it's worth noting that the price of these monitors got cut in half due to this news -- great for the linux users out there
250 karma · joined May 4, 2018
to make this actually work well, consider modifying your switches QoS settings to carve out a priority VLAN for iSCSI traffic
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’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.
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 :)
Carbide [0] is the project that encapsulated some of this in a workable demo. It’s really cool!
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]
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.
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?
[0] https://blog.acolyer.org/2020/03/04/millions-of-tiny-databas...
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.
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.
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
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.
IMO, it's strictly worse than JetBrains for refactoring/autocomplete since JB has really nailed that stuff.
However, it's really useful as a compliment, specifically for git interactions (magit) programmer notes + run configurations (org-mode) and all kinds of minor tools. TRAMP is very useful if you'd like to edit remote files, and I find undo-tree priceless when debugging things that have multiple possible failure modes.
- Propellerheads Reason, a sweet DAW
- emacs, a text editor so elegant it’s a real OS
- Procreate, the nicest iOS drawing app
I stick to the thin-n-light XPS 13/15/17 or the Precision 5570 or 5770. Rock solid build quality.
Note: if you call Dell they’ll give you a couple hundred bucks off the price on the website, and can do custom builds