Treesrule14

https://www.bloomberg.com/news/articles/2024-07-01/dan-davie...

Dan davies did a great interview on odd lots about this he called it accountability sinks

what if people enjoy being in good shape and having engaging workouts in the home?

Thanks, I guess, none of that stuff seemed super useful to cut systematically, but I'm gonna run some tests.

There are a lot of webcrawlers where the chief feature is turning the website into markdown, I don't quite understand what they are doing for me thats useful since I can just do something like `markdownify(my_html)` or whatever, all this to say is that I wouldn't find this useful, but also clearly people think this is a useful feature as part of an LLM pipeline.

Fractional AI | Founding Engineer | San Francisco | Onsite | Full-time

Who: We're Fractional AI -- the dev shop for difficult enterprise applications of genAI.

Looking for: You! Full-stack founding engineers (5+ years of experience, 150k-210k cash, meaningful equity, 99% healthcare premium coverage )

How: apply https://jobs.lever.co/fractional or reach out to me (ben@ fractional.ai)

Why we may be a great fit:

-High impact AI projects - we work on companies' hardest problems to get genAI into production (in the same quarter you might build an AI phone agent for one customer and automate a complex workflow for another customer)

-Culture of a startup but substantive problems to solve that impact millions of users

- Curious, humble, banter-y in-person team comprised of multi-time founders

Why we may not be the best fit:

-Excellence isn't really really important to you (this is less of a 'move fast and break things' role, though we respect that ethos!)

-Predictability is important to you - we work across customers, tech stacks, industries etc

-Interacting with customers isn't your thing

Has anyone else found a good way to swap out models between companies, Langchain has made it very easy for us to swap between openai/anthropic etc

also one funny thing is that the author mentions power constraints, but then doesn't calculate how many terraflops for example the us grid can produce etc.

Just to echo what you are saying, I've read the first chapter and I thought the thesis is interesting and the writing is good but I failed to be convinced becuase it makes a lot of classic mistakes you make in science. Even though logical arguments are being made there is no attempt not to overfit the data.

The author brings out a lot of stats "smart high-schooler", "effective compute", "OOM", "Test Scores", "inference efficiency" but doesn't do a good job of explaining how the author predicted these things before hand (preregistering) and how they actual will result in new technologies or how we can extrapolate past the trend line.

Also in the unhobbling section "Tools: Imagine if humans weren’t allowed to use calculators or computers. We’re only at the beginning here, but ChatGPT can now use a web browser, run some code, and so on. "

This is so non-specific (because no one has really commercialized anything with this yet) that I worry that we don't actually know if we can make the kinds of effective tools the author is talking about. Would love some feedback on these critisms

At least in the US the fed has specifically had full employment as one of their goals at least for the last few years so I'm pretty skeptical of "corporations control the fed arguments"

Like I agree there's a lot of regulatory capture in the US but I just don't see any reason to think the fed is responding to big business rather than their own preferences.

Ye olde Motte and Bailey.

There are in fact people who are saying its only corporate inflation.

The real reason i'm skeptical of the greed argument is that its just a vibes based guess on whats happening and not based on any data or insight into the situation.

New York, NY Remote | Local, Full Time Stack: C, Java, RTOS, Python, Perl Resume: http://careers.stackoverflow.com/benjaminkadish Contact: Baruch.Kadish@gmail.com

I am looking to join an engineering team working on new technology (energy, cars, consumer electronics, ect.)