[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_246":247,"_248":249},"routes/_app.x.$handle.$name",{"_99":98,"_31":44,"_107":89,"_26":108,"_13":14,"_109":-5,"_11":110,"_111":112,"_113":114,"_115":116,"_117":118,"_20":-5,"_119":-5,"_120":-5,"_121":24,"_122":123,"_124":58,"_125":-5,"_126":-5,"_127":128},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_42":43,"_31":44,"_37":45,"_46":-5,"_47":24,"_48":49,"_50":49,"_51":52,"_53":-5,"_18":54,"_55":56,"_57":58,"_59":-5,"_60":-5,"_61":62,"_63":64,"_65":66,"_67":68,"_69":70,"_71":-5,"_72":73,"_74":73,"_75":76,"_77":76,"_78":76,"_79":73,"_80":73,"_81":82,"_83":84,"_85":86,"_87":24},"version",9,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":-7,"_23":24,"_25":24,"_26":27,"_28":27},"file",{"_29":30,"_31":32,"_33":34,"_35":36,"_37":38,"_39":14,"_40":41,"_23":24},"previous","deleted",false,"deletedHere","path","frontend/components/ButtonBar.tsx","latestPath","fileId","1fa234fa-5cc4-11f1-8c4d-1607ee4eb77e","name","ButtonBar.tsx","fileType","script","content","/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { DEVICE } from \"../device.ts\";\n\n// The three physical buttons along the bottom edge. This is the ENTIRE input\n// surface — every screen must express its choices as ≤3 of these. Pass exactly\n// DEVICE.buttons labels; a null label is a dead (unlabelled) key on that screen.\nexport function ButtonBar({\n labels,\n onPress,\n disabled,\n}: {\n labels: (string | null)[];\n onPress: (index: number) => void;\n disabled?: boolean;\n}) {\n const slots = Array.from({ length: DEVICE.buttons }, (_, i) => labels[i] ?? null);\n return (\n \n {slots.map((label, i) => (\n onPress(i)}\n className=\"py-3 text-[13px] font-bold uppercase tracking-wide disabled:opacity-25\"\n style={{\n background: DEVICE.paper,\n color: DEVICE.ink,\n borderLeft: i === 0 ? \"none\" : `1px solid ${DEVICE.ink}`,\n }}\n >\n {label ?? \"·\"}\n \n ))}\n \n );\n}\n","createdAt",["D",1780213283917],"branchVersion","branchId","b3ea59c8-5c81-11f1-90de-ee650bb23af1","id","b3e98642-5c81-11f1-90de-ee650bb23af1","primer",["D",1780184756415],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","Spaced-repetition learning substrate for the Mochi device","imageUrl","21f2e37e-8066-4730-9dc7-d27cf8d3c9f3","pinnedFileId","0123f508-5c84-11f1-8efd-1607ee4eb77e","pinPreview",true,"pinMigrated","screenshotVersion","likeCount",1,"orgId","0630d977-f678-453e-926c-52043293f59f","tursoName","b3e986425c8111f190deee650bb23af1","tursoHostname","b3e986425c8111f190deee650bb23af1-val-town.aws-us-east-2.turso.io","tursoMeasuredAt",["D",1789510270483],"pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_99":98,"_106":102},"org",{"_99":98,"_100":-5,"_101":102,"_18":54,"_103":104,"_105":24},"branch",{"_13":88,"_31":89,"_42":41,"_90":43,"_91":-5,"_92":-5,"_93":-5,"_94":-5,"_95":-5,"_96":44,"_97":98},"isOwned",86,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","c15r","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoicHJveHkiLCJzcmMiOiJodHRwczovL2ltYWdlcy5jbGVyay5kZXYvb2F1dGhfZ2l0aHViL2ltZ18yb2RJNlc1czZ1NlpZRmoxdGJ2eFVNNVhmeTYifQ","tier","pro","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_42":43,"_31":44,"_37":241,"_46":-5,"_47":24,"_48":49,"_50":49,"_51":52,"_53":-5,"_18":54,"_55":56,"_57":58,"_59":-5,"_60":-5,"_61":62,"_63":64,"_65":66,"_67":68,"_69":242,"_71":-5,"_72":73,"_74":73,"_75":76,"_77":76,"_78":76,"_79":73,"_80":73,"_81":243,"_83":244,"_85":245,"_87":24},"tree",[182,188,189,190,191],"files",[182,183,184,185,186,187,188,189,190,191],"counts",{"_172":149,"_173":62,"_174":149,"_175":176,"_177":178},"contents",[129,130,131,132,133],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_42":165,"_31":166,"_29":167,"_136":-5,"_40":41,"_39":168,"_138":58,"_37":169,"_13":149,"_33":170,"_35":-5,"_23":24,"_142":-5,"_26":171},{"_42":159,"_31":160,"_29":161,"_136":-5,"_40":41,"_39":62,"_138":24,"_37":162,"_13":149,"_33":20,"_35":163,"_23":24,"_142":-5,"_26":164},{"_42":152,"_31":153,"_29":154,"_136":-5,"_40":41,"_39":155,"_138":24,"_37":156,"_13":149,"_33":20,"_35":157,"_23":24,"_142":-5,"_26":158},{"_42":144,"_31":145,"_29":146,"_136":-5,"_40":41,"_39":147,"_138":24,"_37":148,"_13":149,"_33":34,"_35":150,"_23":24,"_142":-5,"_26":151},{"_42":134,"_31":135,"_29":56,"_136":-5,"_40":41,"_39":137,"_138":24,"_37":139,"_13":62,"_33":140,"_35":141,"_23":24,"_142":-5,"_26":143},"063badac-5cc4-11f1-822a-1607ee4eb77e","index.ts","parentId",5,"latest",["D",1780213241300],"http","import { parseVal, serveFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport { Hono } from \"npm:hono\";\nimport { applyGrades, getBranches, getProfiles, getSessionBatch, init } from \"./db.ts\";\n\n// Device-scoped bearer token. Provisioned out-of-band (firmware / Val Town secret),\n// never typed into the constrained UI. Defaults to \"dev-token\" so the prototype\n// runs immediately; set PRIMER_DEVICE_TOKEN to lock it down for real.\nconst TOKEN = Deno.env.get(\"PRIMER_DEVICE_TOKEN\") ?? \"dev-token\";\n\nconst app = new Hono();\n\n// --- Frontend (open; it's only the simulator chrome + JS, no data) ---\napp.get(\"/\", () => serveFile(\"/frontend/index.html\"));\napp.get(\"/frontend/**/*\", (c) => serveFile(c.req.path));\napp.get(\"/source\", (c) => c.redirect(parseVal().links.self.val));\n\n// --- API (bearer-gated, device-scoped; profile named per request) ---\napp.use(\"/api/*\", async (c, next) => {\n if (c.req.header(\"Authorization\") !== `Bearer ${TOKEN}`) {\n return c.json({ error: \"unauthorized\" }, 401);\n }\n await init();\n await next();\n});\n\napp.get(\"/api/profiles\", async (c) => c.json({ profiles: await getProfiles() }));\napp.get(\"/api/branches\", async (c) => c.json({ branches: await getBranches() }));\napp.get(\"/api/session/:profile/:branch\", async (c) =>\n c.json({\n cards: await getSessionBatch(\n Number(c.req.param(\"profile\")),\n Number(c.req.param(\"branch\")),\n ),\n }));\napp.post(\"/api/grades\", async (c) => {\n const body = await c.req.json();\n const applied = await applyGrades(Number(body.profile), body.grades ?? []);\n return c.json({ ok: true, applied });\n});\n\n// Surface real stack traces instead of \"Internal Server Error\".\napp.onError((err) => Promise.reject(err));\n\nexport default app.fetch;\n","label",[135],"f99c901a-5c83-11f1-b6d4-1607ee4eb77e","db.ts","f99c90d8-5c83-11f1-b6d4-1607ee4eb77e",3,["D",1780185732336],0,"import { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// primer data layer: schema + seed + Leitner scheduling + the windowed session query.\n// One val-scoped SQLite DB. Generation (the cron/LLM) is NOT here yet — the\n// prototype runs on the seed grid and exercises only the *review* dynamics.\n\nexport type Grade = \"got\" | \"almost\" | \"miss\";\n\n// --- Leitner ---------------------------------------------------------------\n// Intervals by box (1..5), in ms. Box 1 = due again next session.\nconst DAY = 86_400_000;\nconst INTERVALS = [0, 0, 1 * DAY, 3 * DAY, 7 * DAY, 21 * DAY]; // index by box\n\nexport function nextBox(box: number, grade: Grade): number {\n if (grade === \"got\") return Math.min(box + 1, 5);\n if (grade === \"miss\") return 1;\n return box; // \"almost\" holds at the same box\n}\nexport function dueAt(box: number, now: number): number {\n return now + (INTERVALS[box] ?? 0);\n}\n\nconst SESSION_CAP = 12; // capped re-entry: max cards surfaced per session\n\n// --- Init / seed (idempotent, once per warm instance) ----------------------\nlet ready: Promise | null = null;\nexport function init(): Promise {\n return (ready ??= setup());\n}\n\nasync function setup() {\n await sqlite.batch([\n `CREATE TABLE IF NOT EXISTS profiles (id INTEGER PRIMARY KEY, label TEXT NOT NULL)`,\n `CREATE TABLE IF NOT EXISTS nodes (id INTEGER PRIMARY KEY, parent INTEGER, label TEXT NOT NULL, ord INTEGER DEFAULT 0)`,\n `CREATE TABLE IF NOT EXISTS cards (\n id INTEGER PRIMARY KEY,\n node_id INTEGER NOT NULL,\n op TEXT NOT NULL,\n operands TEXT NOT NULL,\n level INTEGER NOT NULL DEFAULT 1,\n q TEXT NOT NULL,\n a TEXT NOT NULL,\n content_hash TEXT,\n superseded_by INTEGER,\n UNIQUE(node_id, op, operands, level)\n )`,\n `CREATE TABLE IF NOT EXISTS box_state (\n profile_id INTEGER NOT NULL,\n card_id INTEGER NOT NULL,\n box INTEGER NOT NULL DEFAULT 1,\n due_at INTEGER NOT NULL DEFAULT 0,\n PRIMARY KEY (profile_id, card_id)\n )`,\n `CREATE TABLE IF NOT EXISTS reviews (\n id INTEGER PRIMARY KEY,\n profile_id INTEGER NOT NULL,\n card_id INTEGER NOT NULL,\n grade TEXT NOT NULL,\n ts INTEGER NOT NULL\n )`,\n ]);\n await seedIfEmpty();\n}\n\nasync function seedIfEmpty() {\n const r = await sqlite.execute(`SELECT COUNT(*) AS n FROM cards`);\n if (Number(r.rows[0].n) > 0) return;\n\n await sqlite.batch([\n { sql: `INSERT INTO profiles (id, label) VALUES (?, ?)`, args: [1, \"Ada (placeholder)\"] },\n { sql: `INSERT INTO profiles (id, label) VALUES (?, ?)`, args: [2, \"Bea (placeholder)\"] },\n ]);\n\n // Categories (parent NULL) and subcategories — the low-n seed grid.\n const nodes: [number, number | null, string, number][] = [\n [1, null, \"Numbers\", 0],\n [2, null, \"Words\", 1],\n [3, null, \"World\", 2],\n [11, 1, \"Addition\", 0],\n [12, 1, \"Counting\", 1],\n [21, 2, \"Spelling\", 0],\n [22, 2, \"Opposites\", 1],\n [31, 3, \"Animals\", 0],\n ];\n await sqlite.batch(\n nodes.map(([id, parent, label, ord]) => ({\n sql: `INSERT INTO nodes (id, parent, label, ord) VALUES (?, ?, ?, ?)`,\n args: [id, parent, label, ord],\n })),\n );\n\n // Seed cards — recall-style ops only (name / recall-instance / compose),\n // each with a pre-rendered question and answer. [node, op, operands, level, q, a]\n const cards: [number, string, string, number, string, string][] = [\n [11, \"name\", \"3+4\", 1, \"3 + 4 = ?\", \"7\"],\n [11, \"name\", \"6+5\", 1, \"6 + 5 = ?\", \"11\"],\n [11, \"name\", \"8+7\", 2, \"8 + 7 = ?\", \"15\"],\n [11, \"name\", \"9+6\", 2, \"9 + 6 = ?\", \"15\"],\n [12, \"name\", \"after-9\", 1, \"What comes after 9?\", \"10\"],\n [12, \"name\", \"after-19\", 1, \"What comes after 19?\", \"20\"],\n [12, \"name\", \"after-99\", 2, \"What comes after 99?\", \"100\"],\n [21, \"name\", \"cat\", 1, \"Spell the word: cat\", \"c — a — t\"],\n [21, \"name\", \"tree\", 1, \"Spell the word: tree\", \"t — r — e — e\"],\n [21, \"name\", \"friend\", 2, \"Spell the word: friend\", \"f — r — i — e — n — d\"],\n [22, \"name\", \"hot\", 1, \"Opposite of hot ?\", \"cold\"],\n [22, \"name\", \"up\", 1, \"Opposite of up ?\", \"down\"],\n [22, \"name\", \"fast\", 1, \"Opposite of fast ?\", \"slow\"],\n [31, \"recall-instance\", \"flies\", 1, \"Name an animal that can fly.\", \"e.g. bird, bat, butterfly\"],\n [31, \"recall-instance\", \"farm\", 1, \"Name a farm animal.\", \"e.g. cow, pig, sheep, hen\"],\n [31, \"compose\", \"red+fish\", 2, \"Say it together: a red fish\", \"a red fish\"],\n ];\n await sqlite.batch(\n cards.map(([node, op, operands, level, q, a]) => ({\n sql: `INSERT INTO cards (node_id, op, operands, level, q, a) VALUES (?, ?, ?, ?, ?, ?)`,\n args: [node, op, operands, level, q, a],\n })),\n );\n}\n\n// --- Queries ---------------------------------------------------------------\nexport async function getProfiles() {\n const r = await sqlite.execute(`SELECT id, label FROM profiles ORDER BY id`);\n return r.rows.map((row) => ({ id: Number(row.id), label: String(row.label) }));\n}\n\nexport async function getBranches() {\n const r = await sqlite.execute(\n `SELECT id, label FROM nodes WHERE parent IS NULL ORDER BY ord`,\n );\n return r.rows.map((row) => ({ id: Number(row.id), label: String(row.label) }));\n}\n\n// The windowed session batch: cards in the chosen branch that are due (or new)\n// for this profile, oldest-due first, capped. Pre-rendered, flat for the device.\nexport async function getSessionBatch(profileId: number, branchId: number) {\n const now = Date.now();\n const r = await sqlite.execute({\n sql: `\n SELECT c.id, c.op, c.q, c.a\n FROM cards c\n LEFT JOIN box_state b ON b.card_id = c.id AND b.profile_id = ?\n WHERE c.superseded_by IS NULL\n AND (c.node_id = ? OR c.node_id IN (SELECT id FROM nodes WHERE parent = ?))\n AND (b.card_id IS NULL OR b.due_at <= ?)\n ORDER BY COALESCE(b.due_at, 0) ASC, c.level ASC, c.id ASC\n LIMIT ?\n `,\n args: [profileId, branchId, branchId, now, SESSION_CAP],\n });\n return r.rows.map((row) => ({\n id: Number(row.id),\n op: String(row.op),\n q: String(row.q),\n a: String(row.a),\n }));\n}\n\n// Apply a batch of grades: advance Leitner boxes and log reviews. Atomic.\nexport async function applyGrades(\n profileId: number,\n grades: { id: number; grade: Grade }[],\n) {\n if (!grades?.length) return 0;\n const now = Date.now();\n const ids = grades.map((g) => g.id);\n const placeholders = ids.map(() => \"?\").join(\",\");\n const cur = await sqlite.execute({\n sql: `SELECT card_id, box FROM box_state WHERE profile_id = ? AND card_id IN (${placeholders})`,\n args: [profileId, ...ids],\n });\n const boxOf = new Map(cur.rows.map((row) => [Number(row.card_id), Number(row.box)]));\n\n const stmts: { sql: string; args: unknown[] }[] = [];\n for (const g of grades) {\n const prev = boxOf.get(g.id) ?? 1;\n const nb = nextBox(prev, g.grade);\n const due = dueAt(nb, now);\n stmts.push({\n sql: `INSERT INTO box_state (profile_id, card_id, box, due_at) VALUES (?, ?, ?, ?)\n ON CONFLICT(profile_id, card_id) DO UPDATE SET box = excluded.box, due_at = excluded.due_at`,\n args: [profileId, g.id, nb, due],\n });\n stmts.push({\n sql: `INSERT INTO reviews (profile_id, card_id, grade, ts) VALUES (?, ?, ?, ?)`,\n args: [profileId, g.id, g.grade, now],\n });\n }\n await sqlite.batch(stmts);\n return grades.length;\n}\n",[145],"d42f2e16-5c81-11f1-9562-ee650bb23af1","SPEC.md","d42f2eb6-5c81-11f1-9562-ee650bb23af1",2,["D",1780184810537],"# primer — design spec\n\nThe detailed design behind `README.md`. Sections are roughly independent; the load-bearing ideas are the **coordinate space** (§2) and the **three-component decomposition** (§1).\n\n## 1. Architecture\n\n### One substrate, three readers\n\nThe shared thing is the **card state** — every card, its coordinate, and per-child review history. Three components observe it independently and act at their own tempo. They never call each other; they coordinate only through the shared state. A blackboard, not a pipeline.\n\n- **Scheduler** (timing) — reads review history, decides what is *ripe*. Per-card, global, topic-blind.\n- **Curriculum** (structure) — reads the whole grid plus mastery and attention; extends the *coordinate space* (new nodes, operands, ladder rungs). Slow, global, low-volume. The control plane.\n- **Cards** (surface) — reads one empty coordinate, renders it. Fast, local, parallel, contextless. The data plane.\n\n### The coordinate space is the seam\n\nCurriculum lays down empty addressable slots; the card process consumes them; the scheduler times whatever exists. None talk directly — the coordinate space *is* the medium between them. Stigmergic: mark where a cell should be, let another component build it.\n\n### Invariant — structure is additive\n\nCurriculum **appends** to the coordinate space; it never mutates or deletes a live slot. A concept that turns out wrong is **superseded**, not deleted — a rarer, deliberate act. Append-only structure means the card process always sees a consistent, monotonically growing space, and the components never race. (Monotone growth ⇒ no coordination needed — the CALM intuition.)\n\n### Federation with Mochi\n\nprimer is a **self-contained val** — own SQLite, own generation, forkable alone. Mochi is its own substrate (an LLM pet with memory and ChatGPT-realtime voice). The **device** stitches them into one world; primer is reached through a *zone* in the pet's world. The zone is the seam — no shared backend.\n\n**Separate for now.** A later, deliberate coupling: the child's progress and current interests feed Mochi's context, so the pet knows what she's learning. That is the one link between the substrates, so it stays an explicit future choice.\n\n### Partitioning\n\nState is per child. The **tree is shared** — one coordinate space both daughters climb — but mastery, Leitner boxes, and the interest window are per child. The 8-year-old lives higher up the same ladders the 6-year-old climbs toward.\n\nAuth is **device-scoped** (bearer token). The **profile (child) is named per request** — one device serves either daughter; the token only gates access to the substrate.\n\n## 2. Data model\n\n### The grid (tech-tree)\n\nA **low-n, shallow** tree: categories → subcategories (nodes). Hand-seeded skeleton; curriculum deepens it. Low-n is load-bearing, not just minimal — it keeps the recombination space small enough that combinations stay coherent.\n\nNavigation is **self-directed**: across branches the child roams free (interest); within a branch, depth is **mastery-ordered** (count to 5 before count to 10). A sustained period of interest makes progress along that branch.\n\n### The card coordinate\n\n (node, operation, operands, level)\n\n- **node** — location in the grid (e.g. `numbers/addition`).\n- **operation** — cognitive template, a small fixed set (below).\n- **operands** — from the node's mastered-primitive vocabulary (words, numbers).\n- **level** — ladder rung.\n\nA **UNIQUE constraint on this tuple enforces DRY**: a proposed card whose coordinate exists fails to insert. Duplication is structurally impossible, not merely discouraged.\n\nA \"harder version of a similar question\" is **not** a near-duplicate — it is a *different coordinate by design* (same operands, higher operation or level). `(colour, name, apple, L1)` and `(colour, recall-instance, {red,3}, L2)` are different addresses. The structure encodes redundant-vs-progressive instead of asking similarity to guess.\n\n### Operations\n\nCognitive templates: `recognise · name · recall-instance · compose · contrast`. This set also doubles as the device's layout vocabulary (§5).\n\n**Note — reconcile with the 3-button grade model (§3).** Recall-style ops (`name`, `recall-instance`, `compose`) fit cleanly: attempt → reveal → self-grade with the three buttons. Answer-selection ops (`recognise`, `contrast`) want answer *input*, which contends for those same buttons — so they are likely **deferred** until there is a separate answer-input affordance (or the audio path). The prototype can ship recall-style only.\n\n### MECE as coverage\n\n- **Mutually exclusive** — unique coordinates plus a curated primitive vocabulary (no synonym primitives; `puppy` and `dog` as separate atoms would slip a real duplicate past the coordinate).\n- **Collectively exhaustive** — a *query*: which eligible slots in the active branch are empty? That set is the generator's worklist.\n\n### Tables (sketch)\n\n- `profiles` — children; id, label.\n- `nodes` — the grid; id, parent, label, order.\n- `operands` — per-node vocabulary; node_id, token.\n- `cards` — id, node_id, operation, operands (canonical), level, q (rendered), a (rendered), opts (rendered; recognition only), content_hash, superseded_by. `UNIQUE(node_id, operation, operands, level)`.\n- `reviews` — profile_id, card_id, grade, ts.\n- `box_state` — profile_id, card_id, leitner_box, due_at.\n- `mastery` — profile_id, node_id/operand, mastered_at (gates ladders and recombination).\n- `attention` — profile_id, node_id, rolling weight (drives windowing, with hysteresis).\n\n`content_hash` is a canonical-form hash of the rendered question — a cheap backstop for surface dupes from overlapping templates. Embeddings, if ever used, are a *same-coordinate* collision flag only, never a cross-card filter (that would eat the ladder).\n\n## 3. Review — grading, scheduling, windowing\n\n### Grading — three-button self-report\n\nThe child grades herself: **got it / almost / didn't know** — hardware-native on a three-button device. (At 6 and 8 they read the labels; `almost` still blurs toward `got it` for the younger — don't over-trust the middle.)\n\nThe **sequence is load-bearing**: attempt → reveal → grade. She must commit to a recall attempt *before* the answer appears, or the grade is noise.\n\nSelf-report is a **soft signal** — kids over-report. Generation treats it as *evidence, not truth*: several consistent `got it`s before laddering up, never one.\n\n**Deferred — audio judging.** The device has audio in/out (ChatGPT-realtime, Mochi side). A *spoken* answer captured before reveal makes the pre-commit real and recorded, and an LLM judge turns the grade from a claim into verification. But it puts the key on the *review* path (a call per answer, latency, cost), so adopt it **selectively** — occasional judging to *calibrate* self-report against ground truth, buttons the rest of the time.\n\n### Scheduling — Leitner\n\nLeitner boxes, over SM-2, for legibility, kid-fit, and a clean mastery signal (top box ⇒ trigger laddering/recombination).\n\nGrade → transition: `got it` promotes, `didn't know` demotes to box 1, `almost` holds. The three grades map onto the three generation moves: `didn't know` → scaffold down, `almost` → hold and add a near-variant, sustained `got it` → ladder up or recombine.\n\nThe scheduler is **independent and global** — per-card, decides what is *ripe*, topic-blind.\n\n### Windowing — self-direction over the ripe set\n\nSelf-direction is a **lens over scheduled content**, not a separate session generator. A card surfaces when it is both **due** (scheduler) and **in the interest window** (branch in focus). Dormant branches still go due but never enter a session — the window is elsewhere.\n\n**Hard window** is the default — purest self-direction, simplest. Wrinkle: returning to a long-dormant branch surfaces a backlog at once, rough for a child. Mitigation: **capped re-entry** — a limit on stale cards per session, or a warm-up.\n\n**Within-session stability, between-session adaptation** — window and generation recompute on the cron *between* sessions, so difficulty never shifts under the child mid-flow.\n\n## 4. Generation\n\nTwo parallel processes, split because global-awareness work and high-volume work are *different* work. Conflating them produces repetitive content.\n\n### Curriculum (control plane)\n\nSlow, global, low-volume. Reads the **whole grid** (small — low-n fits in context) plus mastery and attention; emits **structural edits**: a sub-node, new operands, a ladder rung. The model's judgement about the shape of knowledge lives here, and DRY/MECE are enforced here — it sees everything because everything is small. Edits are **additive**; retraction is **supersede** (§1).\n\n**Triggers** (not every tick): a mastery milestone (node mostly mastered → extend), sustained attention crossing the hysteresis line (deepen the attended branch), or the active region running out of empty slots.\n\n### Cards (data plane)\n\nFast, local, parallel. A pure function from one empty coordinate to a rendered card. **Sees no other card** — the coordinate guarantees uniqueness — so it cannot duplicate. The model does *surface realisation only*: phrasing, distractor choice. Could be a cheaper model. Each tick **fills one slot**, then idles when the region is covered.\n\nGeneration emits small **learning units** (teach card + tests), not bare flashcards — introduce material before quizzing (the Quantum Country move).\n\n### Why the context problem dissolves\n\nYou never send existing cards. Curriculum sends the whole (small) grid; the card process gets specific *empty* coordinates plus the (small) operand vocabulary. Existing cards are structurally unaddressable as targets, so context stays O(gap-list) regardless of deck size.\n\n### Distractors sit on the seam\n\n\"What are the wrong answers\" is more structural than free — draw distractors from the operand vocabulary; don't let the card process invent them, or they get repetitive or absurd. (Applies to recognition ops.)\n\n### Execution — key and cron\n\nGeneration runs **server-side** on a Val.town cron (interval val). The OpenAI key is a **Val.town secret**.\n\nA deliberate trade of the \"no keys on server\" posture — justified for a *private family* substrate on own infra, unlike the giftable Mochi case where that posture is load-bearing. The win: true background generation, matching the original \"periodic consolidation\" intent.\n\n**BYO-key cost shape**: a few expensive structural passes (curriculum) plus many tiny cheap renders (cards) — not many expensive calls each straining to see everything.\n\n## 5. API & device\n\n### Auth\n\nBearer token in `Authorization`. **Device-scoped, not child-scoped** — gates the substrate; the profile is named per request. A deliberate step down from sync's OAuth/passkeys, fit for a private device.\n\n### Efficient query — session-batched, not card-streamed\n\nePaper round-trips are slow, so the device does not fetch per card.\n\n- `GET /session?profile=…` → one **session batch**: the windowed due cards (small, by construction). Each card **pre-rendered** (ready-to-draw strings; the device never templates).\n- The device walks the batch **offline** — render, flip, capture grade.\n- `POST /grades` → all grades in one request at session end.\n\nOne fetch, one post. The server consolidates afterwards on its cron.\n\n### Payload — pre-rendered, flat, parser-friendly\n\nShaped for ArduinoJson: short flat keys, no deep nesting, strings rendered server-side. A card is roughly:\n\n { \"id\": 12, \"op\": \"name\", \"q\": \"…\", \"a\": \"…\" }\n\n`op` is the operation tag; `opts` appears only for recognition-style ops.\n\n### Procedural UI — the middle path\n\nThe device parses the batch and procedurally generates UI. Two extremes: *lean* (payload carries `op` + strings; device owns a fixed layout per operation — the low-n template set is the layout vocabulary; UI evolution needs a firmware flash) and *generic* (server sends an explicit widget list; UI evolves without a flash, at a heavier payload/interpreter).\n\n**Chosen: the middle** — a small declarative payload (`op` + strings + ids) read by a **fixed widget interpreter**. The low-n stable operation set makes the lean coupling cheap, while the interpreter stays generic enough to grow.\n\nUse **partial refresh** for front→back so only the reveal region redraws (full-refresh ghosts and is slow).\n\n### The zone\n\nprimer is reached through a **zone in Mochi's world**. Entering hands control to the embedded primer client (which talks to this API); leaving returns to the pet. The zone is the only integration point.\n\n## 6. Open / deferred\n\n- **Operation set vs buttons** (§2/§3) — ship recall-style ops first; `recognise`/`contrast` deferred pending an answer-input affordance.\n- **Audio judging** (§3) — selective calibration of self-report; not built.\n- **Pet-context coupling** (§1) — progress/interests → Mochi's memory; explicit future choice, the one thing that links the substrates.\n- **Cold start** — who authors the initial grid skeleton and seed primitives, so day-one isn't empty and the card process has known-good operands to combine.\n- **Shared-device profile selection** — the in-world \"who's playing\" mechanism before the zone loads.\n",[153],"b3ef1bf2-5c81-11f1-90de-ee650bb23af1","README.md","b3ef1c9c-5c81-11f1-90de-ee650bb23af1",["D",1780184756442],"# primer\n\nA self-contained spaced-repetition substrate for children, embedded as a zone in the Mochi virtual pet. Cards are generated and consolidated in the background; the deck unfolds along the child's interests rather than following a fixed curriculum.\n\n> Working name — _primer_, after the Young Lady's Illustrated Primer (Diamond Age): an adaptive book that teaches a child and grows with her. Rename freely.\n\n## Lineage\n\n- **Anki / spaced repetition** — the retention engine (Leitner boxes).\n- **Quantum Country** — cards as a mnemonic medium. Generation emits small *learning units* (a teach card + its tests), not bare flashcards.\n- **Diamond Age primer** — adaptive, child-directed, grows with the learner.\n- **Substrate thesis** — one shared card state; independent self-activating components (scheduler, curriculum, cards) observe it and act at their own tempo. Coordination is stigmergic, through the shared coordinate space, not message-passing.\n\n## Who and what\n\n- Two readers, 6 and 8. One shared tree; each climbs to her own depth, gated by her own mastery. State partitioned per child.\n- Form factor: the Mochi device — ESP32 + ePaper. Monochrome, slow refresh, a few buttons, audio in/out. Text-centric: written question, text reveal, graded by button. Content gravitates to text-symbolic domains — arithmetic, spelling, vocabulary, factual recall.\n\n## The core loop\n\n1. Child enters the primer zone in Mochi's world.\n2. Device fetches one **session batch** of due cards (the current interest window) over a bearer-token API.\n3. Per card: attempt → reveal → grade (got it / almost / didn't know). Graded **offline**, on-device.\n4. Device posts all grades back in one request at session end.\n5. Server consolidates between sessions on a cron: updates Leitner boxes, runs curriculum + card generation, recomputes the next window.\n\nAdaptation lands one session later by design — stable within a session, adaptive between them.\n\n## Status\n\nDesign phase. `SPEC.md` holds the converged design; implementation follows.\n",[160],"0fc603ae-5cc4-11f1-b9e7-1607ee4eb77e","frontend","6e562c3d-ef4d-4748-8d41-a53973623f6e",6,["D",1780213257290],"directory",[166],"pulls","branches","forks","envVars",{"_179":62,"_181":149},"connections",{"_179":149,"_180":149},"count","unfilledCount","emptyCount",{"_42":165,"_31":166,"_29":167,"_136":-5,"_40":41,"_39":168,"_138":58,"_37":238,"_13":149,"_33":170,"_35":-5,"_23":24,"_142":-5,"_193":239,"_26":240},{"_42":234,"_31":229,"_29":223,"_136":167,"_40":41,"_39":224,"_138":58,"_37":235,"_13":149,"_33":170,"_35":-5,"_23":24,"_142":-5,"_193":236,"_26":237},{"_42":230,"_31":32,"_29":30,"_136":223,"_40":41,"_39":14,"_138":58,"_37":231,"_13":149,"_33":34,"_35":36,"_23":24,"_142":-5,"_193":232,"_26":233},{"_42":220,"_31":221,"_29":222,"_136":223,"_40":41,"_39":224,"_138":58,"_37":225,"_13":149,"_33":34,"_35":226,"_23":24,"_142":-5,"_193":227,"_26":228},{"_42":212,"_31":213,"_29":214,"_136":167,"_40":41,"_39":215,"_138":24,"_37":216,"_13":149,"_33":34,"_35":217,"_23":24,"_142":-5,"_193":218,"_26":219},{"_42":205,"_31":206,"_29":207,"_136":167,"_40":41,"_39":168,"_138":24,"_37":208,"_13":149,"_33":34,"_35":209,"_23":24,"_142":-5,"_193":210,"_26":211},{"_42":159,"_31":160,"_29":161,"_136":-5,"_40":41,"_39":62,"_138":24,"_37":202,"_13":149,"_33":20,"_35":163,"_23":24,"_142":-5,"_193":203,"_26":204},{"_42":152,"_31":153,"_29":154,"_136":-5,"_40":41,"_39":155,"_138":24,"_37":199,"_13":149,"_33":20,"_35":157,"_23":24,"_142":-5,"_193":200,"_26":201},{"_42":144,"_31":145,"_29":146,"_136":-5,"_40":41,"_39":147,"_138":24,"_37":196,"_13":149,"_33":34,"_35":150,"_23":24,"_142":-5,"_193":197,"_26":198},{"_42":134,"_31":135,"_29":56,"_136":-5,"_40":41,"_39":137,"_138":24,"_37":192,"_13":62,"_33":140,"_35":141,"_23":24,"_142":-5,"_193":194,"_26":195},["D",1780213241300],"children",[],[135],["D",1780185732336],[],[145],["D",1780184810537],[],[153],["D",1780184756442],[],[160],"0fc5db36-5cc4-11f1-b9e7-1607ee4eb77e","device.ts","0fc5dbd6-5cc4-11f1-b9e7-1607ee4eb77e",["D",1780213257290],"// The device envelope. Every UI component reads from here, so the web\n// prototype can never accidentally exceed what the ePaper + ESP32 will do.\n// When real hardware specs land, change them HERE and the whole sim follows.\n\nexport const DEVICE = {\n // ePaper panel, portrait. Logical px ~= device px at 1x.\n screen: { w: 296, h: 416 },\n\n // Monochrome only. No colour is reachable on the panel — the sim renders\n // strictly in these two inks so we never design something we can't ship.\n ink: \"#111111\",\n paper: \"#f4f4ee\",\n\n // Physical buttons. THREE — which is exactly the grade taxonomy.\n // Anything needing more than three discrete inputs is out of scope until\n // an input affordance (or the audio path) exists.\n buttons: 3,\n\n // ePaper is slow. Simulated refresh delays make the rhythm honest:\n // full refresh flashes and is slow; partial refresh (the card flip) is quicker.\n refresh: { full: 700, partial: 200 },\n} as const;\n\n// The three buttons, in fixed order. Doubles as the grade vocabulary.\nexport const GRADES = [\n { key: \"miss\", label: \"Didn't know\" },\n { key: \"almost\", label: \"Almost\" },\n { key: \"got\", label: \"Got it\" },\n] as const;\n\nexport type GradeKey = (typeof GRADES)[number][\"key\"];\n\n// Bearer token. In the sim it's the dev token; on device it's provisioned to\n// firmware. Never entered through this UI — it models a device credential.\nexport const DEVICE_TOKEN = \"dev-token\";\n",[],[166,206],"154c88e8-5cc4-11f1-a41f-1607ee4eb77e","api.ts","154c8988-5cc4-11f1-a41f-1607ee4eb77e",7,["D",1780213266576],"import { DEVICE_TOKEN, type GradeKey } from \"./device.ts\";\n\n// Models the device's entire network surface: two calls per session.\n// Fetch one batch, post the buffered grades. No per-card round trips.\n\nexport type Card = { id: number; op: string; q: string; a: string };\nexport type Profile = { id: number; label: string };\nexport type Branch = { id: number; label: string };\n\nconst auth = { Authorization: `Bearer ${DEVICE_TOKEN}` };\n\nasync function get(path: string): Promise {\n const r = await fetch(path, { headers: auth });\n if (!r.ok) throw new Error(`${path} → ${r.status}`);\n return r.json();\n}\n\nexport async function fetchProfiles(): Promise {\n return (await get<{ profiles: Profile[] }>(\"/api/profiles\")).profiles;\n}\n\nexport async function fetchBranches(): Promise {\n return (await get<{ branches: Branch[] }>(\"/api/branches\")).branches;\n}\n\n// One fetch at session start: the windowed due batch for this profile + branch.\nexport async function fetchSession(profile: number, branch: number): Promise {\n return (await get<{ cards: Card[] }>(`/api/session/${profile}/${branch}`)).cards;\n}\n\n// One post at session end: all buffered grades together.\nexport async function postGrades(\n profile: number,\n grades: { id: number; grade: GradeKey }[],\n): Promise {\n const r = await fetch(\"/api/grades\", {\n method: \"POST\",\n headers: { ...auth, \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ profile, grades }),\n });\n if (!r.ok) throw new Error(`/api/grades → ${r.status}`);\n return (await r.json()).applied;\n}\n",[],[166,213],"1a721234-5cc4-11f1-ae05-1607ee4eb77e","Screen.tsx","1a721310-5cc4-11f1-ae05-1607ee4eb77e","9e07f141-773d-42a7-8481-8834afece6d2",8,["D",1780213275215],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { DEVICE } from \"../device.ts\";\n\n// The bezel. Constrains every child to the panel's exact pixel envelope and\n// two inks, so nothing inside can render at a size or colour the device can't.\nexport function Screen({ children }: { children: React.ReactNode }) {\n return (\n

\n \n \n {children}\n

\n \n

\n {DEVICE.screen.w}×{DEVICE.screen.h} · monochrome · {DEVICE.buttons} buttons\n

\n \n );\n}\n",[],[166,229,221],"components","1fa23432-5cc4-11f1-8c4d-1607ee4eb77e",["D",1780213283917],[],[166,229,32],"1a725302-5cc4-11f1-ae05-1607ee4eb77e",["D",1780213275215],[184,185],[166,229],["D",1780213257290],[183,186,187],[166],["D",1780184756415],["D",1789510270483],{"_99":98,"_106":102},{"_99":98,"_100":-5,"_101":102,"_18":54,"_103":104,"_105":24},{"_13":88,"_31":89,"_42":41,"_90":43,"_91":-5,"_92":-5,"_93":-5,"_94":-5,"_95":-5,"_96":44,"_97":98},"ctx",{"_81":-5,"_126":279,"_280":281,"_282":283,"_284":24,"_285":24,"_286":24,"_287":58,"_288":-5,"_289":-7,"_290":-7,"_291":149,"_292":24,"_293":24,"_294":-5,"_295":24,"_296":24,"_297":24,"_298":-5},"ENV",{"_250":251,"_252":253,"_254":89,"_255":256,"_257":258,"_259":260,"_261":262,"_263":264,"_265":266,"_267":268,"_269":270,"_271":272,"_273":274,"_275":276,"_277":278},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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