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 is sharing one set of agent skills across my team and three coding tools still a bootstrap script?

Teams write skills and instructions for coding agents, then copy them by hand between repos, laptops and tools like Claude Code and Codex, with no versions, no tests and no way to know which copy is current.

Category: AI / Agents · Trend: Agents · Opportunity score: 7.3 / 10

What is the “Why is sharing one set of agent skills across my team and three coding tools still a bootstrap script?” problem in 2026?

Who has this problem?

Engineering teams and solo developers using two or more coding agents who maintain shared skills, AGENTS.md files and conventions.

Recorded source context

Dataset source note: These are supposed to be portable between agents, and so distributing them is currently awkward.

This note may summarize the referenced material rather than quote it verbatim. Source label: Ask HN thread on how people manage skills files, 6 September 2026, 320 points, 299 comments. (primary source).

Existing players in this space

Claude Code plugins and skills: Install skills for one tool, not across every agent a team uses.
chezmoi dotfiles: How commenters sync skills today, by symlinks and their own scripts.
Skillsync: Launch HN product for moving chat sessions between agents, not for versioning and testing team skills.

What existing players are missing

A skills registry for teams: one source of skills with versions, owners and changelogs, installed into each agent's expected folder, with small test tasks that show a skill still works after a model or tool update.

How Real Problem AI scores this opportunity

Aggregate score: 7.3 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 8 / 10
Market signal: 9 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Git-backed skills registry
Per-agent install adapters (Claude Code, Codex, Cursor)
Skill test runner with sample tasks
Usage logging to find unused or broken skills