DEV Community: Mohd Ashab

🦥 The Lazy Payoff: Let AI Handle the Boring Stuff

Mohd Ashab — Tue, 20 Oct 2026 13:42:49 +0000

What if getting organized didn't require organizing everything yourself?

As students and developers, we constantly deal with messy notes, scattered to-do lists, and small repetitive tasks that take time away from the work that actually matters. We often postpone these tasks simply because getting started feels like too much effort.

That's the idea behind The Lazy Payoff — a local-first AI productivity assistant built around a simple philosophy:

Do less repetitive work. Save your energy for what matters.

What I Built

The Lazy Payoff is an open-source AI productivity project designed to make everyday digital tasks easier.

The idea is simple: instead of manually sorting a messy brain dump, rewriting text, or extracting action items, you give the AI your input and let it help organize the work.

The project is designed around practical productivity tasks such as:

Summarizing text: Turn lengthy information into concise, readable takeaways.
Organizing tasks: Transform a messy to-do list into categorized, actionable tasks.
Extracting action items: Identify the next steps hidden inside unstructured text.
Creating checklists: Turn a brain dump into a more manageable plan.

The goal isn't to replace thinking or learning. It's to reduce the small, repetitive tasks that make getting started harder than it needs to be.

Demo

GitHub repository: The Lazy Payoff

I'll share a working demo or screen recording here as soon as it's available.

Code

🚀 Explore the source code: github.com/ashab683/lazyPayOff

The project is being built with a focus on practical AI automation, a simple user experience, and local inference rather than dependence on a paid AI API.

Contributions, feedback, and ideas for making everyday tasks easier are welcome!

How I Built It

The project uses a web application architecture with React, Node.js, and Express, with Gemma 3 4B running locally through Ollama as the intended AI engine.

Here's the role of each component:

React: Provides the user interface for entering text and viewing organized results.
Node.js and Express: Handle application requests and connect the frontend to the AI service.
Ollama: Makes it possible to run the model locally.
Gemma 3 4B: Powers the text-processing and organization workflows.

The important part is that open-weight AI is not just an optional feature or a label on the project. The intention is to make the local model responsible for the actual text-processing work.

I'm focusing on keeping the experience straightforward: provide input, choose an action, and get a useful result without needing to write complicated prompts.

Why Does Open Innovation Matter?

For a productivity assistant, the way AI runs matters almost as much as what it can do.

Using an open-weight model such as Gemma gives developers the freedom to experiment with local inference, change models, customize prompts, and build around their own requirements.

With local inference through Ollama, personal text can stay on the user's machine instead of being sent to an external AI API, provided the application is configured to keep processing local.

It also offers a different cost model. There are no per-request charges from a hosted AI API when inference runs locally, although hardware, electricity, and setup still have costs.

Most importantly, open innovation gives developers the opportunity to understand and control more of their AI stack instead of treating AI as a black box.

For me, this challenge is about building something useful with open technology and making AI-powered automation more accessible.

My Agent Session

I used AI-assisted development to help build and refine the project.

Agent session: I'll add my DevRelay session here if I publish one for this project.

The Lazy Payoff 🦥

Imagine writing this in one go:

Revise DBMS, email the professor about the assignment, buy groceries, finish the project README, and review tomorrow's lecture notes.

Now imagine not having to manually separate every item, decide how to organize the list, and rewrite it into a usable plan.

That's the payoff I'm aiming for with The Lazy Payoff: turn unstructured input into organized next steps with minimal effort.

I haven't measured a reliable time-saving figure yet, so I won't claim a specific number. The next step is to test the workflow on real everyday tasks and measure how much manual effort it actually removes.

Because sometimes the smartest productivity hack isn't working faster.

It's making the unnecessary work disappear.

Prize Categories

Gemma — Best Use of Gemma: Intended entry using Gemma 3 4B through local Ollama inference, subject to verifying the working integration.

I'll update this section if I complete a qualifying integration for any additional partner category.

Built for Hacktoberfest 2026, with open-source AI and a little sloth energy. 🦥

devchallenge #hf26challenge

From Notes to Knowledge: Building StudyBuddy AI with Gemma 3 & Open-Source AI 🚀 | #Hacktoberfest2026

Mohd Ashab — Sat, 17 Oct 2026 10:52:09 +0000

What I Built

What if every tutor could turn study notes into quizzes and revision material without spending hours preparing them manually?

That's the problem I'm tackling with StudyBuddy AI — an AI-powered learning assistant designed to make educational content preparation faster and more accessible.

Students need effective revision materials. Tutors need quality practice questions. Small coaching centres need ways to prepare learning resources without adding more repetitive work to their day.

StudyBuddy AI aims to help by turning study material into structured educational content, including:

📚 Concise revision notes for faster learning.
❓ Multiple-choice questions for topic-wise practice.
🧠 Practice questions to reinforce understanding.
📝 Reusable learning materials for tutors and educators.

Who could pay for it?

Independent tutors and small coaching centres that regularly prepare worksheets and practice tests could benefit from a tool that reduces preparation time. A potential business model could offer basic generation for free, with paid features such as reusable templates and bulk worksheet creation.

The goal is to build something useful beyond a simple AI chatbot: a practical educational tool that solves a recurring problem

Code

💻 GitHub Repository: "Explore StudyBuddy AI" (https://github.com/ashab683/studybudy-ai)

StudyBuddy AI is built as a web application with a frontend, backend, and local AI inference workflow.

Explore the repository for the source code, setup instructions, and implementation details.

How I Built It

The project uses a modern web development stack combined with open-weight AI.

Technology stack:

React + Vite — frontend application.
Tailwind CSS — responsive user interface.
Node.js + Express — backend API.
Ollama — local model runtime.
Gemma 3 4B — open-weight language model.

How it works

A user provides study material through the application.
The frontend sends the request to the backend.
The backend communicates with Gemma through Ollama.
The model generates the requested educational content.
The application displays the output for the user to review and reuse.

The focus is on making open-weight AI useful in a specific domain rather than simply wrapping a general-purpose model in another chat interface.

Why Does Open Innovation Matter?

For StudyBuddy AI, open innovation is about accessibility, control, and the freedom to build without depending entirely on a closed AI service.

🔐 Privacy-conscious design: Local inference can keep study material on the user's machine when the entire workflow is configured locally.

💸 Lower marginal inference costs: Running the model locally avoids per-request cloud API charges, although hardware, electricity, and maintenance still have costs.

🔄 Model flexibility: Open-weight models let developers experiment with different models and adapt their applications to specific educational needs.

🌐 Potential offline use: Once the model and dependencies are installed, local inference can work without an active internet connection, provided the rest of the application is also available locally.

These advantages matter for a product intended for independent educators and smaller institutions that may not have large technology budgets.

Instead of treating AI as a service that users can only access through someone else's infrastructure, open-weight models let developers explore how to make AI more adaptable and accessible.

Prize Categories

Gemma — Best Use of Gemma

StudyBuddy AI uses Gemma 3 4B through Ollama as its local AI inference model. This is the primary partner category I'm targeting, provided the submitted implementation demonstrates the integration.

I'll only include additional partner categories if the project genuinely uses their technology and meets their eligibility requirements.

Building AI That Solves Real Problems

Hacktoberfest Week 2's Side Hustle theme is a reminder that building AI isn't just about choosing a model or writing code. It's about identifying a real problem, understanding who experiences it, and creating something useful enough that people might want to pay for it.

With StudyBuddy AI, I'm exploring how open-source AI can make educational content preparation more practical for students and educators.

The next step is to put the product in front of real users, gather feedback, and improve it based on what they actually need.

If you could automate one repetitive task in studying or teaching, what would it be?

I'd love to hear your ideas. Let's build useful things with open-source AI! 💚

devchallenge #hf26challenge

TouchLess AI 🌱 — AI That Wants You to Stop Using It

Mohd Ashab — Wed, 07 Oct 2026 21:30:13 +0000

🌱 What is TouchLess AI?

TouchLess AI is an AI-powered outdoor mission generator that uses Google Gemma 3 to turn screen time into real-world activity.

Instead of giving you another reason to stay on your screen, it generates a personalized mission that encourages you to leave it.
github->https://github.com/ashab683/touchless-AI

The user chooses:

⏱️ How much time they have
🌳 Their environment
🎯 Their goal
⚡ Difficulty

Gemma then generates a personalized outdoor mission.

And then comes the most important part:

Put the phone away.

💡 The Problem

A lot of technology is designed around keeping us engaged for longer.

More notifications.

More scrolling.

More screen time.

I wanted to explore the opposite idea:

Can AI be useful while encouraging us to spend less time using technology?

That became TouchLess AI.

🔄 How It Works

The experience is designed as a simple loop:

Choose your preferences
↓
Gemma generates an outdoor mission
↓
Start Phone-Away Mode
↓
Put your phone away
↓
Go outside
↓
Complete the mission
↓
Write a short reflection
↓
Gemma generates a grounding response
↓
Save the experience locally

The philosophy is simple:

The better TouchLess AI works, the sooner you should stop using it.

🧠 Gemma at the Core

Gemma is at the core of the TouchLess AI experience.

Before going outside

Gemma receives the user's preferences and generates a structured outdoor mission.

Instead of simply saying:

"Go for a walk."

the application can create a more specific activity based on the user's available time, environment, goal and difficulty.

📸 Insert your actual mission screen showing the Gemma-generated mission here.

This is where the AI turns the user's preferences into an actionable offline activity.

After coming back

The user writes a short reflection about what they noticed or experienced.

Gemma then generates a brief grounding response based on that reflection.

So AI is used at two important moments:

Before: Help me decide what to do.

After: Help me reflect on what I experienced.

The middle part is intentionally human + offline.

📵 Phone-Away Mode

This is one of the most important parts of TouchLess AI.

Once the mission starts, the interface becomes intentionally minimal.

[IMAGE 3 — PHONE-AWAY MODE]

It includes:

⏱️ A timer
🌿 Minimal visual feedback
📵 A reminder to put the phone away
👀 An optional way to briefly check the mission

The feature is designed around one principle:

The application should become less interesting once the mission begins.

The goal isn't to keep you inside the app.

The goal is to get you out of it.

🌳 The Real-World Activity

This is where TouchLess AI becomes different from a typical AI application.

The AI generates the mission.

Then the user leaves the screen behind and actually completes it.

📸 Insert your outdoor photo/video screenshot here.

This could be a photo of you completing the mission, a park/nature scene from the activity, or a frame from your demo video.

The important part is showing that the AI-generated task leads to something outside the screen.

✍️ Reflection

After completing the activity, the user comes back and records a short reflection.

For example:

"I noticed how quiet the park was once I stopped checking my phone."

The reflection becomes the input for the second AI interaction.

Gemma then responds with a short grounding reflection rather than trying to keep the user engaged in another conversation.

🛠️ How I Built It
Frontend
Next.js
React
TypeScript
Tailwind CSS
Backend
Python
FastAPI
Pydantic
AI
Google Gemma 3 4B
Ollama for local inference
Storage
Browser localStorage

The current application has:

No authentication
No database
No user tracking
No unnecessary data collection

The project is designed to keep the experience simple and local-first.

🏗️ Architecture

The current local architecture looks like this:

┌──────────────────────┐
│ Next.js + React │
│ Frontend │
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ FastAPI │
│ Backend │
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ Ollama │
│ Local Inference │
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ Gemma 3 4B │
└──────────────────────┘

I used structured responses and validation so that the frontend receives predictable mission and reflection data from the AI service.

🔓 Why Open Innovation?

The interesting question for me wasn't just:

"What can AI generate?"

It was:

"What should we use AI for?"

Open-weight models make it possible to experiment with AI in different directions and build applications around ideas that don't necessarily fit the usual engagement-driven model.

TouchLess AI is my small experiment in that direction.

Instead of using AI to keep someone on a screen longer, I'm using it to help them leave the screen.

🎥 Demo

The demo follows the complete experience:

Configure
↓
Generate with Gemma
↓
Phone-Away Mode
↓
Go Outside
↓
Complete Mission
↓
Reflect
↓
Gemma Response
↓
History
[VIDEO / GIF — OPTIONAL]

📹 Insert your 60–90 second demo video or GIF here if DEV supports your chosen format.

The demo should show the actual application rather than only the source code.

💻 Source Code

The complete project is open source.

🔗 GitHub:
https://github.com/ashab683/touchless-AI

The repository contains the frontend, FastAPI backend, AI service layer, prompts, configuration and documentation.

📚 What I Learned

Building TouchLess AI helped me learn more about:

Integrating an open-weight AI model into a real application
Building a FastAPI backend
Connecting a Next.js frontend to an AI service
Working with structured AI responses
Validating AI-generated data
Designing a local-first experience
Building an interface where less interaction is actually better
Thinking about AI from a human-behavior perspective

The biggest lesson was probably this:

Good AI doesn't always need to give you more to do. Sometimes it should help you do less on your screen.

🚀 What's Next?

Some improvements I'd like to explore:

🌳 More diverse outdoor missions
🧠 Better personalization
♿ More accessibility improvements
💾 More local-first functionality
🤝 Community-contributed missions
🤖 Additional open-model inference options
📱 Better mobile/PWA experience
📊 More ways to measure meaningful offline activity
🌍 Final Thought

We already have plenty of technology competing for our attention.

Maybe we also need technology that knows when to let us go.

TouchLess AI is an experiment in exactly that. 🌱

If you had 30 minutes away from your phone, what outdoor mission would you want an AI to give you?

🔗 GitHub: https://github.com/ashab683/touchless-AI

devchallenge #hf26challenge #opensource #gemma

StudyBuddy AI: A Local AI Study Companion Powered by Gemma 3 4B

Mohd Ashab — Mon, 05 Oct 2026 00:58:21 +0000

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

What I Built

I built StudyBuddy AI, a local-first AI study companion designed for a fellow college student who struggles with understanding difficult topics and organizing exam preparation.

The problem I wanted to solve was bigger than simply getting an answer from an AI.

When studying a difficult topic, a student usually needs to:

Understand → Practice → Test → Revise → Plan

StudyBuddy AI turns that workflow into one application.

A student can enter a topic and ask StudyBuddy to explain it, then immediately:

Ask for a simpler explanation
Get an analogy
Practice the concept
Take an interactive quiz
Review mistakes
Generate an exam-focused revision sheet
Create a multi-day study plan
Save useful explanations, quizzes, revisions, and plans

The goal is to make AI useful as a learning companion, rather than just another chatbot.

The project is intentionally local-first. The AI runs on the user's machine using Ollama and Gemma 3 4B, without requiring OpenAI, Gemini, Claude, or another cloud AI API.

Demo

GitHub Repository:
https://github.com/ashab683/studybudy-ai

The demo shows the complete learning workflow:

Explanation → Follow-up → Quiz → Revision → Study Plan → Saved Resources

Code

The complete source code is available here:

https://github.com/ashab683/studybudy-ai

The repository contains the React frontend, Express backend, Ollama integration, prompts, validation, local storage utilities, and setup instructions.

How I Built It

The core architecture is:

React + Vite + Tailwind
          ↓
      Express API
          ↓
    Ollama Local Runtime
          ↓
       Gemma 3 4B
          ↓
       Express API
          ↓
        React UI

Tech Stack

Frontend

React
Vite
JavaScript
Tailwind CSS

Backend

Node.js
Express
REST API

Ollama
Gemma 3 4B

Storage

Browser localStorage

The most important part of the project is the local AI pipeline.

The frontend sends requests to the Express backend. The backend validates the request, builds a task-specific prompt, and sends it to the local Ollama API. Ollama runs Gemma 3 4B locally and returns the generated response to the backend, which then sends it back to the React application.

Interactive Quiz Mode

StudyBuddy can generate structured multiple-choice quizzes using Gemma.

The quiz system supports:

Easy, medium, and hard difficulty
3–10 questions
Progress tracking
Immediate feedback
Score calculation
Answer explanations
Mistake review
Bookmarking

The backend requests structured JSON from the model instead of treating the response as plain text.

Conceptually, the generated data looks like:

{
  "title": "Stack in Data Structures",
  "questions": [
    {
      "id": 1,
      "question": "What principle does a stack follow?",
      "options": [
        "FIFO",
        "LIFO",
        "Random access",
        "Priority order"
      ],
      "correctIndex": 1,
      "explanation": "A stack follows the Last In, First Out principle."
    }
  ]
}

Because local models can sometimes return JSON wrapped in markdown or slightly malformed structures, I added parsing, validation, and normalization so the application can handle model output more reliably.

Exam Revision Mode

The revision workflow generates a concise exam-focused study sheet containing:

Core definitions
Important rules and formulas
Key differences
Common exam traps
Important revision questions

Students can copy or save the generated revision material and directly create a quiz on the same topic.

Study Plan Generator

Students can provide:

Subject
Syllabus topics
Hours available per day
Number of days until their exam

StudyBuddy then generates a multi-day schedule.

The frontend turns that response into interactive daily tasks with completion tracking and revision tips.

Contextual Follow-ups

Instead of ending after an explanation, StudyBuddy provides contextual actions such as:

Give me an analogy
Explain more simply
Take a Quiz on this
Create Revision Sheet
Line-by-line code breakdown

This creates a continuous learning flow instead of a single question-and-answer interaction.

Saved Resources and History

I intentionally avoided adding a database to the MVP.

StudyBuddy uses browser localStorage to persist:

Saved explanations
Quizzes
Revision sheets
Study plans
Recent sessions
Theme preference

This keeps the application simple while still allowing users to continue their study workflow after refreshing the browser.

Dark Mode

The application also includes a persistent dark mode designed for longer study sessions.

The theme is saved locally and applied across the complete interface.

Why Does Open Innovation Matter?

Using open-weight AI and local inference changed what I could build.

StudyBuddy does not depend on a proprietary AI API or a paid API key.

Instead, the AI layer runs locally:

StudyBuddy AI
      ↓
    Ollama
      ↓
  Gemma 3 4B
      ↓
Local inference

This provides several advantages.

Privacy

Study questions and learning material can remain on the student's own machine instead of automatically being sent to a third-party AI service.

No API Key

A student can run the application without creating an account with a cloud AI provider or managing an API key.

No Per-request Cloud Cost

Once Ollama and the model are installed, the application does not need a paid cloud AI API for its core AI functionality.

Control and Experimentation

The AI model is configurable through the application's environment.

This allowed me to experiment with prompts, structured outputs, different learning modes, and error handling while keeping the architecture simple.

There is also an important trade-off.

Local inference can be slower than cloud APIs depending on the user's hardware. During development, some Gemma 3 4B requests took around a minute or more.

That trade-off was an important part of the project:

Local AI provides more control and privacy, but performance depends heavily on the user's hardware.

Open innovation made it possible for me to build the AI layer as an actual part of the application instead of simply consuming a proprietary AI service.

What I Learned

One of my biggest lessons from building StudyBuddy AI was that integrating AI into an application is much more than writing a prompt.

The surrounding engineering matters just as much.

I learned about:

Running open-weight models locally with Ollama
Designing prompts for different learning workflows
Structured AI output
Defensive JSON parsing
API validation
React state management
Persistent browser storage
Loading and error states
Building responsive interfaces around asynchronous AI operations
Designing an AI product around a real user problem

The most important lesson was:

AI should be treated as a component of the product, not the entire product.

The value comes from what you build around the model.

Challenges

The biggest technical challenge was dealing with the unpredictable nature of local AI responses.

For normal explanations, plain text was enough.

But quizzes required reliable structured data.

The model could sometimes return:

JSON inside markdown code blocks
Unexpected fields
Incomplete structures
Formatting that wasn't directly usable by React

Instead of assuming the model would always behave perfectly, I added a parsing and validation layer between Ollama and the frontend.

Another challenge was response time.

Because Gemma 3 4B runs locally, generation speed depends on the hardware. This made loading states and error handling important parts of the user experience.

What's Next?

StudyBuddy AI is currently an MVP, but there are several improvements I would like to make:

Streaming local model responses
Faster inference and improved loading UX
Support for additional Ollama models
Flashcards
Spaced repetition
Coding practice
Better personalization based on quiz mistakes
Long-term study progress tracking

The next major improvement would be streaming responses so users can start reading an AI response while Gemma is still generating it.

My Agent Session

Prize Categories

Best Use of Gemma

StudyBuddy AI uses Gemma 3 4B as the core AI model powering explanations, quizzes, revision sheets, follow-up learning, and study-plan generation.

The model runs locally through Ollama, making Gemma an actual part of the application's core architecture rather than an optional feature.

Final Thoughts

StudyBuddy AI started with a simple question:

What if an AI study assistant didn't just answer a student's question, but helped them actually learn the topic?

That question led me to build a workflow around:

Understand → Practice → Quiz → Revise → Plan

Using Ollama and Gemma 3 4B made it possible to build that workflow around local AI instead of relying on a paid cloud API.

The project is still an MVP, and there is plenty I want to improve, especially response speed and personalization.

But I'm happy with what it has become: a working local-first learning companion built around a real student problem.

Instead of building another chatbot, I wanted to build something that helps a student move from:

"I don't understand this."

to:

"I understand it, I practiced it, I tested myself, and I know what to revise next."

That's what StudyBuddy AI is trying to accomplish.

GitHub: https://github.com/ashab683/studybudy-ai

devchallenge #weekendchallenge #hf26challenge

Intro

Mohd Ashab — Sun, 04 Oct 2026 20:54:57 +0000

happy to be the part of the dev.