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This pass re-checks every \"not_found\"\n// name with multiple aggressive search strategies and a much lower match bar,\n// so real shows get many chances to surface. Results are stored with a match\n// score and pod.link so you can review the most-likely-real first.\n//\n// Statuses produced:\n// verified -> confident real match (score >= 0.8)\n// uncertain -> plausible real match (score >= 0.35) — REVIEW, sorted by score\n// not_found -> no plausible match across all strategies (still visible)\n//\n// RESUMABLE + rate-limit-safe, same as the first pass.\n// ============================================================================\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// Free-tier executions cap at ~1 minute. The recheck does up to 4 Apple\n// searches per name, so a small batch is needed to finish in time.\nconst BATCH = 12;\nconst THROTTLE_MS = 600;\nconst COUNTRY = \"US\";\n\nconst CONFIDENT = 0.8;\nconst PLAUSIBLE = 0.35;\n\nconst FILLER = new Set([\n \"podcast\", \"the\", \"show\", \"radio\", \"with\", \"a\", \"an\", \"official\", \"audio\",\n \"pod\", \"network\", \"series\", \"miniseries\", \"dispatch\", \"file\", \"inquest\",\n \"archive\", \"wire\", \"review\", \"report\", \"watch\", \"voice\", \"front\",\n \"expose\", \"today\", \"studies\", \"theory\", \"reader\", \"seminar\", \"workshop\",\n]);\n\nfunction norm(s: string): string {\n return (s || \"\").toLowerCase().replace(/[^a-z0-9]/g, \"\");\n}\nfunction core(s: string): string {\n return (s || \"\")\n .toLowerCase()\n .replace(/[^a-z0-9\\s]/g, \" \")\n .split(/\\s+/)\n .filter((w) => w && !FILLER.has(w))\n .join(\"\");\n}\nfunction tokens(s: string): string[] {\n return (s || \"\").toLowerCase().replace(/[^a-z0-9\\s]/g, \" \").split(/\\s+/)\n .filter(Boolean);\n}\nfunction titleSim(a: string, b: string): number {\n const na = norm(a), nb = norm(b);\n if (!na || !nb) return 0;\n if (na === nb) return 1;\n const ca = core(a), cb = core(b);\n if (ca && cb && ca === cb) return 1;\n let contain = 0;\n if (ca && cb && (ca.includes(cb) || cb.includes(ca))) {\n const ratio = Math.min(ca.length, cb.length) / Math.max(ca.length, cb.length);\n contain = ratio >= 0.7 ? 0.9 : ratio * 0.5;\n }\n const ta = new Set(tokens(a).filter((w) => !FILLER.has(w)));\n const tb = new Set(tokens(b).filter((w) => !FILLER.has(w)));\n let inter = 0;\n for (const t of ta) if (tb.has(t)) inter++;\n const union = new Set([...ta, ...tb]).size || 1;\n const jac = inter / union;\n return Math.max(contain, jac);\n}\n\nasync function itunesSearch(term: string, limit = 10): Promise {\n const url = \"https://itunes.apple.com/search?\" +\n new URLSearchParams({\n term,\n media: \"podcast\",\n entity: \"podcast\",\n limit: String(limit),\n country: COUNTRY,\n }).toString();\n try {\n const r = await fetch(url, { headers: { \"User-Agent\": \"FF-Directory-Verify/1.0\" } });\n if (!r.ok) return [];\n return (await r.json()).results || [];\n } catch (_e) {\n return [];\n }\n}\n\n// Build several query variants so a real show gets many chances to surface.\nfunction queryVariants(q: string): string[] {\n const out = new Set();\n const trimmed = q.replace(/\\b(podcast|audio|pod|show|radio|series|the|network|today|with|a|an)\\b/ig, \"\")\n .replace(/\\s+/g, \" \").trim();\n if (trimmed && trimmed.length >= 2) out.add(trimmed);\n const toks = tokens(q).filter((w) => !FILLER.has(w));\n if (toks.length >= 2) out.add(toks.slice(0, 2).join(\" \"));\n if (toks.length === 1) out.add(toks[0]);\n const c = core(q);\n if (c && c.length >= 4) out.add(c);\n return Array.from(out);\n}\n\nasync function verifyOne(query: string) {\n const variants = queryVariants(query);\n let best: any = null, bestScore = 0;\n\n for (const v of variants) {\n const results = await itunesSearch(v);\n for (const res of results) {\n const cand = res.collectionName || res.trackName || \"\";\n const s = titleSim(query, cand);\n if (s > bestScore) { bestScore = s; best = res; }\n }\n }\n\n if (!best) return { status: \"not_found\", record: null, score: 0 };\n\n const rec = {\n id: best.collectionId || best.trackId || \"\",\n name: best.collectionName || best.trackName || \"\",\n pub: best.artistName || \"\",\n link: (best.collectionId || best.trackId) ? `https://pod.link/${best.collectionId || best.trackId}` : (best.collectionViewUrl || \"\"),\n score: Math.round(bestScore * 100) / 100,\n };\n\n if (bestScore >= CONFIDENT) return { status: \"verified\", record: rec, score: bestScore };\n if (bestScore >= PLAUSIBLE) return { status: \"uncertain\", record: rec, score: bestScore };\n return { status: \"not_found\", record: null, score: bestScore };\n}\n\nexport async function recheckBatch(batchSize = BATCH): Promise {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS recheck_results (\n queried_name TEXT PRIMARY KEY,\n status TEXT NOT NULL,\n record TEXT,\n score REAL\n )`);\n\n const nf = await sqlite.execute(\"SELECT queried_name FROM verify_results WHERE status='not_found'\");\n const nfNames = nf.rows.map((r) => r.queried_name as string);\n\n const doneRows = await sqlite.execute(\"SELECT queried_name FROM recheck_results\");\n const done = new Set(doneRows.rows.map((r) => r.queried_name as string));\n const pending = nfNames.filter((n) => !done.has(n));\n\n console.log(`Not-found names to recheck: ${nfNames.length}`);\n console.log(`Already rechecked: ${done.size}`);\n console.log(`Pending: ${pending.length}`);\n\n if (pending.length === 0) { console.log(\"\\n=== RECHECK ALL DONE ===\"); return; }\n\n const todo = pending.slice(0, batchSize);\n const counts = { verified: 0, uncertain: 0, not_found: 0 };\n\n for (let i = 0; i < todo.length; i++) {\n const query = todo[i];\n const { status, record, score } = await verifyOne(query);\n counts[status as keyof typeof counts]++;\n await sqlite.execute({\n sql: \"INSERT OR REPLACE INTO recheck_results (queried_name, status, record, score) VALUES (?, ?, ?, ?)\",\n args: [query, status, record ? JSON.stringify(record) : null, score ?? null],\n });\n await new Promise((r) => setTimeout(r, THROTTLE_MS));\n }\n\n console.log(`\\nThis run rechecked ${todo.length}.`);\n console.log(` verified: ${counts.verified}`);\n console.log(` uncertain: ${counts.uncertain} (review, sorted by score)`);\n console.log(` still not_found: ${counts.not_found}`);\n console.log(`Progress: ${done.size + todo.length}/${nfNames.length} rechecked.`);\n}\n\n// NOTE: recheckBatch is exported for the worker to call. No top-level auto-run\n// here — the scheduled worker (worker.ts) drives one batch per tick, and direct\n// runs can call recheckBatch() themselves.\n","// ============================================================================\n// SECOND-PASS RE-CHECKER for names previously marked \"not_found\".\n// ----------------------------------------------------------------------------\n// The first pass was too strict: it dropped thousands of names that are real\n// podcasts under slightly-off titles. This pass re-checks every \"not_found\"\n// name with multiple aggressive search strategies and a much lower match bar,\n// so real shows get many chances to surface. Results are stored with a match\n// score and pod.link so you can review the most-likely-real first.\n//\n// Statuses produced:\n// verified -> confident real match (score >= 0.8)\n// uncertain -> plausible real match (score >= 0.35) — REVIEW, sorted by score\n// not_found -> no plausible match across all strategies (still visible)\n//\n// RESUMABLE + rate-limit-safe, same as the first pass.\n// ============================================================================\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// Free-tier executions cap at ~1 minute. The recheck does up to 4 Apple\n// searches per name, so a moderate batch is needed to finish in time.\nconst BATCH = 20;\nconst THROTTLE_MS = 600;\nconst COUNTRY = \"US\";\n\nconst CONFIDENT = 0.8;\nconst PLAUSIBLE = 0.35;\n\nconst FILLER = new Set([\n \"podcast\", \"the\", \"show\", \"radio\", \"with\", \"a\", \"an\", \"official\", \"audio\",\n \"pod\", \"network\", \"series\", \"miniseries\", \"dispatch\", \"file\", \"inquest\",\n \"archive\", \"wire\", \"review\", \"report\", \"watch\", \"voice\", \"front\",\n \"expose\", \"today\", \"studies\", \"theory\", \"reader\", \"seminar\", \"workshop\",\n]);\n\nfunction norm(s: string): string {\n return (s || \"\").toLowerCase().replace(/[^a-z0-9]/g, \"\");\n}\nfunction core(s: string): string {\n return (s || \"\")\n .toLowerCase()\n .replace(/[^a-z0-9\\s]/g, \" \")\n .split(/\\s+/)\n .filter((w) => w && !FILLER.has(w))\n .join(\"\");\n}\nfunction tokens(s: string): string[] {\n return (s || \"\").toLowerCase().replace(/[^a-z0-9\\s]/g, \" \").split(/\\s+/)\n .filter(Boolean);\n}\nfunction titleSim(a: string, b: string): number {\n const na = norm(a), nb = norm(b);\n if (!na || !nb) return 0;\n if (na === nb) return 1;\n const ca = core(a), cb = core(b);\n if (ca && cb && ca === cb) return 1;\n let contain = 0;\n if (ca && cb && (ca.includes(cb) || cb.includes(ca))) {\n const ratio = Math.min(ca.length, cb.length) / Math.max(ca.length, cb.length);\n contain = ratio >= 0.7 ? 0.9 : ratio * 0.5;\n }\n const ta = new Set(tokens(a).filter((w) => !FILLER.has(w)));\n const tb = new Set(tokens(b).filter((w) => !FILLER.has(w)));\n let inter = 0;\n for (const t of ta) if (tb.has(t)) inter++;\n const union = new Set([...ta, ...tb]).size || 1;\n const jac = inter / union;\n return Math.max(contain, jac);\n}\n\nasync function itunesSearch(term: string, limit = 10): Promise {\n const url = \"https://itunes.apple.com/search?\" +\n new URLSearchParams({\n term,\n media: \"podcast\",\n entity: \"podcast\",\n limit: String(limit),\n country: COUNTRY,\n }).toString();\n try {\n const r = await fetch(url, { headers: { \"User-Agent\": \"FF-Directory-Verify/1.0\" } });\n if (!r.ok) return [];\n return (await r.json()).results || [];\n } catch (_e) {\n return [];\n }\n}\n\n// Build several query variants so a real show gets many chances to surface.\nfunction queryVariants(q: string): string[] {\n const out = new Set();\n const trimmed = q.replace(/\\b(podcast|audio|pod|show|radio|series|the|network|today|with|a|an)\\b/ig, \"\")\n .replace(/\\s+/g, \" \").trim();\n if (trimmed && trimmed.length >= 2) out.add(trimmed);\n const toks = tokens(q).filter((w) => !FILLER.has(w));\n if (toks.length >= 2) out.add(toks.slice(0, 2).join(\" \"));\n if (toks.length === 1) out.add(toks[0]);\n const c = core(q);\n if (c && c.length >= 4) out.add(c);\n return Array.from(out);\n}\n\nasync function verifyOne(query: string) {\n const variants = queryVariants(query);\n let best: any = null, bestScore = 0;\n\n for (const v of variants) {\n const results = await itunesSearch(v);\n for (const res of results) {\n const cand = res.collectionName || res.trackName || \"\";\n const s = titleSim(query, cand);\n if (s > bestScore) { bestScore = s; best = res; }\n }\n }\n\n if (!best) return { status: \"not_found\", record: null, score: 0 };\n\n const rec = {\n id: best.collectionId || best.trackId || \"\",\n name: best.collectionName || best.trackName || \"\",\n pub: best.artistName || \"\",\n link: (best.collectionId || best.trackId) ? `https://pod.link/${best.collectionId || best.trackId}` : (best.collectionViewUrl || \"\"),\n score: Math.round(bestScore * 100) / 100,\n };\n\n if (bestScore >= CONFIDENT) return { status: \"verified\", record: rec, score: bestScore };\n if (bestScore >= PLAUSIBLE) return { status: \"uncertain\", record: rec, score: bestScore };\n return { status: \"not_found\", record: null, score: bestScore };\n}\n\nexport async function recheckBatch(batchSize = BATCH): Promise {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS recheck_results (\n queried_name TEXT PRIMARY KEY,\n status TEXT NOT NULL,\n record TEXT,\n score REAL\n )`);\n\n const nf = await sqlite.execute(\"SELECT queried_name FROM verify_results WHERE status='not_found'\");\n const nfNames = nf.rows.map((r) => r.queried_name as string);\n\n const doneRows = await sqlite.execute(\"SELECT queried_name FROM recheck_results\");\n const done = new Set(doneRows.rows.map((r) => r.queried_name as string));\n const pending = nfNames.filter((n) => !done.has(n));\n\n console.log(`Not-found names to recheck: ${nfNames.length}`);\n console.log(`Already rechecked: ${done.size}`);\n console.log(`Pending: ${pending.length}`);\n\n if (pending.length === 0) { console.log(\"\\n=== RECHECK ALL DONE ===\"); return; }\n\n const todo = pending.slice(0, batchSize);\n const counts = { verified: 0, uncertain: 0, not_found: 0 };\n\n for (let i = 0; i < todo.length; i++) {\n const query = todo[i];\n const { status, record, score } = await verifyOne(query);\n counts[status as keyof typeof counts]++;\n await sqlite.execute({\n sql: \"INSERT OR REPLACE INTO recheck_results (queried_name, status, record, score) VALUES (?, ?, ?, ?)\",\n args: [query, status, record ? JSON.stringify(record) : null, score ?? null],\n });\n await new Promise((r) => setTimeout(r, THROTTLE_MS));\n }\n\n console.log(`\\nThis run rechecked ${todo.length}.`);\n console.log(` verified: ${counts.verified}`);\n console.log(` uncertain: ${counts.uncertain} (review, sorted by score)`);\n console.log(` still not_found: ${counts.not_found}`);\n console.log(`Progress: ${done.size + todo.length}/${nfNames.length} rechecked.`);\n}\n\n// NOTE: recheckBatch is exported for the worker to call. 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Runs NOTHING to the \"fake\" bucket without a second,\n// more thorough lookup — because Apple's top-N fuzzy search misses real shows.\n//\n// Each result is one of four statuses:\n// verified -> confidently matched a real Apple show (kept)\n// uncertain -> a plausible near-match existed but wasn't an exact title;\n// kept for HUMAN review so no real show is dropped\n// not_found -> searched generously, no real show found (dropped)\n// (duplicate) -> matched but already in your existing directory\n//\n// RESUMABLE: a single execution has a time limit, so each run:\n// loads names, skips ones already in the DB, verifies BATCH (~150), saves\n// each result, prints totals + how far along. Run repeatedly until ALL DONE.\n// ============================================================================\n\nimport { blob } from \"https://esm.town/v/std/blob/main.ts\";\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// How many names to try per run. A run must finish under the execution time\n// limit. Apple rate-limits rapidly-fired requests (we got 403s on a burst),\n// so the batch size and delay below are tuned to stay well clear of that:\n// BATCH requests, each preceded by a THROTTLE_MS delay, and genuinely short.\nconst BATCH = 50;\nconst THROTTLE_MS = 600; // real politeness delay between Apple calls\nconst COUNTRY = \"US\";\n\n// Title-similarity thresholds:\n// >= CONFIDENT : treat as a verified real match\n// >= PLAUSIBLE : a real show might exist under a slightly different title\n// -> mark \"uncertain\" (human review) instead of \"not_found\"\nconst CONFIDENT = 0.8;\n// Real shows are frequently listed under a slightly different title on Apple\n// (e.g. \"Abolition Science\" vs \"Abolition Science Radio\"), and Apple's fuzzy\n// search often surfaces them imperfectly. So anything with at least a partial\n// word overlap is flagged for human review rather than silently dropped. The\n// PLAUSIBLE bar is deliberately low so real shows aren't thrown away.\nconst PLAUSIBLE = 0.35;\n\n// ---- optional dedupe against existing shows (leave empty to skip) ----------\nconst EXISTING: { ids: number[]; names: string[] } = { ids: [], names: [] };\nconst existingIds = new Set(EXISTING.ids);\nconst existingNames = new Set(EXISTING.names);\n\n// ---- text + matching helpers ----------------------------------------------\nfunction norm(s: string): string {\n return (s || \"\").toLowerCase().replace(/[^a-z0-9]/g, \"\");\n}\nconst FILLER = new Set([\n \"podcast\", \"the\", \"show\", \"radio\", \"with\", \"a\", \"an\", \"official\", \"audio\",\n \"pod\", \"network\", \"series\", \"miniseries\", \"dispatch\", \"file\", \"inquest\",\n \"archive\", \"wire\", \"review\", \"report\", \"watch\", \"voice\", \"front\",\n \"expose\", \"today\", \"studies\", \"theory\", \"reader\", \"seminar\", \"workshop\",\n]);\nfunction core(s: string): string {\n return (s || \"\")\n .toLowerCase()\n .replace(/[^a-z0-9\\s]/g, \" \")\n .split(/\\s+/)\n .filter((w) => w && !FILLER.has(w))\n .join(\"\");\n}\nfunction tokens(s: string): string[] {\n return (s || \"\").toLowerCase().replace(/[^a-z0-9\\s]/g, \" \").split(/\\s+/)\n .filter(Boolean);\n}\n// Similarity score 0..1. Handles filler words, minor punctuation/typos.\nfunction titleSim(a: string, b: string): number {\n const na = norm(a), nb = norm(b);\n if (!na || !nb) return 0;\n if (na === nb) return 1;\n const ca = core(a), cb = core(b);\n if (ca && cb && ca === cb) return 1;\n let contain = 0;\n if (ca && cb && (ca.includes(cb) || cb.includes(ca))) {\n const ratio = Math.min(ca.length, cb.length) / Math.max(ca.length, cb.length);\n contain = ratio >= 0.7 ? 0.9 : ratio * 0.5;\n }\n const ta = new Set(tokens(a).filter((w) => !FILLER.has(w)));\n const tb = new Set(tokens(b).filter((w) => !FILLER.has(w)));\n let inter = 0;\n for (const t of ta) if (tb.has(t)) inter++;\n const union = new Set([...ta, ...tb]).size || 1;\n const jac = inter / union;\n return Math.max(contain, jac);\n}\n\n// ---- Apple search (generous: longer result list) --------------------------\nasync function itunesSearch(term: string, limit = 10): Promise {\n const url = \"https://itunes.apple.com/search?\" +\n new URLSearchParams({\n term,\n media: \"podcast\",\n entity: \"podcast\",\n limit: String(limit),\n country: COUNTRY,\n }).toString();\n try {\n const r = await fetch(url, { headers: { \"User-Agent\": \"FF-Directory-Verify/1.0\" } });\n if (!r.ok) return [];\n return (await r.json()).results || [];\n } catch (_e) {\n return [];\n }\n}\n\n// ---- classification (mirrors original directory's logic) ------------------\nconst FOCUS_KEYWORDS: [string, string[]][] = [\n [\"democracy\", [\"democracy\", \"democratic\", \"voting rights\", \"election integrity\", \"authoritarian\", \"fascism\", \"civic\", \"constitution\"]],\n [\"racial_justice\", [\"racial justice\", \"race\", \"black\", \"blm\", \"civil rights\", \"anti-racis\", \"racism\"]],\n [\"climate\", [\"climate\", \"environment\", \"warming\", \"emissions\", \"sustainab\", \"green energy\", \"fossil\"]],\n [\"environmental_justice\", [\"environmental justice\", \"pollution\", \"frontline communit\", \"toxic\"]],\n [\"labor\", [\"labor\", \"union\", \"worker\", \"strike\", \"wages\", \"gig work\", \"organizing\"]],\n [\"immigration\", [\"immigra\", \"migrant\", \"border\", \"asylum\", \"refugee\", \"ice \", \"deportation\"]],\n [\"housing\", [\"housing\", \"rent\", \"tenant\", \"eviction\", \"homeless\", \"affordable housing\"]],\n [\"reproductive\", [\"reproductive\", \"abortion\", \"roe\", \"pro-choice\", \"reproductive rights\"]],\n [\"lgbtq_rights\", [\"lgbtq\", \"queer\", \"trans \", \"transgender\", \"gay \", \"pride\", \"gender identity\"]],\n [\"disability_rights\", [\"disability\", \"disabled\", \"ableism\", \"accessib\", \"neurodiver\"]],\n [\"criminal_justice\", [\"criminal justice\", \"prison\", \"incarcerat\", \"police\", \"carceral\", \"abolition\", \"mass incarceration\"]],\n [\"health\", [\"health care\", \"healthcare\", \"public health\", \"medicaid\", \"medicare\", \"mental health\"]],\n [\"indigenous\", [\"indigenous\", \"native american\", \"tribal\", \"first nations\", \"sovereignty\"]],\n [\"socialism\", [\"socialis\", \"marxis\", \"communis\", \"anti-capitalis\", \"leftist theory\", \"class struggle\"]],\n [\"feminism\", [\"feminis\", \"women's rights\", \"gender equality\", \"patriarchy\"]],\n [\"education\", [\"education\", \"public school\", \"teacher\", \"student debt\", \"curriculum\"]],\n];\nconst FN_KEYWORDS: [string, string[]][] = [\n [\"local_journalism\", [\"local news\", \"city council\", \"statehouse\", \"municipal\", \"county\", \"regional\", \"community journalism\", \"public radio\", \"local report\"]],\n [\"electoral\", [\"election\", \"campaign\", \"candidate\", \"ballot\", \"voter\", \"primary\", \"electoral\", \"politics\", \"political analysis\", \"congress\", \"senate\"]],\n [\"democratic_institutions\", [\"democracy\", \"institution\", \"court\", \"supreme court\", \"constitution\", \"governance\", \"policy analysis\", \"rule of law\"]],\n [\"issue_advocacy\", [\"advocacy\", \"activis\", \"rights\", \"justice\", \"reform\", \"campaign for\", \"fight for\", \"movement for\"]],\n [\"community_movement\", [\"community\", \"movement\", \"grassroots\", \"organizing\", \"mutual aid\", \"solidarity\", \"collective\"]],\n];\nfunction classify(name: string, desc: string, eps: number, genres: string[]) {\n const text = (name + \" \" + desc + \" \" + (genres || []).join(\" \")).toLowerCase();\n let focus = \"\";\n for (const [f, kws] of FOCUS_KEYWORDS) {\n if (kws.some((k) => text.includes(k))) { focus = f; break; }\n }\n let fn = \"other\";\n for (const [f, kws] of FN_KEYWORDS) {\n if (kws.some((k) => text.includes(k))) { fn = f; break; }\n }\n const corePol = [\"politics\", \"election\", \"campaign\", \"congress\", \"senate\", \"policy\", \"government\", \"democrat\", \"republican\", \"left\", \"progressive\", \"conservative\"];\n const tier = corePol.some((k) => text.includes(k)) ? \"core_political\" : \"civic_adjacent\";\n const reach = eps >= 200 ? \"established\" : eps >= 50 ? \"growing\" : \"emerging\";\n const invest = reach === \"emerging\" ? \"high\" : reach === \"growing\" ? \"medium\" : \"low\";\n return { tier, fn, focus, reach, invest };\n}\nfunction toRecord(res: any) {\n const id = res.collectionId || res.trackId;\n const eps = res.trackCount || 0;\n const genres: string[] = res.genres || [];\n const name = res.collectionName || res.trackName || \"\";\n const cls = classify(name, \"\", eps, genres);\n return {\n id, name, pub: res.artistName || \"\", desc: \"\",\n tier: cls.tier, fn: cls.fn, focus: cls.focus, eps,\n yrs: 0, active: true, reach: cls.reach, invest: cls.invest,\n art: res.artworkUrl600 || res.artworkUrl100 || \"\",\n link: id ? `https://pod.link/${id}` : (res.collectionViewUrl || \"\"),\n rec: [], source: \"verified_apple\",\n feed: res.feedUrl || \"\", genres,\n score: 0,\n };\n}\n\n// ---- name loading ----------------------------------------------------------\nasync function parseNamesTxt(txt: string): Promise {\n try {\n const j = JSON.parse(txt);\n if (Array.isArray(j)) return j.map(String);\n } catch (_e) { /* not JSON */ }\n return txt.split(/\\r?\\n/).map((s) => s.trim()).filter(Boolean);\n}\nasync function loadAllNames(): Promise {\n const parts: string[] = [];\n for (let k = 1; k <= 50; k++) {\n try {\n const res = await blob.get(`names_${k}`);\n if (!res) break;\n parts.push(...(await parseNamesTxt(await res.text())));\n } catch (_e) { break; }\n }\n return parts;\n}\n\n// ---- verify ONE name, generously. Never drops on a single flaky search. ---\n// One search per name (Apple rate-limits bursts, so we keep volume down).\n// We search the filler-trimmed query (e.g. \"5-4 Podcast\" -> \"5-4\") which best\n// matches a real Apple title.\nasync function itunesSearchTrimmed(query: string): Promise {\n const trimmed = query\n .replace(/\\b(podcast|audio|pod|show|radio|series|the|network|today)\\b/ig, \"\")\n .replace(/\\s+/g, \" \").trim();\n return itunesSearch(trimmed && trimmed.length >= 2 ? trimmed : query);\n}\nasync function verifyOne(query: string) {\n const all = await itunesSearchTrimmed(query);\n\n let best: any = null, bestScore = 0;\n for (const res of all) {\n const cand = res.collectionName || res.trackName || \"\";\n const s = titleSim(query, cand);\n if (s > bestScore) { bestScore = s; best = res; }\n }\n\n if (!best) return { status: \"not_found\", record: null, score: 0 };\n\n if (bestScore >= CONFIDENT) {\n const rec = toRecord(best);\n rec.score = Math.round(bestScore * 100) / 100;\n // dedupe\n if (existingIds.has(rec.id) || existingNames.has(norm(rec.name))) {\n return { status: \"duplicate\", record: rec, score: bestScore };\n }\n return { status: \"verified\", record: rec, score: bestScore };\n }\n\n if (bestScore >= PLAUSIBLE) {\n // A real show may exist under a near-but-not-exact title. Store it as\n // \"uncertain\" WITH the best candidate, so a human can confirm. Not dropped.\n const rec = toRecord(best);\n rec.score = Math.round(bestScore * 100) / 100;\n return { status: \"uncertain\", record: rec, score: bestScore };\n }\n\n return { status: \"not_found\", record: null, score: bestScore };\n}\n\n// ---- process ONE batch, safely paced, and persist results -----------------\nexport async function processOneBatch(batchSize = BATCH): Promise {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS verify_results (\n queried_name TEXT PRIMARY KEY,\n status TEXT NOT NULL,\n record TEXT,\n score REAL\n )`);\n\n const names = await loadAllNames();\n const uniqueNames = Array.from(new Set(\n names.map((n) => (n || \"\").trim()).filter((n) => n && n.toLowerCase() !== \"podcast\"),\n ));\n\n const doneRows = await sqlite.execute(\"SELECT queried_name FROM verify_results\");\n const done = new Set(doneRows.rows.map((r) => r.queried_name as string));\n const pending = uniqueNames.filter((n) => !done.has(n));\n\n console.log(`Total unique names: ${uniqueNames.length}`);\n console.log(`Already done: ${done.size}`);\n console.log(`Pending: ${pending.length}`);\n\n if (pending.length === 0) { console.log(\"\\n=== ALL DONE ===\"); return; }\n\n const todo = pending.slice(0, Math.min(batchSize, BATCH));\n const counts = { verified: 0, uncertain: 0, not_found: 0, duplicate: 0 };\n\n for (let i = 0; i < todo.length; i++) {\n const query = todo[i];\n const { status, record, score } = await verifyOne(query);\n counts[status as keyof typeof counts]++;\n await sqlite.execute({\n sql: \"INSERT OR REPLACE INTO verify_results (queried_name, status, record, score) VALUES (?, ?, ?, ?)\",\n args: [query, status, record ? JSON.stringify(record) : null, score ?? null],\n });\n // Real politeness delay so Apple doesn't rate-limit us.\n await new Promise((r) => setTimeout(r, THROTTLE_MS));\n if ((i + 1) % 50 === 0) {\n console.log(`... ${i + 1}/${todo.length} this run | done overall ~${done.size + i + 1}/${uniqueNames.length}`);\n }\n }\n\n console.log(`\\nThis run checked ${todo.length} names.`);\n console.log(` verified: ${counts.verified}`);\n console.log(` uncertain: ${counts.uncertain} (near-match, review before dropping)`);\n console.log(` not_found: ${counts.not_found}`);\n console.log(` duplicate: ${counts.duplicate}`);\n console.log(`Progress: ${done.size + todo.length}/${uniqueNames.length} complete.`);\n}\n\n// Allow running directly (script) OR importing (worker). Only auto-run when\n// executed directly, not when imported as a module.\nconst isMain = import.meta.main;\nif (isMain) {\n await processOneBatch();\n}\n",[156],"60680860-1a8f-487e-850c-c426216c229e","ingest.ts","e041c0d2-a86d-11f1-b23f-1607ee4eb77e",8,["D",1788532529232],"http","// Ingest page for the longlist verifier.\n// Paste your whole list of podcast names here, click Save — it writes them\n// into the val's blob storage under names_1 so main.ts can read them.\nimport { blob } from \"https://esm.town/v/std/blob/main.ts\";\n\nconst PAGE = `\n\nUpload podcast names\n\n

Paste your podcast names here

One per line, or as a JSON array — either works. Then click Save names.

\n \n

\n\n`;\n\nfunction parseNames(txt: string): string[] {\n try {\n const j = JSON.parse(txt);\n if (Array.isArray(j)) return j.map((s) => String(s).trim()).filter(Boolean);\n } catch (_e) {\n /* not JSON */\n }\n return txt.split(/\\r?\\n/).map((s) => s.trim()).filter(Boolean);\n}\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n if (req.method === \"POST\" && url.searchParams.get(\"action\") === \"save\") {\n try {\n const txt = await req.text();\n const names = parseNames(txt);\n await blob.set(\"names_1\", JSON.stringify(names));\n return new Response(\n `Saved ${names.length} names. Now run main.ts (the verifier) to check them.`,\n { headers: { \"Content-Type\": \"text/plain\" } },\n );\n } catch (e) {\n const msg = e instanceof Error ? `${e.name}: ${e.message}` : String(e);\n return new Response(\"ERROR: \" + msg, { status: 500, headers: { \"Content-Type\": \"text/plain\" } });\n }\n }\n return new Response(PAGE, { headers: { \"Content-Type\": \"text/html; charset=utf-8\" } });\n}\n",[163],"efeee165-61d5-41f6-b482-fea85e209155","export.ts","204dde48-a87a-11f1-867b-1607ee4eb77e",27,["D",1788630165888],"// Export page: downloads verification + recheck results grouped into clear\n// buckets so nothing is silently dropped. Recheck results are sorted by match\n// score (highest first) and include pod.link so you review the most-likely-real\n// first and stop when scores get too low to matter.\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nfunction esc(v: unknown): string {\n const s = v === null || v === undefined ? \"\" : String(v);\n if (/[\",\\n\\r]/.test(s)) return `\"${s.replace(/\"/g, '\"\"')}\"`;\n return s;\n}\nfunction toCsv(rows: any[], fields: string[]): string {\n const lines = [fields.join(\",\")];\n for (const r of rows) lines.push(fields.map((f) => esc(r[f])).join(\",\"));\n return lines.join(\"\\n\");\n}\n\nconst PAGE = `\n\nVerification results\n\n

Podcast verification results

Loading…

First pass (original)

\n Verified (keep)\n Review me\n Not found\n

Second pass — recheck of the \"not found\" list (sorted by match score)

👆 Start here: review the highest scores first, then stop when they get too low to bother. Every row has a pod.link to click straight to the show.

\n Recheck verified (0.80+)\n Recheck review (0.35–0.79)\n Still not found (<0.35)\n All recheck results\n

Everything

\n All results (CSV)\n\n`;\n\ntype Row = { queried_name?: unknown; status?: unknown; score?: unknown; record?: unknown };\nfunction recOf(r: Row): any {\n try { return JSON.parse((r.record as string) || \"{}\"); } catch { return {}; }\n}\nfunction rowToOut(r: Row) {\n const rec = recOf(r);\n const score = r.score === null || r.score === undefined ? \"\" : Math.round((r.score as number) * 100);\n return {\n queried_name: r.queried_name ?? \"\",\n status: r.status ?? \"\",\n score,\n matched_name: rec.name || \"\",\n itunes_id: rec.id ?? \"\",\n publisher: rec.pub || \"\",\n podlink: rec.link || \"\",\n };\n}\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const download = url.searchParams.get(\"download\");\n const wantSummary = url.searchParams.has(\"summary\");\n\n const vr = await sqlite.execute(\"SELECT queried_name, status, score, record FROM verify_results\");\n const mainRows = vr.rows as Row[];\n const rr = await sqlite.execute(\"SELECT queried_name, status, score, record FROM recheck_results\");\n const recheckRows = rr.rows as Row[];\n\n if (wantSummary) {\n const c: any = { verified: 0, uncertain: 0, not_found: 0, duplicate: 0, total: mainRows.length, rch_verified: 0, rch_uncertain: 0, rch_notfound: 0, rch_total: recheckRows.length };\n for (const r of mainRows) { if ((r.status as string) in c) c[r.status as string]++; }\n for (const r of recheckRows) {\n if (r.status === \"verified\") c.rch_verified++;\n else if (r.status === \"uncertain\") c.rch_uncertain++;\n else if (r.status === \"not_found\") c.rch_notfound++;\n }\n return new Response(JSON.stringify(c), { headers: { \"Content-Type\": \"application/json\" } });\n }\n\n if (!download) return new Response(PAGE, { headers: { \"Content-Type\": \"text/html; charset=utf-8\" } });\n\n const fields = [\"queried_name\", \"status\", \"score\", \"matched_name\", \"itunes_id\", \"publisher\", \"podlink\"];\n let csvRows: any[] = [];\n\n if (download === \"recheck_verified\" || download === \"recheck_uncertain\" ||\n download === \"recheck_notfound\" || download === \"recheck_all\") {\n const st = download.replace(\"recheck_\", \"\");\n let rows = recheckRows;\n if (st === \"verified\") rows = recheckRows.filter((r) => r.status === \"verified\");\n else if (st === \"uncertain\") rows = recheckRows.filter((r) => r.status === \"uncertain\");\n else if (st === \"notfound\") rows = recheckRows.filter((r) => r.status === \"not_found\");\n // sort by score descending so you review most-likely-real first\n rows = [...rows].sort((a, b) => {\n const sa = (a.score as number) || 0, sb = (b.score as number) || 0;\n return sb - sa;\n });\n csvRows = rows.map(rowToOut);\n const fname = st === \"notfound\" ? \"recheck_notfound\" : `recheck_${st}`;\n return new Response(toCsv(csvRows, fields), {\n headers: { \"Content-Type\": \"text/csv\", \"Content-Disposition\": `attachment; filename=\"${fname}.csv\"` },\n });\n }\n\n // original buckets\n let rows = mainRows;\n if (download !== \"all\") rows = mainRows.filter((r) => r.status === download);\n csvRows = rows.map(rowToOut);\n const fname = (download === \"all\" ? \"all\" : download) || \"all\";\n return new Response(toCsv(csvRows, fields), {\n headers: { \"Content-Type\": \"text/csv\", \"Content-Disposition\": `attachment; filename=\"podcast_${fname}.csv\"` },\n });\n}\n",[171],"pulls","branches",1,"forks","envVars",{"_185":69,"_187":69},"connections",{"_185":69,"_186":69},"count","unfilledCount","emptyCount",{"_49":170,"_38":171,"_36":172,"_142":-5,"_47":48,"_46":173,"_144":31,"_44":206,"_13":146,"_39":167,"_41":175,"_30":31,"_149":-5,"_194":207,"_33":208},{"_49":162,"_38":163,"_36":164,"_142":-5,"_47":48,"_46":165,"_144":65,"_44":203,"_13":69,"_39":167,"_41":168,"_30":31,"_149":-5,"_194":204,"_33":205},{"_49":155,"_38":156,"_36":63,"_142":-5,"_47":48,"_46":157,"_144":65,"_44":200,"_13":159,"_39":40,"_41":160,"_30":31,"_149":-5,"_194":201,"_33":202},{"_49":151,"_38":34,"_36":37,"_142":-5,"_47":48,"_46":14,"_144":65,"_44":197,"_13":153,"_39":40,"_41":43,"_30":31,"_149":-5,"_194":198,"_33":199},{"_49":139,"_38":140,"_36":141,"_142":-5,"_47":48,"_46":143,"_144":31,"_44":193,"_13":146,"_39":147,"_41":148,"_30":31,"_149":-5,"_194":195,"_33":196},["D",1788630175808],"children",[],[140],["D",1788630375713],[],[34],["D",1788538023077],[],[156],["D",1788532529232],[],[163],["D",1788630165888],[],[171],["D",1788530401973],["D",1789488693443],{"_106":105,"_112":59},{"_106":105,"_107":59,"_108":59,"_19":61,"_109":110,"_111":31},{"_13":95,"_38":96,"_49":48,"_97":50,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":-5,"_103":51,"_104":105},"ctx",{"_88":-5,"_131":247,"_248":249,"_250":251,"_252":31,"_253":31,"_254":31,"_255":65,"_256":-5,"_257":-7,"_258":-7,"_259":69,"_260":31,"_261":31,"_262":-5,"_263":31,"_264":31,"_265":31,"_266":-5},"ENV",{"_218":219,"_220":221,"_222":96,"_223":224,"_225":226,"_227":228,"_229":230,"_231":232,"_233":234,"_235":236,"_237":238,"_239":240,"_241":242,"_243":244,"_245":246},"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"]

Diff · v32