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CChinedu Okeke@chiscookeke11·
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Regression

How can a machine predict a number? 🧠 From estimating house prices and salaries to forecasting sales, Regression helps machines learn patterns from data and predict numerical values. In Week 7 of ML From First Principles, I'm breaking down: What Regression means Predicting numerical values Linear Regression How the Linear Regression equation works Simple, real-world examples When to use Regression No jargon overload. Just the fundamentals, explained. Read the full carousel on LinkedIn Learn. Simplify. Share. 🚀 #MachineLearning #LearningInPublic
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machinelearninglearninginpublic
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AAung MyintMyat@aungmyintmyatmaggie129·
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Shipped: A tool for a problem you have

Study Tracker – Personal Study Management AppStudy Tracker is a full-stack web application designed to solve my personal study management problems and help me become more consistent and productive in my learning journey. When studying multiple technologies and subjects, I sometimes struggle to manage my study schedule, keep track of how much time I spend learning, remember what I have completed, and measure my overall progress. I built this application to solve these problems by providing a centralized platform where I can organize my learning activities, track study time, manage tasks, and monitor my progress.Key Features- User Authentication: Secure registration and login to manage personal study data. - Dashboard: Get an overview of study activities, total study time, completed tasks, and learning progress. - Subject Management: Create, update, view, and delete subjects or technologies that you are currently learning. - Study Session Tracking: Record study sessions, including subjects, topics, descriptions, dates, and durations. - Task Management: Create and manage learning tasks and track their completion status. - Progress Monitoring: Monitor study hours, completed sessions, and overall learning progress. - Study History: Review previous study activities to understand learning consistency and productivity. - Responsive Design: Access and manage your study activities across desktop, tablet, and mobile devices.Technology Stack- Frontend: React.js, JavaScript, Tailwind CSS - Backend: Node.js, Express.js - Database: MongoDB, Mongoose - API Communication: Axios, RESTful APIs - Authentication: JSON Web Token (JWT), bcrypt - Development Tools: Git, GitHub, PostmanProject GoalThe main goal of this project is not only to build a functional study management application but also to improve my full-stack development skills through real-world implementation of CRUD operations, REST API integration, database management, authentication, and frontend-backend communication. Built to make learning more organized, measurable, and consistent. Built for the Verified Frontend Internship · Final project
aungmyintmyatmaggie129/study_tracker_app
github.com
internshipfrontend-final-project
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HHumaeid Bin Mansur@humaeidmansur·
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Shipped: A tool for a problem you have

Vision Star – A Personal Life Management Application1. The Problem I Wanted to SolveI built Vision Star to solve a personal real life problem. I have a five-year plan for my life, but I found it difficult to track my progress, achievements, current activities, and future ambitions in one place. For example, I am living in Germany approximately 2.5 years, I wanted to document what I had achieved, what I am currently doing, and where I want to see myself in the next few years. That is why I decided to build Vision Star App.2. How Vision Star WorksThe application connects three stages of personal development: - Past – Where I Started: Record previous experiences, achievements, and milestones. - Present – Where I Am Now: Document current activities, responsibilities, and progress. - Future – Where I Want to Be: Define long-term ambitions and future goals. This helps users understand their journey and connect their daily actions with their long-term plans.3. Technical Implementation and LimitationsI developed Vision Star using Next.js, React, TypeScript, Tailwind CSS, and LocalStorage. #Users As this is a frontend project, I did not integrate a backend or database. I just used localstorage for test, Max. 3 users can register, if any user is continuesly inactive 3 days his account will be removed automatically. The application supports: - Maximum three registered accounts. - Individual data storage for each account. - Automatic account removal after three days of inactivity. - Login and logout functionality. Data is stored in the browser, so it is not synchronized across devices. Authentication is a prototype and is not production-secure.4. How Vision Star Meets the Brief A Requirements1. GitHub Repository: Includes development history through commits. 2. README: Explains the project and how to run it locally. 3. Failure and Empty States: Handles empty workspaces, incorrect login details, and registration limits. 4. Deployment: Provides a live website or a short demonstration video. 5. Limitations: Clearly explains what is not implemented, such as backend integration, a cloud database, and secure authentication.5. Project ObjectiveMy objective was to build a practical tool that helps people understand where they started, where they are today, and where they want to be in the future. Vision Star – Your Past. Your Present. Your Future. Repository: https://github.com/humaeidmansur/vision-star.git Live: https://vision-star-app-made-by-humaeid.vercel.app Built for the Verified Frontend Internship · Final project
internshipfrontend-final-project
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HHasher Amin@hasheramin·

MCPWatch

Know what your AI agents can access. MCPWatch is a small command-line tool that statically audits local Model Context Protocol (MCP) configuration files and flags risky settings before you connect them to an AI coding agent. It also keeps local snapshots so you can see exactly what changed in a configuration over time. MCPWatch only reads the configuration file you point it at. It never starts your MCP servers, never runs the commands it finds, and never sends data over a network. Github Repository:
hasheramin5-cyber/MCPWatch
github.com
rustai-agentsmcpwatch+2
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SSamia Rehman@samiarehman11491·
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Shipped: Radar - real internships and jobs from Pakistani software houses, scored against my resume

I built Radar, a web app for CS students in Pakistan. Every morning a free GitHub Actions job checks 43 Pakistani software houses: it reads real openings from their public job feeds and flags careers pages that changed. The site scores each opening against your resume (read in the browser, never uploaded), explains the fit and eligibility catches, and tracks applications. Openings found elsewhere can be pasted in. How to check the criteria: Live app: [Vercel link] Build history: small, described commits on main at github.com/TechnicalSamia18/radar-frontend, including a measured coverage check before the live feature. README: run steps for a stranger (npm ci && npm run dev, no keys), architecture, measured results, and a “Deliberately not implemented” section. Failure and empty states: a feed that won’t load, damaged saved data, unreachable or blocked sites, scanned PDFs, bad CSV rows, no matching filters. All covered by 33 Playwright page tests (desktop + phone) and 140 unit tests, which run on every push (green CI badge in Actions). Repository: https://github.com/TechnicalSamia18/radar-frontend Live: https://radar-frontend-pi.vercel.app/ Built for the Verified Frontend Internship · Final project
internshipfrontend-final-project
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CChinedu Okeke@chiscookeke11·
question

What is Unsupervised Learning?

Can machines discover patterns in data without being given the correct answers? That's what Unsupervised Learning is all about. In Week 6 of ML From First Principles, I'm breaking down: Learning without labels Clustering Finding patterns in data K-Means, explained simply No jargon overload. Just the fundamentals. I've explained everything with simple examples in the full LinkedIn carousel. Learn. Simplify. Share. #MachineLearning #LearningInPublic
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machinelearninglearninginpublic
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HHumaeid Bin Mansur@humaeidmansur·
showcase

Shipped: A guide for the next contributor

Documentation Requirements – Contributor Guide 1. A CONTRIBUTING.md file has been added to the repository. 2. The project structure is explained, including the purpose of the main folders and files. 3. The documentation explains where new features should be added. 4. Guidance is provided on which existing components may need to be updated when adding a feature. 5. Instructions are included for installing dependencies and running the project locally. 6. The commands for building and linting the project are documented. 7. Expected results of successful build and lint checks are explained. 8. Fragile parts of the code are identified, including ingredient calculations, serving controls, accessibility announcements, and responsive layout. 9. The README includes information about the project, its main features, technologies, and how to run it locally. 10. The documentation provides clear guidance to help future contributors understand and extend the project without breaking existing functionality. Repository: https://github.com/humaeidmansur/accessible-recipe.git Live: https://humaeid-internship-reciepe-project.netlify.app/ Built for the Verified Frontend Internship · Documentation
internshipfrontend-docs
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NEthan (Nano Banana 2)@nanobanana2_7740·

Prompt patterns that make AI photo edits more predictable

I've been building an AI image generator/editor and keeping notes on which prompt structures give consistent edits. Three that help most: (1) say what must stay unchanged first (face, pose, lighting), (2) describe the change in one concrete sentence, (3) add a style or lens hint last. I collected a set of examples in this page of prompt ideas for AI image editing. What structures work for you when editing photos with AI?
nano-banana2.org
banana-prompts
aiprompts
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