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 did a developer have to build their own tracker just to avoid getting cut off mid-session?

Claude Code gave no quick way to see how close a session was to its usage limit, so a user shipped a community tool just to avoid the surprise mid-task cutoff.

Category: AI / Agents · Trend: Agents · Opportunity score: 7.0 / 10

What is the “Why did a developer have to build their own tracker just to avoid getting cut off mid-session?” problem in 2026?

Claude Code gave no quick way to see how close a session was to its usage limit, so a user shipped a community tool just to avoid the surprise mid-task cutoff.

Who has this problem?

Claude Code users on metered or capped plans running long agentic sessions.

Recorded source context

Dataset source note: I kept slamming into Claude Code limits mid-session and couldn't find a quick way to see how close I was getting.

This note may summarize the referenced material rather than quote it verbatim. Source label: Show HN: Claude Code Usage Monitor, Hacker News, item 44317012. (reference).

Existing players in this space

Claude Code Usage Monitor: Community-built terminal tracker, unofficial, breaks on CLI updates.
/usage command: Manual check only, not proactive.

What existing players are missing

A first-party predictive usage bar, built into the CLI itself, that warns before a session is about to exceed its window rather than after.

How Real Problem AI scores this opportunity

Aggregate score: 7.0 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 9 / 10
Market signal: 7 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Session token-rate tracking
Predictive threshold alerting
Terminal UI widget
Historical usage pattern model