I’m porting gpui-component (a Rust UI component library built on GPUI) to C++ as gpui-cpp. By which I mean: my friend Claude does the porting, I’m just directing. It uses markdown-rs (a CommonMark + GFM parser) markdown parser so I ported it too. Then I optimized it. This post describes what I did with the intention of teaching other how to optimize C++ code. The starting point There are 2 kinds of markdown parser: those that stream nodes as they parse those that build an AST in memory markdown-rs builds an AST. The game is about minimizing the size of AST node. In Rust there are various kinds of nodes, the largest being 152 bytes. Claude generated a single Node struct of 232 bytes. I got it down to 16 bytes. Here’s the initial Node struct, before optimizations: Node, 232 bytes k children 24 position 24 8 string fields — 128 bytes align 24 nums 16 grey = padding and small fields · blue = growable vector · yellow = pointer+length strings Node, 16 bytes (same scale) lastKid · sibling · firstStr · kind+flags Where the 232 went: 8 string fields at 16 bytes each (a char* plus a length), two growable vectors at 24 bytes each (children and table alignments), a 24-byte unist Position (line, column and offset at each end), six bools one to a byte, and the padding all of that dragged in. Every node in the tree pays for every field, whichever kind it is. A Text node uses one string field and nothing else. Arena allocator It’s important that all allocations are done in an arena. Nodes in a parse tree all have the same lifetime which makes it a perfect use for an arena: a bump allocator that can only grow. The only way to free memory is to reset the arena. This is different than calling malloc() to allocate each node individually and then having to call free(). It makes it easy to measure memory usage: check the arena size after parsing. It also allows optimization tricks like compressing pointers. How I measured bun cmd/bench.ts markdown parses 64 KB of markdown in four shapes and reports the arena bytes the parse allocated: prose — paragraphs, emphasis, links nested lists — deep blockquotes and lists gfm tables — tables all the way down entities — text that is mostly &amp;-style character references The number is the whole arena: nodes, the tokenizer’s event list, and the strings. Not just sizeof(Node) × node count. We also measure parsing time to make sure we don’t trade size for speed. Baseline, 64 KB of source: prose 1646.1 KB (25.7× the source) nested lists 1067.9 KB gfm tables 2926.0 KB entities 660.2 KB 1. Pointer compression for strings (bed71ee) On 64-bit platforms, pointers are 8 bytes. Pointer compression reduces this to 4 bytes by calculating a 32-bit offset against a base pointer. Google used compressed pointers in v8 with great result. Reduced memory usage and increased speed. Our string type is the simplest possible string: struct Str { char* data; size_t len; }; That’s at least 12 bytes per string, if len is 4 bytes. Due to alignment, the size is 16 bytes. Strings are allocated in Arena so we can use the beginning of an arena as a base pointer and optimize the pointer from 8 bytes to 4 bytes. We typedef ArenaStr as uint64_t. The lower 4 bytes is uint32_t compressed pointer and upper uint32_t is size. We reduced the overhead of strings from 16 bytes to 8 bytes. Times 8 strings that’s 64 bytes saved per node. Added helper functions for allocating ArenaStr in arena and converting ArenaStr to Str. Savings: 8 strings * 8 bytes, 64 bytes per node: 232 → 168 bytes. shape start before after vs before vs start prose 1646.1 KB 1646.1 KB 1285.9 KB -21.9% -21.9% nested lists 1067.9 KB 1067.9 KB 867.5 KB -18.8% -18.8% gfm tables 2926.0 KB 2926.0 KB 2269.7 KB -22.4% -22.4% entities 660.2 KB 660.2 KB 626.2 KB -5.1% -5.1% 2. Growing arena strings in place (a9d4f3a) Some strings had to grow. Arena allocator doesn’t provide freeing or reallocation. You can only allocate new strings, which wastes memory by leaving dead copies of the string we were appending to. We can grow the last allocated string and that’s what this change does. Luckily, most appends were done to the last string. ArenaStrAppend checks whether the string ends exactly where the arena’s next allocation would begin. If it does, the new bytes are pushed straight onto it and nothing is copied. Decoding HTML entities (e.g. &amp;) broke that optimization by doing an allocation before appending to the string. We switched to decoding entities into a 4-byte stack buffer which enabled optimized append. shape start before after vs before vs start prose 1646.1 KB 1285.9 KB 1285.9 KB +0.0% -21.9% nested lists 1067.9 KB 867.5 KB 729.2 KB -15.9% -31.7% gfm tables 2926.0 KB 2269.7 KB 2269.7 KB +0.0% -22.4% entities 660.2 KB 626.2 KB 163.8 KB -73.8% -75.2% 3. Re-order struct fields, pack the bools (5c0ce6e) Unless told to pack the layout of the struct, C++ compilers align struct fields to the size of the largest primitive type. If you sandwich a bool between 2 uint64_t values, the bool will occupy 8 bytes (sizeof(uint64_t)) instead of 1 byte as it should. Our Node had such wasted space due to padding. My friend Claude was careless. A simple fix is to re-arrange fields, putting the largest first. We also had six bool field which we packed into a uint8_t flags field. Result: 168 → 144 bytes, with no padding at all. We’re beating Rust version now. declaration order: bool after vector = 7 bytes of padding, six times over vector b padding strings b padding largest first, bools in one byte: no padding vectors strings nums f shape start before after vs before vs start prose 1646.1 KB 1285.9 KB 1150.9 KB -10.5% -30.1% nested lists 1067.9 KB 729.2 KB 654.0 KB -10.3% -38.8% gfm tables 2926.0 KB 2269.7 KB 2023.6 KB -10.8% -30.8% entities 660.2 KB 163.8 KB 151.0 KB -7.8% -77.1% Free bytes: same fields, same code, different order. 4. Pointer compression for everything (07ec80f) We compress pointer for all objects allocated in the arena, like we compressed a pointer to the string. ArenaVec<Node*> children held 8-byte addresses; ArenaPtr<T> is a 4-byte offset into the arena’s position space, resolved by ArenaAtOffset. Zero is null, which costs nothing because no allocation ever lands at offset zero. The Node itself doesn’t change size — a vector handle is the same three words whatever it holds — so all of the saving is in the child arrays. shape start before after vs before vs start prose 1646.1 KB 1150.9 KB 1091.9 KB -5.1% -33.7% nested lists 1067.9 KB 654.0 KB 611.6 KB -6.5% -42.7% gfm tables 2926.0 KB 2023.6 KB 1866.9 KB -7.7% -36.2% entities 660.2 KB 151.0 KB 144.9 KB -4.0% -78.1% These shapes rank by children-per-node rather than by node count, which is why tables moved most. 5. Varint encoding string length (f9ebc34) ArenaStr was an offset and a length in 8 bytes. Now it’s the offset alone — 4 bytes — and the length is varint-encoded at the beginning of the string data: [varint len][string bytes][NUL] There are many varint encoding schemes. This one is for unsigned number and codes number < 128 as a single byte. Most strings are below that threshold, so they use a single byte for the varint length, saving roughly 3 bytes per string. Node shrinks from 144 → 112 bytes. Str — pointer + length, 16 bytes per field char* s int64 len ArenaStr — offset + length, 8 bytes u32 off u32 len ArenaStr — offset alone, 4 bytes; the length lives in the arena u32 off len characters 0 Caveat: An offset-and-length string can point at a slice of another string, and a length-prefixed one can’t. We weren’t doing it so it doesn’t apply here. shape start before after vs before vs start prose 1646.1 KB 1091.9 KB 918.0 KB -15.9% -44.2% nested lists 1067.9 KB 611.6 KB 512.3 KB -16.2% -52.0% gfm tables 2926.0 KB 1866.9 KB 1543.6 KB -17.3% -47.2% entities 660.2 KB 144.9 KB 128.5 KB -11.3% -80.5% 6. Fusing exclusive fields (5467a18) A List has a start number. A Heading has a depth. No node is ever both, so they became one uint32_t startOrDepth and kind says which it means. It didn’t shrink the size of Node due to the alignment padding but we did it anyway hoping that future optimization would shrink below padding. shape start before after vs before vs start prose 1646.1 KB 918.0 KB 918.0 KB +0.0% -44.2% nested lists 1067.9 KB 512.3 KB 512.3 KB +0.0% -52.0% gfm tables 2926.0 KB 1543.6 KB 1543.6 KB +0.0% -47.2% entities 660.2 KB 128.5 KB 128.5 KB +0.0% -80.5% 7. Compressing text position (ed5e807) Each Node carried the info about its position in parsed text. It was expensive because it was stored as start and end fields and each of them was: a uint32_t line a uint32_t column a uint32_t offset That’s 4*3*2 = 24 bytes. I assume this info is for debugging so not important for me. I replaced it with 2 uint32_t offsets into a source markdown string, srcStart and srcEnd. We can reconstruct the line/column position from that and the source string. shape start before after vs before vs start prose 1646.1 KB 918.0 KB 828.0 KB -9.8% -49.7% nested lists 1067.9 KB 512.3 KB 462.2 KB -9.8% -56.7% gfm tables 2926.0 KB 1543.6 KB 1379.6 KB -10.6% -52.9% entities 660.2 KB 128.5 KB 120.0 KB -6.6% -81.8% 8. Further compression text position (6a558c4) srcEnd is always after srcStart so we can delta-encode it and shrink to uint16_t. What if it’s bigger than 64 KB? I don’t care, we store it as 65535. This is another case where due to padding we didn’t shrink the struct size. But wait for it. shape start before after vs before vs start prose 1646.1 KB 828.0 KB 828.0 KB +0.0% -49.7% nested lists 1067.9 KB 462.2 KB 462.2 KB +0.0% -56.7% gfm tables 2926.0 KB 1379.6 KB 1379.6 KB +0.0% -52.9% entities 660.2 KB 120.0 KB 120.0 KB +0.0% -81.8% 9. Optimizing storing children (d6c4abc) Some nodes have children that were stored as a growable vector. Empty vector was 24 bytes in the node. We replaced it with a ring of compressed pointers: the parent names its last child, each child names the next one, and the last child wraps back to the first. vector: 24 bytes in the node + a separate array of links ptr · len · cap kid0 kid1 kid2 spare spare ring: 4 bytes in the parent, 4 in each child, nothing else allocated parent kid0 kid1 kid2 lastKid We use a ring and not just a linked list because appending is the only thing the parser does to a child list. A single linked list requires walking the list to find the end, while a ring does not. Saving: 96 → 80 bytes. shape start before after vs before vs start prose 1646.1 KB 828.0 KB 619.0 KB -25.2% -62.4% nested lists 1067.9 KB 462.2 KB 308.8 KB -33.2% -71.1% gfm tables 2926.0 KB 1379.6 KB 898.7 KB -34.9% -69.3% entities 660.2 KB 120.0 KB 98.9 KB -17.6% -85.0% Caveat: accessing a child by index would require a walk through the ring, so indexing in a loop would be quadratic. In our code we only ask for the first or the last. 10. Compressing table alignments info (ca0818c) For tables we were storing column alignments in a separate vector on every node, even though only Table nodes have them. Another 24 bytes per node. We switched to a compressed pointer which points to an optimized representation of the column alignments. There are four alignments (left, right, center, none), so a column needs 2 bits: [varint count][2 bits a column, four to a byte] The whole list is known when the table is entered, so it’s counted, allocated once and filled. For an 8-column table that’s 3 bytes in the arena and a 4-byte offset in the node. Saving: 80 → 60 bytes. We saved more than the 20 bytes because with the last pointer-holding member gone alignof(Node) fell from 8 to 4. shape start before after vs before vs start prose 1646.1 KB 619.0 KB 519.3 KB -16.1% -68.5% nested lists 1067.9 KB 308.8 KB 256.3 KB -17.0% -76.0% gfm tables 2926.0 KB 898.7 KB 710.3 KB -21.0% -75.7% entities 660.2 KB 98.9 KB 89.2 KB -9.8% -86.5% The block is pushed byte-aligned rather than through the general allocator, which rounds to 8 and would have handed back exactly what the varint saved. 11. Fusing exclusive fields (07444d6) Previously we fused exclusive fields start of a List node and depth of a Heading node into a single uint32_t. We fused Table node column alignments info from previous optimization into the same field. We called it uint32_t perKind, and kind says what kind of value it is. Saving: 60 → 56 bytes. shape start before after vs before vs start prose 1646.1 KB 519.3 KB 483.9 KB -6.8% -70.6% nested lists 1067.9 KB 256.3 KB 233.7 KB -8.8% -78.1% gfm tables 2926.0 KB 710.3 KB 646.1 KB -9.0% -77.9% entities 660.2 KB 89.2 KB 86.1 KB -3.5% -87.0% 12. Optimizing eight strings (521e32e) We had 8 strings that were not all used by all nodes. Instead of figuring out how many strings we need at most, I created a linked list of strings in the arena. They are different than regular strings in that they carry a 4 byte compressed pointer to the next string within the arena and the kind of the strings. [u32 next][u8 kind][varint len][len bytes][NUL] We can add as many kinds of strings as we need but we only pay for used strings + 5 byte per-string overhead. Some nodes don’t have any strings. 8 fields: 32 bytes on every node, 7 of them empty on almost all of them value url title alt ident label lang meta 1 field: 4 bytes, and a record only for what the node actually carries first next kind len characters 0 a stored string costs 5 bytes more · a node storing none saves 28 New records go on the head, so storing is O(1), and the walk that finds a kind is at most 8 long and is almost always 1 or 0. In-place growth still works, because a record being the newest thing in the arena is the same condition it always was. Saving: 56 → 28 bytes. shape start before after vs before vs start prose 1646.1 KB 483.9 KB 358.3 KB -26.0% -78.2% nested lists 1067.9 KB 233.7 KB 159.1 KB -31.9% -85.1% gfm tables 2926.0 KB 646.1 KB 402.9 KB -37.6% -86.2% entities 660.2 KB 86.1 KB 73.7 KB -14.4% -88.8% 13. Fusing two enums into one (f3c14b9) As it happened we had two enums: one needed 6 bits another needed 2 bits We fused them from 2 bytes to 1 byte. Because this 1 byte saving dropped below padding we saved 4 bytes and went from 28 → 24 bytes. shape start before after vs before vs start prose 1646.1 KB 358.3 KB 321.2 KB -10.4% -80.5% nested lists 1067.9 KB 159.1 KB 136.5 KB -14.2% -87.2% gfm tables 2926.0 KB 402.9 KB 341.9 KB -15.1% -88.3% entities 660.2 KB 73.7 KB 70.4 KB -4.5% -89.3% 14. Remove position, reduce allocator’s alignment (861c803) At this point I decided that I didn’t need the position so I removed it. Other markdown parsers don’t carry it around so it doesn’t seem very useful. I reduced overhead of perKind by converting it to a record in the string list from step 12 — varint-encoded, under its own kind byte. A List, Heading or Table pays ~8 bytes for it; every other node pays nothing, where a field cost 4 bytes on all of them. Savings: 24 → 16 bytes. For safety arena allocator aligns allocations to 8 bytes but a 16 bytes Node can be allocated at 4 bytes, which we did. This reduces wasted space between allocations. shape start before after vs before vs start prose 1646.1 KB 321.2 KB 272.0 KB -15.3% -83.5% nested lists 1067.9 KB 136.5 KB 110.2 KB -19.3% -89.7% gfm tables 2926.0 KB 341.9 KB 250.5 KB -26.7% -91.4% entities 660.2 KB 70.4 KB 65.6 KB -6.8% -90.1% End results The results are pretty dramatic: sizeof(Node) prose nested tables entities start 232 1646.1 KB 1067.9 KB 2926.0 KB 660.2 KB end 16 272.0 KB 110.2 KB 250.5 KB 65.6 KB -93% -83.5% -89.7% -91.4% -90.1% A parse of 64 KB of prose cost 25.7× the source in arena bytes. It costs 4.2× now. The entities shape went from 10.3× to 1.02×. The speed was unchanged. Fastest of 3 runs: prose 8.47 → 8.22 ms nested 9.45 → 9.26 ms tables 12.88 → 12.92 ms entities 5.90 → 5.85 ms Those are within margin of error. The phase of building the tree got a measurable speed up: 0.397 → 0.302 ms, about 24% faster. This is from allocating less and touching fewer cache lines. This is not visible on micro benchmarks, but using less memory will slightly speed up the rest of the application. Lessons learned Arranging struct fields by size is good. It costs literally nothing. Pointer compression is good. 8 bytes become 4 bytes and the cost of converting back and forth is negligible, as Google shown in their v8 blog post and is re-inforced by our benchmarks Varint-encoding is good. Most strings are short so varint encoding can save 3 bytes per string on average. Moving rare fields out of line is good. The way we reduced 8 strings into an out-of-line list. Only pays off if savings is bigger than the cost of additional metadata. sizeof only drops when the saving crosses an alignment boundary. Two of our changes didn’t reduce size of Node struct but it paid off in later optimizations. The allocator’s alignment is part of sizeof. A 28-byte struct from an 8-aligned bump allocator is 32 bytes. We need benchmarks. You can’t improve what you can’t measure. Our benchmarks measured both memory usage and speed, to ensure we didn’t regress speed to save memory.

I maintain SumatraPDF on GitHub. People fork it and make their own changes. I want to know which forks are active and what they’re working on. GitHub has a Network tab for this. I find it lousy. It’s hard to see active forks at a glance. Panning and zooming the UI is slow and fiddly. I just want a sorted list of forks with actual changes. So I wrote a small script: github-active-forks.ts. What it does It uses the GitHub API to find forks that have ahead commits (changes not in upstream) from the last year. For each active branch it prints: a link to the branch on GitHub short sha, author, relative date, first line of commit message Forks are sorted by most recent activity, oldest first. Commits within a branch are oldest first. It compares each fork branch against the matching upstream branch (e.g. rel3.6working vs rel3.6working), not always master. That way you only see commits unique to the fork. How to run it Download the script: curl -O https://gist.githubusercontent.com/kjk/71a7679dd408d52bc612cdf5eecace58/raw/7d7f605dde6b739424e5190a79e3988477312e74/github-active-forks.ts You need Bun and the GitHub CLI logged in (gh auth login). Or set GITHUB_TOKEN. Run it on any repo as owner/repo: bun github-active-forks.ts sumatrapdfreader/sumatrapdf Progress goes to stderr; results go to stdout, so you can redirect to a file: bun github-active-forks.ts sumatrapdfreader/sumatrapdf > forks.txt Example output Excerpt from running on SumatraPDF (74 forks with ahead commits): https://github.com/wackget/sumatrapdf-Feldherren-version/tree/single-page-fit-scrollbar (289d ago) c53a51d wackget 294d ago Added scrollbar usable in Fit a Single Page display mode. f927640 wackget 294d ago scrollbar in single page mode now obeys the hideScrollbars setting. 7f57861 wackget 288d ago improved scrolling speed when zoomed in, in non-continuous single-page view. https://github.com/xyzzyx99/sumatrapdf/tree/rel3.6working (5d ago) c8d1f7b xyzzyx99 13d ago Add GitHub Actions workflow for building project 8a92547 xyzzyx99 13d ago Fix bugs, revert using version 3.6 1f27324 xyzzyx99 13d ago Save and restore CHM scroll position 24e7974 xyzzyx99 6d ago Preserve current CHM URL across tab restore 30b1cb4 xyzzyx99 5d ago Update README with new bug fixes and formatting https://github.com/lsq/sumatrapdf/tree/dev (today) 72d7069 lsq 11d ago feat: support share file via socket a1fc427 lsq 10d ago feat: open home page 938bc07 lsq today add localsend v2.1 api support Much easier to scan than the Network graph.

A new JavaScript library pretext for fast text measuring / layout popped up on social media. Potentially interesting given its focus on speeding up text rendering in web apps and me writing web apps and liking them being fast. I looked at the code and saw a function isCJK(). Given my 3 decades of programming and performance optimization, it looked like it could be sped up. This is a story about ideas on making JavaScript faster and the process of quickly implementing and benchmarking them. The code export function isCJK(s: string): boolean { for (const ch of s) { const c = ch.codePointAt(0)! if ((c >= 0x4E00 && c <= 0x9FFF) || (c >= 0x3400 && c <= 0x4DBF) || (c >= 0x20000 && c <= 0x2A6DF) || (c >= 0x2A700 && c <= 0x2B73F) || (c >= 0x2B740 && c <= 0x2B81F) || (c >= 0x2B820 && c <= 0x2CEAF) || (c >= 0x2CEB0 && c <= 0x2EBEF) || (c >= 0x30000 && c <= 0x3134F) || (c >= 0xF900 && c <= 0xFAFF) || (c >= 0x2F800 && c <= 0x2FA1F) || (c >= 0x3000 && c <= 0x303F) || (c >= 0x3040 && c <= 0x309F) || (c >= 0x30A0 && c <= 0x30FF) || (c >= 0xAC00 && c <= 0xD7AF) || (c >= 0xFF00 && c <= 0xFFEF)) { return true } } return false } My spider sense tingling To make code run fast you have to have mechanical sympathy. You need a good mental model of how CPUs and programming languages work, at the low level. Because I have mechanical sympathy, I know that to evaluate multiple || statements, the program has to check every statement until it finds one that is true. For the case of not matching any range, it has to do all 15 comparisons. My immediate thought was that most characters are ascii (non-cjk) and therefore require 15 comparisons. An early exit should speed things up: if (c < 0x300) continue;. It adds 1 comparison for matching cases but removes 14 (15 - 1) for the most common case of ascii characters. Hunch is good, benchmark is better Micro-benchmarking is subtle. In the past I would google for a JavaScript benchmarking library and trust that it’s sound. Claude wrote a sound, simple benchmark function: function bench(name, fn, iterations = 1_000_000) { for (let i = 0; i < 10_000; i++) fn(); // warmup const start = performance.now(); for (let i = 0; i < iterations; i++) fn(); const elapsed = performance.now() - start; const opsPerSec = ((iterations / elapsed) * 1000).toFixed(0); console.log(`${name}: ${elapsed.toFixed(2)} ms (${opsPerSec} ops/sec)`); } // to call: bench("isCJKRegex ", () => isCJKRegex(input)); The subtle part is warmup phase. JavaScript JIT engines that you’re likely to use (v8 in Chrome, JavaScriptCore in Safari, SpiderMonkey in FireFox) have several optimization tiers and only do most aggressive optimization for frequently called functions. The warmup phase forces JIT to compile fn with most aggressive settings. This is only fair - I assume isCJK is called frequently. If it wasn’t, there’s no point sweating to optimize it. The new AI normal In the past I would have to manually implement each optimization idea. With AI I can delegate the coding and therefore test more ideas. The following was done with Claude 4.6. 100% vibe coding There are levels of delegating things to AI. On the extreme side of giving up control is 100% vibe coding. I had 2 ideas for performance optimization: early exit replace 15 linear checks with binary search But what if I didn’t? What if I just ask AI to come up with ideas? The following is my conversation with Claude 4.6 and what I’ve learned. I created is_cjk.js file and pasted the original function. look at is_cjk.js. come up with 2 ideas for how to speed up isCJK(). Implement those as separate functions and write a javascript micro-benchmark that compares those 2 new implementations against isCJK Two ideas: Pre-compiled regex — push the entire check into the native regex engine, avoiding JS-level for…of iteration and codePointAt() overhead. Typed array bitmap for BMP — for BMP characters (the vast majority of real text), a Uint8Array bitmap gives O(1) lookup with a single array access. Fall back to range checks only for supplementary plane characters. I have to admit: I recognize those as valid optimization ideas but they didn’t immediately occur to me. Regex works because regex implementations in JavaScript engines are insanely optimized. Bitmap is a known optimization for doing true/false lookup for numbers. Let’s say you have total range 0-255 and you want a function that checks if a number is in range 1-10, 20-30, 40-50, 60-70, 80-90. That’s 5 if statements, each doing 2 comparisons. You can instead use 1 bit for each of the 256 numbers (i.e. 256⁄8 = 32 bytes), pre-compute the array of bytes and set the bit for the ranges. Then lookup is just math to get byte offset and bit, load the byte, check the bit. Super fast. Claude not only wrote the benchmark function, 2 new implementations but also ran the benchmarks using node and showed me results in a readable way. The bitmap approach was significantly faster. Human assisted coding Bitmap was the fastest and yet I discarded it using my human judgement. The bitmap needed 65kB of memory and that’s just too much for this function. That’s where human judgement comes in to improve collaboration with AI. I didn’t specify that I also care about memory usage so AI wrote fast code that was using too much memory. You’re benchmarking what? Here’s another tricky part of benchmarking: knowing what you benchmark. There are 3 popular JavaScript engines. Claude ran benchmarks using node which uses Chrome’s v8. That’s good because that’s the most popular browser and therefore most popular JavaScript JIT engine. It’s also good to sanity-check with at least one other JavaScript engine. bun uses Safari’s JavaScriptCore so I asked Claude: run the tests using bun The results were similar, which is good. We don’t want code that is fast in one engine but slow in another. We don’t control which browser the user of our code is running. Continuing collaboration with AI I had some more ideas so here’s the rest of my conversation with Claude. don’t benchmark bitmap, it uses too much memory; write a variant of isCJK that does an early false exit if char is less than smallest Early exit is my first insight I wanted to test. As expected, early exit is good 2x speedup for latin strings, although regex beats it on cjk strings. That is the curse of engineering trade-offs: you have to decide if you want to optimize for cjk strings or non-cjk strings. now implement a variant that does inline bisect / binary search That was my second idea: do a binary search instead of sequential if comparisons. It was faster than original but slower than regex / early exit. Also, Claude didn’t do what I meant. It stored the ranges as array: // Flat array: [lo0, hi0, lo1, hi1, ...] sorted by lo const cjkRanges = new Uint32Array([ 0x3000, 0x303f, 0x3040, 0x309f, ... That’s what I meant: implement a variant isCJBisect that doesn’t use array but unrolls binary search logic into if statements The implementation is gnarly: function inCJKUnrolled(c) { if (c < 0x3000) return false; if (c < 0xff00) { if (c < 0x3400) { if (c < 0x3040) { return c <= 0x303f; } else if (c > 0x309f) { return c >= 0x30a0 && c <= 0x30ff; } return true; // ... more gnarly code But what do I care? Claude wrote it. Arguably it’s hard for me to review for correctness but it’s perfectly testable. We can write a test to compare if it returns the same result as original function for every possible input. As expected, unrolled version was faster than array-based but not always faster than regex or early exit. But Claude wrote something that caught my eye: The for…of + codePointAt iterator overhead remains the bottleneck shared by all non-regex variants on long strings. It’s well known that for ... of loop is slower than for (let i = 0; i < n; i++). It’s one of those things that you need to know. I knew this but I didn’t connect the dots in this particular case. write variant of isCJKEarly which replaces for of with a faster loop Result: isCJKEarlyFast crushes everything across the board on both runtimes The code for isCKJEarlyFast. Looks like we have a winner: Replacing for…of + codePointAt() with charCodeAt() + index loop eliminates the iterator overhead, which was the main bottleneck. The early c < 0x3000 bail then skips all range checks for ASCII/latin characters cheaply. Only regex still wins on Node’s long latin miss case thanks to V8’s native string scanning. There’s a subtlety that makes the rewrite of for .. of into for (let i = 0; i < n; i++) tricky. Logically JavaScript strings are composed from 16-bit numbers (characters). Some Unicode code-points are bigger than 16-bit and are stored as 2 16-bit numbers, called surrogate pairs. So writing a loop over unicode codepoints needs to take this into account. It’s tricky code but again, what do I care? Claude wrote it and it’s perfectly testable. Pressing on The good thing about claude writing most of the code and removing other tedium like running the benchmarks is that we can do more in the same amount of time. So I explored even more. I noticed: export function isCJKUnrolled(s) { for (const ch of s) { if (inCJKUnrolled(ch.codePointAt(0))) return true; } return false; } What happens when inCJKUnrolled is inlined? inline inCJKUnrolled into isCJKUnrolled Result: Done. Inlining didn’t help — it actually got slightly slower in most cases (the engine was already inlining the small function). No change in rankings; EarlyFast remains the winner. Turns out it’s not faster, which was surprising. Inlining is supposed to remove overhead of function calls, which matters in loops. Surprising isn’t bad. We run benchmarks to validate our hunches. For completness, I wanted to compare with original where for .. of was rewritten. also write original with for of replaced by standard for loop and bench all and give results as markdown table Claude was nice enough to show benchmark results in a nicely formatted table, without me even asking. It just knows things. But as you can see above, you can ask it for results in markdown table to e.g. include in a GitHub bug report. Here are final results. The number is operations/second in millions. Higher is better. Bun Scenario Original Regex Early Bisect Unrolled EarlyFast ForLoop Single CJK 11M 22M 12M 12M 16M 79M 57M Single latin 20M 24M 49M 21M 22M 40M 36M CJK string 12M 18M 17M 12M 14M 42M 42M Latin string 1.5M 4.7M 3.6M 3.0M 3.5M 17M 6.5M Mixed string 6.5M 5.9M 9.9M 8.6M 13M 62M 34M Node Scenario Original Regex Early Bisect Unrolled EarlyFast ForLoop Single CJK 85M 54M 51M 44M 56M 112M 110M Single latin 59M 72M 83M 55M 74M 115M 103M CJK string 79M 55M 60M 55M 64M 90M 129M Latin string 4.2M 57M 4.3M 2.8M 4.5M 12M 8.3M Mixed string 14M 15M 16M 10M 15M 41M 38M Conclusions AI is a big unlock. It took me under 30 minutes to test various hypotheses and find out a significant speed up. Without Claude it would take several hours and I would likely not do it at all. It’s just not important enough to spend a working day on it. To get best results we still need to apply human judgement and guide the AI. Programming expert knowledge An expert is simply someone who knows things. We know things because we learn them. If you were paying attention you might have learned the following things: importance of warmup phase when benchmarking JIT compilers for .. of is slower than for (let i = 0; i < n; i++) regex matching in JavaScript engines is fast subtlety of surrogate pairs in JavaScript strings using bitmaps to speed up range lookups Resources All the code is in https://gist.github.com/kjk/bdbea9d90c3bb0454fbe26353c521bfd I like to write fast code. If you want a fast bookmark manager / note taker, try MarkLexis. If you want a fast PDF / ebook / comic book reader for Windows, try SumatraPDF.

TOON stands for a Token-Oriented Object Notation. It’s a new text format that has the same capability as JSON but uses less space. It was invented to lower costs of sending data (tokens) to LLM AIs but it has 2 advantages over JSON: smaller than JSON more readable than JSON It has implementation in many programming languages, including those I care about: JavaScript and Go. Therefore it’s a good use for non-AI cases e.g.: logging structured data sending data from server to client Here’s an example of JSON and TOON formats: { "table": "A", "currency": "dolaramerykański", "code": "USD", "rates": [ { "no": "001/A/NBP/2024", "effectiveDate": "2024-01-02", "mid": 3.9432 }, { "no": "002/A/NBP/2024", "effectiveDate": "2024-01-03", "mid": 3.9909 }, ] } table: A currency: dolaramerykański code: USD rates[252]{no,effectiveDate,mid}: 001/A/NBP/2024,2024-01-02,3.9432 002/A/NBP/2024,2024-01-03,3.9909 As a result, I’m switching to using TOON whenever possible.