47 GitHub Project Ideas for Developers in 2026

Finding project ideas is easy.

Finding projects that actually demonstrate real engineering skills is much harder.

This list contains 47 project ideas across software engineering, data science, AI/ML, cybersecurity, cloud, DevOps, UX/product design, and data analytics.

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Software Engineering

1. Link Shortener with Analytics Dashboard

Build a URL shortener with click tracking and an analytics dashboard.

Reference: https://github.com/jll38/url-shortener

2. Micro-SaaS Link Analytics Dashboard

Create a SaaS-style link management platform with analytics and user accounts.

Reference: https://github.com/priyashanand/Micro-SaaS-Link-Analytics-Dashboard

3. Advanced URL Shortener

Build a production-style URL shortener with device tracking, referrers, and bot detection.

Reference: https://github.com/spoo-me/url-shortener

4. Real-Time Collaborative Notes App

Create a notes application where multiple users can edit the same document simultaneously.

Reference: https://github.com/Dhravya/notty

5. Collaborative Notes App

Build a collaborative editor using WebSockets, authentication, and persistent storage.

Reference: https://github.com/Deepakmyside/collabNotes

6. Collaborative Notes with Go

Create a scalable notes application with collaboration, permissions, search, and offline support.

Reference: https://github.com/TheFozid/go-notes

7. GitHub Activity Visualizer

Visualize GitHub contributions, commits, repositories, languages, and developer activity.

Reference: https://github.com/search?q=github+activity+visualizer&type=repositories

8. Browser Tab Manager & Focus Extension

Build a browser extension combining tab management, reading mode, and productivity tools.

Reference: https://github.com/CubeSeven/tabplusplus

9. Open Source Contribution

Contribute to a real open-source project and document the issue, implementation, and pull request.

Reference: https://github.com/search?q=good-first-issue+language%3ATypeScript&type=issues

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Data Science / ML

10. Employee Salary Prediction

Train a regression model to predict salaries from demographic, education, and employment features.

Reference: https://github.com/saurabh-vit/Employee-Salary-Prediction-using-Machine-Learning

11. Salary Predictor with Explainable AI

Build a salary prediction system using machine learning with SHAP explanations.

Reference: https://github.com/DurgA-5/SalaryPredictor

12. Customer Churn Prediction

Predict which customers are likely to leave a SaaS or subscription business.

Reference: https://github.com/RIT8603/churn_prediction

13. Churn Prediction with XGBoost + SHAP

Combine a powerful ML model with explainability to identify the factors driving customer churn.

Reference: https://github.com/Dorjada/customer-churn-prediction

14. Customer Churn Prediction with FastAPI

Deploy a churn model through an API and connect it to an interactive frontend.

Reference: https://github.com/dcaayushd/Customer_Churn_Prediction

15. Sports Analytics Deep Dive

Analyze player or team performance using statistics, visualization, and machine learning.

Reference: https://github.com/search?q=sports+analytics+machine+learning&type=repositories

16. Fine-Tuned LLM for a Niche Task

Fine-tune an open-source language model for a specific domain or specialized task.

Reference: https://github.com/huggingface/transformers/tree/main/examples

17. Personal Finance Spending Clusters

Use clustering to automatically identify spending patterns from transaction data.

Reference: https://github.com/smartschat/finance-dashboard

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AI / ML Engineering

18. Domain-Specific RAG Application

Build a retrieval-augmented AI assistant that answers questions from a specialized document collection.

Reference: https://github.com/Datyth/RAG-Langchain

19. Production RAG Pipeline

Create a complete RAG system with document ingestion, embeddings, vector search, retrieval, and evaluation.

Reference: https://github.com/langchain-ai/langchain/tree/master/templates

20. Text-to-SQL Interface

Convert natural-language questions into SQL queries and return database results safely.

Reference: https://github.com/search?q=text-to-sql+LLM&type=repositories

21. AI Workflow Agent

Build an agent capable of using tools and executing a multi-step workflow automatically.

Reference: https://github.com/langchain-ai/langgraph/tree/main/examples

22. LangGraph Agent System

Create a stateful AI agent with tools, memory, structured workflows, and human approval steps.

Reference: https://github.com/langchain-ai/langgraph

23. LLM Evaluation Platform

Build a system for comparing prompts and models using automated evaluation datasets and metrics.

Reference: https://github.com/openai/evals

24. LLM Benchmarking Tool

Create a dashboard comparing different models based on accuracy, latency, cost, and task performance.

Reference: https://github.com/arthur-ai/bench

25. Multimodal AI Product

Build an application that combines text, images, audio, or video with an AI model.

Reference: https://github.com/search?q=multimodal+AI+vision+LLM&type=repositories

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Cybersecurity

26. Home Network Vulnerability Scanner

Scan an authorized local network for exposed services and generate a security report.

Reference: https://github.com/PepperBitt/network-vulnerability-scanner

27. CTF Write-Up Collection

Solve and document 5–10 cybersecurity challenges with methodology, vulnerabilities, and lessons learned.

Reference: https://github.com/search?q=CTF+writeups&type=repositories

28. Phishing Awareness Simulation

Build a controlled phishing-awareness platform for security training and reporting.

Reference: https://github.com/jenyraval/Phishing-Simulation

29. Phishing Simulation Lab

Create a safe simulated phishing environment for demonstrating social-engineering techniques.

Reference: https://github.com/Kartik0219/phishing-sim

30. Password Strength Analyzer

Analyze password strength using entropy, pattern detection, and estimated resistance.

Reference: https://github.com/Raghava102/password-strength-analyzer

31. Log Anomaly Detection Dashboard

Analyze system logs and detect unusual behavior using statistical or machine-learning techniques.

Reference: https://github.com/search?q=log+anomaly+detection&type=repositories

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Cloud / DevOps

32. Containerized Application with CI/CD

Dockerize an application and create an automated deployment pipeline.

Reference: https://github.com/docker/awesome-compose

33. Docker Multi-Service Application

Deploy a full-stack application consisting of multiple containerized services.

Reference: https://github.com/docker/awesome-compose

34. Infrastructure as Code with Terraform

Provision AWS infrastructure using reusable Terraform configurations.

Reference: https://github.com/alfonsof/terraform-aws-examples

35. Production AWS Infrastructure

Build infrastructure with VPC, subnets, load balancing, databases, storage, and monitoring.

Reference: https://github.com/choiis/AWS-terraform

36. AWS Cost Optimization Audit

Analyze cloud resource usage and identify opportunities to reduce unnecessary AWS spending.

Reference: https://github.com/search?q=AWS+cost+optimization&type=repositories

37. Kubernetes Autoscaling Demo

Deploy an application to Kubernetes and demonstrate automatic scaling under changing workloads.

Reference: https://github.com/kubernetes/autoscaler

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UX / Product Design

38. App Redesign Case Study

Redesign an existing application and document research, problems, design decisions, and results.

Reference: https://github.com/search?q=app+redesign+case+study&type=repositories

39. Accessibility Audit & Redesign

Audit an application against accessibility standards and redesign problematic experiences.

Reference: https://github.com/search?q=accessibility+audit+redesign&type=repositories

40. 0→1 Product Concept

Take a product from an identified problem through research, wireframes, prototyping, and validation.

Reference: https://github.com/search?q=product+design+case+study&type=repositories

41. Component Library Design System

Create a reusable design system with typography, spacing, colors, components, and documentation.

Reference: https://github.com/search?q=design+system+component+library&type=repositories

42. Usability Testing Study

Test a product with users, identify usability problems, and iterate based on the findings.

Reference: https://github.com/search?q=usability+testing+case+study&type=repositories

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Data Analytics

43. End-to-End SQL Analytics Project

Use SQL to transform raw data, calculate KPIs, discover insights, and present the findings.

Reference: https://github.com/search?q=SQL+analytics+dashboard&type=repositories

44. Marketing Attribution Model Comparison

Compare first-touch, last-touch, linear, time-decay, and other attribution approaches.

Reference: https://github.com/search?q=marketing+attribution+model&type=repositories

45. Business Question Dashboard

Build a dashboard specifically designed to answer an important business question.

Reference: https://github.com/search?q=business+intelligence+dashboard&type=repositories

46. A/B Testing Statistical Analysis

Analyze an experiment using hypothesis testing, confidence intervals, effect sizes, and business interpretation.

Reference: https://github.com/search?q=A%2FB+testing+statistical+analysis&type=repositories

47. Data-Driven Industry Report

Combine public datasets, statistical analysis, visualizations, and market research into a professional report.

Reference: https://github.com/search?q=data+analysis+industry+report&type=repositories

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How to Choose a Project

Don't try to build all 47 projects.

Choose projects that demonstrate different technical abilities rather than creating several projects that all demonstrate the same CRUD skills.

A strong portfolio combination could include:

• Full-Stack: Link Shortener + Analytics
• Distributed Systems: Real-Time Collaborative Notes
• AI Engineering: Domain-Specific RAG Application
• LLM Engineering: LLM Evaluation Platform
• Cybersecurity: Network Vulnerability Scanner
• DevOps: Kubernetes Autoscaling Demo

For every serious portfolio project, aim to include:

• GitHub repository
• Clean README
• Architecture diagram
• Tests
• Docker setup
• CI/CD
• Deployed demo
• Screenshots
• Technical write-up
• Documented design decisions

The goal isn't to have the largest number of projects.

The goal is to have projects that demonstrate that you can design, build, deploy, test, and explain real software systems.