AskLibrary.ai (+ MemoryPlugin)
"AskLibrary.ai lets you talk to your books using advanced RAG techniques, while MemoryPlugin adds long-term memory to ChatGPT, Claude, Gemini, and other AI tools."
0
no rev. info provided
asklibrary.ai
Maker:
asaddhamani
no rev. info provided
Marketing Channels
Secondary
Hacker News
Shared both products in HN yearly projects thread
Primary
Scratch your own itch
Built AskLibrary to solve a personal pain point of finding answers across books; built MemoryPlugin to fill a gap in Claude/TypingMind
Growth Levers
- Position AskLibrary for academic and research users who need to query large book collections
- Market MemoryPlugin to power users of Claude, ChatGPT, and TypingMind who want persistent memory
- Leverage cross-platform browser support (desktop and mobile) as a differentiator for MemoryPlugin
- Publish technical content about RAG techniques (query fanout, reranking, multi-step retrieval) to build authority
- Explore partnerships with e-book platforms to integrate AskLibrary as a feature
First Customer Strategy
Both products were built to solve the creator's own pain points — AskLibrary to query personal books, MemoryPlugin to add long-term memory missing from Claude and TypingMind. No explicit customer acquisition strategy mentioned.
Pricing Insight
No pricing details mentioned for either product.
New Market Opportunities
- Academic and research institutions Researchers often need to search across many books and papers for specific answers
- AI power users across multiple platforms MemoryPlugin works across ChatGPT, Claude, Gemini, TypingMind, and LibreChat — targeting users who switch between AI tools
Key Takeaways
- • Building for your own pain points can yield products with genuine product-market fit
- • Advanced RAG techniques (query fanout, reranking, multi-step retrieval) are becoming table stakes for AI-powered search
- • Cross-platform compatibility (browser extensions for desktop and mobile) can broaden addressable market
- • Fine-tuning AI models with memories increases hallucinations — retrieval-based approaches may be more reliable for personalization
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