Side Project Ideas

The Problem (Pain Level: 6/10) As a developer, it’s difficult to effectively showcase your open source contributions. Current problems: GitHub profile limitations: Only shows commit counts, hard to explain meaningful contributions Manual curation required: Portfolio updates take significant time No verification: Recruiters struggle to assess actual contribution quality Template limitations: Existing portfolio templates are static and require manual input Discovered on Reddit: “I built an AI-powered Open Source Portfolio to help you verify your contributions” ...

The Problem (Pain Level: 8/10) “I’ll start exercising tomorrow”, “I’ll definitely go to bed early tonight” - Do you make these promises every day and break them just as often? Problems with existing habit tracking apps: Simple checklists: Just check and done, no real accountability Self-deception allowed: You can check even if you didn’t actually do it Lack of motivation: No consequences when streaks break Vulnerable to single failures: “Already failed, might as well give up” mentality A developer on Reddit r/SideProject shared: “I built an iOS app for myself because I kept lying to myself.” ...

The Problem (Pain Level: 6/10) Personal data is scattered across numerous data brokers and services, making deletion difficult: Hard to know where your information exists Each site has different deletion request procedures Exercising GDPR/CCPA rights is complex Manual deletion takes dozens of hours Data gets re-collected after deletion A persistent pain for privacy-conscious users. Target Market Global Privacy-Conscious Users - Estimated TAM $500M Privacy-sensitive individual users Residents of Europe (GDPR), California (CCPA) People wanting to reduce their digital footprint Tech users interested in cybersecurity What is Privacy Data Deletion Tool? A tool that automates personal data deletion requests to data brokers and services. ...

The Problem (Pain Level: 4/10) Developers experience friction when navigating large directories and files in the terminal: Basic ls command is useless with thousands of files Finding the right file requires combining find and grep File content preview is cumbersome Understanding dataset structure is difficult A niche pain point, but a real problem for power users. Target Market Global CLI Power Users - Estimated TAM $200M Developers who work terminal-first Data scientists, ML engineers DevOps/SRE engineers Linux/Mac power users What is DAIS (Data/AI Shell)? A CLI tool that adds AI-powered file analysis and intelligent navigation to the existing ls command. ...

Problem (Pain Level: 8/10) “I want to use my API with Claude or Cursor, but I need to build an MCP server from scratch?” - A new barrier every API developer faces in the AI agent era. Current pain points: Manual Work: Even with OpenAPI specs, you must develop MCP servers separately Learning Curve: Time spent learning MCP protocol and SDK usage Deployment Complexity: Hosting and registry registration require separate steps Maintenance Burden: Manual sync when APIs change Fragmented Ecosystem: Tools like Specli (local CLI) and Mpak (package management) are disconnected Target Market Primary Target: API-owning companies, SaaS startups, AI agent developers ...

Problem (Pain Level: 7/10) “7 out of 10 PRs I get these days are AI-generated garbage” - The new pain for open-source maintainers. Current pain points: AI Spam Flood: Low-quality PRs mass-generated by LLMs flooding open source projects Time Waste: Maintainers missing real contributions while reviewing spam PRs Tool Installation Resistance: Security concerns about GitHub App permission requests Complex Setup: Most code review tools require excessive configuration New Contributor Identification: Hard to distinguish real beginners from AI spam accounts Target Market Primary Target: Open source project maintainers, enterprise GitHub administrators ...

The Problem (Pain Level: 5/10) In the age of information overload, reading news and articles takes too much time: Dozens of newsletters arrive daily No time to read long articles Just want to quickly grasp the key points RSS feeds keep piling up Busy professionals want to quickly understand the essentials. Target Market Global Knowledge Workers - Estimated TAM $1B+ Professionals subscribed to many newsletters Analysts monitoring multiple sources RSS reader users Users interested in information curation What is AI News Summarizer? A service that summarizes news, articles, and blog posts into 3 bullet points using AI. ...

Problem Definition As AI coding tools (Copilot, Cursor, etc.) become mainstream, developers face new challenges: No Quality Gate: AI-generated code goes straight to production with placeholder comments, TODOs, and broken imports Lack of Design Thinking: “Vibe Coding” phenomenon - developers accept AI output without critical thinking Security Vulnerabilities: AI-generated code gets committed without security review According to the 2026 Stack Overflow survey, 84% of developers use AI tools, and 41% of new code is AI-assisted. ...

The Problem Teams using Django + Celery need to monitor Celery tasks, but: Flower: Requires a separate process (port 5555) Extra Infrastructure: Additional container, process management Context Switching: Jumping between Django Admin and Flower “Celery observability inside Django admin (no Flower, no extra process)” — HackerNews Show HN (2026.01.21) Developers want to manage everything within the familiar Django Admin interface. Market Analysis Aspect Details Target Market Django + Celery teams TAM Django ecosystem $500M+ Competition Status BLUE (niche market) Competitive Landscape Solution Pricing Weakness Flower Free Requires separate process django-celery-monitor Free Limited features, unmaintained Prometheus + Grafana Free Complex setup, overkill Differentiation: Native Django Admin integration, zero extra processes ...

Problem Definition As AI coding assistants become routine, new problems emerge: No Session Tracking: AI collaboration sessions aren’t recorded, preventing learning Report Writing Burden: Work reports take an average of 30 minutes to write Knowledge Loss: AI conversations are one-time use, not reusable Team Skill Gap: Differences in AI utilization create productivity gaps Market Analysis Metric Value AI IDE Market Size (2032 projected) $37B Cursor Users Millions+ Developers Using AI Tools 84% Target Customers: Development teams using AI coding tools ...