nikhizzle

IMHO from guy working with heavily quantized models and trying to get results. I do think quantization at less than 4-bit requires more advanced mathematics than is usually used. See Google Scholar Babak Hassibi (founder of Prism ML). Generally am amazed about the resiliency of these new data structures to approximation.

Got to my first job out of college and they gave me core dumps and had me debug the kernel for a year. Not my kind of fun, but definitely got me skilled in the art of low level.

I believe I potentially wrote that code in 2006 when working at Apple on HFS. What a remarkable world!

Ex-Apple kernel engineer here, Apple will deal with the memory shortage by making software more efficient in ram usage. Apple will just make every aspect of the system more and more memory efficient. They've done it before over and over and can do it again.

Almost trivial using open source tools, the question is how it performs without calibration/fine tuning.

Will think about it, trying to keep it running snappy with minimal cost since no clear path to monetization

I’ve had luck getting higher rate of interviews for remote jobs by applying first. To that end I built a site which shows job postings as they are posted - https://tangerinefeed.net

I think the interesting thing here for those of us who use open source frameworks is that we can ask the LLM to look at the source to find the answer (eg. Pytorch or Phoenix in my case). For closed source libraries I do not know.

I may eventually get one of these just to use with Claude code. Been looking for the lightest best machine to use with agents.

sample output here - https://www.science.org/action/downloadSupplement?doi=10.112...

This is at the very beginning of begin feasible. I do not know anything about photonics, maybe someone who does can comment on scalability?

Thanks for sharing this. Needed something meaningful this morning.

I mean tether redemptions. Hedge funds liquidating treasuries last year significantly distorted yields for a short while.

Armchair economist here - one implication of this is that a crypto liquidation will cause global interest rates to spike at a time when they will need to be lower to calm the markets.

Selling massive amounts of debt with no additional demand means the required return must be higher.

Thanks for this, this is very useful for a related ai project I’m working on.

Thanks! Will take a look.

A job feed for remote jobs - https://tangerinefeed.net/

This is something I’ve needed myself over the last few years as jobs become shorter and shorter lived. Keep on improving it as some kind of compulsion.

I moved out of the kernel in 2008 and never went back, so don’t have a wise opinion here which would be current.

Ex-Apple File System engineer here who shared an office with the other ZFS lead at the time. Can confirm they link to the wrong profile for Don Brady.

This is the correct person: https://github.com/don-brady

Also can confirm Don is one of the kindest, nicest principal engineer level people I’ve worked with in my career. Always had time to mentor and assist.

I have a rough idea of my friend’s schedules, and I call them when I’m driving the kids around, or we text. Staying in constant contact with a few very close friends about my mental health has floated me through my mid-40s.

A tip I got from a friend in his 60s was that even when you lose friends, life is great because you constantly have the opportunity to find new ones. I am in a new close friends renaissance in my 40s, just be vulnerable and don’t take rejection personally.

So which one is it? Code is contract and he should get to keep the money. Or crypto is governed by laws outside of crypto and so he violated the “spirit” of the code and hence is a criminal?

It seems like right now the crypto industry makes the decision to their convenience on a daily basis.

Perhaps Google is scared about losing its cash cow in search, and is needing to cement their position in cloud compute.

I’m not sure there is a huge advantage at the beginning, perhaps even a disadvantage because of the ease of use of Elixir. As you get more advanced you start using Erlang here and there to access things not in Elixir. For example, Elixir only has lists not arrays, so if you need a good indexed data structure you end up using some Erlang. Also once you start getting into Mnesia etc, Erlang is the way. All this said, still very early on my Erlang journey.

Crawler built in elixir

I’m working on it. This my hobby on my nights and weekends while having many other commitments. Will have that soon. Really appreciate the feedback!

This is super interesting. I run a remote job board as a hobby (see below). My best estimate of jobs my crawler found as well as ones it did not is about 12,000 - 15,000 remote jobs added in February. This is a much higher proportion than I would have guessed otherwise.

(Edit: I am wrong, I am comparing US jobs to global remote jobs, still interesting, just bad proportions)

https://tangerinefeed.net/

Yes this is my nights and weekends while having many other commitments. I’m working on all of that

This is why I built tangerine feed for remote roles. It turns out there are tons of remote roles and you can get an edge by applying first. It has unfortunately come down to a numbers game with low odds.

https://tangerinefeed.net

Still working on filtering etc, pardon the work in progress.

hey Ali - I love your post. Have you considered cofounder matching via YC? I am doing healthcare AI and have found collaborating with physicians is very helpful.

https://www.ycombinator.com/cofounder-matching

I work alot of hours, and aim to solve organizational issues at the root cause with systematic solutions. This way, I minimize hands on management time.

Multi startup CTO/CPO (including one yc, and one growth stage high growth). So AI has been interesting for me across the three businesses I am working on. For two of them, it has enabled us to do things which were not possible at high fidelity before (eg. unstructured data entry). For the third growth stage place, it is allowing us to increase the net margin as we scale by reducing the number of operators we need to hire.

I see the biggest impact in more traditional startups which involve a lot of manual labour. For us, LLMs are letting us scale much faster than we could before.

For the dev teams I manage, LLMs provide a lot of shortcuts whereas before we spent extensive time hand tuning parsers and simple automations.