What is Real Problem AI?

Real Problem AI is a curated directory of 100 real problems worth building an AI startup around. Each problem is scored honestly on severity, AI feasibility, market signal, and competition gap. We're for builders looking for what to build, not for ethics debates about AI.

What is the difference between Real Problem AI and AI ethics or AI risks indexes?

Real Problem AI catalogues problems AI can solve — productive, commercial opportunities for builders. AI ethics indexes catalogue problems with AI itself (bias, environmental cost, copyright, consciousness). Different intent, different audience: founders and indie hackers vs researchers and policy folks.

What should I build with AI in 2026?

Look for problems with high severity (it costs people hours, sleep, or revenue every week), high AI feasibility (LLMs, vision or voice are great at this today), strong market signal (Reddit threads + willingness to pay), and a clear competition gap (incumbents miss the same obvious thing). Real Problem AI scores 100 such problems for you on those four axes.

Where do Real Problem AI's startup ideas come from?

From three sources, scanned each cycle: 1) Reddit threads where people describe a real friction in their own words, 2) podcasts and talks of founders young builders trust (Nikhil Kamath, Aman Gupta, Ankur Warikoo, Raj Shamani, Andrej Karpathy, Greg Isenberg), and 3) a continuous 1,000+ source scan covering forums, app store reviews, regulator filings, and field interviews.

Is Real Problem AI free?

Yes. Browsing every problem, opening every score breakdown, raising your hand on a co-founder match, and the founder vault are all free. There is no paywall.

How is each problem scored?

Four axes, each 1-10, weighted: Problem Severity (30%), AI Feasibility (25%), Market Signal (25%), Competition Gap (20%). Weighted average is the Opportunity Score on each card. Only problems clearing 7.0+ make the live list.

Why does Claude Code re-inject the same skills and CLAUDE.md content instead of remembering it already loaded them?

A community-maintained bug tracker documents 15 separate context-bloat issues stemming from missing dedup, cleanup, scoping, and visibility across skills, plugins, and MCP tool injection.

Category: AI / Agents · Trend: Agents · Opportunity score: 7.2 / 10

What is the “Why does Claude Code re-inject the same skills and CLAUDE.md content instead of remembering it already loaded them?” problem in 2026?

A community-maintained bug tracker documents 15 separate context-bloat issues stemming from missing dedup, cleanup, scoping, and visibility across skills, plugins, and MCP tool injection.

Who has this problem?

Power users running many plugins, skills, and MCP servers inside Claude Code.

Recorded source context

Dataset source note: These 15 issues aren't independent bugs — they're symptoms of missing context lifecycle management: No deduplication... No cleanup... No scoping... No visibility... No budget management.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #29971, Ryan4n6, 2 Mar 2026. (reference).

Existing players in this space

/context command: Underreports actual overhead per the report's own findings.
Manual plugin/skill pruning: Users disable extensions by hand to claw back context.

What existing players are missing

A context-lifecycle manager that deduplicates repeated injections, caps system-overhead budget as a percentage of the window, and shows users exactly what's eating their context before it silently triggers compaction.

How Real Problem AI scores this opportunity

Aggregate score: 7.2 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 8 / 10
Market signal: 7 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Context injection deduplication
Overhead budget enforcement
Scoped plugin/MCP loading per project
Context visibility dashboard