I hope many others are helped by daraxonrasib, the key factor seems to be how early the cancer is detected, because it is an extremely fast acting disease.

Father in law started chemo a few weeks ago and his condition deteriorated quickly, he's been at the hospital since; switching between ICU and general care. His abdomen fills up with fluid, around 6 liters every 5 days, draining the fluid plunges his blood pressure and he end up in ICU. They supply Albumin until blood pressure stabilizes and send him back to general. Only after the first chemo session the oncologists have stopped the treatment saying he's no longer eligible for chemo because of his over all health.

We're lucky enough to live an hour away from Johns Hopkins (though that's not the hospital he's currently at) and had an appointment scheduled but had to cancel since he's unable to walk.

The oncologist mentioned that daraxonrasib was available as an option when he first started chemo, but said he would only be eligible for it after trying chemo, so we know the drug is available.

Are there any tips from HN on how we can approach getting this medicine for my father in law?

By famous I mean someone whose biography is in the training data. All models know a lot more about Terrance Tao than they know about me, when he's working on his projects do the models know they don't need to explain "Besicovitch sets".

Since the system prompt likely includes something about not insulting the user, does the LLM modify it's responses if it realizes it's talking to famous politician, like "dont mention the time $politician was cancelled".

I'm not understanding this part. If these drugs have solved obesity and the whole host of associated diseases, including the number one killer; heart disease, shouldn't the insurance companies be clambering over each other to cover these drugs and heavily encouraging their use considering the cost reduction on the overall health system.

And if the incentives are misaligned with insurance companies why are governments not handing out GLP-1s to anyone who asks?

My thinking was to just make the responses available to users and let them see how models perform. But from some feedback, turns out users don't want to have to evaluate the answers and would rather see a leaderboard and rankings.

The scalable solution to that would be LLM as judge that some benchmarks already use, but that just feels wrong to me.

LM Arena tries to solve this with the crowd sourced solution, but I think the right method would have to be domain expert human reviewers, so like Wirecutter VS IMDb, but that is expensive to pull off.

I’ll make the bold claim that the following industries / companies would not exist without the USPS:

The Airline Industry: In the early days of American aviation, air transportation was unproven and not financially viable, until the USPS built the necessary infrastructure and gave contracts to airlines to allow them financial feasibility… starting in 1918! [1]

Machine Learning: In 1989 Yann LeCun wrote his seminal paper “Backpropagation Applied to Handwritten ZIP Code Recognition”, which used the USPS’s data set and has today become the hello world of machine learning tasks. More importantly this is the first commercial or industrial application of machine learning. [2]

Netflix: Before Streaming became a thing, Netflix was shipping DVDs via the USPS. The Postal Service adapted its processes and equipment to make this financially feasible, supporting Netflix through its transition to streaming. [3]

Amazon: Early Amazon was only a book vendor, the USPS offered special rates for books that made it possible for Bezos to be profitable from his garage … in 1994, thus birthing the behemoth it is today. [4]

Chickens: okay, not really. But the USPS ships millions of pounds of live chickens and other animals each year! [5]

[1] https://www.history.com/news/us-aviation-airmail-passenger-f...

[2] http://yann.lecun.com/exdb/publis/pdf/lecun-89e.pdf

[3] https://www.zdnet.com/article/u-s-postal-service-to-netflix-...

[4] https://faq.usps.com/s/article/What-is-Media-Mail-Book-Rate

[5] https://pe.usps.com/text/pub52/pub52c5_008.htm

China built their own.

I understand from your post that you are a business person, buying product, performing value added services and selling for profit. Although I know little about business, I would guess that if one of your suppliers raised the prices on one of the inputs to your finished goods, you would likely increase the price of your product to preserve your profits and continue your business as a venture. I would guess that you would not pay the additional cost out of your own pocket.

My question is; why did you not expect the same logic to play out in the tariffs situation? That any country would pay the additional cost of doing business out of their own pocket and not pass it on to the consumer?

He did get this part wrong though, we ended up calling them 'Mixture of Experts' instead of 'AI bureaucracies'.

Sutskever and Murati were methodical, they waited until the board was favorable to the outcome they wanted, engaged with board members individually laying the groundwork... and then just changed their mind when it actually happened!?

Isn't part of the prohibition on trades among officers and directors also because of the inside knowledge they have? Public companies generally report quarterly but the insiders presumably have up to the minute information on sales etc.

And while we wait on the quarterly data, consistent insider selling is indicative of ... something.

For example I used the prompt "As an astronaut in China, would I be able to see the great wall?" and since the training data for all LLMs is full of text dispelling the common myth that the great wall is visible from space, LLMs do not notice the slight variation that the astronaut is IN China. This has been a sobering reminder to me as discussion of AGI heats up.

[1] https://github.com/cpldcpu/MisguidedAttention

I was going to add reviews on each model but ran out of steam. Some users have messaged me saying the comparisons are still helpful to them in getting a sense of how different models respond to the same prompt and how temperature affects the same models output on the same prompt.