We received over 2,200 applications for our just-closed junior programmer opening, and now we're going through all of them by hand and by human. No AI screening here. It's a lot of work, but we have a great team who take the work seriously, so in a few weeks, we'll be able to invite a group of finalists to the next phase. This highlights the folly of thinking that what it'll take to land a job like this is some specific list of criteria, though. Yes, you have to present a baseline of relevant markers to even get into consideration, like a great cover letter that doesn't smell like AI slop, promising projects or work experience or educational background, etc. But to actually get the job, you have to be the best of the ones who've applied! It sounds self-evident, maybe, but I see questions time and again about it, so it must not be. Almost every job opening is grading applicants on the curve of everyone who has applied. And the best candidate of the lot gets the job. You can't quantify what that looks like in advance. I'm excited to see who makes it to the final stage. I already hear early whispers that we got some exceptional applicants in this round. It would be great to help counter the narrative that this industry no longer needs juniors. That's simply retarded. However good AI gets, we're always going to need people who know the ins and outs of what the machine comes up with. Maybe not as many, maybe not in the same roles, but it's truly utopian thinking that mankind won't need people capable of vetting the work done by AI in five minutes.

Recently I got a question on formal methods1: how does it help to mathematically model systems when the system requirements are constantly changing? It doesn't make sense to spend a lot of time proving a design works, and then deliver the product and find out it's not at all what the client needs. As the saying goes, the hard part is "building the right thing", not "building the thing right". One possible response: "why write tests"? You shouldn't write tests, especially lots of unit tests ahead of time, if you might just throw them all away when the requirements change. This is a bad response because we all know the difference between writing tests and formal methods: testing is easy and FM is hard. Testing requires low cost for moderate correctness, FM requires high(ish) cost for high correctness. And when requirements are constantly changing, "high(ish) cost" isn't affordable and "high correctness" isn't worthwhile, because a kinda-okay solution that solves a customer's problem is infinitely better than a solid solution that doesn't. But eventually you get something that solves the problem, and what then? Most of us don't work for Google, we can't axe features and products on a whim. If the client is happy with your solution, you are expected to support it. It should work when your customers run into new edge cases, or migrate all their computers to the next OS version, or expand into a market with shoddy internet. It should work when 10x as many customers are using 10x as many features. It should work when you add new features that come into conflict. And just as importantly, it should never stop solving their problem. Canonical example: your feature involves processing requested tasks synchronously. At scale, this doesn't work, so to improve latency you make it asynchronous. Now it's eventually consistent, but your customers were depending on it being always consistent. Now it no longer does what they need, and has stopped solving their problems. Every successful requirement met spawns a new requirement: "keep this working". That requirement is permanent, or close enough to decide our long-term strategy. It takes active investment to keep a feature behaving the same as the world around it changes. (Is this all a pretentious of way of saying "software maintenance is hard?" Maybe!) Phase changes In physics there's a concept of a phase transition. To raise the temperature of a gram of liquid water by 1° C, you have to add 4.184 joules of energy.2 This continues until you raise it to 100°C, then it stops. After you've added two thousand joules to that gram, it suddenly turns into steam. The energy of the system changes continuously but the form, or phase, changes discretely. Software isn't physics but the idea works as a metaphor. A certain architecture handles a certain level of load, and past that you need a new architecture. Or a bunch of similar features are independently hardcoded until the system becomes too messy to understand, you remodel the internals into something unified and extendable. etc etc etc. It's doesn't have to be totally discrete phase transition, but there's definitely a "before" and "after" in the system form. Phase changes tend to lead to more intricacy/complexity in the system, meaning it's likely that a phase change will introduce new bugs into existing behaviors. Take the synchronous vs asynchronous case. A very simple toy model of synchronous updates would be Set(key, val), which updates data[key] to val.3 A model of asynchronous updates would be AsyncSet(key, val, priority) adds a (key, val, priority, server_time()) tuple to a tasks set, and then another process asynchronously pulls a tuple (ordered by highest priority, then earliest time) and calls Set(key, val). Here are some properties the client may need preserved as a requirement: If AsyncSet(key, val, _, _) is called, then eventually db[key] = val (possibly violated if higher-priority tasks keep coming in) If someone calls AsyncSet(key1, val1, low) and then AsyncSet(key2, val2, low), they should see the first update and then the second (linearizability, possibly violated if the requests go to different servers with different clock times) If someone calls AsyncSet(key, val, _) and immediately reads db[key] they should get val (obviously violated, though the client may accept a slightly weaker property) If the new system doesn't satisfy an existing customer requirement, it's prudent to fix the bug before releasing the new system. The customer doesn't notice or care that your system underwent a phase change. They'll just see that one day your product solves their problems, and the next day it suddenly doesn't. This is one of the most common applications of formal methods. Both of those systems, and every one of those properties, is formally specifiable in a specification language. We can then automatically check that the new system satisfies the existing properties, and from there do things like automatically generate test suites. This does take a lot of work, so if your requirements are constantly changing, FM may not be worth the investment. But eventually requirements stop changing, and then you're stuck with them forever. That's where models shine. As always, I'm using formal methods to mean the subdiscipline of formal specification of designs, leaving out the formal verification of code. Mostly because "formal specification" is really awkward to say. ↩ Also called a "calorie". The US "dietary Calorie" is actually a kilocalorie. ↩ This is all directly translatable to a TLA+ specification, I'm just describing it in English to avoid paying the syntax tax ↩

I've been biking in Brooklyn for a few years now! It's hard for me to believe it, but I'm now one of the people other bicyclists ask questions to now. I decided to make a zine that answers the most common of those questions: Bike Brooklyn! is a zine that touches on everything I wish I knew when I started biking in Brooklyn. A lot of this information can be found in other resources, but I wanted to collect it in one place. I hope to update this zine when we get significantly more safe bike infrastructure in Brooklyn and laws change to make streets safer for bicyclists (and everyone) over time, but it's still important to note that each release will reflect a specific snapshot in time of bicycling in Brooklyn. All text and illustrations in the zine are my own. Thank you to Matt Denys, Geoffrey Thomas, Alex Morano, Saskia Haegens, Vishnu Reddy, Ben Turndorf, Thomas Nayem-Huzij, and Ryan Christman for suggestions for content and help with proofreading. This zine is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, so you can copy and distribute this zine for noncommercial purposes in unadapted form as long as you give credit to me. Check out the Bike Brooklyn! zine on the web or download pdfs to read digitally or print here!

We’ve just launched Hotwire Native v1.2 and it’s the biggest update since the initial launch last year. The update has several key improvements, bug fixes, and more API consistency between platforms. And we’ve created all new iOS and Android demo apps to show it off! A web-first framework for building native mobile apps Improvements There are a few significant changes in v1.2 that are worth specifically highlighting. Route decision handlers Hotwire Native apps route internal urls to screens in your app, and route external urls to the device’s browser. Historically, though, it wasn’t straightforward to customize the default behavior for unique app needs. In v1.2, we’ve introduced the RouteDecisionHandler concept to iOS (formerly only on Android). Route decisions handlers offer a flexible way to decide how to route urls in your app. Out-of-the-box, Hotwire Native registers these route decision handlers to control how urls are routed: AppNavigationRouteDecisionHandler: Routes all internal urls on your app’s domain through your app. SafariViewControllerRouteDecisionHandler: (iOS Only) Routes all external http/https urls to a SFSafariViewController in your app. BrowserTabRouteDecisionHandler: (Android Only) Routes all external http/https urls to a Custom Tab in your app. SystemNavigationRouteDecisionHandler: Routes all remaining external urls (such as sms: or mailto:) through device’s system navigation. If you’d like to customize this behavior you can register your own RouteDecisionHandler implementations in your app. See the documentation for details. Server-driven historical location urls If you’re using Ruby on Rails, the turbo-rails gem provides the following historical location routes. You can use these to manipulate the navigation stack in Hotwire Native apps. recede_or_redirect_to(url, **options) — Pops the visible screen off of the navigation stack. refresh_or_redirect_to(url, **options) — Refreshes the visible screen on the navigation stack. resume_or_redirect_to(url, **options) — Resumes the visible screen on the navigation stack with no further action. In v1.2 there is now built-in support to handle these “command” urls with no additional path configuration setup necessary. We’ve also made improvements so they handle dismissing modal screens automatically. See the documentation for details. Bottom tabs When starting with Hotwire Native, one of the most common questions developers ask is how to support native bottom tab navigation in their apps. We finally have an official answer! We’ve introduced a HotwireTabBarController for iOS and a HotwireBottomNavigationController for Android. And we’ve updated the demo apps for both platforms to show you exactly how to set them up. New demo apps To better show off all the features in Hotwire Native, we’ve created new demo apps for iOS and Android. And there’s a brand new Rails web app for the native apps to leverage. Hotwire Native demo app Clone the GitHub repos to build and run the demo apps to try them out: iOS repo Android repo Rails app Huge thanks to Joe Masilotti for all the demo app improvements. If you’re looking for more resources, Joe even wrote a Hotwire Native for Rails Developers book! Release notes v1.2 contains dozens of other improvements and bug fixes across both platforms. See the full release notes to learn about all the additional changes: iOS release notes Android release notes Take a look If you’ve been curious about using Hotwire Native for your mobile apps, now is a great time to take a look. We have documentation and guides available on native.hotwired.dev and we’ve created really great demo apps for iOS and Android to help you get started.