Over on the BruteProtect blog they have a look at the Jetpack Bloat Myth, and find that counter-intuitively even though Jetpack has more comprehensive functionality it’s faster than using individual plugins to do the same things. There are economies of scale to Jetpack’s approach, and it doesn’t even include the impact of doing things more advanced and complex like Related Posts. There’s a reason why some web hosts like WP Engine ban most related post plugins but encourage the use of Jetpack.

The performance of the plugin code, though still faster, is still a small difference when compared to the benefit of offloading certain tasks like image resizing, related posts, stats, video transcoding, and more in the future to the WordPress.com cloud (which is now across 11 datacenters worldwide).

Of course if you don’t need the functionality at all it’s always faster to have nothing, but that’s a shrinking minority. There are still more optimizations to be had, and in line with a performance focus in 2015 look for more improvements to come in the future. In the meantime, check out the Jetpack benchmarks.

How Paul Graham Is Wrong

Asides

I love Paul Graham’s essays and his latest is no exception: Let the Other 95% of Great Programmers In. I agree that the US deserves dramatically better immigration policies, but in the meantime I’m confused with the head-in-the-sand approach most tech companies are taking simultaneously complaining that there are lots of great people they can’t bring into the US, but being stubborn on keeping a company culture that requires people to be physically co-located.

In a region that prides itself on disruption and working from first principles, San Francisco’s scaling problem is pretty humorous if you look at it from the outside: otherwise smart and inventive founders continue to set up offices and try to hire or move people in the most overheated environment since there were carphones in Cadillac Allantes. This is where I feel like Paul Graham misses the most obvious solution to the problem.

If 95% of great programmers aren’t in the US, and an even higher percentage not in the Bay Area, set up your company to take advantage of that fact as a strength, not a weakness. Use WordPress and P2, use Slack, use G+ Hangouts, use Skype, use any of the amazing technology that allows us to collaborate as effectively online as previous generations of company did offline. Let people live someplace remarkable instead of paying $2,800 a month for a mediocre one bedroom rental in San Francisco. Or don’t, and let companies like Automattic and Github hire the best and brightest and let them live and work wherever they like.

Update: There is a vigorous discussion also happening on Hacker News.

Life Hack: Put leftovers on top of your Mac Pro to keep them warm.

“Because the most-popular songs now stay on the charts for months, the relative value of a hit has exploded. The top 1 percent of bands and solo artists now earn 77 percent of all revenue from recorded music, media researchers report. And even though the amount of digital music sold has surged, the 10 best-selling tracks command 82 percent more of the market than they did a decade ago. The advent of do-it-yourself artists in the digital age may have grown music’s long tail, but its fat head keeps getting fatter.” — The Shazam Effect.

In the United States, the Federal Communications Commission in 2002 reclassified high-speed Internet access as an information service, which is unregulated, rather than as telecommunications, which is regulated. Its hope was that Internet providers would compete with one another to provide the best networks. That didn’t happen. The result has been that they have mostly stayed out of one another’s markets.

Why the U.S. Has Fallen Behind in Internet Speed and Affordability. Also has one of my favorite animated GIFs I’ve seen in a Times story.

I like to use the analogy of building bridges. If I have no principles, and I build thousands of bridges without any actual science, lots of them will fall down, and great disasters will occur.

Similarly here, if people use data and inferences they can make with the data without any concern about error bars, about heterogeneity, about noisy data, about the sampling pattern, about all the kinds of things that you have to be serious about if you’re an engineer and a statistician—then you will make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori. You will just produce these outputs and hope for the best.

Today I learned there’s another Michael Jordan that is as awesome in machine learning as #23 is at basketball.  IEEE’s article Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts is worth a read and a re-read.

It’s also worth noting that Professor Jordan did an AMA on Reddit, and actually disagreed with the title and characterization of the IEEE interview and wrote a follow-up and response on a WordPress-powered blog.