cepth

Assuming you're not just here to troll (doubtful given your comment history, but hey I'm feeling generous):

> lol AMD flogged its floundering foundry waaay before Intel ran into any problems.

Not wanting/being able to spend to compete on the leading edge nodes is an interesting definition of "floundering". Today there is exactly 1 foundry in the world that's on that leading edge, TSMC. We'll see how Intel Foundry works out, but they're years behind their revenue/ramp targets at this point.

It's fairly well known that Brian Krzanich proposed spinning out Intel's foundry operations, but the board said no.

The irony is that trailing edge fabs are wildly profitable, since the capex is fully amortized. GloFo made $1 billion in net income in FY2023.

> in fact most of your points about AMD's lack of dough can be traced back to that disaster. The company wasn't hit by some meteorite. It screwed up all by itself

Bulldozer through Excavator were terrible architectures. What does this have to do with what's now known as Global Foundries?

GloFo got spun out with Emirati money in March 2009. Bulldozer launched in Q4 2011. What's the connection?

AMD continued to lose market share (and was unprofitable) for years after the foundry was spun out. Bad architectural choices, and bad management, sure. Overpaying for ATI, yep. "Traced back" to GloFo? How?

> Then lucky it had that duopolistic X86 licence to lean on or it would have gone the way of Zilog or Motorola. 'Cos it sure can't rely on its janky compute offering.

"Janky" when? "Rely" implies present tense. You're saying AMD compute offerings are janky today?

(Part 2 of 2)

> Please show me an AMD GPU with even eight years of support. Back to focus, ROCm isn't even that old and AMD is infamous for removing support for GPUs, often within five years if not less.

As you yourself noted, CDNA vs RDNA makes things more complicated in AMD land. I also think it’s unfair to ask about “eight years of support” when the first RDNA card didn’t launch until 2019, and the first CDNA “accelerator” in 2020.

The Vega and earlier generation is so fundamentally different that it would’ve been an even bigger lift for the already small ROCm team to maintain compatibility.

If we start seeing ROCm removing support for RDNA1 and CDNA1 cards soon, then I’ll share your outrage. But I think ROCm 6 removing support for Radeon VII was entirely understandable.

> Generally agree but back to focus and discipline it's a shame that it took a massive "AI" goldrush over the past ~18 months for them to finally take it vaguely seriously. Now you throw in the fact that Nvidia has absurdly more resources, their 30% R&D spend on software is going to continue to rocket CUDA ahead of ROCm.

> For Frontier and elsewhere I really want AMD to succeed, I just don't think it does them (or anyone) any favors by pretending that all is fine in ROCm land.

The fact is that the bulk of AMD profits is still coming from CPUs, as it always has. AMD wafer allotment at TSMC has to first go towards making its hyperscaler CPU customers happy. If you promise AWS/Azure/GCP hundreds of thousands of EPYC CPUs, you better deliver.

I question how useful it is to dogpile (not you personally, but generally) on AMD, when the investments in people and dollars are trending in the right decision. PyTorch and TensorFlow were broken on ROCm until relatively recently. Now that they work, you (not unreasonably) ask where the other stuff is.

The reality is that NVIDIA will likely forever be the leader with CUDA. I doubt we’ll ever see PhD students and university labs making ROCm their first choice when having to decide where to conduct career-making/breaking research.

But, I don’t think it’s really debatable that AMD is closing the relative gap, given the ROCm ecosystem didn’t exist until at all relatively recently. I’m guessing the very credible list of software partners now at least trying ROCm (https://www.amd.com/en/corporate/events/advancing-ai.html#ec...) are not committing time + resources to an ecosystem that they see as hopeless.

---

Final thoughts:

A) It was completely rational for AMD to focus on devoting the vast majority of R&D spend to its CPUs (particularly server/EPYC), particularly after the success of Zen. From the day that Lisa Su took over (Oct 8, 2014), the stock is up 50x+ (even more earlier in 2024), not that share price is reflective of value in the short term. AMD revenue for calendar year 2014 was $5.5B, operating income negative 155 million. Revenue for 2023 was $22.68B, operating income $401 million. Operating income was substantially higher in 2022 ($1.2B) and 2021 ($3.6B), but AMD has poured that money into R&D spending (https://www.statista.com/statistics/267873/amds-expenditure-...), as well as the Xilinx acquisition.

B) It was completely rational for NVIDIA to build out CUDA, as a way to make it possible to do what they initially called "scientific computing" and eventually "GPU-accelerated computing". There's also the reality that Jensen, the consummate hype man, had to sell investors a growth story. The reality is that gaming will always be a relatively niche market. Cloud gaming (GeForce Now) never matched up to revenue expectations.

C) It’s difficult for me to identify any obvious “points of divergence” that in an alternate history would’ve led to better outcomes with AMD. Without the benefit of “future knowledge”, at what point should AMD have ramped up ROCm investment? Given, as I noted above, in the months before ChatGPT went viral, Jensen’s GTC keynote gave only a tiny mention to LLMs.

D) If anything, the company that missed out was Intel. Beyond floundering on the transition from 14nm to 10nm (allowing TSMC and thus AMD to surpass them), Intel wasted its CPU-monopoly years and the associated profits. Projects like Larrabee (https://www.anandtech.com/show/3738/intel-kills-larrabee-gpu...) and Xe (doomed in part by internal turf wars) (https://www.tomshardware.com/news/intel-axes-xe-hp-gpus-for-...) were killed off. R&D spending was actually comparable to the amount spent on share buybacks in 2011 (14.1B in buybacks vs 8.3B in R&D spending), 2014 (10.7B vs 11.1B), 2018 (10.8B vs 13.B), 2019 (13.5B vs 13.3B) and 2020 (14.1B vs 13.55B). (See https://www.intc.com/stock-info/dividends-and-buybacks and https://www.macrotrends.net/stocks/charts/INTC/intel/researc...).

(Split into two parts due to comment length restrictions)

> I work closely with OLCF and Frontier (I have a job running on Frontier right now). This is incorrect. The overwhelming majority of compute and resource allocation are not "nuclear stockpile modeling code" projects or anything close to it. AMD often gets directly involved with various issues (OLCF staff has plenty of stories about this). I know because I've spoken with them and AMD.

I don't have any experience running a job on one of these national supercomputers, so I'll defer to you on this. (Atomic Canyon looks very cool!)

Just two follow-ups then: is it the case that any job, small or large, enjoys this kind of AMD optimization/debugging support? Does your typical time-grant/node-hour academic awardee get that kind of hands-on support?

And, for nuclear modeling (be it weapons or civilian nuclear), do you know if AMD engineers can get involved? (https://insidehpc.com/2023/02/frontier-pushes-boundaries-86-... this article claims "86% of nodes" were used on at least one modeling run, which I imagine is among the larger jobs)

> It is and has been miles beyond the competition and that's clearly all you need. Nvidia has > 90% market share and is worth ~10x AMD. 17 years of focus and investment (30% of their R&D spend is software) when your competitors are wandering all over the place in fits and starts will do that.

No dispute here that NVIDIA is the market leader today, deservedly so. NVIDIA to its credit has invested in CUDA for many years, even when it wasn't clear there was an immediate ROI.

But, I bristle at the narrative fallacy that it was some divine inspiration and/or careful planning (“focus”) that made CUDA the perfect backbone for deep learning.

In 2018, NVIDIA was chasing crypto mining, and felt the need to underplay (i.e., lie) to investors about how large that segment was (https://wccftech.com/nvidia-sued-cryptocurrency-mining-reven...). As late as 2022, NVIDIA was diverting wafer supply from consumer, professional, and datacenter GPUs to produce crippled "LHR" mining cards.

Jensen has at various points pumped (during GTC and other high profile events):

- Ray tracing (2018) (https://www.youtube.com/watch?v=95nphvtVf34)

- More ray tracing (2019) (https://youtu.be/Z2XlNfCtxwI)

- "Omniverse" (2020) https://youtu.be/o_XeGyg2NIo?list=PLZHnYvH1qtOYOfzAj7JZFwqta...)

- Blockchain, NFTs, and the metaverse (2021) (https://cointelegraph.com/news/nvidia-ceo-we-re-on-the-cusp-...) (https://blockonomi.com/nvidiz-ceo-talks-crypto-nfts-metavers...)

- ETH (2021) (https://markets.businessinsider.com/currencies/news/nvidia-c...)

- "Omniverse"/digital twins (2022) (https://www.youtube.com/watch?v=PWcNlRI00jo)

- Autonomous vehicles (2022) (https://www.youtube.com/watch?v=PWcNlRI00jo)

Most of these predictions about use cases have not panned out at all. The last GTC keynote prior to the "ChatGPT moment" took place just 2 months before the general availability of ChatGPT. And, if you click through to the video, you'll see that LLMs got under 7 minutes of time at the very end of a 90 minute keynote. Clearly, Jensen + NVIDIA leadership had no idea that LLMs would get the kind of mainstream adoption/hype that they have.

On the business side, it hasn't exactly always been a smooth ride for NVIDIA either. In Q2 2022 (again right before the "ChatGPT moment"), the company missed earnings estimates by 18%(!) due to inventory writedowns (https://www.pcworld.com/article/828754/nvidia-preannounces-l...).

The end markets that Jensen forecasts/predicts on quarterly earnings calls (I’ve listened to nearly every one for the last decade) are comically disconnected from what ends up happening.

It's a running joke among buy-side firms that there'll always be an opportunity to buy the NVDA dip, given the volatility of the company's performance + stock.

NVIDIA's "to the moon" run as a company is due in large part to factors outside of its design or control. Of course, how large is up for debate.

If/when it turns out that most generative products can't turn a profit, and NVIDIA revenues decline as a result, it wouldn't be fair to place the blame for the collapse of those end markets at NVIDIA’s feet. Similarly, the fact that LLMs and generative AI turned out to be hit use cases has little to do with NVIDIA's decisions.

AMD is a company that was on death’s door until just a few years ago (2017). It made one of the most incredible corporate comebacks in the history of capitalism on the back of its CPUs, and is now dipping its toes into GPUs again.

NVIDIA had a near-monopoly on non-console gaming. It parlayed that into a dominant software stack.

It’s possible to admire both, without papering over the less appealing aspects of each’s history.

> Depends on what you mean by "real DL workloads". Vanilla torch? Yes. Then start looking at flash attention, triton, xformers, and production inference workloads...

As I mentioned above, this is a chicken-and-egg phenomenon with the developer ecosystem. I don't think we really disagree.

CUDA is an "easy enough" GPGPU backbone that due to incumbency and the lack of real competition from AMD and Intel for a decade led to the flourishing of a developer ecosystem.

Tri Dao (sensibly) decided to write his original Flash Attention paper with an NVIDIA focus, for all the reasons you and I have mentioned. Install base size, ease of use of ROCm vs CUDA, availability of hardware on-prem & in the cloud, etc.

Let's not forget that Xformers is a Meta project, and that non-A100 workloads (i.e., GPUs without 8.0 compute capability) were not officially supported by Meta for the first year of Xformers (https://github.com/huggingface/diffusers/issues/2234) (https://github.com/facebookresearch/xformers/issues/517#issu...). This is the developer ecosystem at work.

AMD right now is forced to put in the lion's share of the work to get a sliver of software parity. It took years to get mainline PyTorch and Tensorflow support for ROCm. The lack of a ROCm developer community (hello chicken and egg), means that AMD ends up being responsbile for first-party implementations of most of the hot new ideas coming from research.

Flash Attention for ROCm does exist (https://github.com/ROCm/flash-attention) (https://llm-tracker.info/howto/AMD-GPUs#flash-attention-2), albeit only on a subset of cards.

Triton added (initial) support for ROCm relatively recently (https://github.com/triton-lang/triton/pull/1983).

Production-scale LLM inference is now entirely possible with ROCm, via first-party support for vLLM (https://rocm.blogs.amd.com/artificial-intelligence/vllm/READ...) (https://community.amd.com/t5/instinct-accelerators/competiti...).

> Compute capability is why code targeting a given lineage of hardware just works. You can target 8.0 (for example) and as long as your hardware is 8.0 it will run on anything with Nvidia stamped on it from laptop to Jetson to datacenter and the higher-level software doesn't know the difference (less VRAM, which is what it is).

This in theory is the case. But, even as an owner of multiple generations of NVIDIA hardware, I find myself occasionally tripped up.

Case in point:

RAPIDS (https://rapids.ai/) is one of the great non-deep learning success stories to come out of CUDA, a child of the “accelerated computing” push that predates the company’s LLM efforts. The GIS and spatial libraries are incredible.

Yet, I was puzzled when earlier this year I updated cuSpatial to the newest available version (24.02) (https://github.com/rapidsai/cuspatial/releases/tag/v24.02.00) via my package manager (Mamba/Conda), and started seeing pretty vanilla functions start breaking on my Pascal card. Logs indicated I needed a Volta card (7.0 CC or newer). They must've reimplemented certain functions altogether.

There’s nothing in the release notes that indicates this bump in minimum CC. The consumer-facing page for RAPIDS (https://rapids.ai/) has a mention under requirements.

So I’m led to wonder, did the RAPIDS devs themselves not realize that certain dependencies experienced a bump in CC?

No, not really?

The customers at the national labs are not going to be sharing custom HPC code with AMD engineers, if for no other reason than security clearances. Nuclear stockpile modeling code, or materials science simulations are not being shared with some SWE at AMD. AMD is not “removing jank”, for these customers. It’s that these customers don’t need a modern DL stack.

Let’s not pretend like CUDA works/has always worked out of the box. There’s forced obsolescence (“CUDA compute capability”). CUDA didn’t even have backwards compatibility for minor releases (.1,.2, etc.) until version 11.0. The distinction between CUDA, CUDA toolkit, CUDNN, and the actual driver is still inscrutable to many new devs (see the common questions asked on r/localLlama and r/StableDiffusion).

Directionally, AMD is trending away from your mainframe analogy.

The first consumer cards got official ROCm support in 5.0. And you have been able to run real DL workloads on budget laptop cards since 5.4 (I’ve done so personally). Developer support is improving (arguably too slowly), but it’s improving. Hugging Face, Cohere, MLIR, Lamini, PyTorch, TensorFlow, DataBricks, etc all now have first party support for ROCm.

A couple of thoughts here.

* AMD's traditional target market for its GPUs has been HPC as opposed to deep learning/"AI" customers.

For example, look at the supercomputers at the national labs. AMD has won quite a few high profile bids with the national labs in recent years:

- Frontier (deployment begun in 2021) (https://en.wikipedia.org/wiki/Frontier_(supercomputer)) - used at Oak Ridge for modeling nuclear reactors, materials science, biology, etc.

- El Capitan (2023) (https://en.wikipedia.org/wiki/El_Capitan_(supercomputer)) - Livermore national lab

AMD GPUs are pretty well represented on the TOP500 list (https://top500.org/lists/top500/list/2024/06/), which tends to feature computers used by major national-level labs for scientific research. AMD CPUs are even moreso represented.

* HPC tends to focus exclusively on FP64 computation, since rounding errors in that kind of use-case are a much bigger deal than in DL (see for example https://hal.science/hal-02486753/document). NVIDIA innovations like TensorFloat, mixed precision, custom silicon (e.g., the "transformer engine") are of limited interest to HPC customers. It's no surprise that AMD didn't pursue similar R&D, given who they were selling GPUs to.

* People tend to forget that less than a decade ago, AMD as a company had a few quarters of cash left before the company would've been bankrupt. When Lisa Su took over as CEO in 2014, AMD market share for all CPUs was 23.4% (even lower in the more lucrative datacenter market). This would bottom out at 17.8% in 2016 (https://www.trefis.com/data/companies/AMD,.INTC/no-login-req...).

AMD's "Zen moment" didn't arrive until March 2017. And it wasn't until Zen 2 (July 2019), that major datacenter customers began to adopt AMD CPUs again.

* In interviews with key AMD figures like Mark Papermaster and Forrest Norrod, they've mentioned how in the years leading up to the Zen release, all other R&D was slashed to the bone. You can see (https://www.statista.com/statistics/267873/amds-expenditure-...) that AMD R&D spending didn't surpass its previous peak (on a nominal dollar, not even inflation-adjusted, basis) until 2020.

There was barely enough money to fund the CPUs that would stop the company from going bankrupt, much less fund GPU hardware and software development.

* By the time AMD could afford to spend on GPU development, CUDA was the entrenched leader. CUDA was first released in 2003(!), ROCm not until 2016. AMD is playing from behind, and had to make various concessions. The ROCm API is designed around CUDA API verbs/nouns. AMD funded ZLUDA, intended to be a "translation layer" so that CUDA programs can run as a drop-in on ROCm.

* There's a chicken-and-egg problem here.

1) There's only one major cloud (Azure) that has ready access to AMD's datacenter-grade GPUs (the Instinct series).

2) I suspect a substantial portion of their datacenter revenue still comes from traditional HPC customers, who have no need for the ROCm stack.

3) The lack of a ROCm developer ecosystem means that development and bug fixes come much slower than they would for CUDA. For example, the mainline TensorFlow release was broken on ROCm for a while (you had to install the nightly release).

4) But, things are improving (slowly). ROCm 6 works substantially better than ROCm 5 did for me. PyTorch and TensorFlow benchmark suites will run.

Trust me, I share the frustration around the semi-broken state that ROCm is in for deep learning applications. As an owner of various NVIDIA GPUs (from consumer laptop/desktop cards to datacenter accelerators), in 90% of cases things just work on CUDA.

On ROCm, as of today it definitely doesn't "just work". I put together a guide for Framework laptop owners to get ROCm working on the AMD GPU that ships as an optional add-in (https://community.frame.work/t/installing-rocm-hiplib-on-ubu...). This took a lot of head banging, and the parsing of obscure blogs and Github issues.

TL;DR, if you consider where AMD GPUs were just a few years ago, things are much better now. But, it still takes too much effort for the average developer to get started on ROCm today.

I think the general idea that "lawmakers are ex-lawyers, and therefore write extremely bureaucratic laws" is a common sentiment, but there are a number of reasons why I think this is not quite the case.

Few (if any) US representatives or senators at the federal level are actually writing out legislation themselves. They hire staffers (see the "Personal staff" section https://en.wikipedia.org/wiki/Congressional_staff), sometimes lift language straight from lobbyist proposals (aka "model legislation" https://en.wikipedia.org/wiki/Model_act, which is widely used at the state level), or defer to committee staffers (who are subject matter experts) to do the heavy lifting. For example, Lina Khan served from 2019-2020 as counsel to the House's "Subcommittee on Antitrust, Commercial, and Administrative Law", and you can see her fingerprints all over the written work that the committee produced. The framing, and sometimes direct language, of committee report sections are clearly lifted from her legal academia work.

This is comparable to the fact that US federal judge at all levels (including SCOTUS) lean on their clerks to write the first drafts of their opinions, and serve primarily as editors of the final text.

---

In regards to the empirical claim about the backgrounds of lawmakers, see page 8 of this report from the Congressional Research Service (https://crsreports.congress.gov/product/pdf/R/R46705). They say that 144 House members (32.7% of the total), and 50 senators (50%) hold law degrees. While I think you may have been using a bit of hyperbole, it is worth pointing out that there are not enough lawyers in Congress for "ALL of the democrats" to be lawyers.

In terms of occupation (page 3), 85 reps and 28 senators were previously educators; 14 reps and 4 senators were physicians; etc.

Yes there are plenty of law degree holders, but it's also worth considering what law-related job they held. Per the CRS report, 29 reps and 9 senators were previously prosecutors, and 1 rep and 6 senators were previously attorney generals. It's unclear to me why a career in the criminal side of our legal system would have much bearing on how someone drafts laws affecting taxation, provision of government services, etc.

There's also the fact that many law degree holders practiced law for not long at all before winning elected office, or had more substantial "chapters" of their life not related to their degree. Take Jason Crow (https://en.wikipedia.org/wiki/Jason_Crow). He spent as much time as an Army Ranger as he did as a lawyer. One could easily construct a narrative that Mr. Crow, who has complained often about the bureaucracy of accessing veterans' healthcare, should be allergic to red tape and bureaucracy. But with the crude taxonomy of "he has a law degree", the other parts of his life would be overlooked.

---

IMO, a big part of the bloated and inhumane parts of the bureaucracy have to do with the outgrowth of "administrative law" and "rulemaking" (https://en.wikipedia.org/wiki/Administrative_law; https://en.wikipedia.org/wiki/United_States_administrative_l...; https://www.everycrsreport.com/reports/RL32240.html). Once a bill has been signed into law, the rulemaking process begins. These are where the actual details of a new law are hashed out. A bill may designate $X in funding for a program. Which contractors receive those contracts, hours of service, the amount of paperwork required, etc. are all handled at the administrative level. And it's certainly the case that for 99.9% of citizens, no one is submitting public comments during this period, and the input of ordinary people is often lacking.

So when the IRS makes a free-file options for taxes difficult to use, in large part due to Intuit's lobbying (https://www.propublica.org/article/inside-turbotax-20-year-f...), this is not the result of a carve-out or giveaway spelled out in actual legislation's text. It's the result of an actor exploiting the opacity of the rulemaking and administrative law practices.

It’s a much more fully featured note taking app, probably most directly comparable to Roam Research.

E.g., tagging, graph views, back links between notes, note/document templates.

All docs are (more or less) plain markdown as well, so if for some reason the open source community ever abandoned Obsidian, in theory it’s easy to export/transfer your notes.

They’re different ratings systems, and not meant to be directly comparable.

Chess.com uses Glicko with an initial rating of 1200 (https://support.chess.com/article/210-how-do-ratings-work-on...), while Lichess uses Glicko-2, and sets their initial rating to 1500 (https://lichess.org/page/rating-systems).

Just anecdotally, I’m an ~1800 rated player on chess.com, and ~2050 on Lichess. Percentile rank wise, I’m in the top 3.5% on chess.com, and closer to top 10% on Lichess.

I think this matches the general perception that Chess.com has many more casual players. This is likely a function of all the cross promotion they’ve done to grow the game, especially on Twitch. Neither site is “good” or “bad”, but my friends who play casually seem more likely to play on Chess.com.

In 2017, Ars Technica did a deep dive into computation in Formula 1 (https://arstechnica.com/cars/2017/04/formula-1-technology/).

Some relevant quotes:

> For example, each Formula 1 team is only allowed to use 25 teraflops (trillions of floating point operations per second) of double precision (64-bit) computing power for simulating car aerodynamics.

> Oddly, the F1 regulations also stipulate that only CPUs can be used, not GPUs, and that teams must explicitly prove whether they're using AVX instructions or not. Without AVX, the FIA rates a single Sandy Bridge or Ivy Bridge CPU core at 4 flops; with AVX, each core is rated at 8 flops. Every team has to submit the exact specifications of their compute cluster to the FIA at the start of the season, and then a logfile after every eight weeks of ongoing testing.

> Everest says that every team has its own on-premises hardware setup and that no one has yet moved to the cloud. There's no technical reason why the cloud can't be used for car aerodynamics simulations—and F1 teams are investigating such a possibility—but the aforementioned stringent CPU stipulations currently make it impossible. The result is that most F1 teams use a somewhat hybridised setup, with a local Linux cluster outputting aerodynamics data that informs the manufacturing of physical components, the details of which are kept in the cloud.

> Wind tunnel usage is similarly restricted: F1 teams are only allowed 25 hours of "wind on" time per week to test new chassis designs. 10 years ago, in 2007, it was very different, says Everest: "There was no restriction on teraflops, no restriction on wind tunnel hours," continues Everest. "We had three shifts running the wind tunnel 24/7. It got to the point where a lot of teams were talking about building a second wind tunnel; Williams built a second tunnel.

With the new cost cap in F1 (https://www.autoweek.com/racing/formula-1/a35293542/f1-budge...) (which notably excludes driver salaries), it would be interesting to know how much these on-prem clusters cost to operate.

You’d be paying for earnings several years out, for sure. But I think it’s hardly fair to compare to Nikola.

Trevor Milton’s experience prior to starting Nikola was selling home security systems. Lucid’s leadership team features various Tesla, Audi, Ford, VW, etc. veterans. (See page 9: https://www.lucidmotors.com/files/lucid-investor-deck-februa...)

Lucid has finished building a factory that can produce roughly 30k cars per year, expandable to 400k.

They’ve given rides in their launch vehicle to various auto journalists (https://youtu.be/gqSN2QNgO5k).

Their battery pack technology is a component of the Formula E drivetrain system (https://lucidmotors.com/media-room/atieva-powers-season-6-fo...).

This isn’t a “Nikola rolling a non functional truck down a hill” situation. They have a working product.

The car could suck, the company could be overvalued. But I think hard to compare Nikola to Lucid.

EDIT:

I should also add for comparison, that at the time that Tesla IPOed in 2010, it was a 1.7B market cap company. Only ~2450 Roadsters (their only car at the time) would be sold in total. By November 29, 2010 Tesla had not yet sold 1400 cars (https://www.tesla.com/blog/race-champions-2010-motorsport-go...).

Tesla's Fremont factory was opened in October 2010. In other words, when the company went public on June 29th, 2010 you would have been buying into a car company without a factory.

The first Model S wasn't delivered until June 2012 (https://www.tesla.com/blog/tesla-motors-begin-customer-deliv...).

Not to say that Lucid will or won't ever reach Tesla's heights, but assigning a 12B valuation to the company isn't loony. The SPAC price though is a different story.

EDIT 2:

There are some fun short videos of Lucid CEO Peter Rawlinson in the workshop from his Tesla days (https://www.youtube.com/watch?v=TrbOLHW8Pec, https://www.youtube.com/watch?v=8YxHp2ot61Y, https://www.youtube.com/watch?v=NGKqPYvtqXE). It's pretty awe inspiring to see where Tesla and the global EV industry as a whole was in 2011 vs today.

I might’ve thought so too, but the account I’m replying to seems to be a genuine CCP critic (which is totally fine!).

A post about Putin and the CCP exploiting US domestic political turmoil: https://news.ycombinator.com/item?id=25830075

A book recommendation by a longtime China-skeptical writer: https://news.ycombinator.com/item?id=25843422 (https://en.wikipedia.org/wiki/Bill_Gertz)

My personal preference is for a HN community where dissenting views don’t lead to assigning ulterior motives (in this case being paid to shill for the CCP) to our debating “opponents”.

Yes, a HN member with a profile created in December of 2008, 5800+ Karma, with links to his Twitter and name of his employer in his profile page is a 五毛(50 center).

The CCP is truly all powerful!

If my dripping sarcasm wasn’t clear, you’re way off base here.

Edit 1: to the downvoters, please feel free to explain why it’s appropriate to accuse a 12-year HN member of being a paid shill for the CCP when all evidence points to the contrary.

Given that a typical garbage truck weighs 25+ short tons (https://www.reference.com/world-view/much-garbage-truck-weig...), it seems likely that you start to run into the legal limits for local roads.

E.g. in NYC (https://www1.nyc.gov/html/dot/html/motorist/sizewt.shtml), the max weight for any vehicle is 80,000 lbs, likely much less if you’re the length of a typical garbage truck.

Can you really go electric? A 2016 bullish Quartz article (https://qz.com/749622/the-economics-of-electric-garbage-truc...) says the typical garbage truck travels 130 miles a day. Unclear what the additional weight of a capable battery pack would be, even after accounting for the saved weight by removing what must be a pretty hefty combustion engine. It’s certainly interesting that the company (Wrightspeed) profiled in the article seems to be doing more than just garbage trucks now. Their Route 1000 powertrain/platform only quotes 24 miles of pure EV range (https://www.wrightspeed.com/the-route-powertrain), so it seems like there are certainly trade offs between range and weight regulations.

I imagine once long and medium haul trucks make it to market (perhaps Tesla or Volvo), maybe we can say we’ve reached the energy densities in packs that make hauling around weight equivalent to an 18 wheeler possible at a reasonable cost.

Other reasons that maybe no one has done this already:

* Garbage trucks cost $250k+ (https://bigtruckrental.com/front-loader-garbage-truck-rental...). I’m guessing there are regulatory requirements around collision safety, and maybe longevity requirements that become a factor.

* Given that a brand new sleeper semi goes for 50% less (https://youngtrucks.com/new-trucks/2020-volvo-vnl64t860-860-...), there are probably significant costs not typical of a combustion engine vehicle. Maybe those front loading bins require powerful pneumatics, or those on-vehicle compactors need to be able to exert tremendous amounts of force, and so on and so on.

* How large is the market for garbage trucks? Some press release claimed that the global garbage truck market in 2019 was ~$22 billion (https://www.globenewswire.com/news-release/2020/07/29/206925...). If we use a unit price of $250k (probably on the low end), that would mean only ~88000 total units sold globally. Of course, I’m sure different countries may have larger and smaller trucks, but you get the gist.

* The customers for these trucks are likely to be municipal governments and a handful of private waste management companies (https://craft.co/waste-management/competitors). I think it’d be a huge risk to build out a plant to put together a garbage truck, have maybe a few hundred plausible decisions makers to pitch on the product, and potentially risk burning hundreds of millions in capital between labor, regulatory certification, endurance testing, battery pack development, power train development, etc.

I have to commend the author for taking on a beast of a task. It's not easy to be learning/relearning chess while also trying to program an engine!

I think programming a chess engine that can beat most club players (let's say sub-2000 rating) is not too hard. At that level, human players will make inaccurate moves (as well as blunders at lower ratings). It is however much harder to develop engines that are competing at GM level (~2500+).

This wiki has some good high level articles (https://www.chessprogramming.org/Main_Page). Even something like a chess bitboard representation is non-trivial to code.

Implementing minimax and comparable algorithms may be straightforward, but the actual evaluation of positions for traditional engines is a distillation of thousands of pieces of expert knowledge. See for example the parameters that can be tweaked in Fritz (http://help.chessbase.com/Fritz/16/Eng/index.html?000038.htm).

If you look at the history of chess engines (https://www.youtube.com/watch?v=wljgxS7tZVE), by the 1990s and later most of the top chess engines have had masters, international masters, and grandmasters intimately involved with development. For example, Deep Blue had several consulting grandmasters. Rybka (the world's best engine 2007-2010) had IM Vasik Rajlich as primary author and GM Larry Kaufman closely involved with tweaking its evaluation functions. Kaufman also went on to write the (still) very strong engine Komodo (https://ccrl.chessdom.com/ccrl/4040/).

Traditional chess engines used to have glaring weaknesses like playing poorly in closed positions, being poor at avoiding disadvantageous endgames, etc. By the mid-2000s many of these weaknesses disappeared, but as recently as Fritz 9 there were well known opening sequences where engines could be tricked into playing losing lines.

In the United States, liberal and leftist are distinct terms.

Even someone like Ben Shapiro recognizes a difference between liberals and leftists (https://twitter.com/benshapiro/status/966081078166421504).

Silicon Valley types would hardly be described as leftists. Numerous studies have been done on the attitudes of Silicon Valley founders and execs (https://www.vox.com/2015/9/29/9411117/silicon-valley-politic...). The distinctions are dramatic.

We see that on average, tech founders are less likely to support vs. even Democrats generally (not just progressives):

* Banning the Keystone XL pipeline (60% vs 78%)

* The individual healthcare mandate (59% vs 70%)

* Labor unions being good (29% vs 73%)

This is to say, the average Silicon Valley type, particularly the C-suite exec or founder, tends not to be on the left wing of the Democratic party.

During the 2020 Democratic primary, even the Silicon Valley billionaires who are openly Democratic-leaning donated to candidates who were not to the left of the field (i.e. Elizabeth Warren and Bernie Sanders) (https://www.cnbc.com/2019/08/13/2020-democratic-presidential...):

* Eric Schmidt -> Cory Booker and Joe Biden

* Reed Hastings -> Pete Buttigieg

* Marc Benioff -> Cory Booker, Kamala Harris, and Jay Inslee

* Reid Hoffman -> Cory Booker, Kirsten Gillibrand, Amy Klobuchar

* Jack Dorsey -> Andrew Yang, Tulsi Gabbard

* Ben Silbermann -> Pete Buttigieg

I'm engaging with you in good faith, and because I was intrigued that in a previous comment you mentioned that you live in Spain (though who's to say you're not a US ex-pat). But calling US tech companies "leftist" is a stretch at best.

The paper referenced ("Gender shades: Intersectional accuracy disparities in commercial gender classification") has been cited 1000+ times per her Google Scholar page (https://scholar.google.com/citations?user=lemnAcwAAAAJ). For a 2-year old paper, this is easily a top 1% most cited paper.

Take for example a retrospective look at 2017 NeurIPS papers done in 2019 (https://archive.is/wip/77YrB).

You can disagree with how she and/or Google has handled this whole situation, but please do not denigrate work that has been cited (https://scholar.google.com/scholar?oi=bibs&hl=en&cites=14954...) by papers accepted at the most competitive/prestigious ML conferences.

EDIT: I also do not see how in good faith you can say that VentureBeat, a company who makes the bulk of its revenue from running conferences catering to C-suite execs who can shell out thousands of dollars for a ticket, is "leftist".

I was happy to write this, since The Great Courses have had such a big impact on my learning. I was just frustrated that what could've been a nice profile (given how much access the author had to the company and its leadership) turned into an ideological screed. IMO, Heather MacDonald sees herself and/or functions as more of a commentator/activist than journalist.

The NY Times has done some fun looks at The Great Courses over the years:

* https://www.nytimes.com/2016/05/29/business/born-in-the-vcr-... * https://www.nytimes.com/2014/07/05/arts/television/the-great...

The Washington Post did a profile a few years back:

* https://www.washingtonpost.com/lifestyle/magazine/before-you...

It's truly a remarkable company. It has reinvented itself multiple times: physical DVDs, online downloads, Netflix style streaming, and now audio partnerships with Audible. The recent lifestyle additions (partnerships with NatGeo and Culinary Institute of America) have been great too.

It's been equally remarkable that the Big History was so well regarded that Bill Gates and Khan Academy then went ahead and spun it out into its own standalone project (https://www.bighistoryproject.com/).

It's in the linked article:

> (A Great Courses lecturer earns a royalty that varies according to how highly viewers rate his performance; the base royalty is 4 percent of the course’s gross revenue, but that rate can rise to 6 percent if a course receives high enough evaluations. The average royalty is about $25,000 a year for a course.)

The Great Courses likes to say that they have a very low "acceptance rate". Maybe a hundred professors are considered for each course that actually makes it to market.

The royalty for older courses will also naturally decay once the content goes "stale".

> This past academic year, for example, a Bowdoin College student interested in American history courses could have taken “Black Women in Atlantic New Orleans,” “Women in American History, 1600–1900,” or “Lawn Boy Meets Valley Girl: Gender and the Suburbs,” but if he wanted a course in American political history, the colonial and revolutionary periods, or the Civil War, he would have been out of luck.

Take a look at the Bowdoin course catalog for the 2010-2011 academic year (https://digitalcommons.bowdoin.edu/cgi/viewcontent.cgi?artic...).

We see courses in:

* The Civil War Era (Fall 2011)

* History of the American West (Fall 2011)

* American Society in the New Nation, 1763-1840 (Fall 2010)

* Borderlands and Empires in Early North America (Fall 2010)

* The History of African Americans, 1619-1865 (Fall 2012) (announced in the 2011-2011 catalog)

* American Political Development (Spring 2011)

* American Political Thought (Spring 2011)

* Political Parties in the United States (Fall 2010)

* Introduction to American Government (Fall 2010)

...and many many more that I got tired of copying over to this post.

This is incredibly lazy research by the writer. At the time this article went to press ("Summer 2011"), these courses already existed, and there are countless others already pre-announced in the 2011-2012 course catalog (https://digitalcommons.bowdoin.edu/cgi/viewcontent.cgi?artic...).

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> The company produces only what its market research shows that customers want.

> This repetition occurs not because the company is on a mission to resuscitate the canon but because customers want it. The insatiability of the demand for such courses surprises even the producers themselves.

> But the incursions of identity studies and other post-sixties academic developments remain minimal—and are inevitably denounced by some customers on the company’s website.

> The biggest question raised by the Great Courses’ success is: Does the curriculum on campuses look so different because undergraduates, unlike adults, actually demand postcolonial studies rather than the Lincoln-Douglas debates? Every indication suggests that the answer is no. “If you say to kids, ‘We’re doing the regendering of medieval Europe,’ they’ll say, ‘No, let’s do medieval kings and queens,’” asserts Allitt. “Most kids want classes on the French Revolution, the Russian Revolution, World War I, and the American Civil War.” Creative writing is such a popular concentration within the English major, Lerer argues, because it is the one place where students encounter attention to character and plot and can non-ironically celebrate literature’s power.

The author spends a good chunk of the article talking about TGC's customer-centric focus as a key reason for the company's success. TGC largely films courses that its customers want!

Yet, the singular piece of evidence she offers that undergrads are having unpopular curriculums pushed upon them is what she herself deems as an "assertion" from a singular professor. It's difficult to square the "excesses" of student protests against what they see as Western-centric canon (a pet topic that the author has written extensively about) with the idea that students actually really do want to learn about a suppressed canon.

Could it possibly be that today's generation might want a greater variety of course offerings beyond the classical Western canon? Could it be that TGC's median customer, who skews older, has different intellectual preferences and tastes from someone who is 30-40 years younger? What happened to respecting the customers' preferences above all else?

> A few professors suggest that the company has pegged the audience as leaning conservative[...] Lerer got an angry e-mail from a customer asking how he could include that “leftist son of a bitch” Noam Chomsky in the course. John McWhorter was told to omit from his linguistics lectures his usual argument that the idea of grammatical “correctness” is an “arbitrary imposition.” Such caveats on the company’s part, however, could simply reflect the desire to avoid alienating any customers.

Where is the outrage from the author that TGC's customers are so close-minded? ;)

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> Predictably, the Great Courses has come under pressure for not having enough “diversity” in its teaching ranks. Rollins has received angry letters from women complaining about the paucity of female lecturers; his nonstop efforts to recruit them have yielded few results, in part because women lecture less than men. As for the truly big-name female professors, they command speaking fees so high that the Great Courses’ pay scale looks insignificant. The same applies to the black superstars, one of whom told Rollins: “Tom, honestly, I make several thousand dollars a night from Martin Luther King Day through Black History Month; you’re not even on my radar screen.”

As someone who has spent almost $5000 with TGC, indulge me for a second to offer some advice to the company. I would've likely spent even more if they had more courses on the non-Western canon. This is something that has improved in recent years, but until ~5 years ago, there was practically nothing in the catalog on eastern philosophy, histories of India or the Middle East, non-European music, etc.

To this day, there is nothing in the catalog about African-American literature or history, Latin American history, or African history. Given that Latin America and Africa are collectively home to over 1/4 of the planet's population, how about some content development in that area? I'd be among the first customers.

Given that TGC's bestselling western philosophy course is taught by an Oxford professor, how about some more recruitment of professors from international universities who would be experts on these subject areas (when we finally emerge from COVID of course).

> But the incursions of identity studies and other post-sixties academic developments remain minimal—and are inevitably denounced by some customers on the company’s website.

Quoted in the previous section as well, but I will say that one of the more amusing things about TGC is that the pre-2008 economics content aged extremely poorly in the wake of the 2008 financial crisis. They had a very popular course (Legacies of Great Economists) that was released in the early 2000's, and has more than a hint of triumphalism around deregulation and the financialization of so many aspects of our modern economy.

Perhaps including some more skeptical voices would've been a good thing.

------------------------------------------

To close, TGC offers great products that I encourage fellow HN readers to try out. They offer a streaming service (The Great Courses Plus) that is likely a better value than purchasing courses outright.

With regards to the author of this piece though, with all due respect she has no idea what she's talking about.

Breaking up my comment because it was "too long" to submit as one (that's a first).

----

There are so many factually incorrect claims and contradictions in this piece that I don't even know where to begin. I write this as someone who owns nearly 100 Great Courses (between DVDs and digital copies). I'm also someone who has had the pleasure of taking an in-person college class with a professor whose course I first watched through The Great Courses (abbreviated as TGC from here on down).

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> Company recruiters sit in on classes of professors who have won awards or been recognized for their teaching; the most promising are invited to the Great Courses headquarters to record an audition lecture. That recording then goes to the company’s most valued customers. If enough of them like it, the company asks the professor to create a lecture course.

> The very fact that the Great Courses has found professors who teach without self-indulgence may suggest that academia is in better shape than is sometimes supposed. But the firm’s 200-plus faculty make up a minute percentage of the country’s college teaching corps. And some Great Courses lecturers feel so marginalized on their own campuses, claims Guelzo, that “if the company granted tenure, they would scramble to abandon their current ships and sleep on couches to work for the firm.” Further, it isn’t clear that the Great Courses professors teach the same way back on their home campuses.

I've spoken with multiple professors who have had their college courses adapted into TGC. For the professor whose class I took in person, their TGC lectures were literally structured identically to their syllabus. Some of the same jokes in TGC lectures made their way into the classroom (lol).

The in-person filming process for TGC may require multiple trips down to their Virginia studios, especially if we're talking about longer 48+ part courses that reach 24+ hours of screentime. Then there are the occasional re-shoots and restructurings necessary if the their customer focus groups (similar to an Amazon Vine/early reviewer program) finds the content not great.

The idea that professors are creating courses from scratch for TGC is in the overwhelming majority of cases not true. A professor who has normal teaching, research, and service requirements is generally not going to have time to create a whole course from scratch for what the author herself notes is typically a $25,000 royalty per year. A religious scholar who I spoke with (eventually decided not to go forward with submitting an audition tape) said that in his mind the biggest value prop of TGC is that you can minimally adapt existing materials to earn a healthy royalty.

-------------------------------------------

> True, the Great Courses emphasizes breadth over depth and offers largely introductory material. In literature and intellectual history, the survey format predominates, with relatively few courses on individual writers or philosophical schools.

This article was written in 2011, but even then there were already courses available about Voltaire, "The Great Ideas of Philosophy", "The Modern Political Tradition: Hobbes to Habermas", "Religious Debate in the Western Intellectual Tradition", Alexis De Tocqueville, "Great Minds of the Western Intellectual Tradition", "The Modern Intellectual Tradition: From Descartes to Derrida", "Legacies of Great Economists", "The Conservative Tradition", "American Ideals", etc. And yes, I double checked my order receipts to make sure these were actually available at the time. In fact many of these courses were on their 2nd or 3rd edition.

Since then, they've added quite a few STEM courses in multivariable calculus, differential equations, linear algebra, statistical computing, complexity theory, college chemistry, organic chemistry, etc.. Several of these are taught at a 2nd year undergrad or beyond level.

> The most striking thing about the Great Courses’ humanities curriculum, however, is how often the same thinkers appear across a large range of courses. The canon has been “problematized” in the academy, but it is alive and well in these recordings. Plato, Aristotle, Cicero, Paul, Erasmus, Galileo, Bacon, Descartes, Hobbes, Spinoza, Dante, Chaucer, Spenser, Shakespeare, Cervantes, Milton, Molière, Pope, Swift, Goethe, and others are foregrounded again and again as touchstones of our civilization.

Again, TGC content is largely carbon copied from college courses' syllabi. If these subjects are "problematized", where is TGC finding these lecturers? Keep in mind that TGC is not plucking unknown professors from lonely heterodox institutions. A majority of their professors teach at "liberal" (in the American sense) campuses.

Just a cursory look at my digital courses shows (in terms of course title and institutional representation):

* Great Ideas of Philosophy, 2nd Edition (Oxford / Georgetown)

* Voltaire / Birth of the Modern Mind: The Intellectual History of the 17th and 18th Centuries (UPenn)

* History's Great Military Blunders and the Lessons They Teach (University of Wisconsin)

* Fall and Rise of China (Cal Berkeley)

* Skeptics and Believers: Religious Debate in the Western Intellectual Tradition (Grinnell)

* Turning Points in Middle Eastern History (Johns Hopkins)

and so on, and so on...

What would you consider a system? Python definitely has more market share than R, but there's still name brand companies of various sizes that use an R stack for data science.

RStudio lists dozens of example clients here: https://rstudio.com/about/customer-stories/.

Use cases include collaborative model development, EDA tools, dashboarding, printed report generation (PDFs and HTML), public facing websites, etc.

I guess it depends on what you’re trying to accomplish (I’ve worked heavily with both R and Python).

If you’re trying to create ETL pipelines that integrate with BigQuery, Mongo, or whatever other database, I think it’s fair to say that the Python packages are generally better documented than their R counterparts.

For most other things, IMO it’s hard to really separate the two languages. Is standing up a Flask API really easier than in plumber?

For dashboarding, it’s is as quick (if not much quicker) to create a decent prototype with Shiny vs Plotly Dash or bokeh.

For simple linear and logistic model training, R’s built-in stats package has much more interpretable outputs vs sklearn, and directly inspired statsmodel. Wes McKinney has acknowledged that pandas draws heavily from R’s native dataframe. And so on and so on.

EDIT:

Also forgot to mention that with R packages like reticulate, you can also directly run Python code within an R environment now. So if there happens to be some Python package that doesn’t have an R equivalent, you can still work in R (though I’ve found the opposite situation to be far more common).

There have been a couple others that come to mind.

With the CEO of Cerebras (a buzzy and well-funded chip startup) on the ARK Invest podcast: https://ark-invest.com/podcast/cerebras-wafer-scale-engine-a.... I will say that the interviewee here was a little bit more coy, and the podcast generally is geared more towards a business audience, though they have had very top tier technical talent on.

Also from ARK Invest was this discussion of the just launched Nvidia A100 GPUs: https://ark-invest.com/podcast/fyi-ep67-nvidia-gpu/.

From the Matroid conference, the chief architect at Groq (another buzzy chip startup): https://youtu.be/q-lBj49iF9w

I can’t speak specifically to Intel, but at most large private companies when you’re an employee you give up the rights to talk to media/press about your work without employer approval.

For the Fortune and Lex Fridman interviews, Keller would likely have had to get Intel PR’s approval to participate. In Lex’s interview, you can see him wearing an Intel guest badge, so I assume the interview took place on-site.

What makes this more bizarre than Keller’s typical short stints at previous companies is that he has done a ton of media in the last year. He’s probably given more time to journalists/interviewers in 2020 than in the previous 3 decades of his career combined.

A Fortune piece from May of this year gave some insights into his plans (as well as provided a nice overview of his career) (https://fortune.com/longform/microchip-designer-jim-keller-i...).

> Keller won’t talk much about the massive chip redesign he’s overseeing—chip designers seldom do—and Intel’s new chip probably won’t be ready for another year or two. Still, both Intel and Keller have scattered some clues about how the chips might work. The new chips will cleanly separate major functions, to make it easier for the company to improve one section at a time—an approach that evokes the chiplet model Keller used at AMD. Keller also hints that Intel’s low-power Atom line of chips may figure more prominently in his future designs for PCs and servers.

It doesn’t sound like at press time he was planning to leave.

Keller also did a great interview on Lex Fridman’s podcast, which was released in February of this year (https://youtu.be/Nb2tebYAaOA).

Keller then did a presentation at the Matroid conference (held at the end of February) (https://youtu.be/8eT1jaHmlx8).

I hope he’s ok, since the Intel statement specifically mentioned “personal reasons”.

The article’s articulation of what goes into returning a serve is a bit simplistic, but the underlying idea is not crazy.

* When you return a serve in tennis, you are doing so from only one side of the court. The opponent’s serve can only land in a service box that provides 13 feet of lateral space.

* Practically, there are relatively few spots in the service box that can be reached by a serve. Because of human physiology (the length of our arms, joints in the arms etc.), it would be extremely painful to try to hit a fast serve to certain parts of the service box. Either that, or the server would have to stand in atypical positions on the service line (i.e. not at the center tick) that would be a dead giveaway of where the server was trying to hit to.

* So, in simplistic terms, most tennis players are choosing between more-or-less staying in place (to return a body serve), or leaping to their left or right. The serve must bounce before you hit it, and it will be bouncing “towards you” vertically. The returner thus is very rarely going to move vertically. This usually only happens when you are moving in to pummel a slow and short serve.

* At the highest levels of tennis, the vast majority (60%+) of serves are going out wide, or down the middle (https://www.atptour.com/en/news/berrettini-infosys-serve-loc...). Mind you, these are also the same player who would have the physical conditioning and athleticism to actually be able to hit these blazing fast serves.

* Additional information for the returner is conveyed by the serve toss. Almost all players are giving away tells here. For example, if I’m a right hander serving from the deuce court, and I toss my ball to the left (the “11 o’clock position”), it’s high unlikely that I’m hitting the ball down the middle. Doing so would require one of those aforementioned contortions in my arms and legs, and I would then be unlikely to generate the power needed to strike the ball in a way that leads to a super fast serve.

* So in reality, by the time that the server is making contact between their racket and the ball, the returner will have a general idea of the direction that the ball is going in.

* The article does conflate getting your racquet on the ball, and making a successful return. Just as with any other tennis shot, there is not guarantee that your return does not go into the net, or go flying out. I think it’s a far more plausible claim that professional tennis players can get their ball on the racquet vs claiming that they can cleanly/successfully return these super fast serves.

Some other thoughts:

* Placement is just as important as speed in determining how returnable a serve is.

* For example, there are plenty of examples of top tennis players returning extremely fast serves. Federer against Isner (140 mph): https://youtu.be/5gcvLbtaNxM, Murray against Raonic (147 mph): https://youtu.be/8GYX4ZIPJsg

* The commonality between these successful returns is that the serves themselves were fast, but poorly placed. By serving right down the middle, the servers allowed Federer and Murray to take one small step, and then make good contact with the serves for an “easy return”.

* One small quibble with the “world record tennis serve” you cite. It’s not 144 mph, but rather 157.2 mph (hit by John Isner). If anything though, this is helps your argument.

* The unofficial record is 160+ MPH (hit by Sam Groth), but this was at a second tier tournament with a questionable radar gun (https://youtu.be/uKeL-W7xft0). Notice how even with this serve, the returner correctly guesses where the serve is headed, and even looks to have gotten a racquet on it.

* It’s a bit of a chicken and an egg problem as well. There is a very tiny sliver of people in the world who are physically fit enough and who possess the natural physical traits (like height and broad shoulders) necessary to hit serves in the 140+ MPH range. These people are likely playing on the ATP against the players in the world best equipped (mentally and physically) to return their serves.

* So all this is to say, returning serves in that 140-160 MPH range is a low probability proposition. Heck, a perfectly placed and well disguised serve even in the 110 MPH range can be unreturnable (as seen in two decades of Federer highlights). But, humans are indeed “capable” of returning serves in that speed range.

In boom times, it’s probably true that people can develop irrational exuberance e.g., taking out a big mortgage because hey, housing prices are going up everywhere. When things are good, maybe you don’t budget so precisely. I know I don’t!

But when ~20% of working age Americans are living off unemployment benefits, and millions more who work in industries like restaurants, hospitality, and travel facing the possibility that they will likely never have a job to go back to, my guess is that you’d have to be pretty well off (or very optimistic) to take on an additional $100/month car payment, not to mention adding a car to your insurance policy etc. :)

The margins for used cars aren’t high enough to support that kind of discount (https://jalopnik.com/how-to-negotiate-for-a-used-car-5570813 and https://www.autonews.com/article/20180428/RETAIL04/180439997...).

Think of it this way: there isn’t nearly enough demand right now, regardless of whether you (the dealer) discount your inventory or not. I.e., you will not move enough cars to pay for fixed costs like the mortgage on your building, financing the inventory on your lot, etc. The Fed has done various studies on household savings account balances and/or net worth (https://www.valuepenguin.com/banking/average-checking-accoun... and https://abcnews.go.com/US/10-americans-struggle-cover-400-em...). The vast majority of Americans would have to finance a car purchase, even for something like a 10-year old used car. Yes interest rates are low right now, but does it make sense for a consumer to buy a depreciating asset and increase their monthly interest costs at this point in time?

To the car dealer (or rental operation like Hertz), the question is does it make sense to have a fire sale on your inventory now (at prices that will almost certainly lead to an accounting loss), or do you take your chances in bankruptcy court? If you believe that in the next year or two that the market for used cars recovers somewhat, you can sell those assets off during bankruptcy proceedings to pay off creditors etc. There’s a strong case that taking this approach will leave the company as a whole better off in 3-5 years, though equity holders will certainly be taking a huge hit.

Incredibly, the Sony WH-1000XM3 (the 3 in XM3 is because this is the 3rd generation of the product) at this point still doesn’t have it. Similar price range, and maybe the closest direct competitor to the QC35/700 series in terms of audio quality and noise cancelling, yet it’s still missing this feature.

Colab is effectively JupyterLab/JupyterHub with Google's own add-ins (like integration with Google Drive). JupyterHub is a huge PITA to manage, and Colab also offers a degree of free compute and limited access to a dated, but still free K80 GPU.

From the tweet thread, it seems like there was some misunderstanding over where the files are being stored and being executed. This is a pretty common issue with Google Drive. I.e. if someone shares a file with me, and I copy it to a folder, it's just a pointer to the original file. Only after clicking "Add to My Drive" does it count against my storage allocation, and only then is it a distinct copy.

My guess is that the researchers expected each user to be able to run the game in their own personal free Colab environment, not be running it against the university's compute and storage budget.