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Measuring Strings Before You Translate Them: Fixing Truncation in 22 Languages Without an App Update

July 29, 2026

The Problem

Localized UI truncation is discovered by customers, not by developers. The loop looks like this:

  1. Ship a screen that fits perfectly in English
  2. Translate it into 25 more languages
  3. Wait for a support ticket that says “the Spanish text is cut off”
  4. Fix that one string, ship an app update, repeat

The reason this loop is so slow is that nothing in the toolchain knows how wide a string will be. A localization catalog stores text. A SwiftUI layout computes widths at runtime, on device, in a specific font at a specific Dynamic Type size. The two facts never meet until a human looks at a screenshot.

Worse, the truncation is systemic rather than incidental. If Air Quality overflows a stat card title in Vietnamese, it overflows in the widget too, because it’s the same catalog key in the same slot class. And if the string comes from your server - a level name, an advisory phrase - then even a one-word fix requires an app release that has nothing to do with the app.

At Hello Weather we hit all of it at once: customer QA reported cut-off text in the small stat cards, in Spanish. Rather than fix the Spanish strings, we went looking for a way to know, ahead of time, every string in every language that would not fit.

The Solution

The pattern has three parts, and none of them require running the app:

  1. A committed JSON registry of layout constraints - which catalog keys render in which width-constrained slots, and what the budget is for each
  2. A dependency-free validator script that compiles and runs standalone, reads the registry plus the localization catalog, and measures
  3. A committed markdown report that is simultaneously the work-list, the diff, and the regression baseline

Plus a lifecycle: the validator starts in audit mode (report findings, exit 0) while there’s a backlog, then flips to gate mode (non-zero exit) once the backlog is cleared.

We built this twice. The first generation counted characters. The second generation measured actual rendered widths. Both were useful, and the difference between them is the interesting part.

Generation 1: A Character-Budget Registry

The registry is a plain JSON file, committed to the repo:

{
  "_readme": "Width budgets for catalog keys rendering in width-constrained slots (stat cards, chart legends, widget rows, complication labels). budget = max Character count for every language value except cjkExempt. scales = per-language values within one group must stay pairwise distinct.",
  "cjkExempt": ["ja", "ko", "zh-Hans", "zh-Hant"],
  "keys": {
    "Air Quality": { "slots": ["statTitle"],         "budget": 17 },
    "Cloudy":      { "slots": ["chartLegend"],       "budget": 9  },
    "AQI":         { "slots": ["complicationLabel"], "budget": 6  },
    "Actual":      { "slots": ["miniTitle"],         "budget": 9  }
  },
  "scales": {
    "uvLegend":       ["Low", "Mid", "High", "Max"],
    "pressureLegend": ["Low", "Normal", "High"],
    "visibilityLegend": ["Good", "Fair", "Poor"]
  }
}

Seventy-four keys across six slot classes. Two things are encoded here that a linter couldn’t infer.

Slot classes. A key is constrained because of where it renders, and one key can render in several places. Writing the slot down makes the budget reviewable - someone can ask “is 17 characters really the stat title budget?” without reading layout code.

Scale groups. These are sets of labels that appear together in one chart legend. The rule isn’t about length at all: within a scale, every language’s values must be pairwise distinct. A translator working key-by-key has no way to know that two English words map to the same natural word in their language.

That second rule found two live shipping bugs on the very first run:

Two identically-labeled swatches, different colors, in a chart that shipped. No amount of width measurement would have caught those; no reviewer scanning a translation file key-by-key would have either.

The validator is about 90 lines of Foundation. It parses the registry and the localization catalog as plain JSON - no app dependency, no test target:

for scale in scales.keys.sorted() {
    for language in checkedLanguages {
        var seen: [String: String] = [:]
        for key in scales[scale] ?? [] {
            guard let translated = value(key, language) else { continue }
            if let previous = seen[translated] {
                findings.append("FINDING: within-scale duplicate in \(scale) " +
                                "\(language): \"\(previous)\" and \"\(key)\" " +
                                "both \"\(translated)\"")
            } else {
                seen[translated] = key
            }
        }
    }
}

A bash wrapper compiles it to a temp directory and runs it, so the whole tool is ./tools/validate-compact-strings with no build system involvement:

#!/usr/bin/env bash
set -euo pipefail
script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
tmp_dir="$(mktemp -d "${TMPDIR:-/tmp}/compact-string-validation.XXXXXX")"
trap 'rm -rf "$tmp_dir"' EXIT

swiftc -parse-as-library "$script_dir/validate-compact-strings.swift" -o "$tmp_dir/validator"
TOOLS_DIR="$script_dir" "$tmp_dir/validator"

First run: 211 findings across 26 languages. That number is not a failure - it’s a work-list, ordered and diffable.

Generation 2: Measuring What Actually Renders

Character counts are a proxy, and a bad one. Ω and l are both one character. Cyrillic is wider than Latin at the same count. And a character budget can’t express the difference between a 13pt regular description and an 11pt semibold uppercased title in the same card.

So the second validator measures real rendered widths using AppKit text measurement on the desktop, with macOS SF Pro standing in for iOS SF Pro:

static func width(_ string: String, _ size: CGFloat, weight: NSFont.Weight = .regular) -> CGFloat {
    let font = NSFont.systemFont(ofSize: size, weight: weight)
    return ceil(NSAttributedString(string: string, attributes: [.font: font]).size().width)
}

The budget side is the part worth copying. Instead of a hand-picked number, it re-derives the layout from the actual grid formula the SwiftUI view uses:

static let deviceWidth: CGFloat = 375        // smallest supported width
static let gridOuterPadding: CGFloat = 32
static let gridSpacing: CGFloat = 10
static let gridMinimumColumn: CGFloat = 165  // adaptive grid minimum
static let cardPadding: CGFloat = 32
static let iconAllowance: CGFloat = 36
static let headroom: CGFloat = 0.95          // proxy-font margin

static var descriptionBudget: CGFloat {
    let available = deviceWidth - gridOuterPadding
    let columns = floor((available + gridSpacing) / (gridMinimumColumn + gridSpacing))
    let column = (available - (columns - 1) * gridSpacing) / columns
    return column - cardPadding
}

static var titleBudget: CGFloat { descriptionBudget - iconAllowance }
static var passBar: CGFloat { descriptionBudget * headroom }

That yields 134.5pt for descriptions and 98.5pt for titles and subtitles. Because the formula mirrors the view, a layout change to spacing or column minimum is a one-line change in the tool - not a re-guess of every budget.

The 5% headroom matters too. Desktop SF Pro is a proxy, not the real thing, so results land in three buckets rather than two: OK, MARGIN (inside the 5% band, needs device verification), and OVER.

Worst-Case Format Arguments

Here’s the part that separates a real measurement tool from a toy: most constrained strings are format templates, not literals. Sunrise at %@. has no width until you fill it in. Measuring the template is meaningless; measuring it with a convenient argument is worse, because it silently passes.

So the tool synthesizes the widest legal argument for each placeholder:

// Widest clock string for this locale (both 12h and 24h are measured)
static func worstTime12(_ language: String) -> String {
    formattedDate(language, pattern: "h:mma", hour: 12, minute: 59)
        .lowercased(with: locale(language))
}

// Widest noun that can fill a precip template
static func worstPrecipNoun(_ language: String) -> String {
    ["Rain", "Snow", "Sleet", "Hail", "Precip"]
        .compactMap { catalogValue($0, language) }
        .max(by: { width($0, 13) < width($1, 13) }) ?? "Rain"
}

The catalog reader does the same thing for plurals - when an entry has plural variations rather than a single string unit, it returns the widest variant, not the other case:

if let plural = (localization["variations"] as? [String: Any])?["plural"] as? [String: Any] {
    let values = plural.values.compactMap {
        (($0 as? [String: Any])?["stringUnit"] as? [String: Any])?["value"] as? String
    }
    return values.max(by: { width($0, 13) < width($1, 13) })
}

The last category of argument is the important one. Many of the widest strings in the app are not in the app at all. Level names (“Very Unhealthy”), advisory phrases (“Health effects possible.”), wind bearings, and composed pollen phrases are all returned by our API, localized on the server. So the tool reads our server repo’s locale files directly, converting YAML to JSON in the wrapper and passing a directory path in:

for yml in "$web_dir"/config/locales/*.yml; do
  lang="$(basename "$yml" .yml)"
  ruby -ryaml -rjson -e 'puts JSON.generate(YAML.safe_load(File.read(ARGV[0])))' \
    "$yml" > "$web_json_dir/$lang.json"
done
web_head="$(git -C "$web_dir" rev-parse --short HEAD)"

The report records the server checkout’s commit SHA and warns when it differs from that repo’s main branch, so a stale baseline announces itself; if the checkout isn’t present at all, the tool degrades to client-key coverage with a warning instead of failing. Rows built from synthesized rather than real values (temperatures, precip amounts, wind units) are tagged [estimate], so a reader knows which findings are inferences.

The Committed Report

The tool writes a markdown file that is checked in:

## Summary

- Rows measured: 1620 (27 languages)
- Over budget at default type size: **483**
- Inside margin (127.8-134.5pt band): 78
- Over budget at the xxLarge cap: 674

| Card | Slot | Language | Width | Verdict | Source | Rendered |
|---|---|---|---|---|---|---|
| AQI | description | de | 242/134pt | OVER | `server:aqiLevelPhrase` | Gesundheitliche Auswirkungen moeglich. |
| AQI | description | en | 144/134pt | OVER | `server:aqiLevelPhrase` | Health effects possible. |
| AQI | subtitle | it | 134/98pt | OVER | `server:aqiLevelName` | Molto Insalubre |

Committing generated output feels wrong until you use it once. It buys three things:

It also surfaced things nobody was looking for: stat card titles truncate today (Vietnamese Chất lượng không khí at 141pt in a 98pt slot, with eight more languages over on the same key), English itself fails 9 rows, and a client bug where a .capitalized call was title-casing Spanish level names mid-sentence.

The Server Loop

Of the 483 over-budget rows, 140 came from server-owned strings - and that’s the architectural payoff. Because those phrases live in our API’s locale files rather than in the app bundle, fixing them is a content change that deploys: no App Store review, no version gate, no waiting for users to update. A truncation bug became a copy edit.

The server-side pass shortened 300+ locale values across 22 languages, under one rule worth stealing:

The dual-surface rule: a shortened value must still read as natural prose on the app’s detail screen and on the web product - not merely fit the card.

This is what stops “make it fit” from degrading into telegraphese. A phrase like a pressure trend name has to work as a standalone card label and inside a sentence:

# before -> after, es
pressure:
  trend_ext_name:
    falling-quickly: "Cae rápido"   # was "Bajando rápido"
    falling: "Bajando"

Note that falling-quickly and falling are adjacent steps in the same scale - so the shortened value still has to stay lexically distinct from its neighbor, which is the generation-1 scale rule showing up again on the server side.

Verification closed the loop: point the client’s width tool at the server branch and re-run. Server-string findings dropped from 140 to 74.

The remaining 74 are not failures, they’re adjudicated keeps - rows where no natural short form exists, recorded explicitly:

Writing keeps down, in the same artifact as the findings, is what makes the report safe to gate on later. A row that stays over budget forever is fine as long as somebody decided that on purpose.

Results

Lessons Learned

How This Post Was Made

Prompt 1: “it’s been a while since we added any blog posts, see recent work in the ~/Code/helloweather projects, dispatch opus agents to search for interesting stuff that we’ve done since the last blog post, perhaps one or more agents per repo, then review and consider and come up with a proposed list of blog posts we might consider.”

Prompt 2: “draft posts for [the approved shortlist] – create one pr for the repo main / skills update we just did, then one pr per post for the approved list”

Research by one Claude agent per repo mining git history since the previous post; this draft was written by a dedicated agent from that research plus the underlying commits and tools, then reviewed before publishing.