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 debugging a Claude Code session still mean manually copy-pasting terminal output back into the chat?

For race conditions and hard-to-reproduce bugs, developers manually add print statements, run the app, copy output back into Claude, and ask it to analyze it, a slow loop that leads to the model guessing without real runtime evidence.

Category: AI / Agents · Trend: Agents · Opportunity score: 6.8 / 10

What is the “Why does debugging a Claude Code session still mean manually copy-pasting terminal output back into the chat?” problem in 2026?

Who has this problem?

Developers debugging complex, hard-to-reproduce issues with an AI coding agent.

Recorded source context

Dataset source note: This manual 'human-in-the-loop' data transfer is slow and error-prone, often leading to 'shotgun debugging' where the model guesses the solution without concrete runtime evidence.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #13865, coygeek, 13 Dec 2025. (reference).

Existing players in this space

Manual log-and-paste loop: The default workaround today, slow and breaks flow every iteration.
AI-written test cases: Works for reproducible bugs, not for race conditions or environment-specific issues.

What existing players are missing

A live runtime-instrumentation mode that lets the agent insert its own logging, run the app, and read output directly, closing the hypothesis-instrumentation-verification loop without a human relay in the middle.

How Real Problem AI scores this opportunity

Aggregate score: 6.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Runtime log injection/removal automation
App execution harness
Hypothesis-verification loop controller
Debug-mode session UI