There's an argument to be made though that hyper-targeting helps niche or smaller businesses more than a P&G who has to run a larger campaign so the IRR could drop more for smaller businesses than a large company on Facebook specifically?
Screen Hero was light years ahead of anything on the market a few years ago. I'm not sure why they didn't integrate it into a simple feature button in Slack.
>>> Moreover their software is finely optimized for it.
What's been impressive is that emulated code optimized for x86 runs faster on the M1. If more developer mindshare focuses on ARM optimizations and specifically writing software for the M1, the performance gains likely will even be more significant. That said, these chips are designed for a single computer, which hasn't been much of a focus area for Intel/AMD the past few years.
If you look at many top professional performers, it usually seems like the younger siblings are better. Haven't read the article yet, but my theory would be that the younger brother gets to practice against his sibling that's a year or two older. My guess would be that this would be particular to sports where 1v1 is a priority like tennis or basketball.
I'd agree, but Apple's position with the iPad likely will ensure enough professional developer mindshare? Most enterprise apps I use typically have a fairly built out version for the iPad (and most only support Windows otherwise). I agree that developers themselves will likely stick with x86 architecture to write code on for the most part given that's what servers will run.
Perhaps it is due to less data copying and configuring the right processor for the job in Apple's benchmarks? Most CPU benchmarks wouldn't be testing the performance characteristics if the matrix heavy computations were done by the GPU, etc.?
Switch statements can work well from a readability standpoint such as the HTTP error example they use. However, I almost feel it is more Pythonic to use a dictionary instead, collapsing the function into:
There is a very real risk that companies spin off completely dead weight (see Honeywell's spinoff of Garrett). eBay spinning off PayPal a few years ago is an example on the opposite end. Assuming the management team is acting in good faith, it usually is because the two divisions lack synergies and have different potential growth trajectories and paths. For instance, the fast growing division might not be able to spend enough money because investors view the overall company as a slow, stable grower while the stable division is upset that the fast growing division is sucking up its profits to grow. In cases like these, it might not be a dead business, but something like eBay.
In Europe, companies like LVMH and L'Oreal has bonus dividends or extra voting rights for long-term institutional shareholders already. Essentially, if you hold onto shares for over 1 year, 2 years, etc., you can offer shareholders additional incentives.
A lot of investors have ingrained that "bundling/unbundling" phases happen across every industry and see Google Search as the ultimate unbundling opportunity in all likelihood. Therefore, I'm not surprised micro/niche search engines continue to see outsized funding relative to their business model potential.
From what I know about the industry, there's also different categories. I don't doubt that Target or Walmart could use digital models for generic clothing. I doubt that would work for a designer launching a new high end collection. Then there's the experiential side where designers like Oscar de La Renta will have events with live models for women to view their red carpet/wedding dresses. Potentially, software could help designers quickly test and iterate on new clothing ideas, but launch brands likely will launch campaigns around real people.
The trick will be whether the code generation works well enough most of the time, but if it generates some 1% weirdness that becomes a needle in the haystack. It seems to be it will be great for product manager/business person prototypes and potentially helpful for "hyper auto-complete" for a developer, but likely not for automating mission critical code.
They really did seem to be firing on all cylinders early on. Prior to their IPO, I thought it was weird they were spending time to migrate off of AWS versus continuing to build around their core offering. As it stands now, they could still target consumers with a higher end offering (lots of caching, etc.) if order to at least offer a superior experience to Google Drive, Microsoft, etc.
I have a significant amount of experience with both of them. For pandas, it is fine for initial exploratory analysis (input, plot, reshape and export, etc). However, it's API has inconsistencies and subtle "features" around silent data coercion that make it hard to use in production. Seaborn is much nicer to work with than matplotlib and Jupyter is useful for making interactive presentations. So I believe Pandas/Jupyter have a place, but then there was a tendency to create a Pandas-like wrapper for all data retrieval such as:
I’m hardly on Facebook, but I hardly notice this. I use Twitter significantly more frequently, but I also notice significantly more divide on politically charged topics, especially on Tweet comments.
This is a bad way to do this. There's executives with multiple c-suite roles, compensation for multi-year performance targets, founders who elect to take low salaries and company size disparities.