use a well-worn framework when it addresses your technical complexities (e.g. expose functionality via the web or build a 3-d game) and your domain complexity (e.g. shopping, social networking, or multi-dimensional bowling) is your paramount concern
once you have some time/experience in your problem domain, start rounding off corners to leave future teammates a metaframework that reduces decision/design burdens and gives them some kind of golden path
frameworks may end up less useful as integration surface area increases
napkin math makes it hard to justify not using a framework; you have to build the thing and accept the cost of not having a community to support you and hire from
to paraphrase Sandi Metz on the wrong abstraction: “(Using) no abstraction is better than the wrong abstraction”; if you’ve had a bad time with a framework, chances it was an inappropriate abstraction or you used the abstraction incorrectly
The first few years of my career, I edited the wrong file all the time. I could spend hours making changes, wondering why nothing was happening, until I realized I’d been tinkering in the wrong place because I was misreading a file path or not paying close enough attention to control flow.
Fast forward to now, and I’m pretty quick to drop a raise "BLORP" in code I’m tinkering with if things aren’t working like I think they should. All hail puts debuggerering.
However, it turns out I found a new class of this operator error today. I was diligently re-running a test case, expecting new results when the test fixture file I thought was changed was the wrong file. Once I deleted the right file, I was back on my way.
Joyful and grumpy are we who can find new ways to screw up time ever day!
I want to connect two Ruby enumerators. Give me all the values from the first, then the second, and so on. Ideally, without forcing any lazy evaluations and flat so I don’t have to think about nested stuff. Like so:
I couldn’t figure out how to do that with Ruby’s standard library alone. But, it wasn’t that hard to write my own:
def chain(*enums)
return to_enum(:chain, *enums) unless block_given?
enums.each { |enum| enum.each { |e| yield e } }
end
But it seems like Ruby’s library, Enumerable in particular, is so strong I must have missed something. So, mob programmers, is there a better way to do this? A fancier enumerator-combining thing I’m missing?
I do my best thinking:
In the shower. I love to take long showers, and I love my tankless water heater.
While talking. Something about my brain is wired directly to my mouth.
When I’m not thinking. See also, the value of letting your mind idle, wander, or just walking away from a tricky problem.
The idea behind Facebook’s Relay is to write declarative queries, put them next to the user interaction code that uses them, and compose those queries. It’s a solid idea. But this snippet about Relay Modern made me chuckle:
The teams realized that if the GraphQL queries instead were statically known — that is, they were not altered by runtime conditions — then they could be constructed once during development time and saved on the Facebook servers, and replaced in the mobile app with a tiny identifier. With this approach, the app sends the identifier along with some GraphQL variables, and the Facebook server knows which query to run. No more overhead, massively reduced network traffic, and much faster mobile apps.
Relay Modern adopts a similar approach. The Relay compiler extracts colocated GraphQL snippets from across an app, constructs the necessary queries, saves them on the server ahead of time, and outputs artifacts that the Relay runtime uses to fetch those queries and process their results at runtime.
How many meetings did they need before they renamed this from “GraphQL stored procedures” to “Relay Modern”?
(FWIW, I worked on a system that exposed stored procedures through a web service for client-side interaction code. It wasn’t too bad, setting aside the need to hand write SQL and XSLT.)
I don’t like using services like Uber, Twitch, or Favor. I want to like them, because the underlying ideas are pretty futuristic. But the reality of these services is that the new boss wants to squeeze their not-even-employess-anymore just as badly the old boss did. It feels manipulative, like buying a car. Except I’m abetting the manipulation too. :(
The contrast between the gig economy’s rhetoric (everyone is always connecting, having fun, and killing it!) and the conditions that allow it to exist (a lack of dependable employment that pays a living wage) makes this kink in our thinking especially clear.
What happens when the gig economy tries to turn a profit? The race downwards will squeeze out all of their contractors until they can replace them all with automated drivers, commoditized personalities, and punitively-low ad revenue sharing rates. This sounds horribly dystopian but I’m pretty sure it’s already happening. See also: when Google kneecapped bloggers as a side-effect of end-of-lifing Reader and changing Pagerank.
Platforms are, in a sense, capitalism distilled to its essence. They are proudly experimental and maximally consequential, prone to creating externalities and especially disinclined to address or even acknowledge what happens beyond their rising walls. And accordingly, platforms are the underlying trend that ties together popular narratives about technology and the economy in general. Platforms provide the substructure for the “gig economy” and the “sharing economy”; they’re the economic engine of social media; they’re the architecture of the “attention economy” and the inspiration for claims about the “end of ownership.”
After reading this, I started substituting “platform company” for “company building its own monopoly”. And then it all makes sense. Businesspeople say they love free markets, but give any rational-thinking business the chance and they will create so many “moats” and “barriers to entry” that they resemble tiny state enterprises more than a private business. See also: telecoms and airlines.
This has been the status quo for most of the last decade. But the next rising wave of tech innovators twist the definition of “market” even further, to a point where they aren’t actually markets at all.
Yes, my confirmation bias is burning. Yes, technologists are doomed to recreate the robber-baron past they didn’t study. Yes, we still have time to change this. Yes, our field needs an ethics refresher. Yes, we should get back to inventing jetpacks!
…watches are one of the key pieces of jewelry I can sport, and while many have no clue what’s on my wrist, those that do… well do. And they are investments. Usually good purchases will not only last forever (with a little love and care), but go up or retain most of their value over time.
When pal Marcos started talking to me about watches, I realized they checked all the boxes cars do, but at a fraction of the price. If cars check your boxes, look into watches. Jeremy’s intro will get you started without breaking the bank.
With feedback, like jokes, timing is everything. Good feedback at a bad time won’t do the trick.
I’ve mostly experienced programming feedback through pull requests. This is way better than no feedback. However, since most pull requests occur at the end of work, and not somewhere in the middle, some kinds of feedback are not conducive to pull requests.
Suppose all feedback falls somewhere on two axes: “timeliness” and “depth”. The narrow sweet spot of code review is apparent:
Pairing and code review are not so similar
The sweet spot in the top-right corner is when code review works best: unhurried and in-depth feedback. I’d hesitate to call the lower-right corner of hurried, minimal feedback a code review at all; it’s more like rubber stamping.
I’ve often referred to code review, flippantly, as the worst form of pairing yet invented. I’ve given a lot of code review feedback in the past that was better suited to the synchronous nature of pairing than the very asynchronous nature of code reviews. That said, I feel like pairing is an excellent way to give all manners of feedback in the moment the code is being conceived or written. You can immediately point out possible incorrectness or better designs and talk it out, with the code at hand, with your collaborator.
However, we can’t all pair all the time. Let me show you how I’m trying to better time my feedback when I can’t share it immediately.
A tale of four pull requests
Consider four PR subject lines. Which ones are appropriate for architectural ideas? What about optimization ideas? When is deep refactoring feedback appropriate? Can I look at one of these in an hour when I’m done with my current task?
“Hotfix Facebook Auth scope”
“Prevent sending email for failed payment jobs”
“Add tagging to admin storylines listing”
“WIP introduce Redis/Lua-based story indexing”
Lately, when I do pull request reviews, I use these guidelines:
Figure out if this PR seems like it’s a hot patch to production, a quick fix on existing work, a PR landing new functionality, or a work-in-progress checkpoint seeking feedback.
Bear in mind that hot patches and quick fixes are more time sensitive and need yes/no feedback on correctness more than detailed feedback.
For hot patches (e.g. “Hotfix FB auth”), I’m only looking for “is this correct” and “will it fix the problem?”; thumbs up or thumbs down and commentary as to what I think is missing to solve the problem. No refactoring ideas. I only touch on performance if I spot a regression.
For quick fixes (e.g. “Prevent sending email…”), I’m again looking for correctness and timeliness. I might leave ideas for how to improve the performance or cleanliness of the code later. Those kinds of notes are entirely up to the gumption of the other developer, though. I know the low-gumption feeling of wanting only to fix something and get on to the next thing.
Landing new functionality (e.g. “Add tagging…”) receives a full review cycle. Beyond baseline correctness, I’m trying to view this code through my crystal ball. When some value of N is grows, will this code slow down noticeably? Is the code structured so that future changes are easy and obvious?
Work-in-progress checkpoints (“WIP introduce Redis/Lua…”) are open to the full spectrum of feedback. Ideas for how to differently structure data, which APIs to export, how to structure objects, how to name the domain model, etc. are all in play. Pretty much the only thing out of play is anything that feels too close to bike shedding.
Bear in mind that everyone exists on a spectrum of coding specificity. More seasoned developers are likely open to ideas for restructuring code or considering novel approaches. Less seasoned developers (including seasoned developers new to the team) likely want specific guidance about which changes to make or factors they need to consider.
Where I may try to respond to hot patches and quick fixes in less than fifteen minutes, I may wait a couple hours before I look at new functionality or WIP reviews.
The most difficult part with these guidelines is how to handle ideas about refactoring on time-sensitive reviews. I want to hold the line against letting lots of little fixes accrete into a medium-sized mess. I don’t want to discourage ideas for refactorings either; I want them separately so I can act on them when I have the energy to really do them.
In short
Use different tactics when sharing feedback for code review; it’s not pairing. Identify patches, reviews, and full feedback pull requests. Sanity check patches, look for correctness in review, look for design in review. Use GitHub’s review process to indicate your feedback is “FYI” vs. “fix this before merging”. Time-to-response is most important for patches and fixes.
Above all: giving feedback is a skill you acquire with practice, empathy, and maintaining a constructive attitude.
The tricky part isn’t the language so much as it is the slang.
Also, the best and worst part of Clojure:
When the going gets tough, the tough use maps
This is probably better now that specs and schema are popular. Before, when they were mysterious maps full of Very Important State, reading Clojure code (and any kind of Lisp) was pretty challenging.
Make sure you stick around for the joke about covariance and contravariance. Those type theories, hilarious!
In Fred Brooks’ terms, this was essential complexity, not accidental complexity. Features interact — intentionally — and that makes the cost of implementing the N+1 feature closer to N than 1.
In other words, the ability to change a product is directly proportional to the size of N (features, requirements, spec points, etc.) for the system that express that product. You may find practices that multiply N by 0.9 so you go a little faster. You may back yourself into a corner that multiply N by 1.1 so you go a little slower. But, to borrow again from Fred Brooks, there is no silver bullet. Essential domain complexity is immutable unless you reduce the size of the domain, i.e. cut existing features.
Not even fancy new technologies are correlated with reducing your multiplier, in the long run:
This perspective does cause one to turn a somewhat jaundiced eye towards claims of amazing breakthroughs with new technologies…What I found is that advocates for these new technologies tended to confuse the productivity benefits of working on a small code base (small N essential complexity due to fewer feature interactions and small N cost for features that scale with size of codebase) with the benefits of the new technology itself — efforts using a new technology inherently start small so the benefits get conflated.
Lastly, this is a gem about getting functionality “for free”:
So “free code” tends to be “free as in puppy” rather than “free as in beer”.
All free functionality eventually poops on your rug and chews up your shoes.
