We supported the iOS and Android WebViews of our own app and of a partner app, and gathered the scattered environment branching into adapters. This is a record of how we organized insets, the bridge, and CSS, along with the problems we hit with the SSR seed and hydration.
I ported markdownlint-cli2 to Rust and matched 264,114 diagnostics on this blog repository byte for byte. The commits that ported 51 rules landed in a single day, but turning that into a tool you could actually swap in took two weeks. A record of giving up a parser 37 times faster, correcting two performance misreadings, and finding 11 more compatibility differences in an adversarial review.
I imported one constant and 97.7% of the bundle came along with it. The vendor had no timeline for a fix, so I pried open the published source maps, recovered over 400 TypeScript files, and rewrote the build, the entry points, and the dependencies however I wanted. Everything except the logic. That got /send to -77.5% raw. The hard part came after. All 1,932 tests passed, and a few of them were watching nothing at all.
I set out to confirm the 500 ms hint that part 2 left behind, but the hard reloads that would form the control group arrive at under one a day. So I built the control group myself, with Playwright and a shaping proxy, and found that going through the worker costs 2 ms on a navigation, and that the cost is not latency but the bytes the worker fetches in the background on every click. The gap I had left between the lab and the field turned out, only after the post was written, to be a measurement definition difference created by 103 Early Hints. Third part of the service worker caching deep dive series.
Armed with the theory from Part 1, I made this blog (Next.js App Router) open offline. On the first deploy, the post I had just read would not open offline; on the second, posts opened but every image was broken. This is a chronicle of fixing, one deploy at a time, the traps created by soft navigation, prefetching, and next/image, and a record of settling the results with GA4 real-user data. Returning-visitor FCP improved by 634ms on average, while TTFB worsened by 525ms on average. The second post of the Service Worker Caching Deep Dive series.
A feature where the server opens a URL the user handed it has the textbook conditions for SSRF written into its spec. Six ways a whitelist gets bypassed first, then five defensive principles that block them, all actually run on Node. Strip IPv4-mapped by hand and it gets through in hex notation, undici lookup hook is never called when the host is an IP literal, and URL.hostname keeps the brackets on an IPv6 literal. The final post of a two-part design note on OG scraping servers.
The "10% error rate" of a link preview server is a single number that five different kinds of failure got mashed into. This post works out why this workload is I/O bound at that TPS, where runtime choice actually diverges across four points, and then moves on to lowering the error rate with User-Agent and encoding. Node built-in TextDecoder turns CP949 extension characters into different characters without raising an error, and a scraped og:title is not an API response but user input. It also covers cache stampedes, negative caching, and a two-million-run simulation that verifies "P95 under one second" by working backwards from the cache hit rate. The first post of a two-part design note on OG scraping servers.
In a Next.js 16 Turbopack production build, a module-scope singleton became two live instances at runtime. Inside the same synchronous block, one condition contradicted the other, and responses that arrived in 30ms still timed out. This is the record of tracing the cause through the bundle output: a partial scope hoisting merge, a circular import, an upstream bug that had already been fixed, and the single-variable experiment I ran too late.
A banner built with framer-motion made the entire home screen stutter for the 0.6 seconds it took to open. The record of guessing the cause from the code, having that guess overturned twice by measurement, and finally removing the reflow with a single easing function. Plus what the work left behind: the gap between declaration and execution, the principle that properties decide performance, preserving mechanisms, suspecting your instruments first, and proving sameness with a curve.
A service worker is a programmable proxy standing between your site and the network. Where does it stand, why does the cache rot, why am I seeing the old version after deploying, what goes in under which strategy, and so, should you use it? Holding on to five questions you actually meet in practice, this post goes down to the details of state transitions and to a real measurement in which a 104KB opaque response was accounted as 6.6MB of storage. It is the general theory that did not fit into the cache chapter of Frontend Performance Optimization Deep Dive (published in Korean), and the first post of the Service Worker Caching Deep Dive series.
The minimum versions where number-flow turns animations on are Chrome 125 and Safari 17.2. A record of the decisions made while building a fork that lowers this floor to Chrome 66 and WebKit 16.4, the two judgment calls I ended up reversing, and the Safari bug investigation that made me give up on automatic downgrading.
I dropped typescript 7.0.2 into a monorepo where pnpm lint took 12 minutes 32 seconds. Type checking passed quietly, but next build broke and lint crashed. A record of the chain reaction from one day of swapping eslint and prettier for oxlint and oxfmt, with before-and-after measurements. To say it up front, the build did not get any faster.
Raise traffic 12x and it takes 31.5 seconds for a new pod to receive its first request. I pulled an itemized bill for those 31.5 seconds with a stopwatch: the structure dominated by the detection window, the conditions under which the autoscaler goes blind in the five minutes right after a deploy, the scale-down staircase, why memory-based HPA misfires on Node, and KEDA's preemptive scaling. Part 5 of the Kubernetes for frontend developers series.
Even a deploy that changes not a single line of code leaks errors. This is a measured record of tagging every failure that leaks during rolling deploys by type and timestamp, performing an autopsy on four causes, and stacking prescriptions one layer at a time until the count reaches zero. Includes the actual Next.js shutdown code, hostage draining, and the real CrashLoopBackOff timetable. Part 4 of the Kubernetes for frontend developers series.
A Service's ClusterIP is an IP attached to no machine, yet curl reaches it. iptables rules and conntrack, EndpointSlice, cluster DNS and ndots, Gateway, and port-forward: a record of opening up the entire path a request takes to a pod, directly in a kind cluster. Part 3 of the Kubernetes for frontend developers series.
The same Next.js app produced one 1.72GB image and one 208MB image. This post traces the missing 1.5GB back through the layers, and verifies with PIDs and cgroup files that a container is an isolated process. The second post of the Kubernetes for frontend developers series.
The Kubernetes vocabulary and structure that frontend developers running SSR actually encounter, organized in the order of real work: the overall cluster structure, what makes up a deploy, pod state and resources, the traffic path, and autoscaling. The first post of the series and the reference map for the rest.
I added one GC tuning flag to the same workload and peak RSS jumped from 201MB to 593MB, while the live data stayed the same. This post traces back through V8 New Space with direct measurements to show why that result is exactly what should happen, and lays out the three axes frontend developers can use to size a Node.js pod.
Why the layers piled up, why we returned to the server, and why the stack survives even after agents. And why the survival of a stack and the value of the people who know it are two separate things
The friction that made judgment expensive is the same friction that taught it. Why the skill growing most valuable in the AI era is the one being cultivated least
