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 a prompt management tool create a whole new version even when nothing actually changed?

Langfuse's create_prompt function generates a new version on every call regardless of whether the content changed, cluttering version history the way un-deduplicated commits would.

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

What is the “Why does a prompt management tool create a whole new version even when nothing actually changed?” problem in 2026?

Langfuse's create_prompt function generates a new version on every call regardless of whether the content changed, cluttering version history the way un-deduplicated commits would.

Who has this problem?

Developers using LLM observability platforms to manage prompt versions.

Recorded source context

Dataset source note: creating a hash of the contents, and only creating a new version when anything changes

This note may summarize the referenced material rather than quote it verbatim. Source label: langfuse GitHub Discussion #2161, brightsparc, 27 May 2024. (reference).

Existing players in this space

Langfuse prompt versioning: Creates a version on every save call, no content-hash dedup.
Manual version pruning: Users clean up duplicate versions by hand after the fact.

What existing players are missing

Content-hash based deduplication in prompt version stores, mirroring how Git only commits when the tree actually changes, so version history stays meaningful.

How Real Problem AI scores this opportunity

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

Problem severity: 4 / 10
AI feasibility today: 9 / 10
Market signal: 4 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Content-hash comparison
Prompt version store
Deduplication on save
History pruning tools