timy2shoes

Another compounding issue is that they had to package the vector into two parts, which then have to both infect the same cell to get any effect. Which means you have to at least double the dose to get similar coverage compared to a single AAV vector (and actually more than double). Seems like a easy recipe for liver toxicity. Which is why most companies doing AAV therapy either target the liver or the eye (where AAV doesn't escape to the liver).

I feel like the parents were not well enough informed of the risks, and the PI rushed the therapy to be famous. Not the first time this has happened, and not the last, sadly.

Benchmarking that I've seen shows that the M5 Max outperforms the DGX Spark, e.g. https://www.reddit.com/r/LocalLLaMA/comments/1tfzsd6/m5_vs_d... or https://www.reddit.com/r/LocalLLaMA/comments/1tr7hzw/psa/. Seems to me that Apple is doing pretty well with local AI inference.

They are paying for the existing earth-bound data centers SpaceXai owns.

> He used xAI to move the Twitter debt so it couldn't be taken away for failure to pay debts.

Exactly. I think it was obvious he was shifting the debt around when xAI merged w/ X right after xAI raised a large funding round and had cash in the bank (which it could use to pay down the X debt).

Probably in a desperate attempt to stall margin calls on the debt, which would cause him to have to sell his Tesla stock, which might start the freefall in stock price, creating a negative feedback loop and cratering his empire. See also the news about Tesla shifting manufacturing to robotics.

This is an opinion piece, similar to her infamous hardware lottery paper. We shouldn't expect a repo on an opinion piece.

My wife refuses to watch it because it hit too close to home.

Of course they are. Why would you want revenue? If you show revenue, people will ask 'HOW MUCH?' and it will never be enough. The company that was the 100xer, the 1000xer is suddenly the 2x dog. But if you have NO revenue, you can say you're pre-revenue! You're a potential pure play... It's not about how much you earn, it's about how much you're worth. And who is worth the most? Companies that lose money!

A for-profit media company that foreign interests can pile money into for a quid pro quo, that’s somehow not legally a quid pro quo anymore.

The down rounds have started for those companies that either haven’t found product market fit, or aren’t in contention on leaderboards. Especially those companies that are a OpenAI wrapper and can be easily replaced by a new, more broadly applicable, foundation model or a new modality model.

https://www.businessinsider.com/ai-down-rounds-rise-valuatio...

The padding in healthcare is part of the system. One part is to have high prices so insurance can negotiate them down. And for hospitals in particular, prices are padded to subsidize emergency care for the indigent (which they have to provide without regard to ability to pay; thanks Reagan).

“the King stay the King.” —- D’Angelo Barksdale

Then take a Cauchy or a t-distribution. Basically anything with a longer tail than exp(x^2). The Gaussian summary will be misleading because of the tails.

They’ve chosen the path of commoditizing their complement. To ensure that ML capabilities are not a differentiating factor in the market, make ML capabilities a commodity available to everyone at the marginal cost.

Everyone talks about the chips, but Nvidia's true competitive advantage is CUDA. Only recently has PyTorch added AMD support, and benchmarks I've seen have shown that AMD has a lot of work to catch up.

From a practitioners perspective, I view this type of behavior as tuning the model more towards the exploitation side of the exploration/exploitation trade-off. I think a lot of recommendation engines do this (looking at you YouTube) because it’s more profitable.

My wife and I visited Paris last summer and biked everywhere. It felt very safe, not as safe as Copenhagen but still safe. I hope that with more time, as more drivers get used to bikes on the road it will get safer and more people will bike, creating a positive feedback loop.

Hi Nishant. Great work, as always.

Was it Clauset, Shalizi, and Newman 2009? That’s the one I know, but I believe there’s many more, especially from Clauset.

https://www.stat.cmu.edu/tr/tr853/tr853.pdf

I completely agree with your assessment and logic, but the parent posted suggested that socialization of healthcare (e.g. ObamaCare) is contributing to auto rates. That I don't understand.

> Socialize healthcare as it is forcing all sorts of market distortions

I don't understand how you come to this conclusion. Can you explain?

My suspicion has been that SNP array data is not that useful for drug discovery. They’re targeting the most common/variable SNPs, which I suspect don’t have a large health effect (except for maybe late in life, otherwise how would they get passed down). I would suspect the more valuable targets would be rarer, or arise de novo (as is common in cancer, eg driver mutations).

Who are you and why should we take your questions so seriously?

Or Zymergen: https://en.wikipedia.org/wiki/Zymergen

There was a puff piece from Sequoia[0] that tries to make the case that he is an altruistic genius and it hasn’t really aged well.

[0]: https://web.archive.org/web/20221027181005/https://www.sequo... (wayback machine is needed because sequoia has taken it down)

“ The best summer intern that I ever hired” supports that the CTO was a better judge of the candidate.

It's interesting that the researchers chose example problems where the minimum norm solution is the best at generalization. What if that's not the case?

Every time someone says “this time it’s different” (e.g. 1998 internet bubble, 2007 housing bubble, 2020 crypto bubble, etc) time proves that this time was not really that different.

> Whether they'd pass they Howey Test or not is irrelevant.

Not if you're the SEC enforcing securities laws, which only apply if they pass the Howey test.

It can also be the case where the interviewer doesn’t really understand the question and are going from prompts. So when you try to get further understanding of their hint from them, they just repeat the hint. I think this reflects poorly on the company culture, that they’re throwing interviewers out there who aren’t ready.