dhash's comments

Post Mendeley shutdown, zotero has been an awesome replacement (while not being controlled by Elsevier). Given the amount of PDF's that I see during researchy times in my life, it's been an absolute godsend. Highly reccommend!

these guys also have some really nice distributed systems explainer videos

I love this kind of research since it correctly identifies some issues with the way the public interacts with LLM’s. Thank you for the evening reading!

I’d love to see future work investigating - how does this compare to expert users (doctors/llm magicians using LLM’s to self diagnose)

- LLM’s often provide answers faster than doctors, and often with less hassle (what’s your insurance?), to what extent does latency impact healthcare outcomes

- do study participants exhibit similar follow on behavior (upcoding, seeking a second opinion, doctors) to others in the same professional discipline

I love the vibe of squishing formats together — like APE.

I’ve had this problem in the past (shudders in Bazel’s WORKSPACE file) and what eventually ended up saving my bacon was org-babel.

Yes, it does mean that emacs is a build-dependency, but honestly literate programming pretty excellent for untangling the complexities of a large build.

fantastic — I’m sure folks who have poked binaries or written patches have all had the desire for something like this. Glad to see that it exists!

I’m definitely going to use this for solving some problems that i’m currently facing.

For anyone curious about what hare-brained scheme that one could hatch with this, i’d like the ability to do something like a PGO’d shared library —- watch the process run and exit, tracing all dlopen’s to create a new shared library with only the right functions from all the referenced libraries.

Hopefully this works, and if not, i’ll at least fail at something interesting :)

One of the cited references is Guillermo Webster — and while the article lists uncmin as the desired artifact, the project that uncmin was developed for takes the concept beyond layout to back solving for desired program outputs.

Carbide [0] is the project that encapsulated some of this in a workable demo. It’s really cool!

[0] https://alpha.trycarbide.com/

My favorite illustrations for the concepts discussed here (in an accessible form, not the processor optimization manuals) has long been [0].

For me, this really makes working with a modern microprocessor a science, as anyone who has written benchmarks knows -- it's difficult to reason about the complex behaviour and performance cliffs without testing.

Another excellent example of the weirdness has to be JVM anatomy quarks [1]

[0] https://www.lighterra.com/papers/modernmicroprocessors/

[1] https://shipilev.net/jvm/anatomy-quarks/

Taking a further step back from LLM’s, this is called portfolio / ensemble techniques in the literature.

A common practice in more formal domains is to have a portfolio of solvers and race them, allowing for the first (provably correct) solver to “win”

In less formal domains, adding/removing nodes/trees in an online manner is part of the deployment process for random forests.

+1 to miscommunication, but host_volume is indeed what I’ve used to allow host files into the chroot. Not all drivers support it, and there are some nomad config implications, but it otherwise works great for storing db’s or configurations.

I loved this post, and patchelf is a real gem of a utility.

Congrats Udayan on the launch!

It’s really neat to see the open-face nature of this product as compared to others doing EWOD with a second immiscible phase / a glass top plate that the droplet is squished under — makes it much easier to do IO to the chip.

Are the chips disposable, to accomodate contamination constraints, or is there some on-site surface reconditioning that we can do to refurb the chips?

Interesting to note that this is down-scaled from some of your older prototypes. What design tradeoffs made you go fron large open faced arrays to a set of smaller arrays?

They will if its cheaper

I believe physalia [0] explores this concept at production scale quite well.

[0] https://blog.acolyer.org/2020/03/04/millions-of-tiny-databas...

Hi! I have tried all the gantt chart libraries, and also ended up building my own. The library isn’t open source (yet) but does this, as well as some other stuff around optimizing for print. Email in profile

This is really cool! I spent the time a couple years back to build a windows sysroot, but having a macOS one was on my mind for a while. Will definitley use

Hi, PL-funded founder here.

This was one of the things that made IPFS a non-starter for us. We ended up grafting Hashicorp Vault into kubo (the go-ipfs implementation) so that we could use IPFS and have things like detetes and access revocation that actually work.

This article is kinda silly, but very true. I have a LUT in my notes that is “VC says” “they actually mean”

I’ve been using ultorg for a couple years now, very happy to see it reach broader audiences

Its’s a game changer tool for me, providing an intuitive graphical query construction interface (via a simple stacked table header, very sum-product like) and cross-db joins. The multi-column report style layouting is also really useful for looking at wide queries with lots of data. I’ve shamelessly ripped of some of eirik’s ideas and wowed people from (especially) the financial world

IMO its one of the truest advancements in SQL clients that i’ve seen in a while.

YC startup founder here,

Mostly agree, except the market is not an optimistic place — it’s the market.

There are a multitude of reasons you lose your optimism, mostly because people take it away — your optimism is their money

So this was Synthego’s OG thesis, but it didn’t validate in the market.

In the last 5 years, the industry has moved to using the LabCyte Echo in high-well-count plates for this kinda work. Zymergen (RIP) Amyris and Ginkgo have this scaled up to something that resembles model train layouts, where plates are shuffled between discrete workcells by little trains.

One of the challenges is the sheer volume of data — Illumina sequencers generate multi-TB files for analysis (synthetic biology context) — with most folks not having “fast datacenter networks” so overwhelmingly I see folks buying Snowballs, AWS direct connect, or running on-prem.

Industry is broadly interested in this kinda thing, with efforts like [1] [2] (me), and many many others integrating into the Design-Build-Test pipeline. Commercial MD (not necessarily only protein folding) has had a huge boost due to NN’s as well, with companies like [3] [4] cropping up in order to sell their analysis as a service.

Academia has also not been sitting idle, with labs like [5] [6] doing cool stuff

Pure, classic microfluidic setups are a huge PITA, but technologies like the Echo or [7] have the potential to change some of the unit economics.

[1] https://atomscience.org/

[2] https://radix.bio/

[3] https://deepcure.ai/

[4] https://syntensor.com/

[5] https://www.damplab.org/

[6] https://www.chem.gla.ac.uk/cronin/

[7] https://www.voltalabs.com/