More from Irrational Exuberance
Back in 2018, I wrote lethain/systems as a domain-specific language for writing runnable systems models, and introduced it with this blog post modeling a hiring funnel. While it’s far from a perfect system, I’ve gotten a lot of value out of it over the last seven years, because it allows me to maintain systems models in version control. As I’ve been playing with writing Model Context Protocol (MCP) servers, one I’ve been thinking about frequently is one to help writing systems syntax, and I finally put that together in the lethain/systems-mcp repository. More detailed installation and usage instructions are in the GitHub repository, so I’ll just share a couple of screenshots and comments here. Starting with the load_systems_documentation tool which loads a copy of lethain/systems/README.md and a file with example systems into the context window. The biggest challenge of properly writing DSLs with an LLM is providing enough in-context learning (ICL) examples, and I think the idea of providing tools that are specifically designed to provide that context is a very interesting idea. Eventually I imagine there will be generalized tools for this, e.g. a search index of the best ICL examples for a wide variety of DSLs. Until then, my guess is that this sort of tool is particularly valuable. The second tool is run_systems_model which passes the DSL (and an optional parameter for number of rounds) to the tool and then returns the result. I experimented with interface design here, initially trying to return a rendered chart of the results, but ultimately even multi-modal models are just much better at working with text than with images. This meant that I had the best results returning JSON of the results and then having the LLM build a tool for interacting with the results. Altogether, a fun little experiment, and another confirmation in my mind that the most interesting part of designing MCPs today is deciding where to introduce and eliminate complexity from the LLM. Introduce too little and the tool lacks power; eliminate too little and the combination rarely works.
At Carta, we recently ran a reading group for Facilitating Software Architecture by Andrew Harmel-Law. We already loosely followed the ideas of an architectural advice process (from this 2021 article by the same Andrew Harmel-Law), but in practice we found that internal tech spec and architecture decision record (ADR) authors tended to exclusively share their documents locally within their team rather than more widely. As we asked authors why they preferred sharing locally, the most common answer was that they got enough feedback from their team that they didn’t want to pay the time overhead of sharing widely. The wider feedback wasn’t necessarily bad or combative. It just wasn’t good enough to compensate for the additional time it cost to process. This made sense from the authors’ perspectives, but didn’t work well for me from the executive perspective, as I was seeing teams make misaligned decisions due to lack of cross-team communication. As one step in reducing the overhead of sharing documents widely, I wrote up and shared this recommended process for providing feedback on documents: Before starting, remember that the goal of providing feedback on a document is to help the author. Optimizing for anything else, even if it’s a worthy cause, discourages authors from sharing their future writing. If you prioritize something other than helping the author, you are discouraging them from sharing future work. Start by skimming the document to understand its structure and where various kinds of topics are addressed. Why? This helps avoid giving feedback on ways the document’s actual structure diverges from how you imagined it would be structured. It also reduces questions about topics that are answered later in the document. Both of these sorts of feedback are a distraction during a discussion on a tech spec. In general, it’s better to avoid them. If you notice an author making the same significant structural mistake over several ADRs, it’s worth delivering that feedback separately. After skimming, reread the document, leaving comments with concerns. Each comment should include these details: What your suggested change or concern is Why you believe this is meaningful to address How important this seems (from ignorable nitpick to critical) If you find yourself leaving more than three or four issues, then you should either raise your threshold for commenting or you should schedule time with the individual to talk over the feedback. If the document is unreasonably weak, then it’s appropriate to nudge their leadership to dig into what’s happening on that team. The most important idea behind these steps is that your goal as a feedback giver is to help the document’s author. It is not to protect your team’s strategy or platform. It is not to optimize for your goals. It’s to help the author. This might feel wrong, but ultimately optimizing for anything else will lead to an environment where sharing widely is an irrational behavior. As a final aside, I think the user experience around commenting on documents is fundamentally wrong in most document editors. For example, Google Docs treats individual comments as first-order objects, similarly to how old version control systems like CVS tracked changes to individual files without tracking an overall state of the project. Ultimately, you want to collect all your comments into a bundle, then review that bundle for consistency and duplicates, and then submit that bundle as commentary, but editors don’t support that flow particularly well.
A few years ago I wrote about reading a Profit & Loss statement, which is a foundational executive skill. I also subsequently wrote about ways to measure your engineering organization. Despite having written those, I still spend a lot of time wondering about effective ways to represent an engineering organization to your board of directors. Over the past few years, one of the most useful charts I’ve found for explaining an R&D organization is a scatterplot of R&D spend as a % of margin versus YoY growth of last twelve months (LTM) revenue. Unlike so many other measures, this is an explicit measure of your R&D organization value as an investment relative to peer organizations. Until recently, I assumed building this dataset required reading financial filings but my strategic finance partner at Carta, Tyler Braslow, pointed out that you can get all of this data for the tech section from Meritech Analytics, for free. When you login to Meritech, you’re dropped into a table of public company comparables for tech companies. This is the exact dataset I’d been looking for to build this chart. After logging in, you can then copy the contents of that table into a Google Sheets spreadsheet or Excel, or whatever you’re most comfortable with. Within that sheet, the columns you care about are: % YoY Growth LTM Rev (column Q for me) – how much “last twelve months revenue” has grown year over year, as a percentage % LTM Margins for R&D (column U for me) – how much R&D spend is as a percentage of last twelve months margin LTM Revenue (column O for me) – although I don’t show this in the scatterplot, I find this one useful for debugging outlier values Hiding the other columns gives you a much simpler table. From that table, you’re then able to build the scatterplot. Note that being “higher” means your R&D spend as a percentage of LTM margin is higher, which is a bad thing. The best companies are to the bottom and to the right; the worst companies are to the top and to the left. With this chart as a starting point, you can then plot your company in and show where you stand. You could also show how your company’s position in the chart has evolved over time: hopefully improving. Finally, you might want to cull some of these data points to better determine your public company comparables. The Meritech dataset has 106 entries, but you might prefer a more representative thirty entries.
Every few years I take a pass at reducing the chaos in my personal inboxes. There are simply too many emails to deal with, and that generally leads to me increasingly failing to follow up on important email. Up to this point, my strategy has largely been filtering out emails that I never want to read. But there’s another category of email which is stuff I often want to read when it’s fresh, but never want to read after it’s fresh. For example, calendar reminders, some mailing lists, some news letters, etc. I decided to figure out how I could setup a system where I could mark a number of things as “filter three days after receipt”. This is a nice compromise, because I do want to see those things, but I don’t want to have to remember to archive them after the fact. You can write a search query for this in GMail: from:(calendar-notification@google.com) older_than:3d However, if you try to create a GMail filter using that, it turns the older_than:3d into a fixed point in time rather than doing what you want. It seems that this is unsolvable within GMail itself. However, some quick searching suggested it was possible to create a Google App Script to solve this, and asked Claude to write the script for me. Following those instructions, I went to script.google.com, which I have not gone to in many years. I edited the generated script from Claude to use the tag “TempMsg”, to archive messages (as originally it had those commented out), and to limit itself to the first fifty items matching that tag. You can find the full code in this gist. I attempted to run this as is, and got an error message that I needed to grant permissions. That requires three clicks within the Google Scripts UI. This also requires approving the somewhat scary message that I trust myself. From there I tried to run this script, and it failed because the TempMsg tag doesn’t exist in my inbox. So I went ahead and created that tag, and setup some filters to assign that tag to certain email senders. After that, I was able to run the script and it worked properly. Note that I convinced myself that it was failing for a bit, because it doesn’t remove messages from the past three days. That is exactly how it’s supposed to work, but I would run it and then see messages with the tag there and think it was failing. Woops. After convincing myself it was working, I added a periodic trigger to run this. I now have this running on a daily basis, and it’s given me a nice new tool for managing my email a bit better. After verifying it, I also used the tag manager to “hide” this tag in the inbox, so I don’t have to see the TempMsg tag everywhere. If I ever need to debug things, I can always make it visible again.
While Stripe is a widely admired company for things like its creation of the Sorbet typer project, I personally think that Stripe’s most interesting strategy work is also among its most subtle: its willingness to significantly prioritize API stability. This strategy is almost invisible externally. Internally, discussions around it were frequent and detailed, but mostly confined to dedicated API design conversations. API stability isn’t just a technical design quirk, it’s a foundational decision in an API-driven business, and I believe it is one of the unsung heroes of Stripe’s business success. This is an exploratory, draft chapter for a book on engineering strategy that I’m brainstorming in #eng-strategy-book. As such, some of the links go to other draft chapters, both published drafts and very early, unpublished drafts. Reading this document To apply this strategy, start at the top with Policy. To understand the thinking behind this strategy, read sections in reverse order, starting with Explore. More detail on this structure in Making a readable Engineering Strategy document. Policy & Operation Our policies for managing API changes are: Design for long API lifetime. APIs are not inherently durable. Instead we have to design thoughtfully to ensure they can support change. When designing a new API, build a test application that doesn’t use this API, then migrate to the new API. Consider how integrations might evolve as applications change. Perform these migrations yourself to understand potential friction with your API. Then think about the future changes that we might want to implement on our end. How would those changes impact the API, and how would they impact the application you’ve developed. At this point, take your API to API Review for initial approval as described below. Following that approval, identify a handful of early adopter companies who can place additional pressure on your API design, and test with them before releasing the final, stable API. All new and modified APIs must be approved by API Review. API changes may not be enabled for customers prior to API Review approval. Change requests should be sent to api-review email group. For examples of prior art, review the api-review archive for prior requests and the feedback they received. All requests must include a written proposal. Most requests will be approved asynchronously by a member of API Review. Complex or controversial proposals will require live discussions to ensure API Review members have sufficient context before making a decision. We never deprecate APIs without an unavoidable requirement to do so. Even if it’s technically expensive to maintain support, we incur that support cost. To be explicit, we define API deprecation as any change that would require customers to modify an existing integration. If such a change were to be approved as an exception to this policy, it must first be approved by the API Review, followed by our CEO. One example where we granted an exception was the deprecation of TLS 1.2 support due to PCI compliance obligations. When significant new functionality is required, we add a new API. For example, we created /v1/subscriptions to support those workflows rather than extending /v1/charges to add subscriptions support. With the benefit of hindsight, a good example of this policy in action was the introduction of the Payment Intents APIs to maintain compliance with Europe’s Strong Customer Authentication requirements. Even in that case the charge API continued to work as it did previously, albeit only for non-European Union payments. We manage this policy’s implied technical debt via an API translation layer. We release changed APIs into versions, tracked in our API version changelog. However, we only maintain one implementation internally, which is the implementation of the latest version of the API. On top of that implementation, a series of version transformations are maintained, which allow us to support prior versions without maintaining them directly. While this approach doesn’t eliminate the overhead of supporting multiple API versions, it significantly reduces complexity by enabling us to maintain just a single, modern implementation internally. All API modifications must also update the version transformation layers to allow the new version to coexist peacefully with prior versions. In the future, SDKs may allow us to soften this policy. While a significant number of our customers have direct integrations with our APIs, that number has dropped significantly over time. Instead, most new integrations are performed via one of our official API SDKs. We believe that in the future, it may be possible for us to make more backwards incompatible changes because we can absorb the complexity of migrations into the SDKs we provide. That is certainly not the case yet today. Diagnosis Our diagnosis of the impact on API changes and deprecation on our business is: If you are a small startup composed of mostly engineers, integrating a new payments API seems easy. However, for a small business without dedicated engineers—or a larger enterprise involving numerous stakeholders—handling external API changes can be particularly challenging. Even if this is only marginally true, we’ve modeled the impact of minimizing API changes on long-term revenue growth, and it has a significant impact, unlocking our ability to benefit from other churn reduction work. While we believe API instability directly creates churn, we also believe that API stability directly retains customers by increasing the migration overhead even if they wanted to change providers. Without an API change forcing them to change their integration, we believe that hypergrowth customers are particularly unlikely to change payments API providers absent a concrete motivation like an API change or a payment plan change. We are aware of relatively few companies that provide long-term API stability in general, and particularly few for complex, dynamic areas like payments APIs. We can’t assume that companies that make API changes are ill-informed. Rather it appears that they experience a meaningful technical debt tradeoff between the API provider and API consumers, and aren’t willing to consistently absorb that technical debt internally. Future compliance or security requirements—along the lines of our upgrade from TLS 1.2 to TLS 1.3 for PCI—may necessitate API changes. There may also be new tradeoffs exposed as we enter new markets with their own compliance regimes. However, we have limited ability to predict these changes at this point.
More in programming
The appeal of "vibe coding" — where programmers lean back and prompt their way through an entire project with AI — appears partly to be based on the fact that so many development environments are deeply unpleasant to work with. So it's no wonder that all these programmers stuck working with cumbersome languages and frameworks can't wait to give up on the coding part of software development. If I found writing code a chore, I'd be looking for retirement too. But I don't. I mean, I used to! When I started programming, it was purely because I wanted programs. Learning to code was a necessary but inconvenient step toward bringing systems to life. That all changed when I learned Ruby and built Rails. Ruby's entire premise is "programmer happiness": that writing code should be a joy. And historically, the language was willing to trade run-time performance, memory usage, and other machine sympathies against the pursuit of said programmer happiness. These days, it seems like you can eat your cake and have it too, though. Ruby, after thirty years of constant improvement, is now incredibly fast and efficient, yet remains a delight to work with. That ethos couldn't shine brighter now. Disgruntled programmers have finally realized that an escape from nasty syntax, boilerplate galore, and ecosystem hyper-churn is possible. That's the appeal of AI: having it hide away all that unpleasantness. Only it's like cleaning your room by stuffing the mess under the bed — it doesn't make it go away! But the instinct is correct: Programming should be a vibe! It should be fun! It should resemble English closely enough that line noise doesn't obscure the underlying ideas and decisions. It should allow a richness of expression that serves the human reader instead of favoring the strictness preferred by the computer. Ruby does. And given that, I have no interest in giving up writing code. That's not the unpleasant part that I want AI to take off my hands. Just so I can — what? — become a project manager for a murder of AI crows? I've had the option to retreat up the manager ladder for most of my career, but I've steadily refused, because I really like writing Ruby! It's the most enjoyable part of the job! That doesn't mean AI doesn't have a role to play when writing Ruby. I'm conversing and collaborating with LLMs all day long — looking up APIs, clarifying concepts, and asking stupid questions. AI is a superb pair programmer, but I'd retire before permanently handing it the keyboard to drive the code. Maybe one day, wanting to write code will be a quaint concept. Like tending to horses for transportation in the modern world — done as a hobby but devoid of any economic value. I don't think anyone knows just how far we can push the intelligence and creativity of these insatiable token munchers. And I wouldn't bet against their advance, but it's clear to me that a big part of their appeal to programmers is the wisdom that Ruby was founded on: Programming should favor and flatter the human.
About half a year ago I encountered a paper bombastically titled “the ultimate conditional syntax”. It has the attractive goal of unifying pattern match with boolean if tests, and its solution is in some ways very nice. But it seems over-complicated to me, especially for something that’s a basic work-horse of programming. I couldn’t immediately see how to cut it down to manageable proportions, but recently I had an idea. I’ll outline it under the “penultimate conditionals” heading below, after reviewing the UCS and explaining my motivation. what the UCS? whence UCS out of scope penultimate conditionals dangling syntax examples antepenultimate breath what the UCS? The ultimate conditional syntax does several things which are somewhat intertwined and support each other. An “expression is pattern” operator allows you to do pattern matching inside boolean expressions. Like “match” but unlike most other expressions, “is” binds variables whose scope is the rest of the boolean expression that might be evaluated when the “is” is true, and the consequent “then” clause. You can “split” tests to avoid repeating parts that are the same in successive branches. For example, if num < 0 then -1 else if num > 0 then +1 else 0 can be written if num < 0 then -1 > 0 then +1 else 0 The example shows a split before an operator, where the left hand operand is the same and the rest of the expression varies. You can split after the operator when the operator is the same, which is common for “is” pattern match clauses. Indentation-based syntax (an offside rule) reduces the amount of punctuation that splits would otherwise need. An explicit version of the example above is if { x { { < { 0 then −1 } }; { > { 0 then +1 } }; else 0 } } (This example is written in the paper on one line. I’ve split it for narrow screens, which exposes what I think is a mistake in the nesting.) You can also intersperse let bindings between splits. I doubt the value of this feature, since “is” can also bind values, but interspersed let does have its uses. The paper has an example using let to avoid rightward drift: if let tp1_n = normalize(tp1) tp1_n is Bot then Bot let tp2_n = normalize(tp2) tp2_n is Bot then Bot let m = merge(tp1_n, tp2_n) m is Some(tp) then tp m is None then glb(tp1_n, tp2_n) It’s probably better to use early return to avoid rightward drift. The desugaring uses let bindings when lowering the UCS to simpler constructions. whence UCS Pattern matching in the tradition of functional programming languages supports nested patterns that are compiled in a way that eliminates redundant tests. For example, this example checks that e1 is Some(_) once, not twice as written. if e1 is Some(Left(lv)) then e2 Some(Right(rv)) then e3 None then e4 Being cheeky, I’d say UCS introduces more causes of redundant checks, then goes to great effort to to eliminate redundant checks again. Splits reduce redundant code at the source level; the bulk of the paper is about eliminating redundant checks in the lowering from source to core language. I think the primary cause of this extra complexity is treating the is operator as a two-way test rather than a multi-way match. Splits are introduced as a more general (more complicated) way to build multi-way conditions out of two-way tests. There’s a secondary cause: the tradition of expression-oriented functional languages doesn’t like early returns. A nice pattern in imperative code is to write a function as a series of preliminary calculations and guards with early returns that set things up for the main work of the function. Rust’s ? operator and let-else statement support this pattern directly. UCS addresses the same pattern by wedging calculate-check sequences into if statements, as in the normalize example above. out of scope I suspect UCS’s indentation-based syntax will make programmers more likely to make mistakes, and make compilers have more trouble producing nice error messages. (YAML has put me off syntax that doesn’t have enough redundancy to support good error recovery.) So I wondered if there’s a way to have something like an “is pattern” operator in a Rust-like language, without an offside rule, and without the excess of punctuation in the UCS desugaring. But I couldn’t work out how to make the scope of variable bindings in patterns cover all the code that might need to use them. The scope needs to extend into the consequent then clause, but also into any follow-up tests – and those tests can branch so the scope might need to reach into multiple then clauses. The problem was the way I was still thinking of the then and else clauses as part of the outer if. That implied the expression has to be closed off before the then, which troublesomely closes off the scope of any is-bound variables. The solution – part of it, at least – is actually in the paper, where then and else are nested inside the conditional expression. penultimate conditionals There are two ingredients: The then and else clauses become operators that cause early return from a conditional expression. They can be lowered to a vaguely Rust syntax with the following desugaring rules. The 'if label denotes the closest-enclosing if; you can’t use then or else inside the expr of a then or else unless there’s another intervening if. then expr ⟼ && break 'if expr else expr ⟼ || break 'if expr else expr ⟼ || _ && break 'if expr There are two desugarings for else depending on whether it appears in an expression or a pattern. If you prefer a less wordy syntax, you might spell then as => (like match in Rust) and else as || =>. (For symmetry we might allow && => for then as well.) An is operator for multi-way pattern-matching that binds variables whose scope covers the consequent part of the expression. The basic form is like the UCS, scrutinee is pattern which matches the scrutinee against the pattern returning a boolean result. For example, foo is None Guarded patterns are like, scrutinee is pattern && consequent where the scope of the variables bound by the pattern covers the consequent. The consequent might be a simple boolean guard, for example, foo is Some(n) && n < 0 or inside an if expression it might end with a then clause, if foo is Some(n) && n < 0 => -1 // ... Simple multi-way patterns are like, scrutinee is { pattern || pattern || … } If there is a consequent then the patterns must all bind the same set of variables (if any) with the same types. More typically, a multi-way match will have consequent clauses, like scrutinee is { pattern && consequent || pattern && consequent || => otherwise } When a consequent is false, we go on to try other alternatives of the match, like we would when the first operand of boolean || is false. To help with layout, you can include a redundant || before the first alternative. For example, if foo is { || Some(n) && n < 0 => -1 || Some(n) && n > 0 => +1 || Some(n) => 0 || None => 0 } Alternatively, if foo is { Some(n) && ( n < 0 => -1 || n > 0 => +1 || => 0 ) || None => 0 } (They should compile the same way.) The evaluation model is like familiar shortcutting && and || and the syntax is supposed to reinforce that intuition. The UCS paper spends a lot of time discussing backtracking and how to eliminate it, but penultimate conditionals evaluate straightforwardly from left to right. The paper briefly mentions as patterns, like Some(Pair(x, y) as p) which in Rust would be written Some(p @ Pair(x, y)) The is operator doesn’t need a separate syntax for this feature: Some(p is Pair(x, y)) For large examples, the penultimate conditional syntax is about as noisy as Rust’s match, but it scales down nicely to smaller matches. However, there are differences in how consequences and alternatives are punctuated which need a bit more discussion. dangling syntax The precedence and associativity of the is operator is tricky: it has two kinds of dangling-else problem. The first kind occurs with a surrounding boolean expression. For example, when b = false, what is the value of this? b is true || false It could bracket to the left, yielding false: (b is true) || false Or to the right, yielding true: b is { true || false } This could be disambiguated by using different spellings for boolean or and pattern alternatives. But that doesn’t help for the second kind which occurs with an inner match. foo is Some(_) && bar is Some(_) || None Does that check foo is Some(_) with an always-true look at bar ( foo is Some(_) ) && bar is { Some(_) || None } Or does it check bar is Some(_) and waste time with foo? foo is { Some(_) && ( bar is Some(_) ) || None } I have chosen to resolve the ambiguity by requiring curly braces {} around groups of alternative patterns. This allows me to use the same spelling || for all kinds of alternation. (Compare Rust, which uses || for boolean expressions, | in a pattern, and , between the arms of a match.) Curlies around multi-way matches can be nested, so the example in the previous section can also be written, if foo is { || Some(n) && n < 0 => -1 || Some(n) && n > 0 => +1 || { Some(0) || None } => 0 } The is operator binds tigher than && on its left, but looser than && on its right (so that a chain of && is gathered into a consequent) and tigher than || on its right so that outer || alternatives don’t need extra brackets. examples I’m going to finish these notes by going through the ultimate conditional syntax paper to translate most of its examples into the penultimate syntax, to give it some exercise. Here we use is to name a value n, as a replacement for the |> abs pipe operator, and we use range patterns instead of split relational operators: if foo(args) is { || 0 => "null" || n && abs(n) is { || 101.. => "large" || ..10 => "small" || => "medium" ) } In both the previous example and the next one, we have some extra brackets where UCS relies purely on an offside rule. if x is { || Right(None) => defaultValue || Right(Some(cached)) => f(cached) || Left(input) && compute(input) is { || None => defaultValue || Some(result) => f(result) } } This one is almost identical to UCS apart from the spellings of and, then, else. if name.startsWith("_") && name.tailOption is Some(namePostfix) && namePostfix.toIntOption is Some(index) && 0 <= index && index < arity && => Right([index, name]) || => Left("invalid identifier: " + name) Here are some nested multi-way matches with overlapping patterns and bound values: if e is { // ... || Lit(value) && Map.find_opt(value) is Some(result) => Some(result) // ... || { Lit(value) || Add(Lit(0), value) || Add(value, Lit(0)) } => { print_int(value); Some(value) } // ... } The next few examples show UCS splits without the is operator. In my syntax I need to press a few more buttons but I think that’s OK. if x == 0 => "zero" || x == 1 => "unit" || => "?" if x == 0 => "null" || x > 0 => "positive" || => "negative" if predicate(0, 1) => "A" || predicate(2, 3) => "B" || => "C" The first two can be written with is instead, but it’s not briefer: if x is { || 0 => "zero" || 1 => "unit" || => "?" } if x is { || 0 => "null" || 1.. => "positive" || => "negative" } There’s little need for a split-anything feature when we have multi-way matches. if foo(u, v, w) is { || Some(x) && x is { || Left(_) => "left-defined" || Right(_) => "right-defined" } || None => "undefined" } A more complete function: fn zip_with(f, xs, ys) { if [xs, ys] is { || [x :: xs, y :: ys] && zip_with(f, xs, ys) is Some(tail) => Some(f(x, y) :: tail) || [Nil, Nil] => Some(Nil) || => None } } Another fragment of the expression evaluator: if e is { // ... || Var(name) && Map.find_opt(env, name) is { || Some(Right(value)) => Some(value) || Some(Left(thunk)) => Some(thunk()) } || App(lhs, rhs) => // ... // ... } This expression is used in the paper to show how a UCS split is desugared: if Pair(x, y) is { || Pair(Some(xv), Some(yv)) => xv + yv || Pair(Some(xv), None) => xv || Pair(None, Some(yv)) => yv || Pair(None, None) => 0 } The desugaring in the paper introduces a lot of redundant tests. I would desugar straightforwardly, then rely on later optimizations to eliminate other redundancies such as the construction and immediate destruction of the pair: if Pair(x, y) is Pair(xx, yy) && xx is { || Some(xv) && yy is { || Some(yv) => xv + yv || None => xv } || None && yy is { || Some(yv) => yv || None => 0 } } Skipping ahead to the “non-trivial example” in the paper’s fig. 11: if e is { || Var(x) && context.get(x) is { || Some(IntVal(v)) => Left(v) || Some(BoolVal(v)) => Right(v) } || Lit(IntVal(v)) => Left(v) || Lit(BoolVal(v)) => Right(v) // ... } The next example in the paper compares C# relational patterns. Rust’s range patterns do a similar job, with the caveat that Rust’s ranges don’t have a syntax for exclusive lower bounds. fn classify(value) { if value is { || .. -4.0 => "too low" || 10.0 .. => "too high" || NaN => "unknown" || => "acceptable" } } I tend to think relational patterns are the better syntax than ranges. With relational patterns I can rewrite an earlier example like, if foo is { || Some(< 0) => -1 || Some(> 0) => +1 || { Some(0) || None } => 0 } I think with the UCS I would have to name the Some(_) value to be able to compare it, which suggests that relational patterns can be better than UCS split relational operators. Prefix-unary relational operators are also a nice way to write single-ended ranges in expressions. We could simply write both ends to get a complete range, like >= lo < hi or like if value is > -4.0 < 10.0 => "acceptable" || => "far out" Near the start I quoted a normalize example that illustrates left-aligned UCS expression. The penultimate version drifts right like the Scala version: if normalize(tp1) is { || Bot => Bot || tp1_n && normalize(tp2) is { || Bot => Bot || tp2_n && merge(tp1_n, tp2_n) is { || Some(tp) => tp || None => glb(tp1_n, tp2_n) } } } But a more Rusty style shows the benefits of early returns (especially the terse ? operator) and monadic combinators. let tp1 = normalize(tp1)?; let tp2 = normalize(tp2)?; merge(tp1, tp2) .unwrap_or_else(|| glb(tp1, tp2)) antepenultimate breath When I started writing these notes, my penultimate conditional syntax was little more than a sketch of an idea. Having gone through the previous section’s exercise, I think it has turned out better than I thought it might. The extra nesting from multi-way match braces doesn’t seem to be unbearably heavyweight. However, none of the examples have bulky then or else blocks which are where the extra nesting is more likely to be annoying. But then, as I said before it’s comparable to a Rust match: match scrutinee { pattern => { consequent } } if scrutinee is { || pattern => { consequent } } The || lines down the left margin are noisy, but hard to get rid of in the context of a curly-brace language. I can’t reduce them to | like OCaml because what would I use for bitwise OR? I don’t want presence or absence of flow control to depend on types or context. I kind of like Prolog / Erlang , for && and ; for ||, but that’s well outside what’s legible to mainstream programmers. So, dunno. Anyway, I think I’ve successfully found a syntax that does most of what UCS does, but much in a much simpler fashion.
I really like RTS games. I pretty much grew up on them, starting with Command&Conquer 3: Kane’s Wrath, moving on to StarCraft 2 trilogy and witnessing the downfall of Command&Conquer 4. I never had the disks for any other RTS games during my teenage years. Yes, the disks, the ones you go to the store to buy! I didn’t know Steam existed back then, so this was my only source of games. There is something magical in owning a physical copy of the game. I always liked the art on the front (a mandatory huge face for all RTS!), game description and screenshots on the back, even the smell of the plastic disk case.
Following up on a previous article I wrote about backwards compatibility, I came across this document from Rick Byers of the Chrome team titled “Blink principles of web compatibility” which outlines how they navigate introducing breaking changes. “Hold up,” you might say. “Breaking changes? But there’s no breaking changes on the web!?” Well, as outlined in their Google Doc, “don’t break anyone ever” is a bit unrealistic. Here’s their rationale: The Chromium project aims to reduce the pain of breaking changes on web developers. But Chromium’s mission is to advance the web, and in some cases it’s realistically unavoidable to make a breaking change in order to do that. Since the web is expected to continue to evolve incrementally indefinitely, it’s essential to its survival that we have some mechanism for shedding some of the mistakes of the past. Fair enough. We all need ways of shedding mistakes from the past. But let’s not get too personal. That’s a different post. So when it comes to the web, how do you know when to break something and when to not? The Chrome team looks at the data collected via Chrome's anonymous usage statistics (you can take a peak at that data yourself) to understand how often “mistake” APIs are still being used. This helps them categorize breaking changes as low-risk or high-risk. What’s wild is that, given Chrome’s ubiquity as a browser, a number like 0.1% is classified as “high-risk”! As a general rule of thumb, 0.1% of PageVisits (1 in 1000) is large, while 0.001% is considered small but non-trivial. Anything below about 0.00001% (1 in 10 million) is generally considered trivial. There are around 771 billion web pages viewed in Chrome every month (not counting other Chromium-based browsers). So seriously breaking even 0.0001% still results in someone being frustrated every 3 seconds, and so not to be taken lightly! But the usage stats are merely a guide — a partially blind one at that. The Chrome team openly acknowledges their dataset doesn’t tell the whole story (e.g. Enterprise clients have metrics recording is disabled, China has Google’s metric servers are disabled, and Chromium derivatives don’t record metrics at all). And Chrome itself is only part of the story. They acknowledge that a change that would break Chrome but align it with other browsers is a good thing because it’s advancing the whole web while perhaps costing Chrome specifically in the short term — community > corporation?? Breaking changes which align Chromium’s behavior with other engines are much less risky than those which cause it to deviate…In general if a change will break only sites coding specifically for Chromium (eg. via UA sniffing), then it’s likely to be net-positive towards Chromium’s mission of advancing the whole web. Yay for advancing the web! And the web is open, which is why they also state they’ll opt for open formats where possible over closed, proprietary, “patent-encumbered” ones. The chromium project is committed to a free and open web, enabling innovation and competition by anyone in any size organization or of any financial means or legal risk tolerance. In general the chromium project will accept an increased level of compatibility risk in order to reduce dependence in the web ecosystem on technologies which cannot be implemented on a royalty-free basis. One example we saw of breaking change that excluded proprietary in favor of open was Flash. One way of dealing with a breaking change like that is to provide opt-out. In the case of Flash, users were given the ability to “opt-out” of Flash being deprecated via site settings (in other words, opt-in to using flash on a page-by-page basis). That was an important step in phasing out that behavior completely over time. But not all changes get that kind of heads-up. there is a substantial portion of the web which is unmaintained and will effectively never be updated…It may be useful to look at how long chromium has had the behavior in question to get some idea of the risk that a lot of unmaintained code will depend on it…In general we believe in the principle that the vast majority of websites should continue to function forever. There’s a lot going on with Chrome right now, but you gotta love seeing the people who work on it making public statements like that — “we believe…that the vast majority of websites should continue to function forever.” There’s some good stuff in this document that gives you hope that people really do care and work incredibly hard to not break the web! (It’s an ecosystem after all.) It’s important for [us] browser engineers to resist the temptation to treat breaking changes in a paternalistic fashion. It’s common to think we know better than web developers, only to find out that we were wrong and didn’t know as much about the real world as we thought we did. Providing at least a temporary developer opt-out is an act of humility and respect for developers which acknowledges that we’ll only succeed in really improving the web for users long-term via healthy collaborations between browser engineers and web developers. More 👏 acts 👏 of 👏 humility 👏 in tech 👏 please! Email · Mastodon · Bluesky
react-router-devtools enhances debugging by adding automatic logging for loaders & actions, plus direct links to code origins in console logs.