Recent submissions

Comment · 10 hours ago
Gives me the impression of being agent-written:

- one page with reasonably looking/appealing structure, tables, bullet points, etc.

- fairly long

- looking more closely, things start to be quite generic, lacking nuance ...

The issue I have with this is not that it is agent-written or -drafted, but rather that the signal-to-noise ratio seems to be off.

Comment · 28 days ago
The crucial question, however, is "Where do the weights come from?" :-)

btw, "kernel smoothing" etc. are still fairly complex terms that sound a bit like math magic: 1-nearest-neighbor (1-nn), k-nearest-neighbor (knn), and weighted knn would be even simpler and possibly more descriptive.

Comment · 6 months ago
So maybe we need programming languages that are really good/supportive at handling errors (while not introducing more of them)?
Comment · 1 year ago
Well, good management/tech leadership is about making sure that the risks coming from individual failure points (10 people in your example) are recognized and mitigated, and that the individuals involved can flag risks and conflicts early enough so that the overall project success probability does not go down as you describe...
Comment · 1 year ago
The problem with collapsed repeated chords comes not only from the data processing -- most Ultimate Guitar songs are written down entirely ignoring how often a chord is repeated -- the classic "lyrics plus chords" format is incomplete and requires the player to somewhat know the structure of the song anyway. The write-up usually just gives hints where, relative to the lyrics, the chord changes.
Comment · 1 year ago
Comment · 1 year ago
While this is from ByteDance, who also are behind TikTok, this algorithm is likely not the one behind TikTok.

Instead, it is likely a component that powers ByteDance's commercial recommender system solution, which they market to e-commerce companies: https://www.byteplus.com/en/product/recommend

This was mentioned in past discussions of the paper on HN.

And even if aspects of this are used for TikTok: (a) it would be just one of many components of their recommendation system, and (b) the TikTok recommendation system has changed a lot during the 2+ years since this has been published.

So take what you see here with a grain of salt. After reading the paper and the code, you will NOT know how TikTok's recommendations work.

Comment · 1 year ago
Most of these sound like team/people problems, not technology problems.

Like most problems in IT in general :-)

Comment · 1 year ago
... and the compound interest of being consistently slightly better makes these people much better than others in the long run.
Comment · 1 year ago
People here in the comments seem to focus on whether it is possible to predict an artist's success based on secondary "civic" virtues, and criticize the author for having subjective criteria for what "success" means. I'd argue that independently of how you measure success, all other things being equal, having diligence and other civic virtues will get you further, on average.

That said, the most interesting lessons are in the first and sixth (the 2nd 6th, the actual 6th) item: How to do a better/more widely scoped job than what you got hired for (by understanding how interests, incentives and responsibilities align in an org) and the fact that in most places, most people are not serious (meaning they tend to not go deeper, look at the big picture, etc.).

Comment · 1 year ago
"The people" are the president's superior.
Comment · 1 year ago
IM software can be closed, too, and in a healthy company the expectations around IMs should not be that they are "instant", but more of an async medium like email.
Comment · 2 years ago
Next step: The Onion buys X.
Comment · 2 years ago
Really interesting story. Different from a standard VC biography.

Thank you for sharing it, Adam!

Comment · 2 years ago
I do not think that guy was only caught by accident. More red flags would have been raised if there had been further interviews.

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