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 my AI assistant forget everything the moment the session ends?

Context window is RAM, not storage, no window size survives a session restart. Every Cursor / Claude / Perplexity / personal-copilot user re-explains their preferences, project conventions, and personal context every single session. Cross-device identity is unsolved. Indie hackers building "AI assistants for X" hit this wall in week two.

Category: Others · Trend: LLM · Opportunity score: 8.6 / 10

What is the “Why does my AI assistant forget everything the moment the session ends?” problem in 2026?

Who has this problem?

Founders building personal AI assistants, agent products, vertical copilots. Anyone shipping a multi-session AI product.

Recorded source context

Dataset source note: Day 1: my AI knows my codebase, my style, my voice. Day 2 (new session): "Hi! How can I help?" I rebuilt the same context 47 times last month. There's a TIL pinned-comment thread on Reddit with 8K upvotes about this.

This note may summarize the referenced material rather than quote it verbatim. Source label: Mem0 "Context Window Is RAM, Not Storage" (May 2026), Cloudflare Agent Memory launch (April 2026), Oracle developers blog "Agent Memory: Why Your AI Has Amnesia". (reference).

Existing players in this space

Mem0: Best-in-class; YC-backed; but framework, not a drop-in product for end-users
Letta (MemGPT): Strong for agent memory; requires self-host + integration work
Supermemory: Personal memory; one ecosystem, no cross-device identity story
ChatGPT memory: OpenAI-only; doesn't help if you use multiple LLMs

What existing players are missing

An LLM-agnostic personal memory layer: install once, attach to Cursor + Claude Code + Perplexity + Notion AI + the chatbot of the day. Cross-device identity resolution (same person on phone + laptop). Decay scoring (which memories are still accurate). Exportable so the user owns it.

How Real Problem AI scores this opportunity

Aggregate score: 8.6 / 10. Four-axis rubric:

Problem severity: 9 / 10
AI feasibility today: 8 / 10
Market signal: 10 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Vector store + episodic memory layer
MCP server that any LLM-client can subscribe to
Identity resolution across sessions + devices
Memory-freshness scorer with decay + accuracy signals
User-owned export (JSON / Markdown)