Side Project Ideas

Problem Developers building document AI pipelines face significant integration pain: Different models required for layout detection, OCR, table parsing, and structured extraction Each provider (Google Document AI, Azure, Nanonets, ABBYY) requires separate preprocessing code, output format handling, and inference setup Testing a new model means rewriting the entire pipeline — days of integration work Answering “Is Azure better than Google for invoices?” requires days of integration effort Managing 5 provider accounts, billing, and API keys creates operational overhead Pain Intensity: 7/10 - Growing demand for unified pipelines as document AI adoption accelerates ...

Problem Individuals and SMBs face compounding pain when using AI services: Concerns about sending sensitive data (contracts, patient records, legal documents) to cloud AI providers Self-hosting is technically complex — requires Docker, Railway, or similar deployment knowledge Multiple AI service subscriptions create redundant spending ($20-100/month each) Existing $1.99 self-deploy gateways lack team sharing, access control, and compliance features Regulated industries (legal, medical, finance) require governance, audit trails, and data residency proof that no OSS tool provides Pain Intensity: 7/10 - Demand surging alongside GDPR/CCPA privacy regulation enforcement ...

Problem Startups and SMB dev teams pursuing SOC2 compliance must manually collect audit evidence even though the data already exists in GitHub: Manually screenshot/export PR reviews, branch protections, commit signatures as evidence 40-100 hours of manual work per audit preparation cycle GitHub audit log retention is only 90 days — evidence is lost if not streamed externally Existing tools like Vanta/Drata cost $10K-30K/year, far too expensive for early-stage startups Pain Intensity: 8/10 - Continuous (compliance posture) + periodic spikes (audit every 6-12 months) ...

Problem Startups and SMBs face a critical “Bus Factor” problem in their infrastructure: All secrets, deploy keys, and server access are concentrated in 1-2 key infrastructure engineers When key personnel become suddenly unavailable (resignation, accident, unreachable), secret rotation is manual Access revocation, backup notification, and emergency procedures are undocumented or unexecutable Existing secret management tools (Vault, Infisical) handle storage/rotation only — no “auto-respond when key person is absent” Pain Intensity: 7/10 - Latent in normal times but catastrophic when triggered; a structural problem every team recognizes ...

Problem MCP (Model Context Protocol) server developers face a persistent testing problem: Must manually write test code to verify tool call correctness every time Covering MCP protocol’s input/output schemas, error handling, and streaming responses manually is difficult MCP Inspector (official tool) is manual/GUI-only — no CI/CD integration possible As of February 2026, 20%+ of Show HN projects reference MCP — explosive ecosystem growth Pain Intensity: 8/10 - Daily friction recurring with every MCP server deployment ...

Problem Developers running multiple AI coding agents (Claude Code, Codex, Aider) simultaneously have no unified management interface: Manual context-switching between multiple terminal windows No unified view of agent status, current tasks, or outputs MCP tool provisioning (search, file access, DB) configured per-agent individually Agent output review and merge conflict resolution is fragmented Pain Intensity: 8/10 - 80% of enterprises plan multi-agent but less than 10% successfully orchestrate Market Primary Market: Power developers and small teams heavily using AI coding agents Segment: Claude Code, Codex, Aider, Cursor users TAM: AI Code Tools market $7-13B (2025), 46% CAGR SAM: Multi-agent orchestration tooling $150-370M Solution Agent Control Plane - Docker-deployable multi-agent orchestration web dashboard with integrated MCP tool provisioning ...

Problem AI coding tools (Copilot, Claude, Cursor) repeatedly introduce specific vulnerability patterns: Veracode: 45% of AI-generated code samples fail security tests 6.4% of GitHub Copilot repos leak at least one secret (40% higher than non-AI repos) AI code security findings hit 10,000+ per month by June 2025 (10x increase) Existing SAST scanners (Snyk, Semgrep) don’t recognize patterns unique to LLM-generated code Pain Intensity: 9/10 - Measurable, costly, and worsening problem ...

Problem AI coding assistants (Copilot, Claude Code, Cursor) degrade in quality as context windows fill up: Developers don’t realize when quality drops, continuing unproductive prompting Models pull in irrelevant details from earlier prompts, reducing accuracy “Instead of speeding up development, creates friction: rework, debugging, copy-pasting errors” 84% of developers use AI tools but 80% incorrectly believe AI code is more secure Pain Intensity: 9/10 - Cited as “#1 problem users have” by multiple sources ...

Problem Local LLM users (Ollama, LM Studio) face significant barriers adding web search capability: Google Custom Search API requires API key setup, 100/day limit, ongoing costs Brave Search API recently dropped free tier, moved to full metered billing SearXNG MCP is free but requires complex settings.yml, Docker Compose, manual mcp.json wiring Paradox: chose local LLM for privacy, but search forces cloud API dependency Pain Intensity: 7/10 - Web search is the #1 requested capability for local LLM users ...

Problem Nginx config files hide dangerous pitfalls behind deceptive simplicity: Header inheritance bugs, path traversal vulnerabilities, conditional logic errors are hard to catch in code review DevOps engineers manually audit configs or rely on tribal knowledge No mature CI/CD-integrated linting standard exists for nginx (unlike ESLint for JavaScript) Existing tools (gixy) are abandoned, nginx-linter has low adoption Pain Intensity: 6/10 - Leads to real outages and security incidents, but nginx -t handles basic syntax ...