You give two critiques relating to the exact numbers the author has choose (equity package at join, valuation at exit), neither of which is really related to the authors hypothesis that people misvalue EV from equity.
You can slide these numbers around however you want and the point still stands, even if you disagree with it for other reasons
Pretty clearly slop, with some of the scandals make no sense. Take Ripplings "scandal":
> Parker Conrad's redemption arc after Zenefits hit a plot twist when Rippling sued competitor Deel for planting an undercover spy inside Rippling who was paid €5,000/month by Deel's CEO to steal trade secrets . The DOJ opened a criminal investigation. Deel allegedly ran the same playbook at crypto HR startup Toku. YC uses Rippling for their own HR — awkward.
I am curious what the motivation for creating this was
> Figma is targeted towards designers who create thoughtful design systems and cohesive UIs and who don't code, while this is targeted towards vibe coders who can't design. Two different circles that intersect to some level.
this overlap has been widening incredibly quickly. lots of designers are now writing code with the help of cursor, claude code, etc.
even if you believe "real designers" wont ever use this product, it's not hard to see how a low barrier-of-entry tool could affect Figams bottom line. slowing down Figma's adoption from the new wave of entry-level designers who dont already have muscle memory would not at all surprise me at all.
The quality with the 1M window has been very poor for me, specifically for coding tasks. It constantly forgets stuff that has happened in the existing conversation. n=1, ymmv
> It's powerful but dangerous, and is intended for developers who understand how to safely configure and test connectors.
So... practically no one? My experience has been that almost everyone testing these cutting edge AI tools as they come out are more interested in new tool shinyness than safety or security.
> "As long as your curve is sufficiently expressive all architectures will converge to the same performance in the large-data regime."
I haven't fully ingested the paper yet, but it looks like it's focused more on compute optimization than the size of the dataset:
> ... and (2) are fully parallelizable during training (175x faster for a sequence of length 512
Even if many types of architectures converge to the same loss over time, finding the one that converges the fastest is quite valuable given the cost of running GPU's at scale.
You can slide these numbers around however you want and the point still stands, even if you disagree with it for other reasons