Hidden ramen spots in Tokyo you must try
@tastingtokyo
AI Summary
Six under-the-radar ramen shops across Shinjuku and Shibuya, with the dish to order at each and how to actually find them.
#tokyo#food#travel
Food & TravelMar 12
Semantic search
Search it the way
you remember it.
Nobody remembers the title. They remember what it was about. Nemivo runs three retrievers over your library at once, meaning included, and fuses them into one ranking, so half-remembered is enough.
Start Free 300 saves free · No credit card required
In short
Semantic search in Nemivo finds a save by what it means rather than by the words it contains. Every save the AI has read is stored as a vector, and a search runs an exact-match tier, a keyword tier and a meaning tier at once, fusing the rankings, so “that video about pricing psychology” finds a talk whose title never used those words.
Try it
Ask this library anything.
A small sample library, searched the way you actually remember things: by meaning, not by keyword.
Async Python: the complete beginner course
CoreDump
AI Summary
A 90-minute walkthrough of asyncio: event loops, coroutines, tasks, and the mistakes every beginner makes.
#python#tutorial#async
Dev LearningFeb 28
How I consistently find dirt-cheap flights
r/travel
AI Summary
Error fares, positioning flights and the three fare-alert tools that actually work, with the exact setup.
#flights#budget#travel
Food & TravelApr 03
The best films you’ve never heard of: a thread
@cinemaghost
AI Summary
25 overlooked films from the last decade, each with one line on why it deserves your evening.
#film#watchlist
WatchlistJan 19
vasilev/css-layout-patterns
Andrei Vasilev
AI Summary
30 modern CSS layout patterns with copy-paste code: grid, container queries, subgrid, and when to use each.
#css#frontend
Dev LearningMar 30
Pack 2 weeks into a carry-on
@minimalmiles
AI Summary
A rolling-and-cube system that fits 14 days of clothes into one carry-on, with the exact fold shown twice.
#travel#packing
Food & TravelApr 11
My endgame 65% mechanical keyboard build
r/MechanicalKeyboards
AI Summary
Full build sheet (board, switches, lube, mods) and what actually made the difference in sound.
#keyboards#diy
Someday ProjectsFeb 07
Banking app onboarding concept
Lena Fischer
AI Summary
A four-screen onboarding flow with soft gradients and restrained motion. A great reference for progressive disclosure.
#design#ui#inspiration
Design InspirationMar 22
Three ways to find it,
one list of results.
Meaning-only search misses the URL you just pasted. Keyword-only search misses everything you cannot spell. So neither of them runs alone.
- 01
Every read save gets a vector
Once the AI has read an item, its platform, title, description, creator, summary and tags are embedded into a vector stored beside it. The vector never leaves the database and never appears in an API response or an export.
- 02
Your query is embedded too
The words you typed become a vector on the same scale, briefly cached so a repeated search is instant. If that step is slow or unavailable, the search quietly degrades to keyword-only rather than failing.
- 03
Three retrievers run at once
An exact tier for a pasted URL, an exact handle or an exact title; a keyword tier over titles, descriptions, creators, summaries and tags; and the meaning tier over the vectors. Each returns its own ranked candidates.
- 04
The lists are fused, not concatenated
Reciprocal rank fusion merges the keyword and meaning rankings, so an item both agree on rises above one that only one of them liked. Exact matches are pinned above the fused list, because someone who pastes a link wants that item back.
The mechanics
What it looks at, and what wins.
Search is a retrieval problem, not a magic trick, and it behaves in ways worth knowing.
The fields it reads
Title, description, creator name and handle, the AI summary and the AI tags. A title match ranks above a summary match, which ranks above a creator match.
A pasted link always wins
Paste a URL, an exact handle or an exact title and that item is pinned to the top, ahead of everything the ranking produced.
Filters apply before ranking
Platform, collection, tag, media type and date narrow the candidates first, so a filtered search ranks within what you asked for rather than around it.
Similar items
The same vectors power "more like this" on an item, which is how you get from one save you remembered to the four beside it that you did not.
It costs no credits
Searching is cheap retrieval, so it is rate-limited rather than metered: 10 meaning-based searches a day on Free, unlimited on Pro. Keyword search is never limited on any plan.
Scoped to you, in the query
Every retrieval filters by your user id as a bound parameter in the query itself, not as a check afterwards. There is no code path that ranks across two libraries.
The limits
Where it stops.
Three of these are consequences of how retrieval works, and one is simply not built yet.
A brand new save is keyword-only
The vector is written after the AI has read the item. In the minute between saving and enrichment, that item is findable by its words, just not yet by its meaning.
Notes are not searched here
Item search reads the save, not the note you attached to it. Notes have their own search on the Notes page. Bringing them into this ranking is a change we have not made yet, and we would rather say so than let you assume.
A save with no text is hard to find
Meaning is derived from words. A post with no caption, no title and no summary has very little for either retriever to match on, whatever it shows on screen.
It ranks, it does not answer
Search hands you the saves. If you want a written answer built out of several of them, with citations back to each, that is Ask, and it is a different button.
The long version, including what is stored and where the AI sits, is on how it works.
Search finds it. Ask explains it.
The same retrieval runs underneath both. Search hands you a ranked library; Ask takes the top of that ranking, reads it, and writes you an answer that cites the saves it used.
Pricing
Start free. Stay for the memory.
A real library on the free plan, forever. Not a trial: no countdown, no card, and no email on day seven asking for one. Pro removes the limits when your memory outgrows them.
Free
$0forever
A plan, not a trial. It does not expire.
- 20 AI credits a month
- 300 saved items
- 10 collections
- 1 automation
- 10 AI searches a day
- JSON export
No card. Ever.
Pro
Recommended$7.99/month
Built for people who save content every day.
- Unlimited AI organization
- Unlimited saved content
- Unlimited AI search
- Unlimited automations
Everything in Free, plus
- Faster AI processing
- AI insights in exports
- AI automation conditions
- Export as JSON, CSV, Markdown
- Public share links
- Bulk library reprocess
Cancel anytime
Free includes 100 welcome AI credits · Secure checkout by Polar
FAQ
Semantic search questions.
That the search compares meaning rather than spelling. Your query and each save are turned into vectors, and closeness between them is what ranks. It is why "that ramen place in Shinjuku" finds a post that never used the word ramen.
Want the mechanism and the limits in full? Read how it works.
Remember everything.
Forget nothing.
Nemivo reads the caption on everything you save, writes the summary, adds the tags and files it away. Months later you ask for it in your own words.
Start Free
A free plan, not a free trial · No credit card · Nothing expires