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item007 | 29 days ago
Out of curiosity, what’s the bigger win for you: full-text search itself, or the tagging/metadata layer that helps narrow results when your memory is fuzzy? And do you mostly search by keywords, or by “context” (project/topic you’re working on)?
I’m validating a similar retrieval-first angle (summarized in my HN profile/bio if you want to compare notes).
flexagoon|29 days ago
I don't manually tag any entries - the automatic AI tags just add extra keywords I can search for that are not included in the original article text. So I mostly search by keywords, yes. Not sure what the difference is between "keywords" and "topic you're working on".
See also https://mymind.com, which takes the AI tagging even further. Potentially similar to what you're building (although, again, your landing page contains a lot of AI generated metaphors and nothing that explains what your product actually does)
item007|29 days ago
This idea stems from my own pain points, and I genuinely hope that while solving my own issues, it might also address broader needs.
Regarding your response: It's interesting that AI tagging primarily aids by adding extra searchable keywords. However, I'd prefer broader content and semantic search/matching capabilities without relying solely on tags—though tagging remains a viable implementation approach. Thanks for the mymind reference—I'll explore it.
PS. Did you perceive an AI-driven approach because I used translation software?