users stream sensitive client notes, deadlines, and project details to third-party servers.

The Solution

PrioritiAI introduces a personal AI productivity assistant that:

Identifies "Your Next Best Task": Evaluates tasks across Urgency, Importance, Effort & Quick Wins, Prerequisite Dependencies, and Mental Energy Requirements, spotlighting the single highest-yield item to tackle next with transparent explanations.
Accepts Natural-Language Task Dumps: Lets users input thoughts naturally ("Finish my DBMS assignment by tomorrow, prepare for Friday's exam, and spend 30 minutes replying to client emails") and extracts actionable task structures with user review before saving.
Generates an Honest Daily Schedule ("Plan My Day"): Slots tasks into realistic working hours, inserts recovery breaks, and honestly warns users when workloads exceed capacity rather than overpromising.
Protects Personal Privacy: 100% of data remains stored locally on device with zero cloud telemetry.

Who Would Pay for It?

Freelancers & Consultants: Juggling multiple client deliverables with strict billable hour constraints who need to protect client confidentiality.
Students & Researchers: Balancing exams, assignment deadlines, and study blocks who need automated workload pacing.
Small Business Owners: Prioritizing operations and revenue-generating activities without expensive monthly SaaS subscription bloat.

Code

The complete source code is open-source under the MIT License on GitHub:

song-code-won / PrioritiAI

PrioritiAI - Smart AI Task Planner with deterministic multi-factor priority scoring and local open-weight AI (Ollama/Qwen).

PrioritiAI — Smart AI Task Planner

A privacy-first, open-weight AI productivity assistant that cuts through decision paralysis, calculates transparent priority rankings, and schedules realistic daily workflows.

📖 1. Problem & Intended Users

Students, freelancers, software developers, and small business operators frequently encounter task overload. When faced with 10–20 concurrent responsibilities across different contexts (deadlines, client emails, exam preparation, code reviews), humans experience decision fatigue and default to either procrastinating or tackling low-impact tasks first.

Most existing todo apps are mere static lists: they record what needs to be done, but provide zero guidance on what to do next and why.

PrioritiAI bridges this gap:

Multi-Factor Priority Scoring: Evaluates urgency, importance, effort, prerequisite dependencies, energy requirements, and impact with a 100% deterministic, explainable algorithm.
"Your Next Best Task": Eliminates ambiguity by spotlighting the single highest-yield item to work on right now.
Open-Weight Local AI: Ingests messy, natural-language…

View on GitHub

Repository link: https://github.com/song-code-won/PrioritiAI

How I Built It

PrioritiAI was built around a hybrid architecture pairing local open-weight AI with deterministic execution:

1. Open-Source AI Engine (Ollama + Qwen 2.5 / Mistral)

Local Inference Runtime: Integrates directly with Ollama running locally at http://localhost:11434.
Open-Weight Models: Designed for Qwen 2.5 (3B/7B) (Apache 2.0 license) and Mistral 7B (Apache 2.0 license).
Task Parsing & Subtask Decomposition: The local model interprets conversational task dumps, identifies entities (durations, deadlines, energy levels), and decomposes complex tasks into 3–5 actionable subtasks with time estimates.
Resilient Offline Fallback: If Ollama is unavailable or offline, PrioritiAI automatically engages an embedded heuristic rule engine. Zero features or priority calculations ever break.

2. Deterministic Prioritization Scoring Formula

Instead of relying on unpredictable black-box rankings, PrioritiAI uses a mathematically transparent priority formula ($0$ to $100$ pts):

$$\text{Priority Score} = \min\left(100, \max\left(0, \text{Base Score} - \text{Dependency Penalty}\right)\right)$$

Where:
$$\text{Base Score} = (\text{Urgency} \times 0.40) + (\text{Importance} \times 0.35) + (\text{Effort} \times 0.15) + (\text{Impact} \times 0.10) + \text{Energy Bonus}$$

Dependency Penalty (-80 pts): Tasks blocked by incomplete prerequisite tasks are penalized and moved to Waiting on Something.
Quick Win Bonus (+40 pts): Sub-15 minute tasks receive an incentive boost to create early momentum.

3. Modern Tech Stack

Frontend: React 19, TypeScript 5.9, Tailwind CSS.
State & Storage: React Context + client-side LocalStorage persistence with full JSON export/import.
Testing: Automated unit tests with Vitest covering priority scoring, blocker penalties, and the daily planner.

Why Does Open Innovation Matter?

Open innovation is what made PrioritiAI possible, practical, and ethically aligned with user trust:

Complete Data Sovereignty & Confidentiality:
With proprietary cloud APIs (OpenAI, Claude, etc.), every task—whether it's confidential client milestone notes or personal deadlines—must travel across the public internet to third-party data centers. Open-weight models running on consumer hardware ensure that zero bits of private task data ever leave the user's laptop.
Zero Recurring API Bills for Side Hustlers:
For students and independent creators, paying $\$20$/month or per-token API charges for daily task management destroys the economics of a personal productivity tool. Open-weight models like Qwen 2.5 run on standard modern hardware at $\$0.00$ operational inference cost forever.
Guaranteed Offline Resilience:
Productivity software cannot be brittle. Freelancers working on trains, flights, or with intermittent connections cannot afford to lose their task prioritizer. Open-source local AI delivers uninterrupted productivity anywhere on earth.
Transparent, Explainable Logic:
Proprietary AI wrappers create black boxes where users don't know why a task was ranked where it was. By open-sourcing the deterministic scoring algorithm alongside local models, users have complete visibility and trust into why their next task was recommended.

Prize Categories

Primary Challenge: Hacktoberfest Open-Source AI Challenge Week 2 — Side Hustle
Open-Source AI & Local Inference Track

CalorieWalk 🌿: Understand Your Meal, Then Step Away and Touch Grass

Mayur P — Tue, 06 Oct 2026 04:34:53 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Most commercial nutrition and food tracking applications are designed to hook you into endless screen time. They force you to weigh ingredients down to the single gram, scroll through overwhelming databases of brand names, and calculate obsessive deficit math ("You ate 500 kcal, now spend 45 minutes on the treadmill to burn it off").

CalorieWalk is built on the opposite philosophy: Screen time should be the shortest part of your day.

CalorieWalk is a lightweight, privacy-first web application that allows you to photograph or quickly describe a meal. Using a locally running open-weight multimodal AI model, it identifies individual meal components, estimates reasonable portions, and calculates approximate calories.

The moment the meal is logged, the app invites you into "Touch Grass Mode"—a dedicated, minimalist outdoor wellness flow with 10-, 20-, and 30-minute nature walk prompts. It features a distraction-free countdown timer designed so you can put your phone in your pocket, walk outside, look at the sky, and reconnect with the physical world.

Key principles:

Zero Exercise Punishment: CalorieWalk never prescribes workouts to "burn off" food. Outdoor strolls are suggested strictly as an independent mental and physical wellness break.
Honest Uncertainty: Visual estimates are clearly labeled as approximate, acknowledging that photographic analysis cannot measure hidden cooking oils or exact sodium levels.
Privacy First: Photos are processed locally with EXIF metadata stripped, never uploaded to third-party cloud servers, and discarded immediately after inference by default.

Code

The complete source code, Docker setup, and test suite are open source on GitHub:

song-code-won / CalorieWalk

Understand your meal. Then go outside. Hacktoberfest Open-Source AI Challenge.

CalorieWalk 🌿 — Local AI Food & Calorie Estimator

Tagline: Understand your meal. Then go outside.
Challenge: Hacktoberfest Open-Source AI Challenge: Week 1 — “Touch Grass”

CalorieWalk is a lightweight, privacy-first web application designed to help you quickly understand what you eat using a locally running open-weight AI model, and then immediately step away from your device into the fresh air.

🎯 The Problem & Philosophy

Most commercial calorie tracking applications trap you in endless screen cycles: typing endless recipe variants, weighing grains to the single gram, and gamifying obsessive "calorie burn" calculations ("You ate 500 kcal, now run on the treadmill for 45 minutes").

CalorieWalk flips this paradigm:

Screen time is the shortest part of the experience: Snap a meal photo or type a quick food note.
Local Open-Weight AI: Processed right on your device—no cloud uploads, no subscription APIs.
Honest estimates with clear uncertainty: Food…

View on GitHub

Repository Link: https://github.com/song-code-won/CalorieWalk

How I Built It

CalorieWalk was designed around an offline-capable, open-weight local inference pipeline without heavyweight cloud dependencies:

Open-Weight Multimodal AI Core:
- Powered by local open-weight Vision-Language Models running via Ollama (e.g., llava:latest, llama3.2-vision:11b, or qwen2-vl:7b).
- The vision service cleans and normalizes user uploads, strips GPS and camera EXIF metadata with Pillow, and requests structured JSON outputs with bounded confidence scores.
- Built with strict schema validation and retry guards to reject hallucinated or invalid model structures.
Backend & Architecture:
- FastAPI (Python 3.11): High-performance asynchronous REST endpoints for image analysis, manual input, settings, and activity logs.
- SQLite: Lightweight local persistence storing meal records and completed walks without needing external database servers.
- Graceful Fallback Dataset: A built-in reference dataset ensures users can still estimate standard meals even if their local model daemon is temporarily offline.
Frontend:
- Built with clean, accessible HTML5, responsive CSS, and vanilla JavaScript—zero bloated client-side JavaScript frameworks or compilation steps.
- Features an immersive Touch Grass screen that replaces nutrition metrics with an outdoor countdown prompt.

Why Does Open Innovation Matter?

Meal photographs and daily eating logs are deeply personal data. They reveal your daily routine, physical location, home interior, dining companions, and health habits.

Relying on closed proprietary AI APIs (like OpenAI or cloud computer vision services) means funneling private personal photos to corporate servers for training and retention.

Open-source and open-weight innovation fundamentally solves this:

Total Data Privacy: Photos are analyzed directly on your own hardware. Not a single byte leaves the machine.
Offline Independence: Once the open-weight model is pulled, your meal logging and outdoor companion function completely without an internet connection.
Auditable & Non-Extractive: Anyone can verify that no hidden trackers or biometric harvesting occurs.
Zero Cost Barriers: Users and developers avoid recurring token fees, subscription paywalls, or sudden proprietary API deprecations.

Prize Categories

Touch Grass Track: Open-weight AI used specifically to reduce screen obsession and encourage users to step outside and experience nature.
Open-Source AI / Local Inference: Built entirely around open-weight models running on local hardware without proprietary closed APIs.

NoteMate AI: A Local-First, Privacy-Preserving AI Note Assistant

Mayur P — Fri, 02 Oct 2026 15:34:04 +0000

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

What I Built

I built NoteMate AI ("Your thoughts, organized by AI. Your notes, under your control.") for my friend, who is currently managing a packed schedule of university lectures, academic study sessions, and team project meetings.

The Problem It Solves

My friend constantly ran into three major friction points:

Scattered, Messy Inputs: Information was coming in from everywhere—spoken lectures, raw audio voice recordings, slide deck PDFs, and quickly typed scratch notes.
Cognitive Overload & Manual Revision: Manually combing through hour-long lecture transcripts or 30-page slide PDFs to extract key definitions, action items, and flashcard revision questions was consuming hours of valuable study time.
Privacy Concerns & API Paywalls: Commercial AI note apps require recurring monthly subscriptions ($15–$25/month) and send private voice recordings and proprietary course materials to third-party cloud servers.

What NoteMate AI Does

NoteMate AI is a local, privacy-first personal AI note assistant designed specifically around my friend's workflow:

Instant AI Summarization: In one click, local AI extracts a concise executive summary, bulleted key takeaways, important terminology, and actionable next steps—without altering or overwriting the original notes.
Voice-to-Notes: Upload spoken audio recordings (.mp3, .wav, .m4a). NoteMate AI transcribes them locally and automatically structures stream-of-consciousness speech into clean notes with clear headings and summaries.
PDF-to-Notes: Upload slide decks and reading materials. NoteMate AI extracts digital text and generates structured study guides while detecting scanned/image-only documents.
Strict Note-Scoped Q&A: A dedicated Q&A assistant scoped strictly to the active note. If a fact isn't present in the note, the AI explicitly states that rather than hallucinating an answer.
Interactive AI Revision Quiz: Generates a 5-question multiple-choice revision test with an interactive in-browser quiz runner, instant scoring, and explanation cards for active recall practice.

Code

The entire codebase is open-source and available on GitHub:

song-code-won / notemate-ai

NoteMate AI: A Personal AI Note-Taking Assistant powered by local open-weight AI (Gemma 3 & faster-whisper). Built for Hacktoberfest 2026.

NoteMate AI — A Personal AI Note-Taking Assistant

Tagline: Your thoughts, organized by AI. Your notes, under your control.
Built for the Hacktoberfest 2026 DEV Challenge — Build for a Friend.

1. Project Overview

NoteMate AI is a lightweight, simple, and functional AI-powered note-taking application designed to capture, organize, summarize, and retrieve knowledge effortlessly.

Rather than being another generic AI chat wrapper, NoteMate AI is engineered as an active cognitive assistant that converts raw thoughts, recorded audio, and PDF documents into clean, structured notes—running 100% locally using open-weight AI models.

2. The Problem & Who It Is Built For

The Friend & The Use Case

This project was built for my friend, who is actively balancing intense study sessions, university lectures, and collaborative team meetings.

The Pain Points

Scattered Information: Information arrives in unpredictable formats—spoken audio from lectures, lecture slide PDFs, and messy quick thoughts.
Cognitive Overload: Manually reading…

View on GitHub

Repository: https://github.com/song-code-won/notemate-ai

Tech Stack Overview

Backend: Python 3.11+, FastAPI, Uvicorn
Local Open-Weight LLM: Google Gemma 3 (gemma3:1b / gemma3:4b) running locally via Ollama
Speech-to-Text: faster-whisper (CTranslate2) running locally on CPU
Document Processing: PyMuPDF (pymupdf)
Database: SQLite with Write-Ahead Logging (WAL) mode for persistent storage
Frontend: Clean, responsive HTML5 / CSS3 / Vanilla JavaScript dashboard with split-view editor
Testing & Containerization: 19 automated Pytest cases, Dockerfile, and docker-compose.yml

How I Built It

1. Open-Weight AI Architecture with Ollama & Gemma 3

Rather than calling commercial cloud APIs like OpenAI or Anthropic, NoteMate AI's core intelligence is powered by Gemma 3 running locally through Ollama (http://localhost:11434).

We designed a dedicated AIService module that handles:

Structured prompt formatting for key-point and action-item extraction.
Strict anti-hallucination prompting for the Q&A engine so answers remain grounded only in the user's note.
JSON-formatted output parsing with robust regex fallbacks for 5-question MCQ generation.
Dynamic fallback error reporting with actionable troubleshooting instructions if Ollama is paused or the model needs to be pulled.

2. Local Speech-to-Text with `faster-whisper`

For voice recordings, we integrated faster-whisper, an optimized CTranslate2 reimplementation of OpenAI's Whisper model. By utilizing the quantized tiny or base weights on CPU, NoteMate AI transcribes audio files up to 4x faster than standard PyTorch models with minimal memory overhead, followed by an automatic Gemma 3 structuring pass.

3. PDF Parsing with PyMuPDF

Text from multi-page PDFs is extracted directly using PyMuPDF. We implemented automated character-density detection so that if my friend uploads a scanned or image-only document, NoteMate AI flags it immediately and prompts for a digital PDF rather than failing silently.

4. Zero Data Loss with SQLite WAL

To ensure notes survive system crashes and restarts without requiring a bulky database server, notes and AI summaries are stored persistently in a local SQLite database configured with PRAGMA journal_mode=WAL; and indexed by modification timestamps.

Why Does Open Innovation Matter?

Building NoteMate AI with open-weight models and open-source tooling demonstrated why open innovation is critical for real-world personal software:

Complete Data Sovereignty & Privacy:

Personal thoughts, course notes, and voice recordings never leave the laptop. For students and professionals dealing with confidential lectures, proprietary team discussions, or sensitive research, closed cloud APIs represent an unacceptable privacy trade-off. With Gemma 3 and faster-whisper, inference happens 100% on localhost.
Democratizing Accessibility (Zero Subscription Barriers):

My friend shouldn't have to budget $20/month just to summarize study notes or prepare for an exam. Open models turn consumer laptops into self-sufficient AI workstations without credit card requirements or surprise monthly billing.
True Model Flexibility:

Because the system communicates with Ollama through open standards, my friend isn't locked into a single provider. With a single environment variable change, the model can switch between gemma3:1b for battery conservation on an airplane, gemma3:4b for deep revision, or open models like llama3 or mistral.
Auditability and Transparency:

Every prompt, processing pipeline, and storage schema is open and verifiable. There are no black-box system prompts or hidden telemetry collecting personal data.

Real User Feedback (From My Friend)

After testing NoteMate AI with actual course materials and lecture recordings on an Intel i7 laptop (16GB RAM):

"The voice-to-notes workflow saved me nearly half an hour after a two-hour lecture. Instead of re-listening to messy audio, having the transcript cleaned up with headings and action items in one place made review instantaneous.

The revision quiz was surprisingly fun and accurate—it asked questions about the exact concepts covered in the notes rather than generic trivia."

Prize Categories

Primary Track: Build for a Friend
Open-Source AI / Open-Weight Model Track: Powered by Google Gemma 3 via Ollama & faster-whisper

Hello there

Mayur P — Thu, 01 Oct 2026 15:56:51 +0000

Hello everyone! Excited to join this amazing developer community, learn new things, share ideas, and connect with fellow developers!

DEV Community: Mayur P

PrioritiAI — Transforming Task Paralysis into Action with Open-Source Local AI

What I Built

The Problem It Solves

The Solution

Who Would Pay for It?

Code

song-code-won / PrioritiAI

PrioritiAI - Smart AI Task Planner with deterministic multi-factor priority scoring and local open-weight AI (Ollama/Qwen).

PrioritiAI — Smart AI Task Planner

📖 1. Problem & Intended Users

How I Built It

1. Open-Source AI Engine (Ollama + Qwen 2.5 / Mistral)

2. Deterministic Prioritization Scoring Formula

3. Modern Tech Stack

Why Does Open Innovation Matter?

Prize Categories

CalorieWalk 🌿: Understand Your Meal, Then Step Away and Touch Grass

What I Built

Code

song-code-won / CalorieWalk

Understand your meal. Then go outside. Hacktoberfest Open-Source AI Challenge.

CalorieWalk 🌿 — Local AI Food & Calorie Estimator

🎯 The Problem & Philosophy

How I Built It

Why Does Open Innovation Matter?

Prize Categories

NoteMate AI: A Local-First, Privacy-Preserving AI Note Assistant

What I Built

The Problem It Solves

What NoteMate AI Does

Code

song-code-won / notemate-ai

NoteMate AI: A Personal AI Note-Taking Assistant powered by local open-weight AI (Gemma 3 & faster-whisper). Built for Hacktoberfest 2026.

NoteMate AI — A Personal AI Note-Taking Assistant

1. Project Overview

2. The Problem & Who It Is Built For

The Friend & The Use Case

The Pain Points

Tech Stack Overview

How I Built It

1. Open-Weight AI Architecture with Ollama & Gemma 3

2. Local Speech-to-Text with faster-whisper

3. PDF Parsing with PyMuPDF

4. Zero Data Loss with SQLite WAL

Why Does Open Innovation Matter?

Real User Feedback (From My Friend)

Prize Categories

Hello there

2. Local Speech-to-Text with `faster-whisper`