Show HN: Map of YC Startups

 (yc-map.vercel.app)

94 points | by yoouareperfect1 天前

19 comments

  • HeyTomesei1 小时前
    Looks nice, but I'm lost. What do the colors represent? What do the axes #s represent?
  • paxys1 天前
    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.
    • ascorbic23 小时前
      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 天前
    Cool project, but missed opportunity to name the arbitrary dimensions Y and C...
    • lovestory1 天前
      My dumb ass was trying to figure out what each dimension meant
      • tptacek1 天前
        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.
        • bravura23 小时前
          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.
          • tptacek22 小时前
            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-knyaz1 天前
        same
    • Bilal_io1 天前
      OP made the change
    • yoouareperfect1 天前
      haha awesome, shipped!
      • ProofHouse23 小时前
        I figure why not plot them with an X and Y (Y,C) of some sort
  • rl_for_energy1 天前
    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
  • crush_robo_153622 小时前
    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.

  • rrr_oh_man1 天前
    Cool concept! What are the X and Y axes?

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

    • tptacek1 天前
      They're semi-arbitrary, dimensionally reduced from OpenAI embedding vectors.
  • tmshapland1 天前
    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.

  • natural2193 小时前
    Wow. This is amazing. Extremely practical to use, I'm glad I checked H.N. yesterday.
  • kure2561 天前
    Love that, what are Axes Y and C?
    • DrawTR1 天前
      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...

  • jb19911 天前
    Filters are unreadable on mobile.
  • zild3d1 天前
    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
    • skeeter20201 天前
      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.

      • gavmor23 小时前
        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.

        • yoouareperfect23 小时前
          OP here, Is there a way to figure that out?
          • gavmor22 小时前
            (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.

  • woodylondon1 天前
    Really nice to see - also, It would be great when filtering if there was a tabular view at the bottom as well.
  • welder1 天前
    Company status isn't up to date... I know there's more than 1 public company that went through YC.
    • yoouareperfect1 天前
      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
  • uncomplexity_1 天前
    hella nice mate very interesting

    what's the x and y axes?

    • jerrygenser1 天前
      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
      • gavmor23 小时前
        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.
  • k-i-r-t-h-i1 天前
    This is awesome! Are you able to also add F24?
  • mring3362123 小时前
    i'd like a filter by target market (US, EU, APAC...)
  • gniting1 天前
    Nice! What's the tech stack?
    • yoouareperfect1 天前
      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)

  • ksec1 天前
    I didn't know YC does Government, Healthcare, Industrials, Real Estate and Construction. All these are great sectors and never made the headline.