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 the same local LLM end up copied into three different folders on your own machine?

Evaluating local inference runners means ending up with the same model duplicated across separate directory structures for Ollama, llama.cpp, and other tools, wasting disk space and creating version-tracking confusion.

Category: AI / Agents · Trend: LLM · Opportunity score: 5.8 / 10

What is the “Why does the same local LLM end up copied into three different folders on your own machine?” problem in 2026?

Who has this problem?

Developers running local LLMs across multiple inference backends.

Recorded source context

Dataset source note: When I evaluated running locally I ended up with 3 different folders containing copies of the same model in different directory structures.

This note may summarize the referenced material rather than quote it verbatim. Source label: Hacker News, item 47788385, comment by user alzoid, on "The local LLM ecosystem doesn't need Ollama." (reference).

Existing players in this space

Ollama model store: Proprietary storage layout, doesn't share weights with other runners.
Manual symlinking: Works but requires knowing every runner's expected directory layout.

What existing players are missing

A shared local model cache/registry that any inference runner (Ollama, llama.cpp, vLLM, LM Studio) can point to, so the same GGUF/safetensors file is stored once and referenced everywhere.

How Real Problem AI scores this opportunity

Aggregate score: 5.8 / 10. Four-axis rubric:

Problem severity: 4 / 10
AI feasibility today: 8 / 10
Market signal: 5 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Shared model cache directory standard
Cross-runner symlink/reference manager
Deduplication by model hash
CLI for local model inventory