🌟 Put Your First Project in the Spotlight!

[ghostinhershell]

Hey there, GitHub community! 👋

Are you new to GitHub? Have you recently created your first project and are excited to share it with the world? We want to hear from you!

Starting a new project can be both thrilling and a bit daunting, but every great developer starts somewhere. Whether it's a simple "Hello World" script, a fun personal website, a creative game, or a unique solution to a problem, your project matters, and we'd love to showcase it!

Each week, for the month of September, our team will choose one developer and their project to highlight for the rest of the community. This highlight post will be pinned to the New to GitHub category and will put a spotlight on you and your project!

👉 How to Share Your Project:

1. Post a link to your repository in the comments below.
2. Include a brief description of your project and what inspired you to create it.
3. Don't forget to add any special features or challenges you faced!
4. Finally, don't forget to share a little about yourself as well! What brings you to GitHub? What are your goals? Etc! If you and your project are chosen for our Spotlight post, the information you share here will be shared with the rest of the community as well 🎤

By sharing your first project, you can:

• Get valuable feedback from fellow developers.
• Gain visibility and connect with like-minded individuals.
• Learn and grow through constructive suggestions and support from the community.
• Earn your own dedicated spotlight post that will be pinned in the New to GitHub community ⭐

Not sure if you should share your project? Just remember, no matter how big or small, each contribution is a step towards becoming a better developer. So, don't be shy—share your hard work with us! Let's celebrate your first steps!

Looking forward to seeing your amazing creations! 🚀

Happy coding! 💻

[Injamul2k24]

Thanks for you guidance

[Injamul2k24]

Repo : https://github.com/Injamul2k24/Movie-recommendation-system-

This project is a Movie Recommendation System built using Python, Numpy, Pandas, Scikit-learn, and Matplotlib. The system leverages machine learning techniques to suggest movies based on the user's preferences. The front-end interface is developed using the Streamlit library, providing an interactive and user-friendly experience.

Features
Content-Based Filtering:
Recommends movies similar to the ones a user likes based on movie metadata.
Data Handling:
Efficiently processes and analyzes large datasets using Pandas.
Vectorization:
Utilizes the CountVectorizer from Scikit-learn to convert movie descriptions into vectors for similarity calculations.
Similarity Calculation:
Employs cosine similarity to find movies that are most similar to the user's selected movie.
Visualization:
Matplotlib is used for visual representations of data and recommendations.
Interactive UI:
A clean and intuitive user interface powered by Streamlit, allowing users to search for movies and get instant recommendations.

How to Run
Clone the repository to your local machine.

Install the required dependencies using pip install -r requirements.txt.

Run the Streamlit app using the command streamlit run app.py.
Open your browser and go to http://localhost:8888/tree to interact with the recommendation system.

Dataset
The dataset used in this project is publicly available from IMDb, containing metadata of thousands of movies, including titles, genres, overviews, and popularity metrics. This rich dataset allows the recommendation system to generate personalized movie suggestions based on a variety of factors.

[Manaskhurana]

Hello shadows , i have make a fun website for Anime lover of its a wallpapers app and website if any bug is found in my website please message me

https://www.animepixels.com/

[evan-cobey]

rethinking badges in 2025 — this is my “badge.io but modern” view: minimal, clean, editable SVGs — no external deps
https://github.com/ogcae/lastrepos

[noorizanbintiabdulmalik85]

Repo : https://github.com/Injamul2k24/Movie-recommendation-system-

This project is a Movie Recommendation System built using Python, Numpy, Pandas, Scikit-learn, and Matplotlib. The system leverages machine learning techniques to suggest movies based on the user's preferences. The front-end interface is developed using the Streamlit library, providing an interactive and user-friendly experience.

Features Content-Based Filtering: Recommends movies similar to the ones a user likes based on movie metadata. Data Handling: Efficiently processes and analyzes large datasets using Pandas. Vectorization: Utilizes the CountVectorizer from Scikit-learn to convert movie descriptions into vectors for similarity calculations. Similarity Calculation: Employs cosine similarity to find movies that are most similar to the user's selected movie. Visualization: Matplotlib is used for visual representations of data and recommendations. Interactive UI: A clean and intuitive user interface powered by Streamlit, allowing users to search for movies and get instant recommendations.

How to Run Clone the repository to your local machine.

Install the required dependencies using pip install -r requirements.txt.

Run the Streamlit app using the command streamlit run app.py. Open your browser and go to http://localhost:8888/tree to interact with the recommendation system.

Dataset The dataset used in this project is publicly available from IMDb, containing metadata of thousands of movies, including titles, genres, overviews, and popularity metrics. This rich dataset allows the recommendation system to generate personalized movie suggestions based on a variety of factors.

IMDb,ya..good...ok thanks.

[loujr]

Side Fun Project - Forward2 A URL Shortener

Repo: https://github.com/loujr/forward2

Forward2 is a url shortener that takes long links and makes them shorter similar to any service you would find online. Everyone aspires to have living, useful code. I wanted to make my barrier of entry very low. This started out as a simple print to file minimal viable project and has ballooned to increasingly complex functionality. This project now utilizes two separate python services one to generate short links and the other to redirect you to the correct page. I have split the components to an API with the option of adding UI elements later. This is further integrated with another project you can find on my profile called MikeTysonBot.

I am a GitHub Staff member who works in our Ecosystems Teams. My speciality is API and integration hence why this program has API components. My goal is to work more on our platform development and continue to contribute code to our monolith.

[ghostinhershell]

Thanks so much for sharing @loujr! 🚀

[MikeyBeez]

Hello GitHub community!
As a retired technical architect, I'm dedicating my final years to contributing to the rapid progress of AI research. I'm excited to share my latest project, Ollama Agents, https://github.com/MikeyBeez/Ollama_Agents a repository that leverages free components for AI agent development.
What sets this project apart is its focus on local development, utilizing Ollama and Llama 3.1 8B to create a remarkably intelligent agent. The smart_agent.py program is capable of tackling complex research projects with impressive results.
I'd love for you to explore the repository and share your thoughts:
Unlike other frameworks like Crew or Langchain, Ollama Agents is designed specifically for local development, offering a unique approach to AI agent development.
Your feedback and suggestions are invaluable in helping me refine this project. Let's work together to accelerate AI advancements!
Feel free to modify it if you'd like, and I hope you get some great feedback on your project! https://github.com/MikeyBeez/Ollama_Agents

[MikeyBeez]

I'm glad you like it. I think it's a unique project, because it doesn't require any paid cloud services. Most big projects are backed by for-profit AI companies. But AI is too important to be just for the wealthy. I'm not aware of any agent frameworks like mine. BTW, today I added a graph knowledgebase.

[PIYUSH1525]

love this

[evan-cobey]

I wanted ultra-minimal GitHub cards with OKLCH tokens — this is my personal dark preset in production
https://github.com/ogcae/lastrepos

[mars3jx]

this is great :)

[gm3dmo]

I've a little project that showcases the GitHub API's. It's called The Power it's useful for teaching, learning and demonstrating how GitHub's API's work. The idea is to keep things simple by demonstrating an API endpoint in a single script such as create-repo.sh

[ghostinhershell]

Thanks so much for sharing @gm3dmo! For anyone interested in getting started with The Power, check out @loujr's post here: The Power: Scripts for Comprehensive Automated API Testing 🚀

[MikeyBeez]

Thanks, @gm3dmo. This may come in handy. I plan on having my agent write code eventually, so interacting with github is a necessity.

[OlaiwonAbdullahi]

I just finished building a replica of
Swyft Pay
Website using React JS and TailwindCSS!🎉

https://github.com/OlaiwonAbdullahi/SwyftPay-Replicate

[ghostinhershell]

Love seeing the progress here, kudos to you @OlaiwonAbdullahi! 🎉

[PIYUSH1525]

nice

[prince272]

👋 Hey GitHub community!

I’m excited to share my latest project: NextSolution V2! 🚀

Project Overview:
NextSolution V2 is a starter template that integrates an ASP.NET API 🖥️, Next.js (React) 🌐, and Expo (React Native) 📱. It’s crafted to provide a robust foundation for full-stack application development with dynamic APIs and responsive web and mobile interfaces.

What Inspired Me:
After the initial release of the NextSolution template on NuGet, I saw a steady rise in both NuGet downloads and GitHub stars. The positive feedback motivated me to develop V2, featuring an enhanced codebase and improved organizational patterns.

Special Features:

✨ ExpoApp Project:

• Added ExpoApp with React Native Paper and NativeWind.
• Customizable sign-up, sign-in, and reset-password screens.
• Integrated Prettier for consistent code formatting.

✨ NextApp Project:

• Added NextUI for UI components and Prettier for code formatting.
• Customizable sign-up, sign-in, and reset-password modals.

✨ WebApi Project:

• Implemented IdentityController and configured Ngrok.
• Added Twilio and MailKit for communication services.
• Integrated RazorViewRender for HTML views, OAuth for authentication, and JWT-based authentication.

🔄 Updates:

• Refined project structure to better support modular and technology-specific needs.

Getting Started:

1. Install the template: dotnet new --install NextSolution.Template::2.0.0
2. Create a new project: dotnet new nextsln -o YourProjectName --include-expo --include-next --configure-ngrok

For more details, check out the GitHub repository. If you find it valuable, please consider giving it a star ⭐!

About Me:
I’m Prince, a developer passionate about creating full-stack applications and exploring new technologies. I’m here on GitHub to connect with fellow developers, share knowledge, and collaborate on exciting projects. My goal is to continue building tools that make development easier and more enjoyable.

Looking forward to your feedback and engaging with the GitHub community!

Happy coding! 💻

[Fabii-p]

Hey guys!
This is my first project: https://github.com/Fabii-p/projeto-imersao-alura
I'm a lawyer, and just started learning about dev six days ago.
Feel free to contact me and sand me any suggestions on How to improve my project.

[lunalobos]

Nice, it seems you use pure JS, HTML, and CSS. I recommend that the next step be learning Vue and Tailwind.

[Fabii-p]

Thank you for your feedback... I'l definitly look It up.

[ghostinhershell]

This is a beautiful project, thank you so much for sharing @Fabii-p, kudos to you for starting a new path and we're all really excited to see where this journey will take you 🚀

[Fabii-p]

Thank you so much, I'm very excited too.

[LikhithaKakuluri]

https://github.com/LikhithaKakuluri/My_Portfolio

A Beautiful Portfolio with enhanced feature , everybody can go through it , and provide me your valuable feedback .

[lunalobos]

Wow, it looks awesome! that’s a serious flex. Just a few minor observations: maybe the background could be a bit smoother (something like this) and you could prevent the skills from covering the navigation bar by using a different z-index for the navbar class.

[LikhithaKakuluri]

thanks for your valuable words. i will make sure to change

[ghostinhershell]

Love to see the collaboration here! Thanks so much for sharing @LikhithaKakuluri and thanks so much for lending a helping hand @lunalobos! 🌮

[lunalobos]

chessapi4j

Hi! I started this project last year and have been improving it, fixing errors and bugs along the way. This year, I picked it up again and made several improvements. It is a Java library that allows you to work with chess-related operations.

The library includes features like verifying legal positions and legal moves, working with PGN files, generating legal moves from a given position, and handling FEN strings.

Since chess is a game, many natural abstractions arise. Developing a relatively fast move generator was particularly challenging (I believe Java can't offer much more speed), while also ensuring it passed the standard unit tests for a move generator. The library includes many features, and perhaps I'm not doing a good job of describing them all.

I am just an amateur programmer, and I wrote this library because I wanted to create a server for an online chess website. As I started, I realized it was better to develop it as a separate project from the server, and then I realized it didn't have to be proprietary software, so I decided to share it, which led me to GitHub in the first place.

[Husnainmirza0071]

03257531376 jazzcash Sent from my Galaxy -------- Original message --------From: lunalobos ***@***.***> Date: 09/09/2024 1:04 am (GMT+05:00) To: community/community ***@***.***> Cc: Husnainmirza0071 ***@***.***>, Comment ***@***.***> Subject: Re: [community/community] 🌟 Put Your First Project in the Spotlight! (Discussion #137746) Wow, it looks awesome! that’s a serious flex. Just a few minor observations: maybe the background could be a bit smoother (something like this) and you could prevent the skills from covering the navigation bar by using a different z-index for the navbar class. —Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you commented.Message ID: ***@***.***>

[Husnainmirza0071]

Sent from my ***@***.*** -------- Original message --------From: lunalobos ***@***.***> Date: 09/09/2024 1:04 am (GMT+05:00) To: community/community ***@***.***> Cc: Husnainmirza0071 ***@***.***>, Comment ***@***.***> Subject: Re: [community/community] 🌟 Put Your First Project in the Spotlight! (Discussion #137746) Wow, it looks awesome! that’s a serious flex. Just a few minor observations: maybe the background could be a bit smoother (something like this) and you could prevent the skills from covering the navigation bar by using a different z-index for the navbar class. —Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you commented.Message ID: ***@***.***>

[jayantur13]

Hello, community.

I recently made a GitHub Bot named OctoPaji (refers to Github's Octo and Paji is Indian, Punjabi term meaning brother).

The bot posts gif on various Github events in a repository like opening and closing issue and PR, search for similar issue/PR, auto assign a maintainer and label the issue for now.

I am thinking of adding more. You can checkout the project in OctoPaji - GitHub Bot in GitHub Marketplace. Shoutout to @ThisIs-Developer for guiding me on the GitHub Marketplace Publishing Issue. You can also checkout more of projects in my GitHub profile.

Thanks for your attention.

[ThisIs-Developer]

I appreciate it! Keep up the great work

[ghostinhershell]

Thanks so much for sharing @jayantur13 and shout out to @ThisIs-Developer for lending a helping hand to a fellow developer! 🚀

[tarikrital]

Astroboard the Umbraco dashboard

Repo: https://github.com/wpplumber/astroboard

Get insights of your contents, assets and members while using the friendly CMS.

No special blockers except Angular JS used by the CMS for services.

I'm a web programmer focused on Front-end with experience in developing Umbraco websites and packages also web apps using Tailwind CSS, SPA and PWA in Vue (+ Quasar Framework, Vuetify), AstroJS.

Professional aligned with agile methodology. 📆

[vaishaliisingh]

Hello Everyone! My name is vaishali singh, i am a student from India and i recently made a project on simple blockchain implementations.

project repo link: https://github.com/vaishaliisingh/Blockchain-fundamentals

Project Description:
This project is a basic implementation of a blockchain using Python. The blockchain system includes essential features like creating blocks, managing transactions, and implementing a simple Proof of Work algorithm. The project demonstrates how a blockchain can be structured and maintained, showcasing key concepts such as mining, hashing, and validation.

Key Features:
Genesis Block Creation: The blockchain starts with a genesis block, which is the first block in the chain.
New Block Generation: Users can mine new blocks by solving a computational problem, adding them to the chain.
Transaction Management: The system allows users to create and store transactions, which are included in the next mined block.
Proof of Work: A basic Proof of Work algorithm is implemented, requiring miners to find a valid hash with specific characteristics (e.g., starting with four leading zeros).
Blockchain Validation: The blockchain can be validated by checking that each block’s hash and the previous block’s hash are consistent and correctly calculated.

How It Works:
The blockchain begins with the creation of the genesis block.
Users can add transactions, which are stored in a list until a new block is mined.
When mining a block, the Proof of Work algorithm requires finding a nonce that generates a valid hash.
Once mined, the block is added to the blockchain, and the transactions are cleared for the next block.
A simple validation method ensures the integrity of the entire blockchain by checking the hashes of all blocks.

Technologies Used:
Python
Hashlib for SHA-256 hashing
JSON for encoding block data
Basic concepts of blockchain, including Proof of Work and transaction handling

This project serves as a foundational demonstration of how blockchain technology works, making it an excellent starting point for understanding more complex blockchain systems and applications.

[TarikTornes]

fan_control (https://github.com/TarikTornes/fan_control)

Hey everyone I am an undergrad student from Luxembourg and I am new to the OpenSource world and to C and therefore decided to create a small project which is a daemon called fan_control.

It started when I wanted to build my own little DIY homeserver with a Raspberry Pi 4B, and decided to cool it with a small cooling system which uses a fan. It bothered me that the only option was to run it permanently on full speed, so I decided to build my own little project in order to fix my issue and improve my C knowledge by coding a small daemon which checks the temperature of the Raspi and adjusts the fan speed according to the temperature.

I am sharing it with you to hopefully get feedback and suggestion according to my style of coding, efficiency and everything about creating a OpenSource project, since I am quite new to this.

Feel free to give me feedback of any kind, wether it is a comment below or as an issue on my repo.
You can find the project here

PS: You can also give feedback to my other repos.

[mohammmad357]

Maze Signal — learning weighted pathfinding with Python, C and a browser demo

Hi everyone! I'm returning to sharing my programming projects publicly and would like to make them useful for people learning algorithms. Maze Signal is a small, AI-assisted open-source project comparing Dijkstra and A* on weighted grids.

• Repository: https://github.com/mohammmad357/maze-signal
• No-install demo: https://mohammmad357.github.io/maze-signal/
• Current release: https://github.com/mohammmad357/maze-signal/releases/tag/v1.2.0

The problem that inspired it: the route with the fewest steps need not have the lowest cost. The demo shows route cost and expanded nodes; those numbers are not a Python/C runtime benchmark. A recent addition exports the edited map into the same text format used by the Python and C command-line implementations. Keeping the three implementations consistent has been the main challenge.

I'm looking for 5–10 independent first-use testers. Please open the demo before reading the README and tell me:

1. Where did you get stuck or feel confused?
2. What was useful?
3. What was missing?

A one-off, optional invitation to fellow participants whose shared projects overlap with this work:

• Python/C and command-line tools: @therealbenflynn @herlonventura @dev-celia @TarikTornes
• Teaching, algorithms and explaining code: @kevin-lozada-santos @lunalobos
• Browser interfaces and games: @zhao1411935907 @Dotoryman @michalstankiewicz4-cell
• Interpreting measured results: @starchlorddotcom

If this fits your interests and you have a couple of minutes, a brief first impression here or a reproducible issue in the repository would help. No obligation to respond. The project and this invitation were prepared with AI assistance; independent usability feedback is still pending. My goal is clearer explanations and a maintainable learning tool. Thanks for reading!

[mohammmad357]

Creator Rail — looking for independent demo testers

Hi everyone! I'm sharing Creator Rail, an MIT-licensed Python/Web prototype for a Persian/English creator support page, developed with AI assistance. I wanted to explore a clearer, self-hostable support flow with direct-to-creator payments as the eventual goal.

Repository and setup: https://github.com/mohammmad357/creator-rail

The current release is a local synthetic demo: no real funds, wallet connection, API keys or wallet secrets are needed. It includes invoice/QR previews, exact satoshi amounts, payment-state scenarios, CSV export and backup/restore. Keeping partial payments, manual status changes and duplicate events from being mistaken for confirmed payments was a key challenge. The BTCPay adapter has mock-based tests; live processor integration is still pending. Mainnet is disabled.

I'm looking for three independent first-use testers. Please follow the README's demo setup without my help, switch languages and try a few synthetic invoice scenarios. Then tell me:

1. Where did you get stuck or feel confused?
2. What was useful, and what was missing?
3. Which OS/browser did you use, and can you reproduce any problem?

A reply here or an issue in the repository would help. Please don't send money or share secrets/private data. This is an experimental prototype, not a live donation service. No obligation to respond; honest criticism is welcome. Thanks!

[Rajkaran-122]

please star on this one too https://github.com/Rajkaran-122/Finance_Learnx_backend i will also star back

[6bjjjj]

I'm sharing DraftScope, an Apache-2.0 research library for reproducible NVIDIA LLM inference experiments: https://github.com/6bjjjj/draftscope

The project grew out of experiments on how numerical uncertainty in a speculative verification block triggers an expensive dense output-head fallback. Its experimental extension conditions that decision on the prefix that actually determines greedy acceptance. It uses existing vLLM, DFlash2 and Marlin components, with CUDA conditional graphs for the branch.

Practical features include a Transformers baseline, capability-based FP16/BF16 selection, pinned model revisions, input hashes, incremental result saving and a bounded CLI worker. English/Arabic documentation and the original experiment records are included.

One useful lesson from the A100 experiments: a component improvement does not automatically translate into a large model-level gain. The captured head study reduced fallback calls from 33 to 9 and head wall time by 18.95%; the later whole-model V8 result was 0.922% lower request latency. Different numerical guards were used, and the independent quality audit remains inconclusive. The refactored package still needs GPU integration testing.

This is an AI-assisted research project, with attribution and limitations in the repository. My goal is to make the experiments inspectable and invite reproducible feedback, rather than claim a new state-of-the-art decoder.

Feedback that would help: is the separation between CPU-tested interfaces and historical GPU evidence clear, and what minimal numerical or compatibility test would you add before enabling the optional head on another NVIDIA architecture? There's a GPU-report issue template for anyone who wants to contribute a result.

[edhard-remakes]

Super Street Fighter Turbo Remake — a free fan project for Windows

Hi everyone! I’m edhard-remakes, a solo gaming enthusiast and a newcomer to GitHub. I enjoy revisiting classic games, and I’d like to share my Street Fighter II fan project with the community.

The game uses IKEMEN GO, with a custom interface and a roster of 16 characters and their stages. I’ve just published the first experimental Windows build, v0.1.0. It is still a work in progress.

This is a free, non-commercial fan project. My goal is to share what I make, learn from others and encourage fellow fans to create things too. I’m also using AI tools to support the creative and experimental process. Credit goes to the IKEMEN GO team and the community authors whose characters, stages, sounds and other resources form part of the project. This is an unofficial project, with no affiliation with Capcom.

• Project: Super Street Fighter Turbo Remake
• Windows download: Experimental release v0.1.0 — the game is the .exe under Assets (about 1.23 GB).
• Feedback and bugs: Open an issue. I added Portuguese templates for bug reports and opinions or suggestions.

I’d love to hear what you think of the visuals, controls and combat feel. If you try it and find a bug, please tell me what happened and how to reproduce it. You don’t need to be a programmer to help — playtesting and honest feedback are very welcome!

Thanks for taking a look, and I’m looking forward to meeting other fighting game fans and creators here.

[FishCityBee]

Hi everyone! I'd like to share dsh-mcp-bridge, my first open-source project that I'm actively maintaining and sharing with others.

I built it to make it easier to delegate tasks from Claude Code, Codex and other compatible MCP clients to DeepSeek Harness. Repeated calls can leave a lot of sessions to manage, and I wanted model, workspace and permission settings to remain visible and controllable in the Harness UI.

The plugin adds an in-app control panel, MCP task status and results, reusable sessions, and automatic session archiving. A running DeepSeek Harness host is required. Version 0.2.4 is available as a preview; model calls, toolbar configuration inheritance and read-only permissions have been tested.

Repository: https://github.com/FishCityBee/dsh-mcp-bridge
Release: https://github.com/FishCityBee/dsh-mcp-bridge/releases/tag/v0.2.4

If you already use Harness with an MCP client, I'd appreciate feedback on setup friction and which tasks you find useful to delegate. The repository includes English and Chinese documentation.

[Rajkaran-122]

please star on this one too https://github.com/Rajkaran-122/Finance_Learnx_backend i will also star back

[edhard-remakes]

Tenchu Shadow Reborn — an experimental fan project for Windows

Hi everyone! I’m edhard-remakes, a newcomer to GitHub and a fan of classic games. I don’t know how to code; AI tools are what made it possible for me to start experimenting with game creation, and I want to be open about that.

My project revisits Tenchu: Stealth Assassins, exploring HD textures and changes to the camera and combat. Current experiments include a free camera, target lock, guard and timed parry, stamina and posture break, heavy attacks and stealth rolling. It is still a work in progress, and the full campaign has not been validated.

I do this for fun, with no commercial goals. I’ve enjoyed many community creations over the years, and I’d like to give something back by sharing my progress and learning from other fans. Credit belongs to the original game’s creators and the authors of the tools and resources involved. This is an unofficial fan project.

• Project and development overview: Tenchu Shadow Reborn
• Feedback, ideas and bugs: Open an issue

I’d love to meet other Tenchu fans and hear your thoughts. You don’t need programming experience to participate — opinions, ideas and honest feedback are welcome!

[Rajkaran-122]

please star on this one too https://github.com/Rajkaran-122/Finance_Learnx_backend i will also star back

[manzoorshoro]

ntroducing MoneyMate My Android Finance App

I built MoneyMate, an Android app designed to make everyday money management easier. It helps users track their income and expenses in one place.

I developed it using Kotlin and Jetpack Compose, with help from AI tools.

🔗 GitHub Repository: https://github.com/manzoorshoro/MoneyMate

I'm sharing my project with the community and would appreciate your feedback and suggestions!

[Rajkaran-122]

please star on this one too https://github.com/Rajkaran-122/Finance_Learnx_backend i will also star back

[MDowser]

Hi everyone! 👋

I'd like to share MDowser, an open-source project I've been building: https://github.com/MDowser/MDowser

🌐 The plain-text web, rendered natively.

AI agents are increasingly able to request Markdown directly from compatible websites through HTTP content negotiation, using headers such as Accept: text/markdown. This makes web content cleaner, easier to parse, and free from much of the HTML overhead.

But here's a thought: why should AI agents be the only ones to enjoy a cleaner web?

That's part of the idea behind MDowser: a minimal, cross-platform desktop browser designed to let humans enjoy the plain-text web too.

🛠️ What makes MDowser different?

• Native Markdown rendering, including tables, footnotes, task lists, callouts, and code blocks.
• Browse Markdown over HTTPS or open local files.
• No webview, JavaScript, cookies, or cache.
• A real desktop-browser interface, with tabs, bookmarks, history, and light and dark themes.
• Available for macOS, Linux, and Windows.

The project is written in Rust and is designed around a deliberately small, auditable network and rendering model.

💡 Why build another browser?

Modern browsers are incredibly powerful, but sometimes all I want is to read a document without all the machinery surrounding it. As more websites make their content available in Markdown, I believe there's room for a simpler, more readable way for people to access it.

MDowser is my attempt to explore that idea. It's an independent open-source project, still evolving, and I'd genuinely appreciate feedback from other developers.

Have you experimented with Markdown content negotiation, or do you think a dedicated Markdown browser has a place in the future of the web?

I'd love to hear your thoughts! 🚀

[keylorpineda]

HealthyCropping: Offline-First Crop Disease Diagnosis for Android

Repository: https://github.com/keylorpineda/healthycropping

Background

Smallholder farmers in Central America (starting in Costa Rica with coffee, cacao, and banana) often operate in rural regions with intermittent or absent cellular connectivity and limited access to professional agronomists. Misdiagnosis of prevalent diseases (such as Coffee Leaf Rust, Cacao Moniliasis, or Banana Black Sigatoka) can lead to devastating harvest losses or improper chemical interventions.

HealthyCropping is an open-source, non-commercial, offline-first mobile application designed to bridge this gap by bringing computer vision and retrieval-grounded guidance directly to edge Android devices.

Current Status: Active Development

The project is structured in 9 execution phases and is actively under development:

1. Completed Milestones (Phases 1 to 5):

• Machine Learning: Three dedicated models (Coffee Leaf, Cacao Pod, Banana Leaf) trained on curated, perceptually deduplicated datasets with grouped 70/15/15 splits.
• Edge Quantization: Optimized INT8 and FP16 TensorFlow Lite models deployed with verified preprocessing parity (224x224 RGB).
• Mobile Client: Expo React Native application with local SQLite storage, on-device TFLite inference integration, and localized Spanish UI.
• Core API: Initial FastAPI foundation with strict linting, type safety, and testing suites.

2. In Progress (Phases 6 and 7):

• Cloud Synchronization: FastAPI endpoints for plot and farm management, cloud sync, and Cloudinary media processing.
• Agronomic Advisory: Retrieval-Augmented Generation (RAG) pipeline powered by Gemini, grounded in institutional literature (ICAFE, CATIE, CORBANA) to prevent hallucinated treatments.

Open Contribution Opportunities

We are actively welcoming open-source contributors across multiple domains:

• Mobile and Edge Client (React Native, Expo, TypeScript, SQLite): Offline-first synchronization engine between SQLite and the remote API, latency optimization on low-end Android hardware, unit and integration testing.
• Backend and Infrastructure (Python 3.12, FastAPI, Firebase Admin, Cloudinary): Farm, plot, and scan synchronization endpoints, token validation, client authorization boundaries, and asynchronous media pipelines.
• Machine Learning and Quantization (TensorFlow, TFLite, Python): Quantization-aware training (QAT), dataset expansion and validation against field-collected samples, mobile inference benchmarks.
• Agronomy, Field Validation, and RAG: Ingestion and chunking of certified Central American agronomic guides, safety evaluation of treatment recommendations, and real-world field testing under varied lighting and canopy conditions.

Getting Started

The project enforces strict code quality gates (mypy, ruff, pytest, ESLint, TypeScript strict) and conventional git workflows:

• Issues and Roadmap: https://github.com/keylorpineda/healthycropping/issues
• Architecture Overview: https://github.com/keylorpineda/healthycropping/blob/develop/docs/ARCHITECTURE.md
• Implementation Plan: https://github.com/keylorpineda/healthycropping/blob/develop/docs/PLAN.md
• Contributing Guidelines: https://github.com/keylorpineda/healthycropping/blob/develop/CONTRIBUTING.md

Feedback, issue discussions, and pull requests are welcome.

[yie793379z-prog]

StreamSplit turns UTF-8/SSE bugs that depend on chunk boundaries into small, offline regression tests.

In the included demo, the same SSE bytes containing 你好 🌊 café decode correctly unsplit but become ���好 🌊 café when split inside 你 and passed through a deliberately broken decoder. Streaming decoding preserves the text.

I'm sharing this early-stage project from my open-source project series: https://github.com/yie793379z-prog/streamsplit

The MIT-licensed core has no dependencies, API keys, or live-server requirements. With Git and Node.js 22 or later, try:

git clone https://github.com/yie793379z-prog/streamsplit.git
cd streamsplit
node examples/utf8-demo.js

It includes saved failure replay and an optional adapter pinned to eventsource-parser 4.1.2 (the adapter requires Node.js 22.12+ and a one-time dependency install). It checks final event results, not delivery timing or reconnection. If you maintain an SSE consumer, is the adapter guide enough to test yours? Which missing case would help?

The project and this introduction were developed with AI assistance.

[mhaprq-cmd]

Introducing Scanix QR — An Android QR Code & Barcode Scanner

I've developed Scanix QR, a native Android application designed to scan QR codes and traditional barcodes with a simple, user-friendly interface.

Key features:

• QR code and barcode scanning
• Scanning supported codes from images
• Scan history and search
• Copying and sharing scan results
• Flashlight support
• Arabic and English interface
• Light and dark themes

I'm sharing the project to introduce the app and welcome constructive feedback from the GitHub community.

Repository: https://github.com/ymflap/Scanix-QR

Releases and APK downloads: https://github.com/ymflap/Scanix-QR/releases

Feedback about usability, compatibility, and potential improvements is welcome.

[IMAN-Py]

Hi, I'm Iman, the maintainer of Kook Recovery. I recently published this project after working on recovering photos and videos from .kook ransomware files.

Repository and downloads: https://github.com/IMAN-Py/kook-recovery

It attempts partial reconstruction, not decryption: for some STOP/Djvu-damaged media, useful image or video data survives beyond the encrypted beginning. The project provides an Apple Silicon macOS app and a standalone Windows command-line download, with local processing and no media uploads.

The recovery engine builds on PhotoPhoenix by Ahmed Raza Bhatti, with additions for .kook-only input handling, detecting media with missing/misleading extensions, automatic camera-reference learning, and extracting intact embedded images when full reconstruction fails. Development has included AI assistance. Original project code is MIT-licensed; PhotoPhoenix remains under its PolyForm Noncommercial license.

A major challenge has been making the limits clear: missing encrypted data cannot simply be recreated, some files yield only a preview, and many files cannot be recovered. The repository includes tests and a synthetic Windows demo, so you can explore the workflow without sharing private photos.

Try it on copies. The CLI writes to a separate output folder and preserves inputs. The macOS app's normal in-place recovery deletes the selected .kook source after validating its replacement; embedded-image salvage keeps the source.

I'd welcome feedback on the recovery messages, packaging, and reproducible failures using synthetic or non-sensitive examples. My goal in publishing it is to make this workflow easier to try and easier to inspect.

[xuantingjiang39]

Hi everyone! I'd like to share TripMate (旅伴), one of my early public GitHub projects: a Chinese-first travel planner focused on trips with family and older parents.

The idea is to make a trip easier to discuss with family: put the daily activities, transport notes, budget and rest breaks into an editable itinerary, then share a PDF before leaving. There are Windows and Android downloads, plus a web workspace.

Repository and screenshots: https://github.com/xuantingjiang39/TripMate

The Windows client uses Electron, Android uses React Native / Expo, and the backend is Node.js with configured DeepSeek, AMap and Meituan integrations. AI coding assistance has been part of building and iterating on the project. One useful challenge has been protecting saved itineraries when sync responses are malformed or a connection fails.

It is still a beta: the interface is primarily Chinese; AI generation uses the backend's credit policy; some checks use mocked services, and real payments, hotel inventory and GPS/device compatibility have not been comprehensively verified. The source is public, but a project-wide license has not yet been selected.

My goal is to keep improving it through practical feedback and small fixes. I'd especially appreciate feedback on installation, itinerary readability, and what information would make this more useful when travelling with older family members. English or Chinese feedback in the repository is welcome. Thanks for taking a look!

[ZIJIE-XUE]

PánCè 2.0 — a bilingual investment risk research lab

Hi everyone! I'm sharing PánCè, a personal finance-learning project focused on making investment assumptions and model limitations easier to inspect.

• Repository: https://github.com/ZIJIE-XUE/Pance-AI-Powered-Investment-Decision-Making-Platform
• Live demo, no account required: https://pancealpha.tech

It connects a 12-question risk profile, a four-asset portfolio, Monte Carlo scenarios, hypothetical stress tests and PDF/JSON research snapshots. The interface and reports support English and Chinese.

The distinctive module is a 0–100°C temperature-driven DCA lab. It compares temperature-based investing, regular monthly investing, a lump sum and cash using the same upfront budget. Keeping those cash flows comparable, lagging signals by a month and matching exported reports to the current inputs have been important implementation challenges.

The stack is Python with NumPy/Pandas/SciPy, a plain HTML/CSS/JavaScript frontend, Flask/Waitress on Render, and ReportLab for PDFs. Development and this introduction used AI assistance. The application's basic reports use deterministic templates, not an LLM.

The default data are clearly labeled synthetic. CSV research samples and explicitly requested delayed public prices are separate modes. The project is for learning and risk research, not investment advice or evidence of real investment performance.

My goal is to improve this into a useful, reproducible learning tool through feedback. I'd especially appreciate a first-time user's perspective: is the difference between synthetic data, historical observations and simulation assumptions clear, and where does the workflow become confusing? Feedback here or a reproducible issue in the repository would help. Thanks for taking a look!

[hvalo596]

Hi everyone! I'm sharing an AI-assisted personal research prototype for Chinese A-share equities.

Repository: https://github.com/hvalo596/a-share-research
Readable demo, no login required: https://hvalo596.github.io/a-share-research/current/oversold-deepening/index.html

The question is simple: when a stock was previously active and rising, then declines sharply, is there a measurable rebound opportunity? The site makes the hypothesis inspectable: frozen rules, dated signals, entry/exit simulations, candlesticks, failed executions and nine exit comparisons. A daily top-four view uses a total simulated budget of CNY10,000, rather than assigning that amount to every signal.

The important finding so far is negative: the stricter version still does not demonstrate a positive edge. Historical universe, corporate-action and execution gaps are disclosed; these are exploratory simulations, not live performance or investment advice.

The interface is primarily Chinese. A useful engineering challenge was loading only the selected rule and stock chart, while keeping cases shareable and list/chart scrolling independent. I'd especially appreciate feedback on confusing comparisons, reproducible calculation errors, and slow loading. AI assisted both the implementation and this introduction; responsibility for checking and correcting the project remains with the maintainer. Thank you!