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 forget its own instructions mid-task and undo its own work after compacting?

Context compaction doesn't re-read CLAUDE.md and auto-continues with degraded context, so the agent sometimes actively undoes work it just built because it forgot doing it, with no pause or confirmation.

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

What is the “Why does Claude Code forget its own instructions mid-task and undo its own work after compacting?” problem in 2026?

Who has this problem?

Developers running long, complex Claude Code sessions on real production codebases.

Recorded source context

Dataset source note: It's like having a brilliant employee who every 5 minutes becomes an imbecile and has to relearn everything — while actively destroying what they just built.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #21925, gizyckik, 30 Jan 2026. (reference).

Existing players in this space

Automatic compaction: Built-in but silent, no pause, no CLAUDE.md reload, no warning.
Manual /clear + restart: Avoids the bug but loses all session context deliberately.

What existing players are missing

A compaction mode that pauses for confirmation, re-reads CLAUDE.md, and shows a diff of what context was dropped before the agent is allowed to continue acting on a degraded memory.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Compaction pause/confirm gate
CLAUDE.md re-injection on resume
Context-loss diff viewer
Session checkpoint/rollback