The reader inputs a name and a skill. On clicking Request Decision, one of 30+ letter templates is selected at random, with {skill} and {action} / {action_noun} substituted from rotating verb lists (integrated, optimised, assimilated, digitized, processed, absorbed, synthesised, catalogued…). A signoff is drawn from ten variants. The letter is displayed on aged paper with typewriter typography. An appeal button triggers one of 20 APPEAL DENIED modal variants — all routed through the same determination system. System interruption dialogs surface at intervals — system notices, productivity tips, philosophical inquiries, efficiency reports — that treat the reader's continued presence as further evidence. A dismissal counter persisted in localStorage increments on each generation and is displayed at page top. A determination archive accumulates in localStorage. Export triggers a download of the current letter as a text file via Blob URL.

Thirteen documents drawn from a hardcoded corpus (AI safety incident, platform moderation variance, biometric deployment, climate modeling, election infrastructure, executive misconduct, contract irregularities, Arctic acquisition feasibility, Greenland infrastructure, regime change operation, military contingency, invasion intelligence, sanctions violations, whistleblower retaliation) are loaded one per session. On Initiate Assessment, the document is rendered word by word with redactions applied at density derived from the confidence slider (1–5; value 3 = 0.6 density). Redacted spans are clickable and reveal the underlying text on click. Pulling the slider to 1 and holding it for 2 seconds triggers epistemic collapse: the system redacts everything, including previously revealed text. Three appeal actions (Increase Exposure) progressively reduce redaction density. After three exposures a FOIA form is unlocked — submitted grounds return one of nine denial variants. Night vision mode applies a phosphor green filter. PDF export via jsPDF renders the document with redaction bars, a random small chance of doubled lines (ghost text), and a footer stating the production of the record neither confirms nor denies the existence of responsive records.

All interactions are captured: keydown, mousedown/up (press duration), touchstart, touchmove (swipe velocity), DeviceOrientationEvent (tilt variance above 5°, direct increment above 30°). Phrases are drawn from four phase vocabularies — Early, Middle, Late, Extreme — each as a second-order Markov chain. Phase threshold is set by contribution count. Simulated other users contribute at stochastic intervals, advancing a collective count persisted in localStorage and synchronised across tabs via storage events. At threshold, the Ghost Training Protocol modal surfaces: on acceptance, a TensorFlow.js sequential dense network is compiled (input [4]: tap rate, swipe velocity, tilt variance, press duration; layers: 8-unit relu → 4-unit relu → 1-unit sigmoid) and trained on the session's behavioural data. A 60Hz sine drone activates on first interaction, gain rising with count. A heartbeat oscillator activates on ghost training acceptance. Session export generates a standalone HTML replay document.

The debt engine measures the gap between keydown events in milliseconds and adds each to a running total. Backspace adds a flat 400ms surcharge. Four thresholds key the phase: SOLVENT (0–10,000ms), LEVERAGED (10,000–30,000ms), INSOLVENT (30,000–50,000ms), FORECLOSURE (50,000–80,000ms). From LEVERAGED: sixteen words in the VULNERABLE set — love, hate, fear, sorry, help, pain, dream, mother, god, die, hope, fail, lost, shame, regret, feel — are replaced with [SENTIMENT_DETECTED] on word completion. From INSOLVENT: 20% of completed words receive a gravity animation, drifting 18px and rotating 3°. The cursor dilates and turns red. At 80,000ms the session freezes and exports a unique SVG: hex Unix timestamp edition ID, timezone, total debt, a per-keystroke latency heatmap (red above 500ms, black below), and the surviving field HTML with all scars intact.

Official sources (White House, UK Gov, EU Commission, SEC, Federal Reserve, Treasury, ECB, SCOTUS, USGS seismic) and dissent sources (Amnesty, EFF, HRW, ACLU, AFL-CIO, Greenpeace, ProPublica, SEIU, The Intercept, RSF) are ingested alternately every 15 seconds. Each fragment enters the ledger as a word-unit span, coloured blue (official) or red (dissent), and immediately begins mutating. The constraint engine selects from 13 Oulipo-derived transforms — LIPOGRAM_E, HOMOPHONIC, PALINDROME, ANAGRAM, SNOWBALL, ROT13, CAESAR_SHIFT, PHONETIC_NATO, NUMERIC_POSITION, XOR_MASK (keyed to 'dearmadta'), VIGENERE (key: CIPHER), REDACTED, STATIC — applied at probability scaling from 0.32 at revision 0–9 to 0.12 at revision 45+. Cross-stage bleed chance (0.15) fires constraints from later stages early. GPT-2 (Xenova, quantized) runs on-device and generates commentary on the constraint drift every 30 seconds; at high mutation counts the observer begins reporting stability while everything decays. Gun.js + SEA keypair connects to a relay network: mutations from peer sessions propagate in. The entropic contract modal (wallet connect) commits a generated witness ID to the Gun graph. Snapshot exports PNG via html2canvas; GIF recording captures 20 frames at 1fps.

Each completed word is evaluated by the adjudication system and assigned one of five intervention types: overprint (CENSORED / REDACTED / VOID stamped in red), block redaction (solid fill), strikethrough, paste intrusion (replacement text injected), or burial. Each intervention is logged to the right-panel sidebar with a reason string, criticality stamp, and critique. The Purity Index bar decrements with each intervention. The Appeal modal accepts formal grounds as a text field: on submission, grounds are SHA-256 hashed via Web Crypto API, appended to an in-session Merkle tree, and returned DISPOSITION: NON-ADJUDICABLE | RELIEF: DENIED. Export: SVG Official Record with text in the left two-thirds, annotations ruled off to the right, all appeal hashes at the foot as MERKLE APPEAL RECORD, surviving text below as SURVIVING TEXT. Mobile receives a hard block screen.

SmolLM2-360M-Instruct is loaded via WebLLM and WebGPU, cached after first download. The model is prompted with one of four rotating templates asking it to describe a canonical painting poetically but ambiguously, as if half-remembering, at temperature 1.1, max 60 tokens. Artworks are drawn from a corpus spanning Renaissance through NFT era; each attempt runs five times before advancing. A gallery card shows title, artist, year, and live Wikipedia provenance. Each sentence is rendered character-by-character. A mutation interval (1.8s or 3.5s, randomly chosen) replaces characters with glitch symbols at probability 0.015 per character per tick. After 25 mutations the text collapses to em-dashes. Meta-commentary fires at up to 10% probability at session open, decaying linearly to zero over 6 hours: the model receives one of 15 hardcoded self-referential prompts instead of an artwork. The corpus degrades on a timer — eras eliminated in sequence — until the pool empties and the frame holds white. Template-based fallback substitutes when the model is unavailable.

Seven RSS feeds (BBC, NYT, TechCrunch, The Verge, Rolling Stone, Bloomberg, ScienceDaily) are fetched via rss2json proxy on load and every 90 seconds. Each headline is cleaned, deduped by a 60-character key, and passed to the Warholification engine: the headline is matched against ten thematic signature regexes (death/disaster, finance, technology, politics, celebrity, climate, science, conflict, food, identity), and a frame is selected from the matched set's array of ten variants — or a generic frame from fifteen options. The frame text is then optionally passed through one of eight STRUCT transforms (repetition, dash repeat, tripling, edition numbering, ellipsis, NO. prefix, parenthesis, slash repeat) at 38% probability. The Warholified text is displayed below the raw headline in a colour-blocked cell. Twelve palettes cycle with no duplicate background on screen. Each cell is stamped with a series label from twelve options and a unique four-digit serial. Clicking a cell copies an image generation prompt to clipboard; a toast confirms. A ticker runs the raw pool. SNAPSHOT triggers a canvas composite export as PNG.

Phi-3.5-mini-instruct is loaded via WebLLM and WebGPU (Chrome/Edge 113+). The model outputs strict JSON with three keys: title (1–4 uppercase words), instructions (1–6 imperatives, one of which must be impossible or must change the meaning of the others), and duration_seconds (15–70). Subject matter is drawn from ~80 seed prompts: AI systems, crypto, attention, latency, notification liturgy, late-session Vesper seeds, and Frame seeds addressing the act of reading on a device. The decay bar drains over duration_seconds. The PERFORM button requires an 800ms hold to register. Outcome is stamped: PERFORMED, EXPIRED, or MISSED. All outcomes are logged to localStorage. After five or more scores, a behavioural observation surfaces — flat, non-evaluative. Once per session, generation is bypassed for a hardcoded Confession score. When JSON generation fails, the error surfaces as a score. Mobile receives one of eight static scores.

Llama-3.2-3B-Instruct-q4f16_1-MLC is loaded via WebLLM and WebGPU (Chrome/Edge 113+; desktop only). Five prompt templates cycle in random order, each seeding a different episode type: paternity and infidelity, family implosion, addiction and recovery, workplace transgression, secrets returning. Each template requests strict JSON output with two keys: name (first name, era-correct working-class American) and descriptor (6–12 words, reductive, present or perfect tense, written by a production assistant who feels nothing). Output is sanitised, validated for length and character set, and rejected if it contains code-like characters, CJK runs, or JS keywords; up to three retries before a fallback fires. An image is selected from a curated set served from the ./images/ directory, discovered on boot via an images.json manifest (required for Netlify static hosting); a directory-listing fallback covers local dev. Images are shuffled without immediate repeat. A 30-second countdown ring triggers each new cycle; the next chyron is prefetched during the countdown so display is near-instantaneous. Space bar forces an immediate cycle. P pauses. Every generated chyron is logged to a session panel (Shift+L); clicking any log entry recalls it to screen. A boot sequence animates on first load. Mobile and non-WebGPU browsers are blocked with a message overlay.

Phi-3.5-mini-instruct is loaded via WebLLM and WebGPU. Seven advertising registers (Direct Response, Corporate Manifesto, Pseudo-Scientific, Testimonial, Minimalist Luxury, Surreal Sincere, Emotional Leverage) are crossed against six thematic territories (Odd Household, Body Futures, Private Infrastructure, Chain Grocery, AI Domestic, Platform Self) via a weighted collision table of predefined mode/theme pairs. Each register carries a structured prompt specifying subhead and CTA rules. The model is asked to output strict JSON: product_name, qualifier, headline, subhead, features (array), testimonial_text, testimonial_source, fine_print, price_marker, cta. Output is sanitised and parsed; Phi-3.5-mini escaping artefacts (literal backslash-quote) are repaired before parse. A gate filter rejects generic or prohibited tokens; up to three attempts before fallback fires. At approximately 8% probability, once per run, a meta modifier is injected: the model is instructed to sell the sign-generation system itself. A handwritten fallback corpus (authored to the benchmark standard the model is trying to clear) is served while the model loads and substituted on generation failure. On no-WebGPU hardware a degraded-capacity notice is injected as the first corpus entry. On mobile the store-closed screen is displayed and the model does not initialise. Signs advance on a 30-second auto-timer; Space bar forces immediate advance. Lighting modes cycle (S toggles sound, F toggles fullscreen). Blue indicator dot: corpus active. Green: model ready.

Live price and 24h-change data for BTC, ETH, and SOL is polled from the CoinGecko markets endpoint. Direction (bull/bear) sets horizontal scroll bias and colour register (warm neutrals-to-black for bull, burnt umbers-to-near-black for bear); magnitude of volatility selects one of five intensity tiers per direction (flat, stable, active, turbulent, extreme), each carrying its own row count, wave frequency, amplitude, focal-compression strength, and shear. Ten resulting states are labelled with psychological vocabulary rather than trading terms (HESITATION → MANIA bull-side, UNEASE → EXHAUSTION bear-side). The field itself is rows of repeating characters, wave-displaced via a shared sine-displacement function, rendered inside a circular disc clipped to 48% of the shorter viewport dimension. A temporal-scar buffer (one float per row, 55 rows) accumulates bias proportional to volatility intensity and decays at 0.000001 per frame — turbulence experienced weeks earlier still measurably increases wave amplitude today. A hidden canvas render is hashed via SHA-256 to fingerprint the rendering machine (GPU/driver/font-rasterisation dependent), deriving a deterministic per-device phase offset, shear bias, and row stagger that is stable across reloads on the same machine. Rare events (flash, collapse, inversion, silence) are probability-gated — 0.0012/frame at extreme intensity, 0.0004/frame at turbulent, zero otherwise — with a minimum 3-minute gap between occurrences. An attention region locally suppresses wave amplitude near the cursor's dwell point, briefly clarifying the field, then lets it degrade again. Three render modes (TEXT, TICKER, SIGNAL/moiré) change presentation only; the underlying data and state are shared.

Five phrase-level corpus banks (Bureaucrat, Net, Fiction, Sticker, Signal) supply a three-line stamp template, selected by weighted random draw (Bureaucrat 26%, Net 26%, Fiction 4%, Sticker 22%, Signal 22%). The Fiction archetype occasionally builds from live r/worldnews top headlines (fetched daily, JSON endpoint) rather than the static corpus, blending real geopolitical language into the invented-bureaucracy register. Each badge is rendered as layered SVG: a radial-gradient base tint per archetype, a mylar dome highlight and secondary specular lobe, an edge tension ring, a curvature shadow, and a procedural weathering group — turbulence-driven scratches and tarnish accumulate per-badge via feTurbulence and displacement filters, seeded per stamp so no two badges weather identically. A CMYK misregistration ghost fires once on the title SVG during the stamp animation, then settles. Stamps are archived via the storage API (capped run, oldest dropped on overflow) and can be exported individually as PNG or as a single contact-sheet image of the full archived run.

A live microphone is captured at 16kHz via a custom AudioWorklet VAD processor — a noise-floor tracker with a 2.0× multiplier threshold separates speech from ambient room sound before any model sees the audio. Detected speech is passed to Whisper-tiny.en, loaded via Transformers.js. Recognised text is filtered against a Whisper-hallucination token list ([BLANK_AUDIO], [silence], non-speech sound annotations) and treated as silence if matched. Valid transcripts are sent to Llama-3.2-1B-Instruct via WebLLM, with one of three system prompts selected by state: VOID (room speaking into silence — Ruscha/Sanchez register, one sentence, max 14 words, roughly 1-in-4 collapsed to a 1–3 word fragment), SALESMAN (advertising-closer register responding to the last utterance, max 16 words, must contain "you"/"your" and one sales-register keyword), and SALESMAN_ACQUIRE (a rare ~1-in-6 inversion where the closer becomes a buyer of something intangible the listener revealed). A departure-aware variant fires when the listener falls silent after speaking. Output is spoken back via Kokoro-82M TTS. Utterances render as a 3D-perspective receding stack (current + three prior, each pushed back and faded) with an SVG turbulence/displacement filter simulating letterpress bite and a per-utterance misregistration ghost. Room lighting and warmth shift with state (municipal blue for listening, nicotine yellow for speaking).

Five public-domain texts (Moby-Dick, Frankenstein, Alice's Adventures in Wonderland, Dracula, The Metamorphosis) are fetched and triaged sentence-by-sentence into three regions — head, torso, legs — with a per-book quota to counter Moby-Dick's length dominance. Each region maintains a hundred-generation ancestry stack. Selection runs two channels: a slow channel (ecologyWords) reads each region's own ancestry only, weighted by recency via exponential decay — pure gravitational pull from a region's own history, no cross-region bleed. A fast channel (resonanceScore) compares a candidate fragment against a same-tick snapshot of the other two regions; a fast word already present in the receiver's own slow ecology gets multiplicatively amplified rather than linearly added — a resonance event rather than a steady lock — and decays back to a flat floor (RESONANCE_BASE 0.6) almost immediately. Final candidates are chosen by weighted roulette-wheel selection over the combined slow/fast score, not by maximum. Five behavioural states (obsession, decay, exploration, convergence, fragmentation) each carry distinct pullMult / fusionProb / wordMutProb values and persist for 8–20 generations before a weighted transition to the next state, with early-session bias toward exploration. A reader may upload their own txt/pdf/json library via the footer control, which clears the canonical corpus and ancestry and re-triages entirely from the uploaded material; an eject control restores the five-novel default.

On load, the piece runs a synchronous device-profile pass collecting: browser identity and version (Chrome/Firefox/Safari/Edge/Brave/Opera/IE, version-gated outdated flags), GPU renderer and vendor strings via WEBGL_debug_renderer_info (classified as virtual, software-rendered, or hidden), hardware concurrency and device memory, cookie availability, connection effective type, dark-mode preference, screen resolution and pixel ratio (flagged non-standard if below 480,000px², above 5120px wide, or DPR above 3), timezone (three blocked zones), primary language family, mobile detection, canvas fingerprint (SHA-256-derived 8-character hex from a seeded 200×50 canvas draw), and an asynchronous OfflineAudioContext acoustic profile (triangle oscillator through a dynamics compressor, 44,100 sample render, 500-sample window sum, normalised to 8-character fingerprint). Each property is mapped to a detection trigger with a zone classification: temporal (weekend, outside operating hours), boundary (blocked timezone, non-English language family), pedestrian (mobile device, non-standard resolution, canvas fingerprint present), surface (IE, Brave, Opera, outdated browser, virtual/software/hidden GPU, low core count, low memory), or flow (ad blocker, cookies disabled, slow connection, dark mode). Triggered conditions are counted; the active zone is selected from the triggered set by a seeded hash of session timestamp. A matching notice layout is selected from 5 zone-specific banks (26 total variants) and rendered via a typed span assembly system (bar-black, bar-red, bar-white at variable widths; sub-black; rule; gap; data; label; label-inv) with per-element pseudo-random micro-registration jitter applied via a seeded hash function keyed to session timestamp. A 25% chance fires a generic non-detection layout from a separate pool of 8. The about-modal text (10 lines, partially pre-baked with ██ redactions) has additional lines programmatically redacted in proportion to the detection score: 0 triggers → only line 8 redacted; 1–2 → also lines 6, 2; 3+ → also lines 4, 1, 3, 7. Inspector-open state (outerWidth/outerHeight delta > 160px, polled at 800ms) adds 2 to the redaction score and flags the about-btn. Copy events of 4+ words have [SECTION REMOVED] spliced at the 30th percentile word boundary before clipboard delivery. Print media query renders odd children invisible (black-on-black) and even children near-transparent, with a piece-title pseudo-element reading PUBLIC REALM IMPROVEMENT — REDACTED FOR PLANNING PURPOSES. After 180 seconds of continuous session time, stage padding transitions over 8 seconds to narrow the corridor. After 120 seconds of pointer/keyboard/touch/scroll inactivity, the frame fades to 0.3 opacity and recovers. Returning after a background tab triggers a frame inset pulse. The console receives a formatted maintenance log on load and reports surface ID and acoustic profile on idle callback.