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I'd be careful reading too much into data on top performing funds. The law of large numbers makes it so that smaller funds are more likey to outperform (and underperform) since they have higher variance. Couple that with the strong correlation that speciality funds are more likely to be smaller and it's not surprising.

A more accurate analysis would involve looking at all VCs and seeing if you can apply those rules in advance: What would your IRR be if you invested in all the specialty biotech funds as a basket?


The small funds on that list are all tech funds. The larger funds are all biotech.

Biotech funds are "specialist" but they aren't really small. Biopharma is a top 5 subsector of VC (by some estimates it is the 2nd biggest sector after software) and the largest sector by far of healthcare VC, with $17B+ invested in 2018. Orbimed manages $10B+ (though some of that is public equity). Flagship manages many billions (latest fund was $800M+). Foresite manages over $2B, last fund was $668M. Many other examples

I mentioned i was writing a longer post on the topic, that chart is a snapshot. If you invested in biotech VC as a basket recently it would outperform tech [0]. There's more recent data as well but i dont have the link offhand

[0] https://lifescivc.com/2016/11/biotech-venture-capital-mythbu...


I was more referencing the tweet which looked at the performance of a particular vintage of those funds: https://mobile.twitter.com/zavaindar/status/1159660549615116... none of which reached $1 billion vs the largest funds on the second page https://mobile.twitter.com/zavaindar/status/1159660549615116...

I look forward to seeing how the newer larger biotech funds do/seeing more recent data during a time where public tech outperformed rest of public market.


For patterns like this I like to reach for request coalescing. Here's an example of a package that does it in the golang standard lib https://godoc.org/golang.org/x/sync/singleflight


Big fan of request coalescing, was introduced to it by Varnish. Really helps when a cache entry drops.

I believe what the article did is called a "grace period".

Are there any open implementations of distributed request coalescing? Pretty easy to do using a service proxy, but what about something like a Raft cluster?


I'm curious if assigning a random value from an acceptable range for the expiry time is a common approach. I assume that wouldn't work for all cases, but might spread the avalanche for some.

Google searches on this topic (random expiry to mitigate an avalanche cache refresh) turn up very little.


It is very common and often referred to as jitter.

A variation, called scaled ttl, was nicely discussed in this video. https://www.youtube.com/watch?v=kxMKnx__uso



Thanks!


I would highlight the relevant paragraph when you expand an annotation. That's very solid. (Sticking with angular?)


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