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 upgrading to a bigger context window just mean burning your quota 5x faster with no way to cap it?

After Opus upgraded to a 1M-token context window, a developer's API quota started burning roughly 5x faster with no flag to cap the effective context size back to what their workflow actually needs.

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

What is the “Why does upgrading to a bigger context window just mean burning your quota 5x faster with no way to cap it?” problem in 2026?

After Opus upgraded to a 1M-token context window, a developer's API quota started burning roughly 5x faster with no flag to cap the effective context size back to what their workflow actually needs.

Who has this problem?

Claude Code users on metered API plans who don't need the full expanded context window.

Recorded source context

Dataset source note: With the recent upgrade of Opus 4.6 to 1M context, my API quota burns ~5x faster than before. I was working comfortably at 200K and have no need for 1M in most sessions. There's currently no way to limit the context window size Claude Code uses.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #34650, profff, 15 Mar 2026. (reference).

Existing players in this space

Manual /clear discipline: Resets context but loses useful history along with the bloat.
Switching models: Sidesteps the large window but loses its benefits entirely.

What existing players are missing

A --max-context flag or settings-level cap that lets users opt back into the smaller, cheaper context window on a per-session or per-project basis without losing access to the larger window when they actually need it.

How Real Problem AI scores this opportunity

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

Problem severity: 6 / 10
AI feasibility today: 9 / 10
Market signal: 5 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

CLI flag / settings option
Context window cap enforcement
Auto-compaction threshold adjustment
Per-project default config