Show HN: Map of YC Startups

 (yc-map.vercel.app)

86 points | by yoouareperfect1 day ago

17 comments

  • paxys9 hours ago
    There's no need to include an X & Y axis, labels and gridlines if they all have no meaning. A simple cluster diagram is enough.
    • ascorbic7 hours ago
      I agree it would be less confusing if they weren't there. I'm sure I'm not alone in spending some time trying to work out what the axes were.
  • Liftyee1 day ago
    Cool project, but missed opportunity to name the arbitrary dimensions Y and C...
    • lovestory20 hours ago
      My dumb ass was trying to figure out what each dimension meant
      • tptacek20 hours ago
        That doesn't make you dumb; there is no intuitive meaning for the axes chosen; you can think of them, roughly, as statistically chosen to maximize clustering.
        • bravura7 hours ago
          Statistically chosen to maximize *some particular loss measure, which in this case might be the t-SNE or UMAP criterion, and is computed only globally and not for different filters.
          • tptacek6 hours ago
            Right (I mean, I'm saying "right" but really I should just say "I'm taking your word for it"), but even more fundamentally this is dimensionality reduction from an OpenAI embedding vector, which seems almost like the asymptotic limit of inscrutability.
      • alex-knyaz19 hours ago
        same
    • Bilal_io22 hours ago
      OP made the change
    • yoouareperfect22 hours ago
      haha awesome, shipped!
      • ProofHouse7 hours ago
        I figure why not plot them with an X and Y (Y,C) of some sort
  • crush_robo_15366 hours ago
    Love this! It'd be interesting if some builds this but adds more dimensions (similar to Company status) to it that you can query or group by. For example, if I look at S21 and W21 batches, then it'd be nice to know things like -

    1. How many of these companies made it to series A, series B, etc

    2. How many of these companies have > x employees (where x can be 5, 10, 20, etc)

    3. How many of these companies had a founder that moved on to something else

    This does require a lot more intelligent data scraping or manual data collection though.

  • rl_for_energy19 hours ago
    It’d be nice to just see the name of the company on click instead of going to the website (I’m on mobile). Trying to find our company
  • rrr_oh_man1 day ago
    Cool concept! What are the X and Y axes?

    Oh, and your website has an unchanged Wordpress favicon...

    • tptacek1 day ago
      They're semi-arbitrary, dimensionally reduced from OpenAI embedding vectors.
  • tmshapland1 day ago
    Really neat! We were Tule, in the industrials part of the map in grey.

    There's something wonky when I zoom in on Chrome on my laptop. It abruptly shifts to another part of the map.

  • kure25616 hours ago
    Love that, what are Axes Y and C?
    • DrawTR15 hours ago
      Apparently inspired by a comment on this very post! (Above yours, right now.)

      > Cool project, but missed opportunity to name the arbitrary dimensions Y and C...

  • zild3d16 hours ago
    fun, though I also got stuck on what the Y and C axes represent initially. IMO just hide the axes altogether, since the goal is just some visual clustering/similarity
    • skeeter202010 hours ago
      Maybe I'm slow, but clustering on what dimension? The lack of axes and labeling makes it pretty confusing to me, but I'm a dinosaur.

      Visuals that are not self-explanatory make me feel dumb.

      • gavmor7 hours ago
        We don't know what to label those features/dimensions, because they're a reduction form higher dimensions that we also didn't bother to interrogate.

        It's possible to figure them out. I wish OP would.

        • yoouareperfect7 hours ago
          OP here, Is there a way to figure that out?
          • gavmor6 hours ago
            (Not OP) I can think of a convoluted and expensive pair-wise comparison method, but I hope there's also a way to figure this out during the application of principal component analysis in a way I don't understand.

            Edit: I'm thinking it can't be done without experimentation on the embedding model.

            Edit2: Ah, even that might not yield results, because as the basis is derived interstitially through computation, there's no guarantee the features of the final coordinate system will have any accessible relationship to those of the initial basis.

  • welder17 hours ago
  • woodylondon12 hours ago
    Really nice to see - also, It would be great when filtering if there was a tabular view at the bottom as well.
  • jb199123 hours ago
    Filters are unreadable on mobile.
  • k-i-r-t-h-i8 hours ago
    This is awesome! Are you able to also add F24?
  • welder17 hours ago
    Company status isn't up to date... I know there's more than 1 public company that went through YC.
    • yoouareperfect14 hours ago
      Check the filters, not all batches are selected as default. Only the latest ones. If you select all of them, then there are many public companies
  • mring336217 hours ago
    i'd like a filter by target market (US, EU, APAC...)
  • uncomplexity_1 day ago
    hella nice mate very interesting

    what's the x and y axes?

    • jerrygenser1 day ago
      they don't have meaning by themselves. they are two dimensions that umap projected the original embeddings down to in order to show a combination of local neighborhood similarity or closenes
      • gavmor7 hours ago
        Well, they do have meaning by themselves, but it's more work to figure that out. All regular, predictable relationships "have" meaning because all meaning is prescribed. And since we've captured many such prescriptions in LLMs, they can do a decent job approximating those.
  • gniting16 hours ago
    Nice! What's the tech stack?
    • yoouareperfect15 hours ago
      For scraping and all the processing, typescript. Embeddings: openai

      For visualizing react (nextjs) + plotly (though the lack of mobile zoom makes me question if I should chsnge it)

  • ksec12 hours ago
    I didn't know YC does Government, Healthcare, Industrials, Real Estate and Construction. All these are great sectors and never made the headline.