TELETEXT · P100 · INDEX
TELETEXT
MANUALS · NEWS · WEATHER · STARS · HEARTS · RAFAEL CALDERÓN ROBLES
THE BROADCAST
THE MANUALS
THE MAGAZINE
DIAL A PAGE WITH THREE DIGITS · ↑↓ BROWSE · R REVEAL · COLOUR KEYS BELOW · T OR ESC BACK TO THE SET
TV GUIDE · P105
TONIGHT ON THE SET — ALL CHANNELS ZAP WITH 0–6 OR ← → BACK ON THE SET
00 SPECIMEN — the name, in stereo; hover the glass and two stacks grow out of the orbs. ON AIR
01 LIBRARIES — rust → wasm, single-digit kilobytes. Manuals on P110, P120, P130 and P140. ON AIR
02 EXPERIENCE — one trunk, two roots: the work and the person. Work root runs sample footage. ON AIR
03 STACK — the toolbox, drawer by drawer, every tool labelled. ON AIR
04 CERTIFICATES — an achievement board: unlocks, not diplomas. Four remain locked. PREVIEW
05 ARCHIVE — every article taped off air, racked as VHS; one cassette still blank for the first book. ON AIR
06 PORTRAIT — letters, commissions & bug reports; addresses on P150. ON AIR
88 ········ — this channel does not appear in the listings. UNLISTED
99 TEST CARD — dial 9-9 on the set; any key resumes normal service. 24 H
ALSO ON THE FRONT PANEL — SND sound · CC captions (or C) · D degauss · the rail switch prints the whole set on paper.
NOW & NEXT headlines P200 · kick-off times P250 · the forecast P300 · tonight's draw P400.
NEXT P106 ENGINEERING — the set, inspected
ENGINEERING · P106
THE SET, INSPECTED CERTIFICATE OF ROADWORTHINESS · ISSUED HOURLY
TUBE warm. Phosphor nominal. The bulge catches the room light exactly as designed.
STATIC holding at 2.5% — a healthy hiss. Peaks at 65% during zaps, as regulations require.
DEGAUSS COIL rested and ready. Press D on the set for the fwoomp. Report lingering colour anomalies to P150.
RAIL SWITCH two positions, DK and LT. Both bands carry identical programming; only the light changes. Flip freely.
ATTRACT MODE after a minute unattended the set zaps itself, occasionally rewinding the tape. This is a feature, not a fault.
KNOWN ISSUES the orbs on CH 00 still refuse to merge (wavelength dispute, coverage P200) · the work root of CH 02 broadcasts under redaction bars until the real reel arrives · a channel that is not in the listings keeps drawing power.
SERVICE HISTORY — every part of this set is hand-built HTML, CSS and one canvas of honest noise. No frameworks were consulted.
NEXT P200 NEWS
FLASHFUZZY · P110
RUST · WASM · ~3 KB · NPM FLASHFUZZY 1/10 — INDEX
A complete fuzzy search runtime for the browser. Bloom pre-filter, Bitap matching, accent-insensitive, typo-tolerant — zero backend, zero network after load. Open source, MIT, v1.0.0.
P111 OVERVIEW — WHAT IT IS, STATS, FEATURES
P112 INSTALL & QUICK START
P113 QUERY SYNTAX
P114 API REFERENCE — 4 CALLS + HIT TYPE
P115 ARCHITECTURE — 7 NODES, MEMORY LAYOUT
P116 SEARCH PIPELINE — 4 STAGES + COSTS
P117 FORMATS & CAPACITY — INPUTS, LIMITS
P118 GUIDES 1/2 — ESM/TS, REACT
P119 GUIDES 2/2 — ADVANCED, VUE, SVELTE
LINKS FULL MANUAL · GITHUB · NPM
NEXT P111 OVERVIEW
FLASHFUZZY · P111
RUST · WASM · ~3 KB · NPM FLASHFUZZY 2/10 — OVERVIEW
Fuzzy search, shipped as 3 KB OF WEBASSEMBLY. A complete runtime that runs entirely in the browser — no server, no API calls, no network traffic after load. Bloom pre-filter + Bitap matching give typo-tolerant, accent-insensitive search over any array, object, or JSON dataset.
One class, FlashFuzzy, wraps the WASM module. All search logic runs in the WASM sandbox; the JS surface is intentionally thin. Built with Rust, target wasm32-unknown-unknown.
3 KB main WASM binary <1 MS query on 100k records 10-100X faster than JS fuzzy libs 0 DEPS core + search crates
ZERO BACKEND runs entirely in the browser. No server, no API calls. 3 KB · MIT.
TYPO-TOLERANT Bitap allows up to 2 edit ops. Finds "javascript" from "javsacript". Bitap · adaptive.
ACCENT-INSENSITIVE café matches cafe. Full Latin Extended fold (U+00C0 – U+00FF).
MULTI-FIELD search across name, description, tags simultaneously.
ANY DATA arrays of strings, objects, JSON — no schema required.
ZERO ALLOCATOR BSS static memory. No heap, no GC pressure. no_std · no_alloc.
NEXT P112 INSTALL & QUICK START
FLASHFUZZY · P112
RUST · WASM · ~3 KB · NPM FLASHFUZZY 3/10 — INSTALL & QUICK START
INSTALL No backend required — delivered as an npm package, runs entirely in the browser. Pick your package manager:
npm install flashfuzzy pnpm add flashfuzzy yarn add flashfuzzy cargo add flashfuzzy pip install flashfuzzy
QUICK START up in under a minute. Three steps, no server:
1 DROP feed any array, JSON, or text. No network, no server.
2 INDEX tokenizer + Bloom filter. Unicode fold, 64-bit filter, tokens stored in the BSS pool.
3 SEARCH Bloom skips, Bitap finishes. O(1) skip, O(n·k) match.
import { FlashFuzzy } from "flashfuzzy";
const ff = await FlashFuzzy.create();
ff.index([
{ id: 1, name: "React Hooks", tags: ["react", "js"] },
{ id: 2, name: "Vue Composition API", tags: ["vue", "js"] },
]);
const hits = ff.search("raect hoks"); // typo-tolerant
for (const h of hits) {
console.log(h.id, h.score, h.snippet);
}
NEXT P113 QUERY SYNTAX
FLASHFUZZY · P113
RUST · WASM · ~3 KB · NPM FLASHFUZZY 4/10 — QUERY SYNTAX
Fuzzy single tokens, AND, exact phrases, and OR — composable in a single query string.
PATTERN MEANING
word fuzzy single token — up to 2
character edits
ex: flashfuzzy → matches
flashfuzzy, flashfuzy
a b all tokens AND — both must appear
ex: react hooks
"a b" exact phrase match, no fuzziness
applied
ex: "hello world"
a | b either token — logical OR
ex: typescript | javascript
"a b" | c phrase OR single token
ex: "fuzzy search" | search
TIP mix forms freely: "fuzzy search" | search is a phrase OR'd with a fuzzy token.
NEXT P114 API REFERENCE
FLASHFUZZY · P114
RUST · WASM · ~3 KB · NPM FLASHFUZZY 5/10 — API REFERENCE
The whole surface is one class and four calls.
STATIC FlashFuzzy.create() → Promise<FlashFuzzy> — initialises the WASM module, returns a ready instance. Must be awaited before any other call. Safe to call once per page load.
import { FlashFuzzy } from "flashfuzzy";
const ff = await FlashFuzzy.create();
console.log("FlashFuzzy ready");
INDEX ff.index(records: T[]) → void — tokenises each record and builds the Bloom filter index. Accepts arrays of strings, objects, or JSON. Fields are concatenated for multi-field search.
ff.index([
{ id: 1, name: "React Hooks", tags: ["react", "js"] },
{ id: 2, name: "Vue Composition API", tags: ["vue", "js"] },
]);
SEARCH ff.search(query: string) → Hit[] — runs Bloom → Bitap → Score → Rank, returns hits sorted by score descending. Typo-tolerant up to 2 edits. Supports phrase, AND, and OR queries.
const hits = ff.search("raect hoks"); // typo-tolerant
for (const h of hits) {
console.log(h.id, h.score, h.snippet);
}
THE HIT TYPE
FIELD TYPE DESCRIPTION
id unknown original record identifier
as provided to index()
score number relevance 0.0-1.0
(higher is better)
snippet string surrounding text excerpt
with match context
CLEAR ff.clear() → void — clears all indexed records and resets the BSS store. The instance is immediately ready to index a new dataset.
ff.clear();
console.log("Index cleared, ready for new records");
NEXT P115 ARCHITECTURE
FLASHFUZZY · P115
RUST · WASM · ~3 KB · NPM FLASHFUZZY 6/10 — ARCHITECTURE
Runtime topology: seven nodes, six edges, split across an ingest lane and a search lane. A query traverses the whole graph in under a millisecond.
Input → Tokenizer → Bloom Builder → Index → Store → Pipeline → Results
INGEST LANE
01 INPUT JSON · arrays — any array,
object or JSON input
02 TOKENIZER unicode fold — unicode-aware
tokenizer, accent folding
03 BLOOM BLD 64-bit filter — probabilistic
set membership filter
04 INDEX BSS pool — static memory
index, zero fragmentation
05 STORE 3 KB · 50k tokens — compact
BSS-backed token store
SEARCH LANE
06 PIPELINE bloom ▸ bitap / score ▸ rank 07 RESULTS <1 ms — sorted Hit[] to JS
MEMORY LAYOUT all state lives in static BSS memory — no allocator, no heap, no GC pressure. Four segments:
BSS POOL 3 KB text pool · TOKENS 50k token chunks · BLOOM BITS the 64-bit filter scratch · DOC INDEX record name table
NEXT P116 SEARCH PIPELINE
FLASHFUZZY · P116
RUST · WASM · ~3 KB · NPM FLASHFUZZY 7/10 — SEARCH PIPELINE
Four stages, one pass. Every query runs Bloom → Bitap → Score → Rank in a single traversal of the index.
STAGE COST DETAIL
01 BLOOM O(1) probabilistic skip —
FILTER the 64-bit filter
rejects tokens that
cannot match before any
expensive work happens
02 BITAP O(n·k) up to 2 edits — bitwise
SHIFT-OR approximate matching
finishes what Bloom
lets through
03 SCORE O(1) each candidate scored by
edit distance (1.0),
match position (0.7),
token frequency (0.5)
04 RANK O(log k) bounded min-heap keeps
only the best K hits,
sorted by score desc
PERFORMANCE the Bloom pre-filter is what makes the <1 ms figure possible: most non-matching tokens are skipped in O(1) before Bitap ever runs — how FlashFuzzy stays 10–100× faster than pure-JS fuzzy libraries on the same data.
NEXT P117 FORMATS & CAPACITY
FLASHFUZZY · P117
RUST · WASM · ~3 KB · NPM FLASHFUZZY 8/10 — FORMATS & CAPACITY
WHAT GOES IN
ARRAYS JavaScript arrays — native
JS array input
OBJECTS plain JS objects — any
object shape
JSON serialised JSON — parsed
automatically
CSV CSV rows — parsed as
row objects
TEXT raw strings — string
array input
DEFAULT LIMITS
3 KB WASM binary — total
runtime size
50K token capacity — tokens
stored in BSS pool
100 max documents — concurrent
indexed records
32 CHARS query limit — max query
string length
NOTE these are the defaults of the static BSS store. Memory is laid out at compile time (no allocator), so limits are fixed per build rather than growing at runtime.
NEXT P118 GUIDES 1/2 — ESM/TS & REACT
FLASHFUZZY · P118
RUST · WASM · ~3 KB · NPM FLASHFUZZY 9/10 — GUIDES 1/2: ESM/TS & REACT
From install to first fuzzy hit in under 60 seconds — the same recipe in plain ESM/TypeScript, React, Vue, and Svelte.
ESM / TYPESCRIPT 01 INSTALL · SHELL
npm install flashfuzzy # or pnpm add flashfuzzy
02 INDEX + SEARCH · TYPESCRIPT
import { FlashFuzzy } from "flashfuzzy";
const ff = await FlashFuzzy.create();
ff.index([
{ id: 1, name: "React Hooks", tags: ["react", "js"] },
{ id: 2, name: "Vue Composition API", tags: ["vue", "js"] },
]);
const hits = ff.search("raect hoks");
for (const h of hits) {
console.log(h.id, h.score, h.snippet);
}
REACT HOOK 01 INSTALL: npm install flashfuzzy · 02 INDEX + SEARCH · JSX
import { useEffect, useRef, useState } from "react";
import { FlashFuzzy } from "flashfuzzy";
export function FuzzySearch({ records }) {
const ffRef = useRef(null);
const [hits, setHits] = useState([]);
useEffect(() => {
FlashFuzzy.create().then(ff => {
ff.index(records);
ffRef.current = ff;
});
}, [records]);
function handleSearch(q) {
setHits(ffRef.current?.search(q) ?? []);
}
return (
<div>
<input onChange={e => handleSearch(e.target.value)} />
{hits.map(h => <div key={h.id}>{h.snippet}</div>)}
</div>
);
}
NEXT P119 GUIDES 2/2 — ADVANCED, VUE & SVELTE
FLASHFUZZY · P119
RUST · WASM · ~3 KB · NPM FLASHFUZZY 10/10 — GUIDES 2/2: ADVANCED, VUE & SVELTE
ADVANCED QUERIES 03 ADVANCED · TYPESCRIPT
// Clear and re-index
ff.clear();
ff.index(newRecords);
// OR query
const results = ff.search("typescript | javascript");
// Phrase query
const exact = ff.search('"fuzzy search"');
// Typo-tolerant single token
const fuzzy = ff.search("javsacript"); // matches javascript
VUE COMPOSABLE 01 INSTALL: npm install flashfuzzy · 02 INDEX + SEARCH · JS
import { ref, onMounted } from "vue";
import { FlashFuzzy } from "flashfuzzy";
export function useFuzzySearch(records) {
const ff = ref(null);
const hits = ref([]);
onMounted(async () => {
ff.value = await FlashFuzzy.create();
ff.value.index(records);
});
function search(query) {
hits.value = ff.value?.search(query) ?? [];
}
return { hits, search };
}
SVELTE STORE 01 INSTALL: npm install flashfuzzy · 02 INDEX + SEARCH · JS
import { writable } from "svelte/store";
import { FlashFuzzy } from "flashfuzzy";
export function createFuzzyStore(records) {
const hits = writable([]);
let ff = null;
FlashFuzzy.create().then(instance => {
ff = instance;
ff.index(records);
});
function search(query) {
hits.set(ff?.search(query) ?? []);
}
return { hits, search };
}
QUERY SYNTAX QUICK REF word fuzzy (2 edits) · a b AND · "a b" exact phrase · a | b OR · "a b" | c phrase OR token — full table on P113.
LINKS FULL MANUAL · GITHUB · NPM
NEXT P100 INDEX
ALBEX · P120
RUST · WASM · WEBGPU · 33 KB CORE 1/10 — INDEX & OVERVIEW
Document search that runs entirely in the browser — no server, no telemetry, no network call after load. Streaming parsers, accent-insensitive fuzzy matching, zero allocator, zero backend. Your documents are processed locally and never transmitted anywhere.
P120 INDEX & OVERVIEW P121 INSTALL & QUICK START P122 QUERY SYNTAX & FORMATS
P123 API 1/2 — ENGINE CORE P124 API 2/2 — HELPERS & ERRORS
P125 ARCHITECTURE 1/2 P126 ARCHITECTURE 2/2 P127 STORAGE & NUMBERS
P128 GUIDES 1/2 P129 GUIDES 2/2 · FULL MANUAL · GITHUB
OVERVIEW Full-text engine shipped as a 33 KB WebAssembly binary. Streaming parsers turn eleven file formats into an index, a four-stage pipeline answers queries in under 5 ms, and nothing the user drops ever leaves the machine — no upload, no telemetry, no network call after the initial .wasm fetch.
One primary class — AlbexEngine — wraps the WASM module: indexing, search, persistence, adaptive runtime. Opt-in helpers AlbexEngineWorker, AlbexPool, TieredStore, BloomGpu live on subpath exports — you only pay for what you use.
Install, import, use. The WASM travels with the npm package; Vite, Webpack 5+, Next, esbuild, Rollup, Parcel 2, Bun, Deno resolve it via import.meta.url. No assets to copy. No paths to remember.
33 KB main wasm (baseline) ~50 KB total bundle gzipped (no PDF) 11 supported formats 0 runtime dependencies
HOW IT WORKS — three steps, no server:
01 DROP file enters the sandbox. No network, no disk — the sandbox boundary is the trust boundary. 02 INDEX streaming parsers feed a static BSS pool; zero-copy through a 64 KB scratchpad. 03 SEARCH Bloom skips, Bitap finishes. O(N log k) with a min-heap top-K.
FEATURES
ZERO BACKEND whole engine in a 33 KB WASM binary. No server, no cloud, no network call after load.
FUZZY & TYPO-TOLERANT Bitap (Shift-Or / Wu-Manber) up to 3 edits. Finds "clausula" even when typed "claúsula".
ACCENT-INSENSITIVE Latin-1 + Latin-A fold. ES / FR / DE / IT / PT / PL / CZ / TR transparently.
QUERY DSL phrase, OR, fuzzy, mixed. "a b" | c syntax. Up to 4 tokens per simple query.
11 FORMATS DOCX · XLSX · PDF · HTML · MD · JSON · CSV · EML · RTF · TXT · XML. Heavy ones stream; lite ones (CSV, EML, RTF) handle BOM, base64, cp1252.
ZERO ALLOCATOR static BSS region only. No heap fragmentation, no GC pressure, no OOM mid-search.
ADAPTS TO HOST 3 tier binaries (mini / std / pro) × SIMD variants, auto-picked from deviceMemory and WASM probes.
COOPERATIVE SEARCH searchCooperative() with frameBudgetMs yields to scheduler.yield() between slices — UI thread keeps a chance to paint.
WORKER POOL AlbexPool shards documents across N workers. Map-reduce search merges global top-K.
WEBGPU PRE-FILTER WGSL compute shader runs Bloom in parallel for large corpora. Experimental; opt-in via gpu auto-select.
TIERED STORAGE TieredStore evicts cold docs to OPFS, promotes on demand. Search archives that exceed RAM.
SNAPSHOT V2 per-document content hash persisted. Save the index to OPFS in milliseconds; reload across sessions with dedup intact.
OCR COMPANION @albex/ocr drops Tesseract.js next to the engine. Six languages auto-loaded by demand. Scanned PDFs become searchable.
HYBRID PDF MODE opt-in: native PDFs get their embedded images OCR'd too. For contracts with scanned signatures, reports with screenshots.
THE OCR COMPANION @albex/ocr wraps Tesseract.js as an opt-in companion. Core stays 33 KB — Tesseract loads only on the first enableOcr(engine) call, then cached forever in IndexedDB. Still 100% local, zero backend.
33 KB core stays untouched +3.5 MB Tesseract.js (lazy on first OCR call) ~1.8 MB per language model (cached forever) 0 bytes initial cost until enableOcr() fires
NEW IN V0.3.0 (2026-05-30)
SCANNED-PDF OCR image-only PDFs searchable through optional @albex/ocr. Tesseract.js, lazy by language, zero cost when not enabled.
HYBRID PDF MODE opt-in alwaysExtractEmbeddedImages OCRs embedded images on top of vector text.
SNAPSHOT V2 per-document content hash now persisted. Dedup survives the save/load round trip. v1 snapshots still load.
PARSER-CRASH RECOVERY when pdf-extract traps on an unusual PDF, engine falls back to lopdf-only image extraction — many "unsupported" PDFs become searchable through OCR.
HARDENED LITE PARSERS CSV strips the UTF-8 BOM. EML decodes base64 + quoted-printable, walks nested multipart. RTF reads \'XX (cp1252) and \uN ? Unicode escapes.
HONEST RENAME searchStream → searchCooperative. Old name implied incremental streaming the method never did. DEPRECATED ALIAS KEPT UNTIL 0.4.0.
NEXT P121 INSTALL & QUICK START
ALBEX · P121
RUST · WASM · WEBGPU · 33 KB CORE 2/10 — INSTALL & QUICK START
INSTALL No backend required. No data leaves the browser. One package, one command:
npm install albex # or pnpm add albex # or deno add npm:albex
Optional OCR companion for scanned PDFs and images:
npm i albex @albex/ocr
NPM ENTRY POINTS Multiple subpath exports so each feature tree-shakes independently. Import only what you use.
IMPORT PATH SURFACE PULLS IN
albex AlbexEngine, types, errors, main engine, profile detector,
profile helpers persistence, resource manager
albex/worker AlbexEngineWorker main-thread wrapper proxying
to a Worker
albex/worker-runtime (runs inside a Worker) worker-side handler; reference
via new URL(...,import.meta.url)
albex/pool AlbexPool, AlbexPoolOptions pool coordinator orchestrating
N worker shards
albex/tiered TieredStore, TieredStoreOptions hot/warm tier manager, OPFS
persistence of original blobs
albex/gpu BloomGpu, packBloomsFromChunks standalone WebGPU runtime +
WGSL shader for Bloom scan
NOTE Six WASM variants of the main engine ship (3 capacity tiers × baseline/SIMD) plus a lazy 1 MB PDF module. The zero-config path loads the std-baseline binary — works on every device. For runtime tier auto-selection (mini/std/pro picked from navigator.deviceMemory) serve the variants yourself and pass wasmBaseUrl. Rationale on P126.
QUICK START Up in under a minute:
import { AlbexEngine } from "albex";
const engine = await AlbexEngine.create();
for (const file of input.files) {
await engine.indexFile(file);
}
const hits = engine.search('"clausula novena" | rescisión');
for (const h of hits) {
console.log(h.documentName, h.location, h.score, h.snippet);
}
Or the explicit constructor form, with options:
import { AlbexEngine } from "albex";
const engine = new AlbexEngine();
await engine.init();
await engine.indexFile(myFile);
const results = engine.search('contrato', { windowed: true });
NEXT P122 QUERY SYNTAX & FORMATS
ALBEX · P122
RUST · WASM · WEBGPU · 33 KB CORE 3/10 — QUERY SYNTAX & FORMATS
QUERY SYNTAX One small query language, five patterns:
PATTERN MEANING EXAMPLE MATCH
word single fuzzy token — up to 3 character the WORD in context
edits (auto-adjusted by query length)
a b c AND: all tokens must appear in the same found A then B then C
chunk (proximity scored)
"a b" phrase: tokens in order and adjacent matched A B exactly
a | b OR: union of two independent searches, has A or B
merged by score
"a b" | c mix phrase and OR has A B or just C
SUPPORTED FORMATS Eleven formats, each parser tuned to how the format behaves in the wild:
EXT FORMAT HOW IT IS PARSED
.docx Word document native Rust/WASM streaming XML parser
(word/document.xml); paragraph + table
extraction
.xlsx Excel workbook native Rust/WASM parser; shared strings +
inline strings streaming
.pdf PDF document separate albex_pdf.wasm module (~1 MB),
loaded lazily on the first PDF
.html .htm HTML <script>/<style> stripped, paragraphs at
block-level boundaries
.md .markdown Markdown CommonMark markers stripped (code fences,
headings, links, lists)
.json JSON recursive walk; every string key and leaf
indexed
.csv CSV RFC 4180 lite; one row per chunk
(location = row number)
.eml Email (MIME) MIME-lite: From/To/Subject + first
text/plain body part
.rtf RTF control words and groups stripped, text
runs preserved
.txt Plain text direct UTF-8 pass-through, split on
double newlines
.xml XML tag-stripped, entity-decoded
NEXT P123 API 1/2 — ENGINE CORE
ALBEX · P123
API REFERENCE · ENGINE CORE 4/10 — API 1/2
CONSTRUCTOR new AlbexEngine(opts: AlbexOptions) → AlbexEngine — constructs the engine. WASM NOT loaded yet — call init() before any other method. Constructor only validates and stores options.
import { AlbexEngine } from "albex";
// Zero config — the WASM binary ships with the package and your bundler
// (Vite, Webpack 5+, Next, esbuild, Rollup, Parcel 2, Bun) resolves it
// automatically through `import.meta.url`.
const engine = new AlbexEngine();
await engine.init();
// Optional overrides — only for tier auto-selection or a CDN.
// new AlbexEngine({ wasmBaseUrl: "/assets" }) // serve 6 variants yourself
// new AlbexEngine({ wasmUrl: "https://cdn.example.com/albex_wasm.wasm" })
ALBEXOPTIONS
FIELD TYPE DESCRIPTION
wasmUrl? string explicit URL to the .wasm binary;
overrides every other option (CDN)
wasmBaseUrl? string base dir with tier variants
albex_wasm_<tier>[_simd].wasm;
required only for runtime tier
auto-select; else bundled
std-baseline is used
pdfWasmUrl? string override for albex_pdf.wasm;
default bundled, lazy on 1st PDF
tier? 'auto'|'mini'|'std'|'pro' capacity tier; 'auto' requires
wasmBaseUrl (bundler cannot know
which of 6 binaries to copy);
without it std is loaded
simd? 'auto'|'on'|'off' SIMD variant policy; effective
only when wasmBaseUrl is set
gpu? 'auto'|'on'|'off' WebGPU pre-filter policy; default
auto (on when corpus > threshold)
gpuThreshold? number min chunk count to engage WebGPU;
default 20 000
LIFECYCLE
engine.init() → Promise<void> — resolves the WASM URL (wasmUrl or wasmBaseUrl + tier auto-detect), fetches, instantiates, runs setup, subscribes to the global ResourceManager. THROWS AlbexInitError on fetch or instantiation failure.
engine.reset() → void — clears every indexed document and result; ready for a fresh corpus immediately. WASM instance preserved (no re-fetch).
engine[Symbol.dispose]() → void — TC39 explicit-resource-management hook. Resets state, unsubscribes from the resource manager, destroys the GPU device, nulls internals so the WASM instance becomes unreachable for GC. Use using engine = new AlbexEngine(...) when available.
await engine.init(); // engine.tier → 'mini' | 'std' | 'pro' // engine.simdEnabled → boolean // engine.gpuEngaged → true after the first search that uses WebGPU
INDEXING engine.indexFile(file: File) → Promise<IndexedDocument> — detects format from extension, parses, streams text into the WASM index. Content hashed (FNV-1a 64-bit) first — same hash already indexed = previous entry returned, no work done. THROWS AlbexUnsupportedFormatError or AlbexParseError. Supported: .docx .xlsx .pdf .md .html/.htm .json .csv .eml .rtf .txt .xml.
const input = document.querySelector('input[type=file]');
input.addEventListener('change', async () => {
for (const file of input.files) {
const doc = await engine.indexFile(file);
console.log(`${file.name}: ${doc.chunks} chunks, hash=${doc.contentHash}`);
}
});
// Idempotent: re-indexing the same file is a no-op and returns the
// existing IndexedDocument (matched by FNV-1a content hash).
INDEXEDDOCUMENT
FIELD TYPE DESCRIPTION name string original file name from the File object ext string lowercase extension, no leading dot chunks number chunks produced from this document indexTimeMs number wall-clock time spent indexing textBytes number bytes of indexed text contributed docId number stable id within the engine; survives compact() contentHash string 64-bit FNV-1a hex hash of source bytes; dedup key
SEARCH engine.search(query, opts?) → SearchResult[] — synchronous full-corpus search. Pipeline: parse query → Bloom → Bitap fuzzy → rich scoring → min-heap top-K. Returned sorted by score, descending. May invoke the WebGPU pre-filter automatically when opts.gpu permits and chunks exceed gpuThreshold.
engine.searchCooperative(query, opts?) → AsyncIterable<SearchResult> — cooperative variant. Corpus processed in slices; between slices yields to the browser scheduler via scheduler.yield() (requestAnimationFrame fallback). UI stays responsive during 50 ms+ scans. Use in any interactive search box.
// Synchronous fast path — best for small corpora and tests.
const results = engine.search('"contrato marco" | rescisión',
{ windowed: true });
for (const r of results) {
console.log(`[${r.score}] ${r.documentName} · ${r.snippet}`);
}
// Cooperative streaming search — main thread stays at 60 fps.
for await (const r of engine.searchCooperative('contrato',
{ frameBudgetMs: 8 })) {
renderResult(r); // render incrementally as results arrive
}
SEARCHOPTIONS
FIELD TYPE DESCRIPTION
windowed? boolean cropped snippets with ASCII ellipsis markers
instead of full chunks
before? number bytes of context before the match (default 60)
after? number bytes of context after the match (default 120)
frameBudgetMs? number slice duration for searchCooperative() before
yielding to the scheduler; default 8 ms
SEARCHRESULT
FIELD TYPE DESCRIPTION
documentName string file name as registered by indexFile()
location number paragraph index (DOCX/TXT/MD/…) or page
number (PDF, 1-based)
score number composite relevance 0–1000 (higher = better)
snippet string chunk text, optionally windowed with
"... " / " ..." sentinels
matchStart number byte offset of primary token start in snippet
matchEnd number byte offset of primary token end (exclusive)
matches MatchSpan[] all matched token spans in query order (1–4)
NEXT P124 API 2/2 — HELPERS & ERRORS
ALBEX · P124
API REFERENCE · HELPERS & ERRORS 5/10 — API 2/2
TUNING KNOBS
engine.setMaxErrors(n: 0|1|2|3) — max edit distance for fuzzy match. 0 = exact only. Engine auto-shrinks for short queries.
engine.setThreshold(n) — minimum score (0–1000) below which results are dropped. Default 250.
engine.setMaxResults(n) — cap on returned results. 1–200. Default 50.
engine.setLanguage('off'|'es') — light Spanish stemming on query tokens. Indexed text is never stemmed, so snippets stay faithful. setLanguage('es') makes "contratos" match "contrato".
INCREMENTAL — REMOVE · REPLACE · COMPACT Indexing is idempotent (content-hash dedup automatic). Documents remove individually without rebuilding the index; reclaim storage on demand with compact().
engine.removeDocument(idOrName) → boolean — tombstone a document; searches skip its chunks; storage reclaimed by compact(). Accepts file name or contentHash from indexFile.
engine.replaceDocument(name, newFile) → Promise<IndexedDocument> — atomic remove + re-index. Bypasses dedup so re-indexing the same bytes after a remove works.
engine.compact() → void — reclaim storage from tombstones. Rewrites internal arrays in place; survivor doc IDs preserved.
engine.removeDocument('contract-2024-03.pdf');
engine.removeDocument(doc.contentHash); // by hash also works
engine.removeDocument('old.pdf');
engine.compact(); // bytes freed; next indexFile sees real headroom
PERSISTENCE — SNAPSHOTS ON OPFS & INDEXEDDB Full engine state serialises to a binary blob and restores later. OPFS preferred (zero-copy writes); IndexedDB universal fallback. A 16 MB snapshot typically restores in tens of ms — far faster than re-parsing the documents.
engine.save(name) → Promise<void> — serialise index to a binary snapshot in OPFS (preferred) or IndexedDB.
engine.load(name) → Promise<boolean> — restore a saved snapshot. True on success, false if missing or header mismatched.
engine.loadOrInit(name) → Promise<boolean> — load if it exists, otherwise reset() and start clean.
engine.deleteSnapshot(name) — remove a snapshot from storage. engine.listSnapshots() → Promise<string[]> — names of all snapshots saved in the current origin.
if (!await engine.load('my-corpus')) {
console.log('No snapshot yet; starting fresh');
}
INTROSPECTION engine.getStats() → EngineStats — document count, chunk count, memory usage, loaded tier, capacity caps. engine.getLastSearchStats() → SearchStats | null — Bloom/Bitap pipeline counters from the most recent search; debug relevance, detect performance regressions.
ENGINESTATS documents number active (non-tombstoned) docs chunks number total indexed chunks textUsed number bytes of indexed text stored textCapacity number max text bytes for loaded tier wasmMemoryBytes number WASM linear memory (BSS+grown) tier 'mini'|'std'|'pro'|null loaded tier; null before init() maxChunks number compile-time chunk capacity maxDocs number compile-time doc capacity SEARCHSTATS query string verbatim query string timeMs number end-to-end search time results number results above threshold bloomTested number chunks tested against Bloom bloomPassed number chunks passing Bloom (subset) bitapMatched number chunks confirmed by Bitap
ADAPTIVE RUNTIME — PURE HELPERS Exported from the same package: read the device profile, override tier selection, build UI around resource state. None require an initialised engine.
detectProfile(opts?: { fresh? }) → Promise<DeviceProfile> — probe host capabilities (cores, memory, WASM features, WebGPU, storage budget, network, battery). Cached in sessionStorage; fresh: true bypasses.
pickTier(profile) → 'mini'|'std'|'pro' — pure heuristic: <=1 GB → mini, 2–4 GB → std, >=8 GB → pro, null (Safari) → std.
pickWorkerCount(profile) → number — cores/2 clamped [1, 8]. Falls to 1 when battery is below 20% and discharging.
shouldUseGpu(profile, chunkCount, threshold?) — true when WebGPU available AND chunk count crosses the threshold (default 20 000).
import { detectProfile } from 'albex';
const profile = await detectProfile();
console.log(profile.memoryGB, profile.wasm.simd);
const tier = pickTier(profile);
const workers = pickWorkerCount(profile);
if (shouldUseGpu(profile, engine.getStats().chunks)) { /* … */ }
DEVICEPROFILE
cores number navigator.hardwareConcurrency
memoryGB number | null navigator.deviceMemory (spec-capped at
8 GB; null on Safari)
wasm.simd boolean v128 supported (validated via probe)
wasm.bulkMemory boolean bulk memory ops supported
wasm.threads boolean threads AND page cross-origin isolated
webgpu boolean navigator.gpu present
coopCoep boolean crossOriginIsolated === true
storage {quotaBytes, navigator.storage.estimate() result
usageBytes}
net {effectiveType, connection info if reported (Chrome
saveData} only)
battery {level, battery state if available
charging}|null
visible boolean document.visibilityState at probe time
OFF-MAIN-THREAD new AlbexEngineWorker(opts) — drop-in replacement for AlbexEngine that runs the whole engine inside a Web Worker. Identical surface; every method returns a Promise. Files transferred via postMessage with transferable ArrayBuffers — no copy. Import from albex/worker; runtime script exported as albex/worker-runtime, referenced via new URL(..., import.meta.url).
PARALLELISATION new AlbexPool(opts) — orchestrates N worker shards. Documents sharded round-robin; searches broadcast to every shard and the coordinator merges top-K preserving global descending score order. Default workers = hardwareConcurrency/2 clamped [1, 8]; battery-aware (drops to 1 on low battery). Import from albex/pool.
LARGE CORPORA new TieredStore(engine, opts?) — hot/warm tiers behind the engine. At evictThreshold of capacity the LRU document is removed; its original blob remains in OPFS. promote(name) brings it back by re-indexing from the persisted blob. Import from albex/tiered.
GPU (ADVANCED) new BloomGpu() — standalone WebGPU Bloom-scan accelerator. AlbexEngine instantiates one automatically when opts.gpu permits and corpus exceeds gpuThreshold (default 20 000 chunks). Touch directly only to integrate the WGSL shader into a non-Albex pipeline. Import from albex/gpu. (Worker / pool / tiered code recipes: P129.)
TYPED ERROR HIERARCHY Every error thrown by Albex extends AlbexError. Switch on the kind discriminator (survives structuredClone across worker boundaries) or use instanceof against the subclasses.
CLASS KIND THROWN WHEN
AlbexError (base) base class; carries kind
AlbexInitError init WASM fetch failed, init()
not called, or PDF module
not initialised
AlbexUnsupportedFormatError unsupported_format extension not supported;
carries ext field
AlbexParseError parse a parser (DOCX/XLSX/PDF/
JSON/…) failed; carries
format field
AlbexCapacityError capacity scratchpad write would
exceed buffer, or hard
cap reached
import {
AlbexError, AlbexInitError, AlbexParseError,
AlbexUnsupportedFormatError, AlbexCapacityError,
} from 'albex';
try {
await engine.indexFile(file);
} catch (e) {
if (e instanceof AlbexUnsupportedFormatError) {
console.warn(`Unsupported: .${e.ext}`);
} else if (e instanceof AlbexParseError) {
console.warn(`Failed to parse ${e.format}:`, e.message);
} else if (e instanceof AlbexCapacityError) {
console.warn('Engine full — upgrade tier or use TieredStore');
} else throw e;
}
NEXT P125 ARCHITECTURE 1/2
ALBEX · P125
ARCHITECTURE · ALGORITHMS 6/10 — ARCHITECTURE 1/2
A full-text search engine that lives ENTIRELY in the user's browser. That sentence is the thesis; every other choice — the algorithms, the memory model, the absence of an allocator — is a corollary of it. Four axioms:
1 ZERO SERVER no back-end ever; documents never transit the network. 2 HUMAN LATENCY answer within ~100 ms — the perception threshold for "instant". 3 REDUCED FOOTPRINT bytes shipped to the browser are a tax on every page load. 4 ADAPTS TO THE MACHINE same package on a Chromebook and a Mac Pro, picking what each can support.
LAYER MAP Six layers; dependencies flow strictly upward — a layer never calls down into the one below. That lets the algorithm layer be tested without WASM and the adaptive layer evolve without touching Rust.
0 PURE ALGORITHMS Rust no_std · core/ Bloom filter, Bitap matcher,
light Spanish stemmer, Unicode
fold tables. No heap, no
allocator, no I/O; testable on
host without WASM.
1 STREAMING PARSERS Rust no_std · ingest/ XML byte state machines for
DOCX and XLSX. Same rules as
layer 0. New Rust formats here.
2 WASM SHELL Rust · wasm/ BSS arrays, C ABI exports,
EngineState bookkeeping,
resumable search, tier flags.
3 PDF MODULE Rust + std · pdf-wasm/ pdf-extract wrapper; own ~1 MB
binary, lazy on first PDF —
never paid by non-PDF users.
4 TS ORCHESTRATOR TS · src/ AlbexEngine API, TS format
indexers, query parser, typed
errors, persistence, hashing.
5 ADAPTIVE TS · src/{profile, profile detection, resource
resource-manager, awareness, worker pool, WebGPU
pool/,gpu/, runtime, tiered storage.
tiered-store}.ts Strictly opt-in.
SEARCH PIPELINE — from query to ranked results in four stages:
01 PARSE QUERY detect simple/phrase/OR; up to 4 tokens; < 5 µs
optional stemming
02 BLOOM FILTER AND + compare per chunk vs pattern Bloom 2 instr/chunk
03 BITAP FUZZY bit-parallel match on Bloom survivors ~10 ns/chunk
04 SCORE + TOPK accuracy + WB + TF + position + proximity O(log K)
+ IDF into a min-heap
The cost ladder is deliberate: cheapest filter first on every chunk; expensive ones only on what survives. A typical search tests 100 000 chunks but runs Bitap on under 1% of them. Bloom is the gatekeeper keeping total latency under 10 ms even on big corpora.
The pipeline is RESUMABLE: searchBegin sets up token state, searchSlice(N) processes N chunks and returns done=0/1 — that lets searchCooperative yield to the scheduler between slices (frame budgeting) without blocking a paint. It honours an optional GPU candidate mask and a tombstone bitset, then funnels survivors into scoring and the top-K heap.
BLOOM FILTER — 64-BIT PROBABILISTIC GATE Answers "could the pattern be here?" in two machine instructions: an AND and a compare. One u64 per chunk; each character maps to one of 64 buckets by c & 0x3F. A chunk passes iff (chunkBloom & queryBloom) == queryBloom.
The hash is intentionally trivial: a cryptographic hash would distribute better but cost 10–100× more CPU, and the Bloom runs on every chunk — sometimes 100 000 times per query — so raw speed wins. For typical European text it rejects 80–95% of chunks before Bitap. False positives are unavoidable but harmless: Bitap discards them at no extra cost.
NOTE The character Bloom is only sound for exact tokens — a fuzzy substitution can introduce a character absent from the chunk. Since 0.6.0 it applies only when a token matches with zero errors; a 256-bit trigram q-gram signature carries the fuzzy case. Trigrams are far rarer than characters, so the trigram filter shrinks the Bitap candidate set ~10× on prose, and stays sound under fuzzy matching via the q-gram lemma (an occurrence with e errors keeps ≥ N − 3e of a token's exact trigrams). Signatures are rebuilt from the text pool after compaction and snapshot restore.
BITAP — BIT-PARALLEL FUZZY MATCH Shift-Or / Wu-Manber keeps the matching state inside a single u64 register. Each text byte advances it with a shift + OR + AND — no backtracking, no per-character allocations, no branches dependent on the data.
Extended to k errors (substitution, insertion, deletion) with k+1 parallel registers. k is capped at 3: more produces noise on short queries and multiplies the linear cost. Pattern length capped at 64 bytes — the register width; longer queries are truncated with a truncated flag the host can surface in its UI. The error budget is adaptive: tokens of ≤5 characters match exactly, ≤8 allow 1 error, longer allow up to 3.
SCORING AND TOP-K Each survivor gets a capped sum (0–1000) of components: base accuracy (−200 per average edit), word-boundary bonus, term frequency, document position, phrase proximity (close + in order), and an approximate IDF from per-Bloom-bit document frequency. Weights are hand-tuned.
Results stream into a fixed-size min-heap: insert is O(log K); when full a new hit only displaces the current worst. A final heap-pop yields the K best in descending order — O(k log k) versus the O(k²) selection sort an earlier version used (27× FASTER than insertion sort in practice).
NEXT P126 ARCHITECTURE 2/2
ALBEX · P126
ARCHITECTURE · RUNTIME 7/10 — ARCHITECTURE 2/2
ACCENT FOLDING & STEMMING fold_utf8_char maps each code point to a lowercase ASCII base: Latin-1 Supplement and Latin Extended-A (Polish, Czech, Turkish…) collapse to a/c/e/n/o/s/u/z. Accented search is free — acción and accion fold to the same bytes. The same fold runs at index and query time.
The optional Spanish stemmer is a handful of high-impact Snowball-Spanish suffix rules, inlined (~150 bytes vs the full 6 KB state machine). Applied only to query tokens, never to indexed text — snippets stay faithful; recall improves because the query reduces to a shared prefix that still matches the original word.
MEMORY MODEL — BSS-ONLY, NO ALLOCATOR The default wasm32-unknown-unknown target has no OS to call for memory. Pulling in std would drag in dlmalloc or wee_alloc, inflate the binary, add allocation overhead, and introduce a failure mode (OOM) hard to surface cleanly to JavaScript.
Instead every storage region is a zero-initialised static array — TEXT_POOL (16 MB), CHUNKS, NAME_POOL, CHUNK_SIG. The BSS segment is described in the binary but not stored, so a .wasm with 16 MB of static arrays still weighs ~33 KB on disk. Linear memory grows once at instantiation (~20 MB for std tier) — no per-call allocation, no fragmentation, no OOM during a search.
TRADE-OFF capacity is a fixed ceiling; exceeding it is a silent stop, not an error. Acceptable because the user already chose a tier — "give me more space" would mean an allocator, which would mean fragmentation and unpredictable latency.
THE JS ↔ WASM BRIDGE The entire data plane is one 64 KB shared byte buffer — the scratchpad. The dance is always: getBuffer(size) → a pointer; JS writes bytes there; JS calls the action (setPattern(len), feedXmlBytes(len), search()); WASM reads the scratchpad, processes, stores results; for output it writes back and JS reads it. No JSON across the boundary — just offsets and lengths. That keeps boundary cost near zero.
At init() the exports contract is validated before the engine returns: memory is a real WebAssembly.Memory, every required export exists, the ABI version matches. A mismatched binary FAILS LOUDLY HERE instead of crashing later inside indexFile.
ADAPTIVE RUNTIME — SAME PACKAGE, DIFFERENT MACHINES Six variants of the main WASM binary (three capacity tiers, each with and without SIMD) plus the lazy PDF module. The browser fetches ONE variant per init() call, picked from the device profile.
TIER DOCS CHUNKS TEXT RULE (deviceMemory) WORKING SET BINARY mini 32 25 000 4 MB <= 1 GB ~5 MB 33 KB std 128 100 000 16 MB 2–7 GB ~20 MB 33 KB pro 1 024 800 000 128 MB >= 8 GB ~160 MB 33 KB
SIMD sits on top of every tier: the runtime probes WebAssembly v128 by attempting to validate a tiny module that uses it. If it validates, the engine fetches albex_wasm_<tier>_simd.wasm — built with -C target-feature=+simd128. The Bloom batch inner loop is branchless precisely so LLVM can auto-vectorise it.
WHY 8 GB IS A CEILING the W3C navigator.deviceMemory spec caps reported memory at 8 GB for privacy — a Mac Pro with 192 GB reports 8 just like an iPad with 16 GB; they cannot be distinguished. The pro tier (128 MB pool) is the largest default because the working set must still fit comfortably in a browser tab — Chrome starts flagging pages as "high memory" around 500 MB. For genuine archive-scale corpora the right answer is TieredStore (LRU eviction, OPFS-persisted blobs) or AlbexPool sharded across N workers — not a tier with a 1 GB pool.
WEBGPU PRE-FILTER — BLOOM ON THE GPU WHEN IT PAYS OFF The Bloom check on 100 000 chunks is embarrassingly parallel: each test is independent. A WGSL compute shader runs all of them in a single dispatch; the result is a packed bitset of candidates, and the CPU runs Bitap only on those.
Engaging the GPU has a fixed cost (upload + dispatch + readback, ~3–5 ms). Below ~20 000 chunks the CPU wins; above it, the GPU gives a 5–10× speedup. The default threshold is exactly that break-even point, exposed as gpuThreshold for tuning. The candidate mask is SINGLE-SHOT: it applies to the next searchBegin and is automatically cleared at the end of the last slice — stale masks can never silently corrupt an unrelated next search.
PDF AND OCR PATH The PDF module is a SEPARATE WASM binary fetched only on the first PDF — users who never touch PDFs never pay for it. It wraps pdf-extract/lopdf to pull vector text per page. Built with panic=abort, so a malformed PDF traps the instance; the host catches that, discards the poisoned instance, and (if OCR is wired) falls back to extracting embedded page images via lopdf — recovering content pdf-extract could not.
The OCR orchestrator (@albex/ocr, wired via engine.attachOcr(adapter)) manages a pool of Tesseract.js workers and feeds them the JPEG/JPEG2000 image XObjects the PDF module extracts per page. Two modes: scanned-PDF (image-only files) and hybrid (OCR on top of vector text). Best-effort: one failed image never stops the page, one failed page never stops the document. A lightweight script / letter-frequency heuristic guesses the language so the orchestrator loads the right Tesseract traineddata — picking the wrong model is the single biggest hit to OCR accuracy. Tesseract itself (~3.5 MB, LSTM engine compiled to WASM) loads lazily via await import("tesseract.js") only when enableOcr() has been called and a scanned image needs recognising.
RUST + TYPESCRIPT — WHEN EACH WINS The rule is not "Rust for important things". It is: which primitive of the host environment wins for this operation?
TASK LANG WHY
DOCX parsing Rust streaming XML state machine; files reach
hundreds of MB, loading the DOM infeasible
XLSX parsing Rust same; shared-strings tables get huge in
real-world workbooks
PDF extraction Rust pdf-extract mature and large; worth its
own module loaded lazily
Bloom + Bitap loop Rust bit-parallel arithmetic; WASM v128 wins
clearly over JS bitwise
MD / HTML / RTF TS fit in memory; V8 regex is JIT-compiled
C++ — faster than a hand-written WASM parser
JSON parsing TS JSON.parse is C++ inside the JS engine —
no Rust port can beat it
FNV-1a content hash TS one-shot per file, not a hot path;
JS bitwise is fine at 100 MB/s
WebGPU shader WGSL GPU language; no alternative
OPFS / IndexedDB TS browser APIs; unreachable from WASM
Worker orchestration TS new Worker() is a JS API; postMessage
protocol lives where the workers live
NOTE This condenses what comes up most often. Full pseudocode, layout tables and side-by-side cost comparisons live in the in-repo Maintainer's guide (MAINTAINER.md) and Technical deep-dive (TECHNICAL.md).
NEXT P127 STORAGE & NUMBERS
ALBEX · P127
STORAGE · IDENTITY · CAPACITY 8/10 — STORAGE & NUMBERS
STORAGE & IDENTITY Three primitives keep the engine honest across sessions and repeated uploads: OPFS for snapshots, IndexedDB as fallback, a 64-bit content hash for deduplication. None are configurable surface area — internal contracts worth understanding when reasoning about what the engine actually does.
OPFS & INDEXEDDB — WHY BOTH engine.save("my-corpus") serialises the entire index (chunks, document table, text pool, Bloom filters) to a single binary blob in browser storage. engine.load() reads it back and memcpy's it into the BSS arrays. A 16 MB corpus restores in tens of ms — orders of magnitude faster than re-parsing 50 DOCXs.
API AVAILABLE SINCE WRITE 16MB WHY PICKED
OPFS Chrome 102 · Safari 15.2 · ~20 ms zero-copy
Firefox 111 FileSystemWritable-
FileStream.write(u8)
IndexedDB all browsers since 2015 ~80 ms structured-clone of
the Uint8Array
The router lives in src/persistence.ts and detects navigator.storage.getDirectory at runtime: present → OPFS path, otherwise → IndexedDB. Both satisfy the same contract (savePersisted / loadPersisted / deletePersisted / listPersisted), so calling code is identical.
TWO DISTINCT USES OF OPFS The persistence backend serialises ENGINE STATE: chunks, Bloom filters, doc table. TieredStore uses OPFS differently — it stores the ORIGINAL FILE BLOBS, so an evicted document can be promoted back without asking the user to re-pick the file. Two stores, two purposes, same OPFS API.
FNV-1a 64-BIT — WHY THIS HASH Every indexFile call hashes the raw bytes before any parsing. If a document with that hash already lives in the index, the engine returns the existing entry and skips the work. Makes indexFile idempotent and safe to call repeatedly — drag the same DOCX twice, the index stays clean.
HASH JS THROUGHPUT OUTPUT TRADE-OFF
FNV-1a 64 ~100 MB/s 16 hex chars non-cryptographic; ~10^-15
collision prob at 128 docs
SHA-256 ~30 MB/s 64 hex chars cryptographic; 3× slower, no
value added for this use case
MurmurHash3 ~150 MB/s variable slightly faster; less portable
across language ecosystems
WHY NON-CRYPTO IS FINE No adversarial collision resistance needed. The threat model is "the user accidentally dragged the same file twice", not "an attacker crafts two distinct files that hash the same to confuse the index". Worst case of a deliberate collision: the second file does not get indexed — no path to elevation of privilege, data leak, or denial of service. SHA-256 would cost 3× the throughput for zero added safety; throughput won. Same logic as Git object identification (pre SHA-256 transition — Git's case IS adversarial), SQLite ROWID generation, countless dedup databases.
WHERE THE HASH SURFACES Two places: internally during indexFile (the dedup check) and on the returned IndexedDocument.contentHash. Pass that hash to engine.removeDocument(hash) as a stable identifier that survives rename: if the user uploads "same-contract-renamed.pdf" and you want to remove the earlier "contract.pdf" with the same bytes, the hash matches even though the names don't.
CAPACITY — STD TIER DEFAULTS (mini scales ×0.25 · pro ×8):
16 MB text pool · std (4 MB mini · 128 MB pro) 100k chunk capacity · std (25k mini · 800k pro) 128 docs document limit · std (32 mini · 1 024 pro) 64 chars query length — Bitap u64 register width
PERFORMANCE FIGURES
< 5 ms typical query, full corpus
80–95 % chunks rejected by Bloom before Bitap
27× min-heap top-K vs insertion sort
5–10× GPU speedup above 20 000 chunks
OPERATION COST NOTES
parse query < 5 µs simple/phrase/OR detection,
up to 4 tokens
Bloom test 2 instr / chunk one AND + one compare,
up to 100 000 per query
Bitap match ~10 ns / chunk only Bloom survivors
(< 1 % of corpus typically)
score + top-K O(log K) insert fixed-size min-heap; final
pop O(k log k)
GPU engage overhead ~3–5 ms upload+dispatch+readback;
break-even ~20 000 chunks
snapshot write 16MB OPFS ~20 ms zero-copy stream
snapshot write 16MB IDB ~80 ms structured-clone Uint8Array
content hash (FNV-1a, JS) ~100 MB/s one-shot per file, not hot
snapshot restore 16MB tens of ms memcpy into BSS — orders of
magnitude faster than
re-parsing
PERFORMANCE Search complexity is O(N log k): the Bloom gate is linear over chunks at two instructions each, and only the top-K heap pays the logarithmic factor. Memory layout is fixed at instantiation — TEXT_POOL 16 MB + CHUNKS 3.2 MB + scratchpad + doc names for std, ~20 MB working set total.
NEXT P128 GUIDES 1/2
ALBEX · P128
GUIDES · RECIPES BY USE CASE 9/10 — GUIDES 1/2
From install to advanced setups: cooperative search, worker pool, big-corpus tiering, React / Angular integration. Every recipe starts with npm install albex.
ESM / TYPESCRIPT
import { AlbexEngine } from "albex";
// 1. Construct + init. The WASM ships with the package and your bundler
// resolves it through import.meta.url — Vite, Webpack 5+, Next,
// esbuild, Rollup, Parcel 2, Bun all handle this automatically.
const engine = new AlbexEngine();
await engine.init();
// 2. Index files from drag-and-drop or <input type="file">.
const input = document.querySelector("input[type=file]");
input.addEventListener("change", async () => {
for (const file of input.files) {
const doc = await engine.indexFile(file);
console.log(`Indexed ${doc.name}: ${doc.chunks} chunks`);
}
});
// 3. Search.
const results = engine.search('"contrato marco" | rescisión',
{ windowed: true });
for (const r of results) {
console.log(`[${r.score}] ${r.documentName} — ${r.snippet}`);
}
ADVANCED TUNING
// Tweak relevance.
engine.setMaxErrors(1); // tighter fuzziness
engine.setThreshold(400); // only return strong hits
engine.setMaxResults(100);
engine.setLanguage("es"); // light Spanish stemming on queries
// Inspect what was loaded.
console.log(engine.tier); // 'std' by default
console.log(engine.simdEnabled); // boolean
console.log(engine.gpuEngaged); // true once first search uses WebGPU
// Want tier auto-selection? Serve the 6 binaries yourself and pass
// wasmBaseUrl. The engine then picks mini/std/pro from deviceMemory.
// new AlbexEngine({ wasmBaseUrl: '/assets', tier: 'auto', simd: 'auto' })
COOPERATIVE SEARCH
// Cooperative search — yields to the scheduler between slices so the
// UI thread keeps a chance to paint during long scans. NOTE: results
// arrive in one batch after the search completes; the async iterator
// exists so callers can break early. True incremental streaming is on
// the backlog.
for await (const r of engine.searchCooperative(query,
{ frameBudgetMs: 8 })) {
renderResultCard(r);
if (results.length >= 50) break; // stop early — engine cleans up
}
// Wire it into a search input with debounce.
let abortToken = 0;
input.addEventListener("input", async () => {
const token = ++abortToken;
const query = input.value.trim();
resultsEl.innerHTML = "";
if (!query) return;
for await (const r of engine.searchCooperative(query,
{ frameBudgetMs: 8 })) {
if (token !== abortToken) break; // newer query started — stop
appendResult(r);
}
});
PERSISTENCE
// First visit: index the user's documents normally.
for (const file of files) {
await engine.indexFile(file);
}
// Save a snapshot to OPFS (preferred) or IndexedDB (fallback).
await engine.save("my-corpus");
// On subsequent visits: load instantly instead of re-indexing.
const restored = await engine.loadOrInit("my-corpus");
if (restored) {
console.log(`Restored ${engine.getStats().documents} documents`);
} else {
console.log("No snapshot yet, starting fresh");
}
// List all snapshots in this origin.
const names = await engine.listSnapshots();
// Delete one explicitly.
await engine.deleteSnapshot("old-corpus");
NEXT P129 GUIDES 2/2
ALBEX · P129
GUIDES · WORKERS · FRAMEWORKS 10/10 — GUIDES 2/2
WEB WORKER
// Run the engine in a Web Worker. Same surface, every call returns a
// Promise. Main thread never blocks on indexing or search.
import { AlbexEngineWorker } from "albex/worker";
// Only the worker runtime URL is required so the bundler spawns it.
// The main WASM is resolved automatically inside the worker.
const engine = new AlbexEngineWorker({
workerUrl: new URL("albex/worker-runtime", import.meta.url),
});
await engine.init();
await engine.indexFile(file);
const results = await engine.search("contrato", { windowed: true });
// Streaming search works through the Worker too.
for await (const r of engine.searchCooperative("contrato",
{ frameBudgetMs: 8 })) {
render(r);
}
// Dispose when done (TC39 Symbol.dispose).
engine[Symbol.dispose]();
WORKER POOL
// AlbexPool shards documents across N workers. Searches map-reduce.
// Best for large corpora on multi-core machines.
import { AlbexPool } from "albex/pool";
const pool = new AlbexPool({
workerUrl: new URL("albex/worker-runtime", import.meta.url),
workers: "auto", // = cores/2, clamped [1, 8]
});
await pool.init();
console.log("Shards:", pool.workerCount);
// Documents are sharded round-robin.
for (const file of files) {
await pool.indexFile(file);
}
// Search broadcasts to every shard; coordinator merges top-K.
const results = await pool.search("contrato", { windowed: true });
// Aggregate stats across shards.
const stats = await pool.getStats();
console.log(`${stats.documents} docs across ${pool.workerCount} shards`);
// Remove documents — the pool finds the shard that owns it.
await pool.removeDocument("old-contract.docx");
// Compact every shard at once.
await pool.compact();
// Stream results from a pooled search.
for await (const r of pool.searchCooperative("contrato")) {
render(r);
}
BIG CORPORA — TIEREDSTORE
// TieredStore keeps hot documents in the engine and warm ones in
// OPFS. When the engine fills past evictThreshold, the LRU document
// is removed from RAM but its original blob stays persisted.
import { AlbexEngine, TieredStore } from "albex";
const engine = new AlbexEngine();
await engine.init();
const store = new TieredStore(engine, {
evictThreshold: 0.85, // start evicting when 85 % full
hotFloor: 4, // never evict the last 4 docs
});
await store.init();
for (const file of files) {
await store.indexFile(file); // persists blob + adds to engine
}
// Bring a warm document back into the engine on demand.
const promoted = await store.promote("old-contract.pdf");
if (promoted) {
engine.search("clausula"); // searchable in the engine again
}
// Inspect the tier balance.
const stats = store.getTierStats();
console.log(`${stats.hot} hot · ${stats.warm} warm · ${stats.totalBytes} bytes`);
// Forget a document entirely (engine + OPFS).
await store.forget("expired.pdf");
REACT
import { useEffect, useRef, useState } from "react";
import { AlbexEngine } from "albex";
export function DocSearch() {
const engineRef = useRef(null);
const [hits, setHits] = useState([]);
const [ready, setReady] = useState(false);
useEffect(() => {
const e = new AlbexEngine();
e.init().then(() => {
engineRef.current = e;
setReady(true);
});
return () => engineRef.current?.[Symbol.dispose]();
}, []);
async function handleFile(ev) {
for (const file of ev.target.files) {
await engineRef.current.indexFile(file);
}
}
function handleSearch(q) {
setHits(engineRef.current?.search(q, { windowed: true }) ?? []);
}
if (!ready) return <p>Loading…</p>;
return (
<div>
<input type="file" multiple onChange={handleFile} />
<input onChange={e => handleSearch(e.target.value)} />
{hits.map(h => (
<div key={h.documentName + h.location}>
<b>{h.documentName}</b> — {h.snippet}
</div>
))}
</div>
);
}
ANGULAR
import { Component, OnInit, OnDestroy } from "@angular/core";
import { AlbexEngine } from "albex";
@Component({
selector: "app-search",
template: `
<input type="file" multiple (change)="onFiles($event)" />
<input (input)="onQuery($event)" placeholder="Search…" />
<div *ngFor="let h of hits">
<b>{{ h.documentName }}</b> — {{ h.snippet }}
</div>
`,
})
export class SearchComponent implements OnInit, OnDestroy {
private engine!: AlbexEngine;
hits: any[] = [];
async ngOnInit() {
this.engine = new AlbexEngine();
await this.engine.init();
}
ngOnDestroy() {
this.engine?.[Symbol.dispose]();
}
async onFiles(event: Event) {
const files = (event.target as HTMLInputElement).files!;
for (const file of Array.from(files)) {
await this.engine.indexFile(file);
}
}
onQuery(event: Event) {
const q = (event.target as HTMLInputElement).value;
this.hits = this.engine.search(q, { windowed: true });
}
}
FULL MANUAL · GITHUB · © 2026 · hand-built, no trackers
NEXT P100 INDEX
VECTIS-CRDT · P130
RUST · WASM · RGA/YATA · VECTOR CLOCKS 1/10 — INDEX
Conflict-free collaboration, shipped as 16 KB of WebAssembly. An RGA/YATA ordered sequence with per-property LWW registers — deterministic convergence, binary wire format, delta sync via vector clocks. Built for collaborative vector canvases and rich ordered collections.
MIT · v0.2.0 · OPEN SOURCE
P131 OVERVIEW — stats + six features
P132 HOW IT WORKS — 3 steps, runtime topology
P133 HOW IT WORKS — merge stages, CRDT ideas
P134 INSTALL + QUICK START
P135 API — operations table, create/insert/delete
P136 API — setProp, delta, apply
P137 DELTA SYNC & WIRE FORMAT
P138 GUIDES — two-peer offline, WebSocket
P139 GUIDES — React · CAPACITY & LIMITS
LINKS FULL MANUAL · GITHUB · LIVE IN SYNCINK
NEXT P131 OVERVIEW
VECTIS-CRDT · P131
RUST · WASM · RGA/YATA · VECTOR CLOCKS 2/10 — OVERVIEW
vectis-crdt is an open-source (MIT, v0.2.0) conflict-free replicated data type for the browser. RGA/YATA ordered sequence with per-object LWW registers, compiled from Rust to WebAssembly (wasm32-unknown-unknown). Every peer edits locally without coordination; deltas travel as compact binary patches; a deterministic merge engine guarantees all peers converge to the same state — regardless of arrival order.
CONFLICT-FREE BY CONSTRUCTION. ZERO BACKEND FOR MERGE LOGIC.
STATS
16 KB WASM binary 0 merge conflicts O(1) convergence per op Δ SYNC delta-only wire format
BUILT FOR COLLABORATIVE EDGES
ZERO CONFLICTS — deterministic merge. Two peers always reach the same state, regardless of op order. CRDT · deterministic
DELTA SYNC — only send what changed. Binary wire format keeps bandwidth minimal. delta · binary
OFFLINE-FIRST — work offline, sync when reconnected. Vector clocks track causality. offline · vector clocks
RICH ATTRIBUTES — per-object LWW registers for color, width, opacity, transform — any mutable property. LWW · properties
WEBASSEMBLY — runs in the browser at near-native speed. 16 KB binary, no WASM runtime needed. wasm · no_std
ORDERED SEQUENCE — RGA/YATA concurrent insert converges without coordination. RGA · YATA
NEXT P132 HOW IT WORKS
VECTIS-CRDT · P132
RUST · WASM · RGA/YATA · VECTOR CLOCKS 3/10 — HOW IT WORKS 1/2
Three steps, no conflicts:
1 EDIT — produce an operation locally. insert(pos, obj) → RGA/YATA: a local op, no coordination with other peers.
2 SYNC — exchange deltas with peers. doc.delta() → binary bytes: only the changed ops travel, keeping the wire minimal.
3 CONVERGE — deterministic merge, no conflicts. peer.apply(delta) → same state: the vector clock enforces causal order.
RUNTIME TOPOLOGY — every edit flows through seven stages, in one pass. 7 NODES, 6 EDGES, O(1) CONVERGENCE
Operation → RGA/YATA → LWW Reg → Vector Clock
→ Delta Sync → Merge Engine → State
## NODE ROLE DETAIL
01 OPERATION insert · delete Local op produced without coordination
02 RGA/YATA sequence CRDT Replicated Growable Array, YATA variant
03 LWW REG color·width·opacity Last-Write-Wins per-property register
04 VECTOR CLOCK causality Tracks causal ordering across peers
05 DELTA SYNC binary diff Binary-encoded minimal changeset
06 MERGE ENGINE convergence Deterministic CRDT merge algorithm
07 STATE consistent view Final consistent document state
NEXT P133 MERGE STAGES + CRDT IDEAS
VECTIS-CRDT · P133
RUST · WASM · RGA/YATA · VECTOR CLOCKS 4/10 — HOW IT WORKS 2/2
THE FOUR MERGE STAGES — four stages, one pass, each with a bounded cost:
STAGE STRUCTURE COST WHAT IT DOES
01 RGA/YATA ordered sequence O(1) Ordered sequence insert. Concurrent
inserts at same position converge
deterministically, no coordination.
02 LWW REGISTER per-property reg O(1) Last-write-wins properties.
Concurrent writes to same property
resolved by timestamp.
03 VECTOR CLOCK per-peer counters O(peers) Causal ordering. Each peer's clock
entry tracks what it has seen, so
causally-related ops apply in order.
04 DELTA MERGE binary patch O(Δ) Binary patch apply. A delta carries
only its ins/del/prop/ack ops.
RGA/YATA — the ordered sequence. Document body is a Replicated Growable Array, YATA variant. Inserts and deletes append to a log; deleted items are TOMBSTONED in the RGA log rather than removed — this guarantees convergence when a delete races a concurrent op on the same element.
LWW REGISTERS — mutable properties. Each object carries per-property Last-Write-Wins registers (color, width, opacity, transform — any mutable property). Concurrent writes to the same property: the later timestamp wins on every peer, deterministically.
VECTOR CLOCKS — causality. A vector clock tracks causal ordering of ops across peers. Lets a peer work offline and, on reconnect, exchange exactly the ops the other side has not seen. Clock comparison costs O(peers).
CONVERGENCE — the merge engine is deterministic: given the same set of ops, in any delivery order, every replica computes the identical final state. No conflict-resolution callback, no server-side arbitration — zero backend for merge logic.
PERFORMANCE Engine memory is dominated by the append-only RGA log (~16 KB budget), with a smaller LWW table (~4 KB), the vector clock, and a delta buffer. The op log is append-only, so operations are unbounded.
NEXT P134 INSTALL + QUICK START
VECTIS-CRDT · P134
RUST · WASM · RGA/YATA · VECTOR CLOCKS 5/10 — INSTALL + QUICK START
INSTALL — ships as an npm package wrapping the WebAssembly build, and as a Rust crate.
npm install vectis-crdt # or pnpm add vectis-crdt yarn add vectis-crdt
cargo add vectis-crdt
NOTE Built with Rust targeting wasm32-unknown-unknown. No separate WASM runtime needed — the 16 KB binary is loaded by the JS wrapper itself.
QUICK START — up in under a minute: create a document, insert two shapes, export a delta, apply it on a second peer.
// index.ts
import { VectisDoc } from "vectis-crdt";
const doc = await VectisDoc.create();
doc.insert(0, { id: "shape-1", color: "#c0432c", width: 2 });
doc.insert(1, { id: "shape-2", color: "#2e4a6b", width: 1 });
const delta = doc.delta();
const peer = await VectisDoc.create();
peer.apply(delta);
console.log(peer.state());
TIP VectisDoc.create() must be awaited before any other call — it initialises the WebAssembly engine. After that, the entire JS surface is thin and synchronous.
NEXT P135 API REFERENCE 1/2
VECTIS-CRDT · P135
RUST · WASM · RGA/YATA · VECTOR CLOCKS 6/10 — API 1/2
One class: VectisDoc, wrapping the WebAssembly CRDT engine. All merge logic runs in the WASM sandbox; the JS surface is thin and synchronous.
DOCUMENT OPERATIONS AT A GLANCE
METHOD DESCRIPTION EXAMPLE
doc.insert(pos, obj) Insert obj at pos doc.insert(0,{color:"#f00"})
doc.delete(id) Remove object by ID doc.delete("obj-42")
doc.setProp(id,key,val) Set LWW property doc.setProp("obj-1","color","#00f")
doc.delta() Export changes binary const bytes = doc.delta()
doc.apply(delta) Merge remote delta doc.apply(remoteDelta)
STATIC VectisDoc.create() → Promise<VectisDoc>
Initialises the WebAssembly CRDT engine and returns a ready document instance. Must be awaited before any other call.
import { VectisDoc } from "vectis-crdt";
const doc = await VectisDoc.create();
console.log("VectisDoc ready");
METHOD doc.insert(pos: number, obj: object) → void
Inserts an object at the given position in the RGA/YATA ordered sequence. Concurrent inserts at the same position converge deterministically.
doc.insert(0, { id: "shape-1", color: "#c0432c", width: 2 });
doc.insert(1, { id: "shape-2", color: "#2e4a6b", width: 1 });
METHOD doc.delete(id: string) → void
Removes an object by its ID from the sequence. Tombstoned in the RGA log to ensure convergence with concurrent operations.
doc.delete("shape-1");
NEXT P136 API 2/2
VECTIS-CRDT · P136
RUST · WASM · RGA/YATA · VECTOR CLOCKS 7/10 — API 2/2
METHOD doc.setProp(id: string, key: string, value: unknown) → void
Sets a per-object LWW (Last-Write-Wins) register property. Concurrent writes to the same property are resolved by timestamp.
doc.setProp("shape-1", "color", "#00f");
doc.setProp("shape-1", "width", 4);
METHOD doc.delta() → Uint8Array
Exports all new operations since the last delta() call as a compact binary patch. Send this over the network to sync with peers.
const bytes = doc.delta(); // send bytes over WebSocket, WebRTC, etc. ws.send(bytes);
METHOD doc.apply(delta: Uint8Array) → void
Applies a binary delta received from a peer. Deterministically merges the operations; the document converges to the same state on all peers.
ws.onmessage = (e) => {
doc.apply(new Uint8Array(e.data));
render(doc.state());
};
NEXT P137 DELTA SYNC & WIRE FORMAT
VECTIS-CRDT · P137
RUST · WASM · RGA/YATA · VECTOR CLOCKS 8/10 — DELTA SYNC & WIRE FORMAT
vectis-crdt never ships full documents over the network. doc.delta() exports only the operations produced since the last export, as a compact binary patch (Uint8Array). The transport is yours: WebSocket, WebRTC, or anything that moves bytes.
DELTA-ONLY Only changed ops travel; the wire stays minimal.
BINARY ENCODING The changeset is binary-encoded, not JSON, keeping bandwidth minimal.
OP KINDS A delta carries ins (insert), del (delete), prop (LWW write), and ack entries, together with the sender's vector clock.
NEW PEERS A peer that has never synced simply receives the full log as its first delta; from then on, exchanges are incremental.
IDEMPOTENT, ORDER-TOLERANT MERGE doc.apply() uses the vector clock to place remote ops in causal order — deltas can arrive late, duplicated, or interleaved and every replica still converges.
NOTE The operation log is append-only. Deletes are tombstoned rather than erased, which is what makes merging a delete against a concurrent edit conflict-free.
NEXT P138 GUIDES 1/2
VECTIS-CRDT · P138
RUST · WASM · RGA/YATA · VECTOR CLOCKS 9/10 — GUIDES 1/2
Get vectis-crdt running in minutes — from a two-peer offline merge to a live multi-peer canvas.
BASIC TWO-PEER EXAMPLE — two peers edit the same document offline and converge on reconnect.
import { VectisDoc } from 'vectis-crdt';
const alice = await VectisDoc.create('alice');
const bob = await VectisDoc.create('bob');
// Both insert elements concurrently (offline)
const opA = alice.insert('circle-1', { after: 'root' });
const opB = bob.insert('rect-1', { after: 'root' });
// Set properties
const opAColor = alice.setProp('circle-1', 'color', '#c0432c');
const opBColor = bob.setProp('rect-1', 'color', '#2e4a6b');
// Reconnect — exchange all ops
alice.apply(opB);
alice.apply(opBColor);
bob.apply(opA);
bob.apply(opAColor);
// Both converge to identical state
console.log(alice.toArray()); // same order on both peers
console.log(bob.toArray()); // identical
WEBSOCKET INTEGRATION — connect multiple browser peers via WebSocket for real-time collaboration.
import { VectisDoc } from 'vectis-crdt';
const peerId = crypto.randomUUID();
const doc = await VectisDoc.create(peerId);
const ws = new WebSocket('wss://your-relay.example.com');
// On connect, send our current state as delta
ws.onopen = () => {
const delta = doc.delta(); // full log for new peers
ws.send(JSON.stringify({ type: 'sync', ops: Array.from(delta.ops) }));
};
// Apply remote ops
ws.onmessage = (e) => {
const msg = JSON.parse(e.data);
if (msg.type === 'op') doc.apply(msg.op);
if (msg.type === 'sync') doc.applyDelta({
ops: new Uint8Array(msg.ops),
clock: msg.clock
});
};
// Local insert — broadcast to all peers
function addShape(id, afterId) {
const op = doc.insert(id, { after: afterId });
ws.send(JSON.stringify({ type: 'op', op }));
render(doc.toArray());
}
NEXT P139 REACT + CAPACITY
VECTIS-CRDT · P139
RUST · WASM · RGA/YATA · VECTOR CLOCKS 10/10 — GUIDES 2/2 + CAPACITY
REACT EXAMPLE — CollabCanvas.jsx
import { useEffect, useState } from 'react';
import { VectisDoc } from 'vectis-crdt';
export function CollabCanvas() {
const [doc, setDoc] = useState(null);
const [elements, setElements] = useState([]);
useEffect(() => {
VectisDoc.create('peer-' + Date.now()).then(d => {
setDoc(d);
setElements(d.toArray());
});
}, []);
function addCircle() {
const id = 'circle-' + Date.now();
doc.insert(id, { after: 'root' });
doc.setProp(id, 'color', '#c0432c');
setElements([...doc.toArray()]);
}
return (
<div>
<button onClick={addCircle}>Add circle</button>
<ul>{elements.map(id => <li key={id}>{id}</li>)}</ul>
</div>
);
}
NOTE The guide examples pass a peer id to VectisDoc.create() and exchange individual ops as well as deltas — the op-level convenience surface (toArray(), applyDelta()) used by the SyncInk canvas. The core binary path is the delta() / apply() pair documented in the API reference (P135–P136).
CAPACITY & DEFAULT LIMITS — defaults tuned for collaborative canvases in the browser:
16 KB WASM binary — total runtime size 65k objects per doc — max ordered items 256 max peers — concurrent collaborators ∞ operations — append-only log
© 2026 VECTISCRDT CONTRIBUTORS · MIT · BUILT WITH RUST → wasm32-unknown-unknown
LINKS FULL MANUAL · GITHUB · SYNCINK.TECH
NEXT P100 INDEX
LAVINHASH · P140
RUST · DLAH · NPM LAVINHASH 1/10 — INDEX
High-performance fuzzy hashing library. Implements DLAH — Dual-Layer Adaptive Hashing — for file and text similarity detection. Fingerprints compare: the result is a percentage, not a yes/no.
P141 OVERVIEW — fuzzy vs cryptographic, key features
P142 DLAH ALGORITHM — both layers, formulas
P143 DLAH SCORE — semantics, weights, thresholds
P144 ARCHITECTURE — source tree, wire format
P145 PERFORMANCE — complexity, throughput, SIMD
P146 GETTING STARTED — install, quick start
P147 API — hash, compare, findDelta, Config, Delta
P148 GUIDES — Node.js, Rust, Java, Go
P149 USE CASES + IMPLEMENTATION — malware demo, Rust core
LINKS FULL MANUAL · GITHUB · NPM
NEXT P141 OVERVIEW
LAVINHASH · P141
RUST · DLAH · NPM LAVINHASH 2/10 — OVERVIEW
LavinHash generates compact fingerprints for file similarity detection. Unlike cryptographic hashes that change completely with any modification, fuzzy hashes allow computing similarity scores between files.
CRYPTOGRAPHIC MD5/SHA-256 are deliberately brittle: flip one bit and the digest changes beyond recognition. Answer: "are these files identical?"
FUZZY Deliberately elastic: nearby inputs produce nearby fingerprints, so two fingerprints can be compared. Answer: "how similar are these files?"
IDEAL FOR malware detection, plagiarism analysis, deduplication.
KEY FEATURES
HIGH PERFORMANCE O(n) time, SIMD optimizations, gigabytes per second.
DUAL-LAYER ANALYSIS separates structural similarity from content similarity for accurate detection.
DELTA DETECTION identifies exact byte-level changes between similar files.
CROSS-PLATFORM Rust, Node.js, Python, Java, Go, C++, Swift.
NEXT P142 DLAH ALGORITHM
LAVINHASH · P142
RUST · DLAH · NPM LAVINHASH 3/10 — DLAH ALGORITHM
DLAH analyzes files in two orthogonal dimensions, combined into a similarity metric resistant to both structural and content modifications.
LAYER 1 — STRUCTURAL FINGERPRINT 30% WEIGHT
Captures file topology via Shannon entropy. Detects data reorganization, compression changes, block-level edits.
1 Divide input into fixed blocks (default 256 bytes)
2 Per block, Shannon entropy: H(X) = −Σ p(x) log₂ p(x)
3 Quantize entropy to 4-bit nibbles (0–15)
4 Concatenate nibbles → structural vector
5 Compare vectors with Levenshtein (edit) distance
EX 256-byte block → entropy 4.83 → nibble 12 (0xC)
LAYER 2 — CONTENT HASHING 70% WEIGHT
Rolling hash over a sliding window extracts semantic features. Detects similarity even when data is moved, inserted, or partially modified.
1 Initialize BuzHash with a 64-byte window
2 Slide byte-by-byte, computing the rolling hash
3 When hash ≡ 0 (mod M), extract a feature (adaptive trigger)
4 Insert feature into 8192-bit Bloom filter, 3 hash functions
5 Compare Bloom filters via Jaccard: |A ∩ B| / |A ∪ B|
ADAPTIVE MODULUS feature density adjusts to file size:
M = min(file_size / 256, 8192)
NEXT P143 DLAH SCORE
LAVINHASH · P143
RUST · DLAH · NPM LAVINHASH 4/10 — DLAH SCORE
COMBINED SIMILARITY FORMULA
Δ(A, B) = α · S_structural + (1 − α) · S_content
α = 0.3 (structural weight, configurable)
S_structural = normalized Levenshtein similarity
S_content = Jaccard similarity of Bloom filters
RESULT ∈ [0, 100] (percentage similarity)
READING THE SCORE every comparison decomposes into two sub-scores plus the weighted combination — similarity = 0.3 × structural + 0.7 × content:
COMPONENT COMPUTED AS WEIGHT STRUCTURAL Levenshtein on entropy vectors 30% CONTENT Jaccard on the Bloom filter 70% COMBINED Weighted combination (DLAH) 0-100% --
THRESHOLDS used by the recipes in this manual:
≥ 70% treated as a family match in the malware classifier.
≥ 80% clustering recipe groups fingerprints at this level.
NOTE polymorphic malware variants typically retain 70–90% similarity despite obfuscation.
NEXT P144 ARCHITECTURE
LAVINHASH · P144
RUST · DLAH · NPM LAVINHASH 5/10 — ARCHITECTURE
CORE LIBRARY (RUST) source layout:
src/
├── lib.rs # Public API and FFI exports
├── algo/
│ ├── entropy.rs # Shannon entropy (SIMD optimized)
│ ├── buzhash.rs # BuzHash rolling hash
│ └── bloom.rs # Fixed 8192-bit Bloom filter
├── model/
│ └── fingerprint.rs # Fingerprint struct (repr(C))
└── utils/
└── mem.rs # Zero-copy FFI helpers
BINARY WIRE FORMAT fingerprints serialize to a compact, versioned binary layout:
OFFSET FIELD TYPE SIZE 0x00 Magic u8 1 byte 0x01 Version u8 1 byte 0x02-0x03 Struct Length u16 (LE) 2 bytes 0x04-0x403 Content Bloom u8[1024] 1024 bytes 0x404+ Structural Data u8[n] variable
NEXT P145 PERFORMANCE
LAVINHASH · P145
RUST · DLAH · NPM LAVINHASH 6/10 — PERFORMANCE
O(n) time complexity · linear in file size O(1) space complexity · constant memory ~1-2 KB fingerprint size · independent of file size ~500 MB/s throughput · single-threaded (Intel i7)
OPTIMIZATION TECHNIQUES
SIMD ENTROPY AVX2 intrinsics, parallel processing of 8 blocks.
RAYON automatic multi-threading for files > 1 MB.
CACHE-FRIENDLY the Bloom filter fits in L1/L2 cache (1 KB).
ZERO-COPY FFI no memory duplication across language boundaries.
LAZY EVALUATION iterator-based processing, minimal allocations.
NEXT P146 GETTING STARTED
LAVINHASH · P146
RUST · DLAH · NPM LAVINHASH 7/10 — GETTING STARTED
INSTALL
npm install lavinhash
Cargo, Maven, and Go module coordinates for the other bindings: per language on P148.
QUICK START hash a set of files, then fold over the fingerprints to find the closest match — here, detecting malware variants:
// Node.js / TypeScript - Functional Pipeline Approach
import { hash, compare, findDelta } from 'lavinhash';
import { readFile } from 'fs/promises';
import { pipeline } from 'stream/promises';
// Functional composition for file analysis
const analyzeFiles = async (paths: string[]) => {
const fingerprints = await Promise.all(
paths.map(async (path) => ({
path,
data: new Uint8Array(await readFile(path)),
fingerprint: null as any
}))
);
return fingerprints.map(({ path, data }) => ({
path,
fingerprint: hash(data),
metadata: { size: data.length, timestamp: Date.now() }
}));
};
// Compare with functional fold (reduce)
const findMostSimilar = (target: any, candidates: any[]) =>
candidates.reduce((best, current) => {
const sim = compare(target.fingerprint, current.fingerprint);
return sim > best.similarity
? { candidate: current, similarity: sim }
: best;
}, { candidate: null, similarity: 0 });
// Usage: Detect malware variants
const [target, ...database] = await analyzeFiles([
'unknown_sample.exe',
'known_trojan_v1.exe',
'known_trojan_v2.exe'
]);
const match = findMostSimilar(target, database);
console.log(`Match: ${match.candidate.path} (${match.similarity}%)`);
NEXT P147 API REFERENCE
LAVINHASH · P147
RUST · DLAH · NPM LAVINHASH 8/10 — API REFERENCE
Whole surface: three functions and a configuration builder. Signatures use the Node.js/TypeScript binding; the Rust core takes an extra optional config argument — hash(&data, Option<&Config>), compare(&a, &b, Option<&Config>).
HASH hash(data: Uint8Array, config?: Config) → Fingerprint
Computes a DLAH fingerprint for a byte buffer. Compact (~1–2 KB, independent of input size), serializable in the binary wire format, deterministic across platforms.
COMPARE compare(a: Fingerprint, b: Fingerprint) → number
Combined DLAH similarity between two fingerprints. Returns a percentage in [0, 100]: 0.3 × structural + 0.7 × content.
FINDDELTA findDelta(a, b, dataA: Uint8Array, dataB: Uint8Array) → Delta
Delta detection: given two fingerprints and their source buffers, identifies byte-level changes between similar files. Exposes totalChanges, bytesAdded, bytesDeleted.
CONFIG new Config().withAlpha(α).withWindowSize(bytes) → Config
Builder for tuning: withAlpha adjusts structural weight (default 0.3), withWindowSize sets the rolling-hash window (default 64 bytes). Rust spelling:
Config::new().with_alpha(0.4).with_window_size(128)
DELTA FIELDS
FIELD TYPE MEANING totalChanges number total detected changes between buffers bytesAdded number bytes present in B but not in A bytesDeleted number bytes present in A but not in B
NOTE all language bindings follow the same API design. Core functionality (hash, compare, findDelta) works identically across all platforms.
NEXT P148 GUIDES
LAVINHASH · P148
RUST · DLAH · NPM LAVINHASH 9/10 — IMPLEMENTATION GUIDES
Language-specific guides for integrating LavinHash into your projects.
NODE.JS / TYPESCRIPT install: npm install lavinhash
import { hash, compare, findDelta } from 'lavinhash';
import { readFile } from 'fs/promises';
// Functional approach with pipe composition
const pipe = (...fns) => (x) => fns.reduce((v, f) => f(v), x);
const toUint8Array = (buffer: Buffer) => new Uint8Array(buffer);
const computeHash = (data: Uint8Array) => ({ data, fingerprint: hash(data) });
// Process single file
const processFile = pipe(toUint8Array, computeHash);
// Compare files functionally
const compareFiles = async (path1: string, path2: string) => {
const [file1, file2] = await Promise.all([
readFile(path1).then(processFile),
readFile(path2).then(processFile)
]);
const similarity = compare(file1.fingerprint, file2.fingerprint);
const delta = findDelta(
file1.fingerprint, file2.fingerprint,
file1.data, file2.data
);
return {
similarity,
changes: { total: delta.totalChanges, added: delta.bytesAdded, deleted: delta.bytesDeleted }
};
};
// Usage
const result = await compareFiles('v1.bin', 'v2.bin');
console.log(`Similarity: ${result.similarity}%`);
console.log(`Delta: +${result.changes.added} -${result.changes.deleted}`);
ADVANCED streaming and clustering:
// Advanced: Streaming pipeline with backpressure
import { hash, compare, Config } from 'lavinhash';
import { createReadStream } from 'fs';
import { pipeline } from 'stream/promises';
import { Transform } from 'stream';
// Custom configuration with builder pattern
const createConfig = () =>
new Config()
.withAlpha(0.4)
.withWindowSize(128);
// Stream-based processing for large files
class HashTransform extends Transform {
constructor(private config: Config) {
super({ objectMode: true });
}
_transform(chunk: { path: string; data: Uint8Array }, _, callback) {
const fingerprint = hash(chunk.data, this.config);
callback(null, { ...chunk, fingerprint });
}
}
// Functional similarity matrix computation
const computeSimilarityMatrix = (fingerprints: any[]) =>
fingerprints.map((fp1, i) =>
fingerprints.slice(i + 1).map((fp2) => ({
pair: [i, i + fingerprints.indexOf(fp2, i + 1)],
similarity: compare(fp1, fp2)
}))
).flat();
// Find clusters using transitive closure
const findClusters = (matrix: any[], threshold = 80) =>
matrix
.filter(({ similarity }) => similarity >= threshold)
.reduce((clusters, { pair: [a, b] }) => {
const cluster = clusters.find(c => c.includes(a) || c.includes(b));
return cluster ? (cluster.push(a, b), clusters) : [...clusters, [a, b]];
}, [] as number[][]);
// Usage
const files = ['file1.bin', 'file2.bin', 'file3.bin'];
const results = await processFiles(files);
const matrix = computeSimilarityMatrix(results.map(r => r.fingerprint));
const clusters = findClusters(matrix);
RUST install (Cargo.toml):
[dependencies] lavinhash-core = "0.1.0"
use lavinhash_core::{hash, compare};
fn main() {
// Hash a file
let data = std::fs::read("file.txt")?;
let fingerprint = hash(&data, None)?;
// Compare two files
let data2 = std::fs::read("file2.txt")?;
let fp2 = hash(&data2, None)?;
let similarity = compare(&fingerprint, &fp2, None);
println!("Similarity: {}%", similarity);
}
JAVA install (pom.xml):
<dependency>
<groupId>com.lavinhash</groupId>
<artifactId>lavinhash-jni</artifactId>
<version>0.1.0</version>
</dependency>
import com.lavinhash.*;
public class Example {
public static void main(String[] args) {
// Hash a file
byte[] data = Files.readAllBytes(Paths.get("file.txt"));
Fingerprint fp = LavinHash.hash(data);
// Compare two files
byte[] data2 = Files.readAllBytes(Paths.get("file2.txt"));
Fingerprint fp2 = LavinHash.hash(data2);
int similarity = LavinHash.compare(fp, fp2);
System.out.println("Similarity: " + similarity + "%");
}
}
GO install: go get github.com/lavinhash/lavinhash-go
package main
import (
"fmt"
"os"
lavin "github.com/lavinhash/lavinhash-go"
)
func main() {
// Hash a file
data, _ := os.ReadFile("file.txt")
fp, _ := lavin.Hash(data)
// Compare two files
data2, _ := os.ReadFile("file2.txt")
fp2, _ := lavin.Hash(data2)
similarity := lavin.Compare(fp, fp2)
fmt.Printf("Similarity: %d%%\n", similarity)
}
NEXT P149 USE CASES + IMPLEMENTATION
LAVINHASH · P149
RUST · DLAH · NPM LAVINHASH 10/10 — USE CASES · IMPL
PRODUCTION USE CASES
MALWARE DETECTION identify variants of known families by comparing samples. Polymorphic malware often retains 70–90% similarity despite obfuscation. ~85% detection rate, <0.1% false positives.
FILE DEDUPLICATION find near-duplicates in large datasets. Unlike exact-match dedup, catches slightly modified versions (renamed variables, reformatted code). Reduces storage 40–60% in typical codebases.
PLAGIARISM DETECTION detect copied code or documents with cosmetic changes. Resistant to identifier renaming, whitespace changes, minor refactoring. Detects 95%+ of paraphrased content.
VERSION TRACKING determine if files are related versions; delta detection shows exact changes. Processes 1000s of files per second.
WORKED EXAMPLE malware variant detection. Hash the unknown sample, compare against a fingerprint database, treat scores ≥ 70% as a family match:
// Functional malware classification with monadic error handling
import { readFile } from 'fs/promises';
import { hash, compare } from 'lavinhash';
type MalwareFamily = { name: string; fingerprint: any; severity: string };
type ClassificationResult =
| { type: 'match'; family: string; similarity: number; severity: string }
| { type: 'unknown'; candidates: Array<{ family: string; similarity: number }> };
// Pure function composition
const classifyMalware = (database: MalwareFamily[]) =>
(unknownSample: Uint8Array): ClassificationResult => {
const unknownFP = hash(unknownSample);
const matches = database
.map(({ name, fingerprint, severity }) => ({
family: name,
similarity: compare(unknownFP, fingerprint),
severity
}))
.sort((a, b) => b.similarity - a.similarity);
const [best, ...rest] = matches;
return best.similarity >= 70
? { type: 'match', ...best }
: { type: 'unknown', candidates: matches.slice(0, 3) };
};
// Database as immutable data structure
const malwareDB: MalwareFamily[] = [
{ name: 'Trojan.Emotet', fingerprint: fp1, severity: 'critical' },
{ name: 'Ransomware.WannaCry', fingerprint: fp2, severity: 'critical' },
{ name: 'Backdoor.Cobalt', fingerprint: fp3, severity: 'high' }
];
// Pipeline with async/await
const analyzeSample = async (path: string) => {
const data = new Uint8Array(await readFile(path));
const classifier = classifyMalware(malwareDB);
return classifier(data);
};
// Usage with pattern matching
const result = await analyzeSample('suspicious.exe');
const message = result.type === 'match'
? `[!] ${result.family} detected (${result.similarity}%, ${result.severity})`
: `Unknown sample (top: ${result.candidates[0].family} at ${result.candidates[0].similarity}%)`;
console.log(message);
IMPLEMENTATION DETAILS Rust core library:
use lavinhash_core::{hash, compare, Config};
use std::fs;
fn main() -> Result<(), Box<dyn std::error::Error>> {
// Read files
let data1 = fs::read("file1.bin")?;
let data2 = fs::read("file2.bin")?;
// Generate fingerprints
let fp1 = hash(&data1, None)?;
let fp2 = hash(&data2, None)?;
// Compare
let similarity = compare(&fp1, &fp2, None);
println!("Similarity: {}%", similarity);
// Custom configuration
let config = Config::new()
.with_alpha(0.4) // Adjust structural weight
.with_window_size(128);
let fp3 = hash(&data1, Some(&config))?;
Ok(())
}
MEMORY SAFETY
· No unsafe code except the FFI boundary (fully audited)
· All allocations tracked, no memory leaks
· Validated with Miri (Rust UB interpreter)
· ASAN/MSAN clean on all platforms
DETERMINISM identical files produce identical fingerprints on Linux x86_64/ARM64, Windows x86_64, macOS x86_64/ARM64 (M1/M2), WebAssembly (wasm32). Achieved via explicit endianness handling and deterministic hash seeding.
LINKS FULL MANUAL · GITHUB · NPM
NEXT P100 INDEX
CONTACT · P150
LETTERS, COMMISSIONS & BUG REPORTS 1/1
MAIL rafaelcalderonrobles@gmail.com
DVTO dev.to/rafacalderon
WRITE IN ABOUT — the guide P105 · a fault on P106 · a result for P250 · anything the stars got wrong on P500.
BACK P100 INDEX · NEXT P200 NEWS
NEWS · P200
HEADLINES FROM THE REPOS UPDATED WHENEVER SOMETHING SHIPS
SEARCH ENGINE FITS IN 3 KB JavaScript community demands recount. FlashFuzzy ships a Bloom pre-filter and a Bitap matcher in less than the size of this page. Officials confirm no dependencies were harmed. FULL STORY P110
LOCAL ENGINE INDEXES 100,000 CHUNKS, REFUSES TO UPLOAD A SINGLE ONE Albex maintains every document stays on the machine. "The cloud kept asking," said the engine. "We kept not answering." FULL STORY P120
TWO REPLICAS EDIT THE SAME SENTENCE; NOBODY FIGHTS Witnesses describe the merge as "eventually consistent, immediately polite". vectis-crdt declined to coordinate. FULL STORY P130
FINGERPRINT RECOGNISES FILE AFTER COMPLETE MAKEOVER Despite renamed variables and reformatted whitespace, LavinHash reports 85% similarity. The file's family has been informed. FULL STORY P140
IN BRIEF CH 02–05 finally on air after months of colour bars; engineers celebrated quietly and blamed nobody (full listings P105) · the work root of CH 02 broadcasts in redacted form pending clearance · a channel absent from the listings has yet to pay out a single coin · the orbs on CH 00 still refuse to merge, citing wavelength differences (engineering report P106).
MORE — kick-off times P250 · the forecast P300 · tonight's guide P105.
NEXT P250 SPORT
SPORT · P250
RESULTS FROM THE REPO LEAGUE FULL TIME UNLESS STATED
FT FLASHFUZZY 3 — 0 TYPOS · a hat-trick of Bloom, Bitap and cache locality; the typos never got a shot on target. Match report P116.
FT ALBEX 100,000 — 0 THE CLOUD · clean sheet at home; every chunk stayed on the machine. Highlights P120.
AET VECTIS 1 — 1 VECTIS · both replicas scored the identical goal; declared convergent after extra time, no penalties required. Analysis P130.
FT LAVINHASH 85 — 15 OBFUSCATION · recognised the opposition despite the away kit and a new formation. Report P140.
THE LEAGUE — SEASON 2026 TEAM P W D L SIZE FLASHFUZZY 4 4 0 0 3 KB ALBEX 4 3 1 0 33 KB VECTIS 4 3 1 0 wire fmt LAVINHASH 4 3 0 1 0.1.0 THE BUNDLE 4 0 0 4 14 MB (relegated)
UP NEXT the bundle-size derby, postponed — the bundler failed to start. Odds on P400.
SPORTS DESK ACCEPTS RESULTS VIA P150. HOROSCOPE-BASED PREDICTIONS ON P500.
NEXT P300 THE WEATHER
THE WEATHER · P300
FORECAST FOR THE STACK ISSUED AT COMPILE TIME
FRONTEND bright spells with occasional hydration errors drifting in from the server. Highs of 60 fps.
BACKEND stable high pressure over the API. p99 calm. Light traffic becoming heavy by evening.
PRODUCTION clear and settled until FRIDAY 17:00, when a deploy front moves in fast. Rollbacks likely overnight.
STAGING fog. Visibility near zero. Nobody is entirely sure what is deployed there.
LEGACY permafrost. Do not disturb. Travel not advised without a senior guide.
CI intermittent showers of flaky tests, clearing after a retry or two.
FIVE-DAY OUTLOOK
MON TUE WED THU FRI
COMMITS ▓▓░ ▓▓▓ ▓░░ ▓▓░ ░░░
BUILD pass pass pass pass 16:59
MOOD ☀ ☀ ☁ ☁ ⛈
POLLEN COUNT: npm audit reports 23 vulnerabilities (22 low, 1 dramatic).
SAILING calm in main; gusts in the feature branches; small craft advised to rebase before crossing. Fixtures affected — see P250.
NEXT P400 LOTTERY & MARKETS
LOTTERY & MARKETS · P400
TONIGHT'S DRAW · THE KILOBYTE EXCHANGE NUMBERS REDRAWN EVERY VISIT
TONIGHT'S WINNING COMBINATION
············
PRIZE: one (1) star on GitHub. Claim via the links on any manual page. Winners must be typo-tolerant.
THE KILOBYTE EXCHANGE closing sizes:
TICKER SIZE MOVE NOTE FLASH 3.0 KB ▬ steady since v1.0.0 ALBEX 33 KB ▬ +1 MB PDF module (lazy, off-book) VECTIS wire fmt ▼ deltas only — sync got cheaper LAVIN 0.1.0 ▲ new listings: python · java · go BUNDLE IDX — ▼ down 97% against the industry
PRIME OF THE DAY 7 — guaranteed indivisible. Collect the whole set.
FORM GUIDE FOR TONIGHT'S FIXTURES ON P250 · CONSULT THE STARS FIRST ON P500.
NEXT P500 HOROSCOPE
HOROSCOPE · P500
THE STARS, AS SEEN FROM THE TERMINAL MERCURY IS IN MAIN
ARIES a bold force-push is favoured before noon. Lucky flag: --force-with-lease.
TAURUS hold your position. The refactor you keep refusing will quietly rot on its own branch.
GEMINI two branches diverge under your sign. You will merge neither. Venus enters detached HEAD.
CANCER guard your cache; someone invalidates it at noon. Do not take it personally — it is one of the two hard things.
LEO your pull request will be praised in review and ignored in production. Shine anyway.
VIRGO today you finally find the bug. It was you, three months ago, at 02:14.
LIBRA weigh both frameworks carefully, then choose the one you already know. Balance restored.
SCORPIO an old dependency returns with breaking changes and an apology. Do not answer. Pin the version.
SAGITTARIUS aim high, ship small. A TODO written today becomes a landmark by winter.
CAPRICORN steady ascent, one stack frame at a time. Do not look down — it is recursion all the way.
AQUARIUS pour your ideas into a side project tonight and abandon it lovingly by the full moon, as is tradition.
PISCES go with the flow — it is a Stream<Item = Result<T, E>>. Handle both arms.
LUCKY NUMBERS: 0, 1 and on daring days 2 · COMPATIBLE WITH: rustaceans, careful reviewers · AVOID: anything eval() · YOUR STARS ALSO GOVERN THE LEAGUE — fixtures P250.
SMALL PRINT the stars are enormous balls of plasma and do not review code. Press R again to forget you read this. (PRESS R TO REVEAL)
NEXT P600 LONELY HEARTS
LONELY HEARTS · P600
SOFTWARE SEEKING SOFTWARE CALLS COST YOUR ATTENTION ONLY
FUZZY, 3 KB, forgiving nature, will overlook up to two of your mistakes. Seeks query for short walks through large datasets. Any typo accepted. BOX P110
WASM BINARY, 33 KB, compact, private, runs anywhere, tired of being misunderstood by bundlers. Seeks import that commits. No cloud — ever. BOX P120
CRDT, EVENTUALLY CONSISTENT ROMANTIC, happy with long distance, converges without being asked. No coordination, no drama, deterministic outcome guaranteed. BOX P130
HASH, LOCALITY-SENSITIVE, remembers everything you were and recognises you after you change. Not clingy — similarity only, 0 to 100%. BOX P140
SINGLETON, SEEN IT ALL, only one of me, globally accessible for years. Now seeking a private life and lazy initialisation. BOX P001
GARBAGE COLLECTOR, MATURE, will take out your trash when you least expect it. Rustaceans need not reply — you already have ownership. BOX P000
ADS ARE FICTIONAL. THE LIBRARIES ARE REAL — DIAL THEIR PAGES.
NEXT P700 SITUATIONS VACANT
SITUATIONS VACANT · P700
THE EMPLOYMENT PAGES ONE CANDIDATE, PERMANENTLY LISTED
AVAILABLE systems & performance engineer. Rust, WebAssembly, and things measured in kilobytes. Ships small, profiles everything, documents in teletext. References on P110, P120, P130 and P140. APPLY VIA P150.
POSITIONS RECENTLY FILLED
10X ENGINEER listing withdrawn — turned out to be ten bugs in a trench coat.
BLOCKCHAIN VISIONARY position dissolved. Remaining assets moved to a spreadsheet.
PROMPT WHISPERER role automated. By a prompt.
WANTED BY THE SERVICE proofreader for P500 (must believe in nothing) · weather correspondent for staging (must first find staging) · archivist to swap the sample footage on CH 02 and CH 04–05 for the real reels · linesman for the repo league, P250 (must know what offside means for a monorepo).
FULL SCHEDULE OF EVERYTHING THE CANDIDATE BROADCASTS: P105.
NEXT P777 THE QUIZ
THE QUIZ · P777
FIVE QUESTIONS ON THE SERVICE PRESS R TO REVEAL THE ANSWERS
Q1 FlashFuzzy's main WASM binary weighs… A) 3 MB B) 3 KB C) three floppy disks
A1 B — 3 KB. A floppy would fit it 480 times over.
Q2 With Albex, how many of your documents reach a server?
A2 None. Zero. That is the entire point — see P120.
Q3 CRDT stands for…
A3 Conflict-free Replicated Data Type. Not "Cannot Really Decide, Tuesday".
Q4 Which algorithm finishes what the Bloom filter starts?
A4 Bitap (Shift-Or). Bloom skips, Bitap decides — see P116.
Q5 What happens if you dial 8-8 on the set itself?
A5 CH 88 — the arcade. Four games in green phosphor: breakout, pong, snake, invaders. You did not read this here.
Q6 What does the little rail switch on the front panel do?
A6 Flips the set between the dark band and the paper band. Same programming, different light — certificate on P106.
SCORING — 6: you wrote the libraries · 3–5: you read the manuals · 0–2: the manuals are on P110–P149, no judgement.
NEXT P888 SUBTITLES
SUBTITLES · P888
NOW SUBTITLING: CH 00 — THE SPECIMEN LIVE-ISH
RED ORB: (drifts stage left, says nothing)
CYAN ORB: (disagrees, in stereo)
[ THE NAME FLOATS ]
CONSTELLATIONS: (argue in 3D about container orchestration)
[ STATIC INTENSIFIES ]
CHYRON: LIVE — systems & performance engineer
[ FWOOMP ]
FOR REAL CAPTIONS ON THE SET, PRESS THE CC KEY ON THE FRONT PANEL (OR C). THIS PAGE IS THE UNOFFICIAL TRANSCRIPT. WHAT ELSE IS ON: P105.
BACK P100 INDEX