VectorVFS

Data

vector search

SHOW HN2025

279points
138comments
#202of 3,837 in 2025
Top 6%of 2025 by points

How HN took it

MixedNo clear lean

7 positive, 16 neutral or asking, 3 mixed, 10 critical, out of 36 comments about the project. Thread score +0.03.

Idea: 6 for, 2 againstPerformance: 0 for, 6 againstDesign: 0 for, 2 against

The thread, comment by comment

Each row is a top-level comment and its replies, in HN's order. Select a cell to open that comment on HN.

positive neutral or mixed critical about another commenter or something else the maker

What HN said

What people liked

  • Clever idea of storing embeddings in inodes

    From 2 comments
    • Fun idea storing embeddings in inodes! Very clever! I want to point out that this isn’t suitable for any kind of actual things you’d use a vector database for. There’s no notion…
      malcolmgreaves on HN
    • this is actually a great idea
      natas on HN
  • Nostalgia for BeFS and WinFS style metadata search

    From 1 comment
    • This immediately made me nostalgic for BeOS's BeFS or Windows Longhorn's WinFS database filesystems, and how this kind of thing would have fit them perfect. So much cool stuff…
      pseudosavant on HN

Objections

  • Search is linear over all files, not suited to real vector database use

    From 2 comments
    • Fun idea storing embeddings in inodes! Very clever! I want to point out that this isn’t suitable for any kind of actual things you’d use a vector database for. There’s no notion…
      malcolmgreaves on HN
    • If there is no indexing, how will your search time not increase linear or worse with the number of files?
      PeterStuer on HN
  • Zero-overhead indexing claim is misleading

    From 1 comment
    • > Zero-overhead indexing Embeddings are stored as extended attributes (xattrs) on each file, eliminating the need for external index files or services. Ain't no such thing as…
      yencabulator on HN
  • Docs lack GPU backend and uninstall details

    From 1 comment
    • Great idea indeed. The documentation needs a bit more information to be useful. What GPU backends are supported for example? How do I delete the embedding information after I…
      anotherpaul on HN

Requests

  • Support for macOS

    From 1 comment
    • I wonder if I could use this locally on my macbook. The finder applications built-in search is kinda meh.
      adenta on HN
  • Explain how to delete embeddings

    From 1 comment
    • Great idea indeed. The documentation needs a bit more information to be useful. What GPU backends are supported for example? How do I delete the embedding information after I…
      anotherpaul on HN
  • Add an embedded vector index such as Weaviate

    From 1 comment
    • Might be interesting to add an optional embedded Weaviate [1] with a flat-index [2] to the project. It wouldn't use external services and is fully disk-based. Would allow you to…
      thirdtrigger on HN

Questions

  • Is the embedding for the whole file or per chunk?

    From 1 comment
    • is the embedding for the whole file? or each 1024/512 byte chunk?
      asadawadia on HN
  • How does it compare with LanceDB?

    From 1 comment
    • Files-as-vector stores is LanceDB's value proposition. How do you compare in performance, etc.?
      esafak on HN
  • How can users debug why a file surfaced?

    From 1 comment
    • If VectorVFS obscures retrieval logic behind opaque embeddings, how do users debug why a file surfaced—or worse, why one didn’t?
      b0a04gl on HN

Alternatives named

  • LanceDB

    From 1 comment
    • Files-as-vector stores is LanceDB's value proposition. How do you compare in performance, etc.?
      esafak on HN
  • Finder search

    From 1 comment
    • I wonder if I could use this locally on my macbook. The finder applications built-in search is kinda meh.
      adenta on HN
  • BeFS and WinFS

    From 1 comment
    • This immediately made me nostalgic for BeOS's BeFS or Windows Longhorn's WinFS database filesystems, and how this kind of thing would have fit them perfect. So much cool stuff…
      pseudosavant on HN

Category, keywords, summaries and labels on this page were made by AI (Thread Reader v2) and not checked by hand. Each point links to the comments it came from.

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