spacemanspiff01

From reading online, linux battery seems to be a-lot better on the new intel board, and also if you configure linux with sleep to hibernate, you get really good behavioral performance. My experience is that the sleep states really drain the battery since the low power mode is not _that_ low power, at least with the ram I have.

For context I have a 13th gen intel with the old battery and only get ~4hrs of battery life, but generally go a week or 2 between full shutdowns, since I hibernate all the time. I can close the lid, throw it in my backpack, not use it for the weekend, and when I open it it is ready to go, not having lost charge.

I generally have a better experience than when I was on windows.

I had good experience within warranty period, but there was still some funky-ness with a non-posting board... I do sometimes wonder if it was a bios corruption - It died after running the battery 100% to zero...

That being said, I think the particular bios-chip is a design flaw that they should communicate about and fix for future boards... Probably a teething issue thing, but they should fix it. Either a usable replaceable chip so they can just send out a fix, or the fallback bios chip....

So one of my thoughts is maybe AI makes this more viable. You continually refactor the code base. Often when you start something you do not know the correct abstractions anyway - so vibe-code it.

Then once you have the kinda-sorta prototype, do a full rewrite, try to do component modules with interfaces.

(maybe do multiple rewrites depending on architecture complexity)

Then incrementally improve the software quality of each of the components.

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My thesis in this is that the cost of a refactor has dropped precipitously, LLMs are really good at doing translations, and are pretty good at finding good abstractions if you work them a bit. It is also much faster, becuase in your refactor you try and focus on the big picture and interfaces, and yolo the components... Make sure that they work, but if there is a particular edge case you can fix it later.

I don't know for sure, but I am testing this out and am halfway through a rewrite of a large project into rust from c++ (second step from above) - I am mostly vibe-ing the components, But am paying a lot of attention to the interfaces and layout.

This is really cool - I really like the beam search idea,

You dont have to repay per hour? It seems like that is a design issue - Like yes we will keep these in whatever cache you want, but you will have some sort of renewal period

You should have a hosted site for this - It is entirely frontend - right?

Not knowledgable, but irradiated flies should not be expected to be irradiated again. There are 3 population pools:

1. The Factory spawning population - This is self-sustained, and never encounters radiation.

2. A subset of the spawned males from the factory population are irradiated, making them sterile.

3. The wild population, consisting of the sterile males + wild males + wild females.

If for some reason the sterile population is not fully sterile (unlikely), then maybe there is a gene that helps for radiation resistance, but the children of that strain will not encounter radiation, so it fades away.

The factories are not going out to the regions where the flies are deployed to get new fly studs.

The US (and Europe) have been under investing in shell production since the end of the cold war.

North Korea is a dictatorship, which one of its main deterrents is to shell soul to oblivion.

but some tokens are not really needed? This is probably bad because it is mismatched with training set, but if you trained a model on a dataset removing all prepositions (or whatever caveman speak is), would you have a performance degradation compared to the same model trained on the same dataset without the caveman translation?

> How do they audit that Anthropic can't alter model outputs for contexts they (the ethics board or whatever it's called, can't remember) don't like?

I was thinking that Anthropic would just be providing the models/setup support to run their models in aws gov cloud. They do not have any real insight into what is being asked. Maybe a few engineers have the specific clearances to access and debug the running systems, but that would one or two people who are embedded to debug inference issues - not something that would be analyzed by others in the company.

The whole 'do not use our models for mass surveillance' is at the end of the day an honor system. Companies have no real way of enforcing that clause, or determining that it has been violated. That being said, at least historically, one has been able to trust the government to abide by commercial agreements. The people who work in cleared positions are generally selected for honesty, and ability, willingness to follow rules.

Is it a voice to voice model, or a voice->text->voice?

I might have missed it in their writeup.

Anyone know the tokens/sec for llm inference?

How about Nvidia, they have both good hardware/software.