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In general, relying only on natural keys is a nightmare. Double nightmare if it’s PII. Natural keys only work if you are flawlessly omniscient about the domain. And you aren’t.
If you measure things by foot traffic we [the SFO Museum] are one of the busiest museums in the world. If that is the case we are also one of the busiest museums in the world that no one knows about. Nothing in modern life really prepares you for the idea that a museum should be part of an airport. San Francisco, as I’ve mentioned, is funny that way.
This teaches us that—when it’s a big enough deal—Amazon will lie to us. And coming from the company that runs the production infrastructure for our companies, stores our data, and has been granted an outsized position of trust based upon having earned it over 15 years, this is a nightmare.
GitHub, by default, writes five replicas of each repository across our three data centers to protect against failures at the server, rack, network, and data center levels. When we need to update Git references, we briefly take a lock across all of the replicas in all of our data centers, and release the lock when our three-phase-commit (3PC) protocol reports success.
When you have to mock a collaborator, avoid using the Mock object directly. Either use mock.create_autospec() or mock.patch(autospec=True) if at all possible. Autospeccing from the real collaborator means that if the collaborator’s interface changes, your tests will fail. Manually speccing or not speccing at all means that changes in the collaborator’s interface will not break your tests that use the collaborator: you could have 100% test coverage and your library would fall over when used!
When building a tool, it’s easy to forget how much you’ve internalized: how much knowledge and context you’ve assumed. Your tool can feel familiar or even obvious to you while being utterly foreign to everyone else. If your goal is for other people to use the darn thing — meaning you’re not just building for yourself, or tinkering for its own sake (which are totally valid reasons) — you gotta help people use it! It doesn’t matter what’s possible or what you intended; all that matters is whether people actually succeed in practice.
I strongly suspect that the single most impactful thing I did during my 5+ years at Linden Lab was shortly before I left: set up a weekly meeting between a couple of leads from Support and Engineering to go over the top 10 support issues.
Business rules engines are li’l Conway’s Law devices: a manifestation of the distrust between stakeholders, client and contractor. We require BREs so that separate business units need not talk to each other to solve problems. They are communication and organizational dysfunction made silicon.
One of the hardest things I’ve had to learn is that humans aren’t pure functions: an input that works one day and gets one result, then again another day and get an entirely different result.
Litestream runs continuously on a test server with generated load and streams backups to S3. It uses physical replication so it’ll actually restore the data from S3 periodically and compare the checksum byte-for-byte with the current database.
Finally, remember that whatever choice is made, you’re going to need to get behind it! You should be able to make a compelling positive case for any of the options you present. If there’s an option you can’t support, don’t present it.
Technology does not need vast troves of personal data stitched together across dozens of websites and apps in order to succeed. Advertising existed and thrived for decades without it, and we’re here today because the path of least resistance is rarely the path of wisdom.
Tuesday’s chaos arose after China Railway Shenyang failed to deactivate Flash in time, leading to a complete shutdown of its railroads in Dalian, Liaoning province. Staffers were reportedly unable to view train operation diagrams, formulate train sequencing schedules and arrange shunting plans. Authorities fixed the issue by installing a pirated version of Flash at 4:30 a.m. the following day.
When you know something it is almost impossible to imagine what it is like not to know that thing. This is the curse of knowledge, and it is the root of countless misunderstandings and inefficiencies. Smart people who are comfortable with complexity can be especially prone to it! If you don’t guard against the curse of knowledge it has the potential to obfuscate all forms of communication, including code. The more specialized your work, the greater the risk that you will communicate in ways that are incomprehensible to the uninitiated.
Generally, product-aligned teams deliver better products more rapidly. Again, Conway’s Law is inescapable; if delivering a new feature requires several teams to coordinate, you’ll struggle compared to an org where a single team can execute on a new feature.
You know Google Maps? What I do is, like, build little pieces of Google Maps over and over for people who need them but can’t just use Google Maps because they’re not allowed to for some reason, or another.
While copywriting is used to persuade a user to take a certain action, technical writing exists to support the user and remove barriers to getting something done. Good technical writing is hard because writers must get straight to the point without losing or confusing readers.
At GitHub, we want to protect developer privacy, and we find cookie banners quite irritating, so we decided to look for a solution. After a brief search, we found one: just don’t use any non-essential cookies. Pretty simple, really. 🤔 So, we have removed all non-essential cookies from GitHub, and visiting our website does not send any information to third-party analytics services.
I get asked a lot about learning to code. Sure, if you can. It’s fun. But the real action, the crux of things, is there in the database. Grab a tiny, free database like SQLite. Import a few million rows of data. Make them searchable. It’s one of the most soothing activities known to humankind, taking big piles of messy data and massaging them into the rigid structure required of a relational database. It’s true power.
If you are pre-product market fit it’s probably too early to think about event based analytics. If you have a small number of users and are able to talk with all of them, you will get much more meaningful data getting to know them than if you were to set up product analytics. You probably don’t have enough users to get meaningful data from product analytics anyways.
Discoverable CLIs have comprehensive help texts, provide lots of examples, suggest what command to run next, suggest what to do when there is an error. There are lots of ideas that can be stolen from GUIs to make CLIs easier to learn and use, even for power users.
The value of a product is the number of problems it can solve divided by the amount of complexity the user needs to keep in their head to use it. Consider an iPhone vs a standard TV remove: an iPhone touchscreen can be used for countless different functions, but there’s very little to remember about how it works (tap, drag, swipe, pinch). With a TV remove you have to remember what every button does; the more things you can use the remote for, the more buttons it has. We want to create iPhones, not TV remotes.
The open secret Jennings filled me in on is that OpenStreetMap (OSM) is now at the center of an unholy alliance of the world’s largest and wealthiest technology companies. The most valuable companies in the world are treating OSM as critical infrastructure for some of the most-used software ever written. The four companies in the inner circle— Facebook, Apple, Amazon, and Microsoft— have a combined market capitalization of over six trillion dollars.
Seniors generally report having more trust in the people around them, a characteristic that may make them more credulous of information that comes from friends and family. There is also the issue of context: Misinformation appears in a stream that also includes baby pictures, recipes and career updates. Users may not expect to toggle between light socializing and heavy truth-assessing when they’re looking at their phone for a few minutes in line at the grocery store.
Apple now receives an estimated $8 billion to $12 billion in annual payments — up from $1 billion a year in 2014 — in exchange for building Google’s search engine into its products. It is probably the single biggest payment that Google makes to anyone and accounts for 14 to 21 percent of Apple’s annual profits.
Writing the code to sign data with a private key and verify it with a public key would have been easier to get correct than correctly invoking the JWT library. In fact, the iOS app (which gets this right) doesn’t use a JWT library at all, but manages to verify using a public key in fewer lines of code than the Android app takes to incorrectly use a JWT library!
The stampede of the affluent into grim-faced, highly competitive sports has been a tragicomedy of perverse incentives and social evolution in unequal times: a Darwinian parable of the mayhem that can ensue following the discovery of even a minor advantage. Like a peacock rendered nearly flightless by gaudy tail feathers, the overserved athlete is the product of a process that has become maladaptive, and is now harming the very blue-chip demographic it was supposed to help.
It’s probably a bad idea to risk paying your ransom, though — the US Treasury Dept has issued clarifying guidance that companies paying off ransomware, and all companies facilitating the payment, can be charged with sanctions violations if the bitcoins end up at North Korea or sanctioned cybercrime groups.
I’ve often joked with other internet culture reporters about what I call the “normie tipping point.” In every emerging internet trend, there is a point at which “normies” — people who don’t spend all day online, and whose brains aren’t rotted by internet garbage — start calling, texting and emailing us to ask what’s going on. Why are kids eating Tide Pods? What is the Momo Challenge? Who is Logan Paul, and why did he film himself with a dead body? The normie tipping point is a joke, but it speaks to one of the thorniest questions in modern journalism, specifically on this beat: When does the benefit of informing people about an emerging piece of misinformation outweigh the possible harms?
Inevitably we got round to talking about async. As much of an unneeded complication as it is for so many day-to-day use-cases, it’s important for Python because, if and when you do need the high throughput handling of these io-bound use-cases, you don’t want to have to switch language. The same for Django: most of what you’re doing has no need of async but you don’t want to have to change web framework just because you need a sprinkling of non-blocking IO.