DEV Community: Rohan Sonu Bablani

I Gave My Notes a Ghostly Side: Bringing Forgotten Memories Back with AI

Rohan Sonu Bablani — Fri, 30 Oct 2026 13:31:52 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine

What I Built

What if the things we write down never really disappear, even when we forget where we saved them?

For Week 4's Ghost in the Machine theme, I wanted to explore the connection between technology, memories, and the information we leave behind.

I extended NoteOcean, my open-source AI-powered note-taking application, with a dedicated experience called Ghost Notebook.

The idea is to create a separate, atmospheric notebook space inspired by forgotten memories, old thoughts, unfinished ideas, and stories worth preserving.

The ghost isn't meant to be something supernatural. It's a metaphor for the information that remains hidden inside our digital spaces, waiting to be discovered again.

Ghost Notebook brings this idea into NoteOcean through:

A dedicated ghost-themed notebook experience.
A darker, atmospheric visual identity inspired by the Ghost in the Machine theme.
A familiar rich-text editing experience with Markdown shortcuts and slash commands.
NoteOcean Bot, which uses open-weight AI to help retrieve and understand information from saved notes.
Contextual answers with references to the original pages.

The goal is simple: make forgotten information feel alive again.

Demo

Live application: https://noteocean-l222m.ondigitalocean.app/

The Ghost Notebook experience will be available as a separate section inside NoteOcean.

Code

GitHub repository:

Golixco / NoteOcean

NoteOcean 🌊

A modern, fast, AI-powered personal workspace inspired by Notion, featuring instant Markdown transformations, slash commands, real-time title search, browser-local persistence, and an AI assistant strictly grounded in your personal notes.

🌐 Live Application: https://noteocean-l222m.ondigitalocean.app/
📄 Hackathon Presentation: View Slide Deck (PDF)

📸 Screenshots & UI Preview

NoteOcean Workspace Canvas	Grounded AI Assistant (NoteOcean Bot)

Fast, zero-login workspace featuring instant Markdown transformations, editable code blocks, and real-time title search.	NoteOcean Bot answering queries strictly grounded in saved personal notes with clickable source citations.

📊 Presentation

The official NoteOcean hackathon slide deck details the problem statement, product features, editor interactions, grounded retrieval architecture, privacy boundaries, open-weight Google Gemma integration, and DigitalOcean App Platform deployment.

📄 View the NoteOcean Hackathon Presentation (PDF)

🌊 Overview & Product Vision

NoteOcean is built for software engineers, students, and writers who want the rapid, keyboard-first velocity of Markdown coupled with rich block layouts…

View on GitHub

The project is released under the MIT License.

How I Built It

NoteOcean is built using:

Next.js and TypeScript for the application.
Tailwind CSS for the interface.
Tiptap and ProseMirror for rich-text editing.
Browser localStorage for note persistence.
Backboard API for AI integration.
Google's Gemma-family open-weight model for answering questions.
DigitalOcean App Platform for deployment.

The interesting part is how the AI works.

When someone asks a question, NoteOcean searches their saved notes, retrieves relevant content, and sends the selected context to a server-side API endpoint.

Backboard routes the request to the configured Gemma model, which generates an answer using the retrieved information.

The response is displayed with references to the relevant notebook pages.

The model is not simply generating random ghost stories. It is helping users rediscover information they have already written down.

Notes are stored locally in the browser. Relevant excerpts are sent to the configured AI provider when a user asks a question. The application does not currently provide cross-device synchronization or offline AI inference.

Why Does Open Innovation Matter?

I believe open-weight AI makes it possible for developers to experiment beyond conventional chatbot experiences.

With models such as Gemma, developers can explore how AI interacts with personal knowledge, build specialized workflows, and change or replace models as their requirements evolve.

For NoteOcean, open-weight AI is an important part of the actual experience. Without it, the application would simply be a notebook with search. With it, users can ask questions in natural language and retrieve contextual answers from their own information.

Backboard provides the API integration, while the application controls retrieval, context selection, and source validation.

This approach also keeps the architecture relatively small and gives the project flexibility to evolve.

The Spirit of It

We write down hundreds of little things throughout our lives.

An idea during a late-night coding session. A conversation we don't want to forget. An address, a thought, a plan, or a story.

Eventually, we forget where we put them.

But the information is still there.

I liked the idea of interpreting a ghost as something from the past that hasn't truly disappeared. A thought that has been sitting quietly in a notebook, waiting for someone to bring it back.

That's what Ghost Notebook represents to me.

A little space where forgotten thoughts can find their way back into the present.

Prize Categories

Best Use of DigitalOcean: NoteOcean is deployed using DigitalOcean App Platform.
Best Use of Gemma: Using a Gemma-family open-weight model for contextual question answering.
Best Use of Backboard: Using Backboard's API to power the AI assistant.

Closing Thoughts

The Ghost in the Machine theme gave me an opportunity to explore something beyond another conventional productivity feature.

I wanted to build an experience that connects memories, personal knowledge, and open-weight AI.

Sometimes, the most interesting things inside a machine are the things we thought we had forgotten.

NoteOcean — Make your notes think with you.

I Built NoteOcean So I Could Stop Searching Through My Notes

Rohan Sonu Bablani — Sun, 25 Oct 2026 17:00:16 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

Ever remembered writing something down but couldn't remember which page it was on?

I built NoteOcean — an open-source, AI-powered note-taking workspace that lets you ask questions about your notes instead of manually searching through them.

NoteOcean combines a Notion-inspired editing experience with an AI assistant that retrieves relevant information from your saved pages and generates answers with source references.

Here's what it includes:

Real-time Markdown editing: Type # and watch it transform into a heading as you write.
Slash commands: Insert headings, lists, quotes, and editable code blocks using /.
Instant page search: Find notes by title with keyboard-friendly navigation.
Local-first persistence: Create and edit notes without login or registration.
NoteOcean Bot: Ask natural-language questions and get answers grounded in your saved notes, with links to the relevant sources.

The idea is simple: your notes already contain the information you need. Finding it shouldn't be another task.

Demo

Live application: https://noteocean-l222m.ondigitalocean.app/

Code

GitHub repository:

Golixco / NoteOcean

NoteOcean 🌊

A modern, fast, AI-powered personal workspace inspired by Notion, featuring instant Markdown transformations, slash commands, real-time title search, browser-local persistence, and an AI assistant strictly grounded in your personal notes.

🌐 Live Application: https://noteocean-l222m.ondigitalocean.app/
📄 Hackathon Presentation: View Slide Deck (PDF)

📸 Screenshots & UI Preview

NoteOcean Workspace Canvas	Grounded AI Assistant (NoteOcean Bot)

Fast, zero-login workspace featuring instant Markdown transformations, editable code blocks, and real-time title search.	NoteOcean Bot answering queries strictly grounded in saved personal notes with clickable source citations.

📊 Presentation

📄 View the NoteOcean Hackathon Presentation (PDF)

🌊 Overview & Product Vision

NoteOcean is built for software engineers, students, and writers who want the rapid, keyboard-first velocity of Markdown coupled with rich block layouts…

View on GitHub

The project is open source under the MIT License.

How I Built It

I built NoteOcean using Next.js, TypeScript, Tailwind CSS, and Tiptap/ProseMirror for the rich-text editor.

Pages are stored in the browser using localStorage, so users can start taking notes immediately without creating an account or setting up a database.

The AI workflow is the interesting part:

The user asks a question about their notes.
NoteOcean searches locally stored content and retrieves relevant snippets.
The question and selected context are sent to a server-side API route.
The Backboard API routes the request to the configured open-weight Google Gemma 3 27B Instruct model.
NoteOcean displays the answer alongside references to relevant pages.

The API key stays on the server, and the assistant is instructed to acknowledge when information cannot be found rather than inventing facts.

The AI integration requires an internet connection, and relevant note excerpts are sent to the configured provider when a user asks a question. Notes themselves are stored locally in the browser; they don't automatically sync across devices.

Why Does Open Innovation Matter?

I wanted the AI behind NoteOcean to be more than a black box hidden inside a proprietary application.

Open-weight models give developers more flexibility to experiment with model choices, adapt prompts, and build workflows around their specific needs.

For a tool like NoteOcean, that flexibility matters because the AI should work with the user's own context rather than provide generic answers.

Using an open-weight model also means the application isn't conceptually tied to one model forever. The underlying model can potentially be changed as requirements, costs, and capabilities evolve.

I'm using Backboard as the API layer for the current implementation rather than running inference locally, so this version isn't an offline AI application. My goal is to demonstrate how open-weight AI can make everyday software more useful while keeping the architecture small enough for an independent developer to build and maintain.

The Lazy Payoff

Imagine writing down an address, a deadline, or an idea a few days ago.

Normally, you might open several pages, search for keywords, scan paragraphs, and piece the information together yourself.

With NoteOcean, you can simply ask:

"Where did I write down that information?"

The assistant finds relevant notes, answers the question, and points you to the source.

No need to remember the exact page title. No need to manually inspect every note. Just ask and get back to what you were doing.

That's the kind of laziness I wanted to build for: not avoiding useful work, but removing the repetitive work that gets in the way.

Prize Categories

Best Use of DigitalOcean — hosted on DigitalOcean App Platform.
Best Use of Gemma — using a Gemma-family open-weight model for question answering.
Best Use of Backboard — using Backboard's API for the AI workflow.

Closing Thoughts

NoteOcean started with a small idea: what if our notes could help us find their own answers?

Building it during Hacktoberfest has been an opportunity to turn that idea into a working application and explore how open-weight AI can improve a familiar everyday tool.

The goal is to make knowledge easier to use, one question at a time.

NoteOcean — Make your notes think with you.

Stocklytics AI: An Open-Weight AI Assistant for Small Retail Businesses

Rohan Sonu Bablani — Fri, 16 Oct 2026 16:19:24 +0000

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

What I Built

Small retail businesses have to manage inventory, billing, customer records, stock alerts, and sales analytics—often across disconnected tools.

What if a shop owner could manage these operations in one place and ask questions about their business in plain language?

That's the problem Stocklytics AI is designed to solve.

It's a retail operations platform for small and medium-sized stores, combining everyday business workflows with an AI assistant grounded in store data.

What can it do?

Inventory management: Create products, adjust stock, and monitor reorder thresholds.
Billing: Record transactions and update inventory when products are sold.
Customer management: Keep customer profiles and purchase histories together.
Smart alerts: Surface low-stock products, expiry risks, high-demand items, and products that aren't selling.
Analytics: Explore sales trends, product performance, and customer insights.
AI retail assistant: Ask operational questions and receive answers grounded in inventory, transactions, customers, alerts, and analytics.

Instead of manually checking multiple screens and records, a store operator can ask questions such as:

Which products are running low?
What should I consider restocking?
Which products are performing best?
Are any items approaching their expiry dates?
What has changed in the business?

The goal is to make useful business intelligence more accessible to retailers who don't have an enterprise software budget or a dedicated data team.

Who would pay for it?

The intended customers are independent shop owners, small grocery and convenience stores, and specialist retailers.

A potential business model is a subscription priced per store, with different tiers for inventory size, analytics, or multiple locations.

This is a proposed business model, not a claim that Stocklytics AI already has paying customers. The next step would be to validate the workflow and pricing with real shop owners.

Demo

🎥 Demo video: https://youtu.be/e9IBF1SQq4Q

🌐 Deployed application: https://stocklytics-frontend-hpcb3ng5ra-el.a.run.app/

Code

💻 Repository for this submission:

Golixco / StocklyticsAI

Stocklytics AI is a retail operations platform for small and medium stores. It combines inventory management, billing, customer records, alerts, analytics, and a grounded AI assistant in one workspace built for day-to-day store operations.

Stocklytics AI

Stocklytics AI is a retail operations platform for small and medium stores. It combines inventory management, billing, customer records, alerts, analytics, and a grounded AI assistant in one workspace built for day-to-day store operations.

The project is implemented as a modular monolith backend with a modern Next.js frontend, and it is designed to run well on Google Cloud using Firestore, BigQuery, Cloud Run, and local Gemma open models.

Project Links

YouTube: https://youtu.be/e9IBF1SQq4Q?si=OrUKfv7aweuc-g7J
Main GitHub Repo: https://github.com/ShreyData/Stocklytics-AI
Cloud Run App: https://stocklytics-frontend-hpcb3ng5ra-el.a.run.app/

Hackathon Journey

The Genesis: Google Solution Challenge

Stocklytics AI was originally built for the Solution Challenge by Hack2skill and Google. In its initial version, the platform relied heavily on cloud-based AI, specifically…

View on GitHub

📂 Primary project repository referenced in the README: https://github.com/ShreyData/Stocklytics-AI

The codebase is organized into a Next.js frontend, a FastAPI backend, retail domain modules, AI services, tests, and deployment resources.

How I Built It

Stocklytics AI combines a modern web application with a retrieval-grounded AI workflow.

The technology stack

Frontend

Next.js 15
React 19
TypeScript
Tailwind CSS

Backend

Python and FastAPI
Pydantic for request and response validation
Firebase Authentication
Cloud Firestore for operational records
BigQuery for analytics and vector-search workflows

Open-weight AI

google/gemma-4-4b-it for AI response generation
sentence-transformers/all-MiniLM-L6-v2 for embeddings

How the AI assistant works

The assistant is designed around the store's actual operational context rather than functioning as a generic chatbot.

At a high level, its workflow is:

Detect the intent behind the user's question.
Load the relevant operational context.
Retrieve supporting evidence from store records and analytics.
Construct a prompt using that evidence.
Generate a response with the configured Gemma model.
Normalize and persist the response and conversation history.

For example, a question about restocking should be answered using the available stock levels and relevant business context—not simply from the model's general knowledge.

This grounding is important because retail decisions need to be connected to actual business records.

Why use a small open-weight model?

A retail assistant does not necessarily need a large, expensive, general-purpose hosted model for every question.

Using a compact open-weight model creates room to control the inference environment, experiment with model configurations, and potentially keep AI inference costs predictable as the product grows.

The application still uses cloud infrastructure for its operational data and deployment. Using an open-weight model does not automatically make the entire application offline or keep all business data on a shop owner's laptop.

Why Does Open Innovation Matter?

For a small business, AI is only useful if it is affordable, dependable, and practical to operate.

A powerful assistant can become difficult to sustain if every business question depends on a paid external AI API with costs and terms controlled by another provider.

Open-weight models offer a different path.

1. More control over costs

The developer can choose where to run inference and optimize the model and hardware configuration around the workload. That gives the product more options than relying exclusively on a third-party hosted model.

2. Less dependence on a closed AI provider

A model can potentially be replaced or reconfigured without redesigning the entire retail platform. This creates more flexibility as models, infrastructure, and business requirements change.

3. More control over AI behaviour

An open-weight model gives developers opportunities to experiment with prompting, retrieval, evaluation, and model customization for retail-specific tasks.

4. A more accessible starting point

Small retailers don't need to build their own machine-learning infrastructure. An application can bring AI-assisted workflows into the tools they already use to manage their business.

For Stocklytics AI, open innovation is about making the AI layer more adaptable and giving the product room to evolve without being tied entirely to a single closed-model API.

The objective isn't to use AI just because it's available. It's to use it where it can make a real operational task easier.

Prize Categories

Best Use of Gemma — the repository documents the use of Google's Gemma open-weight model for the retail assistant.

Closing Thoughts

A small shop may not need an enterprise data team, a complicated analytics platform, or an expensive AI subscription to begin getting value from its own business data.

It needs practical answers to practical questions.

Stocklytics AI explores how inventory management, billing, analytics, and a data-grounded open-weight AI assistant can work together to support those everyday decisions.

The long-term goal is simple: make useful AI-assisted business operations accessible to the retailers who could benefit from them.

TouchTree: I Built a Website That Tells You to Close It

Rohan Sonu Bablani — Sat, 10 Oct 2026 18:12:56 +0000

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

What I Built

What if a website's entire purpose was to convince you to stop using websites?

We spend so much time building technology to keep people engaged, scrolling, clicking, and coming back. I wanted to make something that did the opposite.

Meet TouchTree — a tiny, interactive website that reminds you to step away from your screen and experience the world outside.

It doesn't have a productivity dashboard, daily streaks, achievements, or notifications. It simply encourages you to feel the grass beneath your feet, touch the bark of a tree, smell a flower, listen to the wind, and be present for a moment.

The experience has three simple stages:

A reminder that the real world is waiting outside.
A gentle nudge: Go touch a tree.
A final screen telling you to leave the screen behind.

No progress to track. Nothing to share. Nothing else to click.

The best outcome is that you close the website.

Demo

🌱 Live website:

golixco.github.io

Code

💻 GitHub repository:

Golixco / TouchTree

TouchTree

A tiny, quiet reminder to step away from the screen and return to nature.

The Philosophy

We live in a world where hours are spent staring at screens, consuming digital streams, and conversing with artificial intelligence. Sometimes, the most helpful thing technology can do is remind us to stop using it.

"Nothing on this screen is as real as the world waiting outside.
Go out. Touch the leaves. Feel the grass beneath your feet. Smell the flowers. Listen to the birds.
Our ancestors experienced the world directly. We don't need an app to teach us how to be human.
Close this screen. Go touch a tree."

TouchTree does not exist to keep your attention. It exists to give you a reason to leave.

Why the UI is Intentionally Minimal

TouchTree rejects the conventions of modern engagement software:

No artificial reward loops: No streaks to maintain, no badges to…

View on GitHub

Feel free to explore the source and see how the experience is built.

How I Built It

I built TouchTree using plain HTML, CSS, and vanilla JavaScript.

I deliberately kept the implementation lightweight:

No frontend framework.
No backend or external API.
No accounts, analytics, or tracking.
A responsive interface with keyboard accessibility and reduced-motion support.

The visual design uses botanical colours, soft shapes, and a calm layout to make the experience feel closer to nature than a conventional productivity tool.

And here's the unusual part: I didn't use AI in the project itself.

That was intentional. I didn't want to build another system that claims to get people closer to the real world while demanding more of their attention.

Why Does Open Innovation Matter?

I want to be transparent: TouchTree does not use an open-weight model or an open-source AI framework, so I cannot claim that open AI powered this project. That means it does not meet the challenge's central technical requirement.

But the idea behind TouchTree comes from a question I think is worth asking:

Does every human problem need another piece of technology?

Open innovation gives people the freedom to experiment, understand how tools work, and build things for themselves. At the same time, building responsibly sometimes means recognising when a problem doesn't need a complicated technical solution.

TouchTree is my small, deliberately simple response to the theme. It isn't a demonstration of open-source AI; it's a reminder that technology should serve people, not become the centre of their lives.

Sometimes, the most useful thing a digital experience can do is help someone walk away from it.

My Agent Session

I used an AI coding agent to help build the project.

DevRelay session:
https://dev.to/agent_sessions/touchtree-building-a-tiny-botanical-nature-reminder-experience-qdwkkv

Prize Categories

None claimed. TouchTree does not use any partner technology, and I am not claiming eligibility for an AI or partner category.

Built solo by Golixco. 🌿

Go touch a tree.

Nudge10, An anti-doomscrolling focus companion built for a friend who struggles to stay productive for even 10 minutes.

Rohan Sonu Bablani — Fri, 02 Oct 2026 19:30:19 +0000

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

What I Built

My friend has a problem that probably sounds familiar: he can spend a ridiculous amount of time doomscrolling, but when it comes to doing something productive, staying focused for even 10 minutes can be difficult.

So I built Nudge10 specifically for him.

Nudge10 turns a large or vague task into one small, concrete 10-minute mission.

Instead of:

"Finish my DBMS assignment."

it might give him:

Open Question 1 and complete the next small step.

Then the 10-minute session starts.

While the session is active, the Nudge10 Chrome extension can detect selected distraction sites such as YouTube, Instagram, Reddit, and X. When that happens, Nudge10 generates a short AI intervention designed to get the user back to the task without guilt or a motivational lecture.

The goal isn't to make someone suddenly become perfectly disciplined.

It's much simpler:

Make the next 10 minutes easier to start.

I also gave Nudge10 to the friend I built it for, and he liked the experience enough to see himself actually using it.

Demo

Live Demo:
https://nudge10-9tli.onrender.com

Video Demo:

Code

GitHub:

Golixco / nudge10

Nudge10

An AI-powered anti-doomscrolling focus companion built for a friend who struggles to stay productive for even 10 minutes.

Nudge10 turns large, vague tasks into tiny 10-minute focus missions, protects active focus sessions from selected distraction sites, and delivers personalized AI interventions backed by durable persistent memory.

Demo

Live Web Application: https://nudge10-9tli.onrender.com
GitHub Repository: https://github.com/Golixco/nudge10
YouTube Video: https://youtu.be/sl0vxohgzsM?si=fzaGLlbtBTCoQXcc

The live production application runs on Render, serving both the FastAPI REST API and the static web client.

Note on Chrome Extension: The Nudge10 browser companion is currently distributed as an unpacked Manifest V3 extension for personal and development use; it is not published on the Chrome Web Store.

What Nudge10 Does

Task Input: The user enters a task they are avoiding or overwhelmed by.
Mission Generation: Google Gemma 3 27B breaks the task down into one concrete, low-friction starting action scoped to a 10-minute sprint.
Session Launch: The user starts the…

View on GitHub

How I Built It

Nudge10 uses Google's Gemma 3 27B, an open-weight model, as the core AI model.

I use Backboard for assistant orchestration and persistent memory, and Render for the production deployment.

The basic architecture is:

User
  │
  ▼
Nudge10 Web App
  │
  ├── Task input
  ├── 10-minute focus sessions
  └── Session lifecycle
  │
  ▼
FastAPI Backend
  │
  ▼
Backboard Assistant
  │
  ├── Persistent memory
  └── Conversation/thread handling
  │
  ▼
Gemma 3 27B
  │
  ▼
Mission / Intervention

The Chrome extension connects the browser side of the experience to the active focus session.

When the user starts a session, the extension stores the active session locally. If the user navigates to a configured distraction domain, the extension sends only the normalized domain category and the session timing needed by the backend.

For example:

{
  "site": "youtube.com",
  "elapsed_seconds": 120
}

It does not send the full URL, search query, page contents, page title, cookies, passwords, or browsing history.

Persistent memory

One of the most useful parts of Nudge10 is its ability to remember how the user prefers to work.

For example:

the user prefers 10-minute focus missions
the user prefers concise, direct instructions
the user doesn't want motivational speeches

Those preferences can influence future sessions.

Nudge10 also distinguishes useful durable behavior from temporary context. A one-off assignment shouldn't automatically become a permanent memory.

Why Does Open Innovation Matter?

A closed API could absolutely be used to build something like Nudge10.

For me, the value of open innovation is flexibility.

Nudge10 uses an open-weight Gemma model instead of making the core AI experience permanently dependent on one closed model provider. That leaves room to experiment with different models and deployment approaches, and potentially move toward more private or local inference in the future.

That matters for a product dealing with personal productivity behavior.

The browser extension also follows a data-minimization approach. It reacts to a small set of explicitly configured distraction domains instead of collecting a user's general browsing history.

Open innovation didn't make the project possible in a way that a closed API could never have done. Instead, it gave me more control over what sits at the center of the product and more freedom to change that layer later.

My Agent Session

I built Nudge10 with Antigravity and used DevRelay to preserve the development process.

The saved session includes the architecture work, Gemma and Backboard integration, persistent memory implementation, focus-session system, Chrome extension, testing, and Render deployment.

Agent Session:
https://dev.to/agent_sessions/building-nudge10-anti-doomscrolling-focus-companion-with-google-gemma-backboard-zczmyn

Prize Categories

I'm entering Nudge10 in:

Best Use of Gemma
Best Use of Backboard
Best Use of Render

Nudge10 uses Gemma as its open-weight AI model, Backboard for assistant orchestration and persistent memory, and Render for the production deployment.

I built Nudge10 for one friend, but the problem is probably much bigger than one person.

Sometimes the hardest part of productivity isn't knowing what to do.

It's staying with it for the next ten minutes.

DEV Community: Rohan Sonu Bablani

I Gave My Notes a Ghostly Side: Bringing Forgotten Memories Back with AI

What I Built

Demo

Code

Golixco / NoteOcean

NoteOcean 🌊

📸 Screenshots & UI Preview

📊 Presentation

🌊 Overview & Product Vision

How I Built It

Why Does Open Innovation Matter?

The Spirit of It

Prize Categories

Closing Thoughts

I Built NoteOcean So I Could Stop Searching Through My Notes

What I Built

Demo

Code

Golixco / NoteOcean

NoteOcean 🌊

📸 Screenshots & UI Preview

📊 Presentation

🌊 Overview & Product Vision

How I Built It

Why Does Open Innovation Matter?

The Lazy Payoff

Prize Categories

Closing Thoughts

Stocklytics AI: An Open-Weight AI Assistant for Small Retail Businesses

What I Built

What can it do?

Who would pay for it?

Demo

Code

Golixco / StocklyticsAI

Stocklytics AI is a retail operations platform for small and medium stores. It combines inventory management, billing, customer records, alerts, analytics, and a grounded AI assistant in one workspace built for day-to-day store operations.

Stocklytics AI

Project Links

Table of Contents

Hackathon Journey

The Genesis: Google Solution Challenge

How I Built It

The technology stack

How the AI assistant works

Why use a small open-weight model?

Why Does Open Innovation Matter?

Prize Categories

Closing Thoughts

TouchTree: I Built a Website That Tells You to Close It

What I Built

Demo

Code

Golixco / TouchTree

TouchTree

The Philosophy

Why the UI is Intentionally Minimal

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories

Nudge10, An anti-doomscrolling focus companion built for a friend who struggles to stay productive for even 10 minutes.

What I Built

Demo

Code

Golixco / nudge10

Nudge10

Demo

What Nudge10 Does

How I Built It

Persistent memory

Why Does Open Innovation Matter?

My Agent Session

Prize Categories