Cool project, but missed opportunity to name the arbitrary dimensions Y and C...
haha awesome, shipped!
I figure why not plot them with an X and Y (Y,C) of some sort
Show HN: Map of YC Startups
31–40 of 47 posts
Re: Show HN: Map of YC Startups
#32Earlier quoted context omitted.
My dumb ass was trying to figure out what each dimension meant
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.
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.
Re: Show HN: Map of YC Startups
#33There's no need to include an X & Y axis, labels and gridlines if they all have no meaning. A simple cluster diagram is enough.
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.
Re: Show HN: Map of YC Startups
#34i'd like a filter by target market (US, EU, APAC...)
Re: Show HN: Map of YC Startups
#35Earlier quoted context omitted.
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.
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.
OP here, Is there a way to figure that out?
Re: Show HN: Map of YC Startups
#36i'd like a filter by target market (US, EU, APAC...)
Coming for v2
Re: Show HN: Map of YC Startups
#37Earlier quoted context omitted.
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.
OP here, Is there a way to figure that out?
(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.
Re: Show HN: Map of YC Startups
#38Earlier quoted context omitted.
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.
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.
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.
Re: Show HN: Map of YC Startups
#39Love 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.
Re: Show HN: Map of YC Startups
#40There's no need to include an X & Y axis, labels and gridlines if they all have no meaning. A simple cluster diagram is enough.
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.
That’s why I’m here