I'm very happy with my Leopold with Cherry MX Clears, having come from a Model M as my first board. Alternatively, at this price point one could put together a very decent custom from a kit + switches + keycaps.
I have an original production Model M and don't think I would ever go back to it. It's extremely loud (friends would always complain if we were gaming together), keypresses are very heavy, and it takes up a ton of deskspace.
If the Model M or F are appealing to you, at this price point you should also consider some more modern production keyboards or even building your own from a kit + switches + keycaps. If you're after the heavy & tactile keypresses of buckling springs a board with Cherry MX Clear (common) or Green (less common) will probably satisfy you - I can highly recommend any of Leopold's boards with Clears in them.
Definition, martial law: Martial law is the temporary imposition of direct military control of normal civil functions or suspension of civil law by a government, especially in response to a temporary emergency where civil forces are overwhelmed, or in an occupied territory. [1]
The key phrases are "imposition of direct military control of normal civil functions" and "suspension of civil law by a government".
The Canadian Emergencies Act, which was invoked by the Liberal government today, specifically states the following: "For greater certainty, nothing in this Act derogates from the authority of the Government of Canada to deal with emergencies on any property, territory or area in respect of which the Parliament of Canada has jurisdiction" [2].
I'd do a deeper reading but I'm a bit lazy, but my understanding is that the EA does not allow, in any way, a shift in governance that could be described as "martial law" - where the military is in control of civil functions and can create or remove laws as military leadership desires. Even with the EA invoked, the federal government still controls the Canadian military (but can be assisted in enforcing civil law _by_ the military).
I'm no fan of Trudeau either, but we should seek to be precise when discussing hot situations like this. People can get very inflamed off of internet posts and the idea that we're under "martial law" is riling people up.
Disclaimer: I work in this space (not at Xilinx), comments are strictly my own opinions and do not reflect any positions of my employer, etc.
Broadly speaking, FPGA-based ML model accelerators are in an interesting space right now, where they aren't particularly compelling from a performance (or perf / Watt, perf / $, etc.) perspective. If you just need performance, then a GPU or ASIC-based accelerator will serve you better - the GPU will be easier to program, and ASIC-based accelerators from the various startups are performing pretty well. Where an FPGA accelerator makes a lot of sense is if you otherwise need an FPGA anyways, or the other benefits of FPGAs (e.g. lots of easily-controlled IO) - but then you're just back to square 1 of "there's some cases where an FPGA makes sense and many where it doesn't". Besides that, a few niche cases where a mid-range FPGA might beat a mid-range GPU on perf / Watt or whatever metric is important for you.
Again, opinions are my own and all that. As someone in the space, I am very much hoping that someone - whether an ASIC startup or Xilinx / Intel come up with a "better" (performant, cheaper, easier to use, etc.) solution than GPUs for ML applications. If the winner ends up being FPGAs, that would be really really cool! Just at the moment it's not too compelling, and I'm trying to be realistic.
All that said, FPGAs and their related supports (software, boards, etc.) are an $Xb / Y market - nothing to shake a stick at, and there are many cases where an FPGA makes sense. Just doesn't currently make sense for every dev to buy an FPGA card to drop in their desktop to play with.
You've struck on the fundamental problem that the FPGA industry has been trying to solve for 30+ years - how to get an FPGA into the hands of every developer, like how GPUs have propagated to be essential tools.
Nobody has come up with a good answer yet. Developing for an FPGA still requires domain-specific knowledge, and because place & route (the "compile" for an FPGA) is a couple of intertwined NP-hard problems development cycles are necessarily long. Small designs might take an hour to compile, the largest designs deployed these days ~24H.
All this to say is that while they are neat, nobody has found the magic bullet use case that will make everyone want one enough to put up with the pain of developing for them (a la machine learning for GPUs). Simultaneously, nobody has found the magic bullet to make developing for them any easier, whether by reducing the knowledge required or improving the tooling.
Effort has been made in places like High-Level Synthesis (HLS, compiling C/C++ code down to an FPGA), open-source tooling, and (everyone's favorite) simulation, but they all still kinda suck compared to developing software, or even the ecosystem that exists around GPUs these days. You'll often hear FPGA people saying stuff like "just simulate your design during development, compiling to hardware is just a last step to check everything works" - but simulation still takes a long time (large designs can take hours) and tracking down a bug in waveforms is akin to Neo learning to see the Matrix.
I've done it as well with my parents, who love wine. At the shops I've been to, you buy the grape concentrate up front and they handle to process of making it up until bottling. Once the juice has fermented, you go in and they walk you through cleaning and sanitizing the bottles, filling them, corking them, and usually shrink-wrapping the tops. They provide all the supplies except the bottles themselves - customers bring their own, saved from buying wine the "normal" way.
Never seen a shop do ageing, so the wine will be noticeably "young". My parents like dryer and sharper white wines anyways (Pinot Grigio, Riesling, etc.) so it doesn't bother them. Also note that due to taxes and such, the cheapest wine you'll find commercially is C$11 a bottle, so even at C$20 / gallon you're getting a great deal if you like the resulting wines.
Personally, I quite like the wines my folks get through these shops - properly chilled they make a wonderfully refreshing beverage in the summer, and we'll often drink a few bottles on the back deck together when I go to see them.
It might very well be the DAC doing the work then, or maybe the placebo effect is just making me pay more attention to music I first heard years ago and I'm picking up more :)
Thanks for the information though, I'll do some A/B testing to see if it makes any difference to me.
Anecdotally, recently my dad got me a Tidal subscription (which has masters at supposedly higher quality than CDs) and an entry-level DAC. The two put together have blown me away and I've been having a blast the last couple of weeks re-listening to all my favourite records from as far back as the 70s.
I think the 70s are also around the same time that a lot of familiar genres started to emerge, while music from before then is often dismissed as "oldies" or saved for special occasions - e.g. old crunchy recordings of Christmas songs.
Adding to the comment about difficulty, the number and ratio of likes : dislikes on a problem is a good measure of difficulty. A Medium with 5K likes and 300 dislikes is probably quite a good (well-written, constrained, fair) problem. A Medium with 300 likes and 800 dislikes I'd steer clear of.
I think an older GPU would definitely be useful. Used GTX 1080 cards (I picked one up used for C$375 back in September) are listed for C$750-850 where I live. I'm not as familiar with AMD's products but I think 1-2 generation old GPUs would definitely have high demand until current gen ones are more available.
>spend years working on getting acquired instead of working on new chips (which happened to Altera)
That's interesting, can you expand on this? I'm curious what could have impacted them that much (I'm a student about to finish my undergrad in comp eng)
I'll take a crack at it, though I'm only in undergrad (took a course on VLSI this semester).
Making a device at a specific technology node (e.g. 14nm, 10nm, 7nm) isn't just about the lithography, although litho is crucial too. In effect, lithography is what allows you to "draw" patterns onto a wafer, but then you still need to do various things to that patterned wafer (deposition, etching, polishing, cleaning, etc.). Going from "we have litho machines capable of X nm spacing" to "we can manufacture a CPU on this node at scale with good yield" requires a huge amount of low-level design to figure out transistor sizings, spacings, and then how to actually manufacture the designed transistors and gates using the steps listed above.
Hugely anecdotally, I think ketamine might have fixed my depression for about 6 months with one heavy dose. It was early 2018, I had just gone through a really bad breakup the December previous. Around March, my friends invited me on a ski trip - where I promptly had a bad fall and ended up in the hospital with a dislocated shoulder. The doctors had a hard time getting it back in while I was conscious, so they knocked me out with ketamine (smaller, more rural hospital) to put it back in.
In the days and weeks following I felt amazing, with almost all of the post-breakup blues gone and a fantastic outlook on life - I chalked it up to the "near-death" experience, but later found out about the potential anti-depressive properties of the drug and I've wondered ever since.
> Hamburg build from 2008 on a new district, the HafenCity, a soulless place of concrete, expensive apartments, and chain stores.
I find your description here fascinating, because it's exactly how I've described the CityPlace district [1] here in Toronto that was built over the last 20 years, with most development in the last 10. Makes me wonder how common this style of modern, sterile, "ideal" development is in big cities around the world.
> Are there available AI accelerators on the market at the same level as Google's?
Not sure if they're quite at the same level (hard to measure apples against apples and all that), but there's a few companies in the space - namely, Groq, Cerebras, Tenstorrent, and Untether. Besides that, both major FPGA vendors have ML inference IP available.
I'd also bet other FAANG-ish companies are trying, besides just Google, but I would expect anything to come out of them to also be compute-as-a-service like Google's hardware.