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 getting GPU acceleration working for a local model still mean hours of crashes and half-supported backends?

Getting Vulkan acceleration working with Ollama took hours of trial and error, with half of models unsupported and crashing the runtime outright, an unresolved reliability gap in local inference tooling.

Category: AI / Agents · Trend: LLM · Opportunity score: 5.2 / 10

What is the “Why does getting GPU acceleration working for a local model still mean hours of crashes and half-supported backends?” problem in 2026?

Who has this problem?

Developers running local LLMs on consumer GPUs without CUDA.

Recorded source context

Dataset source note: I spend like 2 hours trying to get vulkan acceleration working with ollama, no luck (half models are not supported and crash it).

This note may summarize the referenced material rather than quote it verbatim. Source label: Hacker News, item 47788385, comment by user throw9393rj, on "The local LLM ecosystem doesn't need Ollama." (reference).

Existing players in this space

Ollama Vulkan backend: Partial support, crashes on unsupported model architectures.
CUDA-only setups: Reliable but locks users into NVIDIA hardware.

What existing players are missing

A pre-flight compatibility checker that tells a user, before they try, whether their specific GPU/backend/model combination is known to work, sourced from a shared community compatibility matrix.

How Real Problem AI scores this opportunity

Aggregate score: 5.2 / 10. Four-axis rubric:

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

How to build a solution: stack hints

GPU/backend/model compatibility matrix
Pre-flight diagnostic CLI
Community-reported compatibility data
Fallback backend suggestion