{\n if (!OPENAI_API_KEY || !tweets.length) {\n return buildLocalArticle(topic, tweets);\n }\n\n const top = tweets.slice(0, 10);\n const sourceBlock = top\n .map((t, i) => {\n const clean = cleanTweetProse(t.text).slice(0, 300);\n return `[${i + 1}] @${t.handle} (${t.likes}❤, ${t.retweets}🔁): ${clean}`;\n })\n .join(\"\\n\");\n\n const dateline = `NEW DELHI, ${istDateline()} —`;\n\n const system =\n `You are a senior wire-service correspondent. You MUST write a SINGLE news paragraph of EXACTLY 600 words (minimum 580, maximum 640).\n\nSTRICT RULES:\n- The body MUST be one continuous paragraph. No line breaks.\n- The body MUST contain at least 580 words. Do NOT stop early. Do NOT summarize. Keep expanding with context, background, implications, and forward-looking framing until you reach 600 words.\n- If the tweets are thin, expand by providing context: what led to this, who is affected, historical background, market context, and what to watch next — all inferred from the beat and topic, never inventing specific facts, quotes, or numbers.\n- Open the body with this exact dateline: \"${dateline}\"\n- Third person, past tense, wire-service style.\n- No bullet points, no hashtags, no emojis, no URLs, no markdown.\n\nOutput format — respond with exactly two lines:\nHEADLINE: \nBODY: \n\nBefore you finish, count your words. If under 580, keep writing. This is non-negotiable.`;\n\n const user = `Beat: ${topic.name} (${topic.section})\nContext: ${topic.context}\nDateline to open the body with verbatim: \"${dateline}\"\n\nTweets from Indian X:\n${sourceBlock}\n\nWrite the 600-word single-paragraph dispatch now. Count words as you write.`;\n\n for (let attempt = 1; attempt <= 2; attempt++) {\n try {\n const res = await fetch(\"https://api.openai.com/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"content-type\": \"application/json\",\n authorization: `Bearer ${OPENAI_API_KEY}`,\n },\n body: JSON.stringify({\n model: OPENAI_MODEL,\n messages: [\n { role: \"system\", content: system },\n { role: \"user\", content: user },\n ],\n temperature: 0.5,\n max_tokens: 2200,\n presence_penalty: 0.1,\n frequency_penalty: 0.1,\n }),\n });\n\n if (!res.ok) {\n console.warn(\n `[llm ${topic.name}] HTTP ${res.status} attempt ${attempt}`,\n );\n if (attempt === 2) return buildLocalArticle(topic, tweets);\n continue;\n }\n\n const data = await res.json();\n const text: string = data?.choices?.[0]?.message?.content?.trim() ?? \"\";\n if (!text) {\n if (attempt === 2) return buildLocalArticle(topic, tweets);\n continue;\n }\n\n const headlineMatch = text.match(/HEADLINE:\\s*(.+)/i);\n const bodyMatch = text.match(/BODY:\\s*([\\s\\S]*)/i);\n\n const headline = (headlineMatch?.[1] ?? \"\").trim();\n let body = (bodyMatch?.[1] ?? \"\")\n .replace(/\\s+/g, \" \")\n .replace(/^[\"“]|[\"”]$/g, \"\")\n .trim();\n\n if (!headline || !body) {\n if (attempt === 2) return buildLocalArticle(topic, tweets);\n continue;\n }\n\n if (!body.startsWith(dateline)) {\n body = `${dateline} ${body.replace(/^[A-Z ,0-9—–-]+—\\s*/, \"\")}`;\n }\n\n const wordCount = body.split(/\\s+/).length;\n\n if (wordCount < 500 && attempt === 1) {\n console.warn(\n `[llm ${topic.name}] attempt 1 too short (${wordCount} words), retrying…`,\n );\n continue;\n }\n\n return {\n name: topic.name,\n emoji: topic.emoji,\n section: topic.section,\n headline,\n body,\n wordCount,\n sources: top.slice(0, 4).map((t) => `@${t.handle}`),\n sourceKind: \"llm\",\n };\n } catch (err) {\n console.warn(\n `[llm ${topic.name}] attempt ${attempt} failed:`,\n (err as Error).message,\n );\n if (attempt === 2) return buildLocalArticle(topic, tweets);\n }\n }\n\n return buildLocalArticle(topic, tweets);\n}\n\n// ─── 1. Trends for a WOEID region ───────────────────────────────\nasync function fetchTrendsForRegion(\n woeid: number,\n regionName: string,\n): Promise {\n try {\n const res = await fetch(\n `https://x.com/i/api/1.1/trends/place.json?id=${woeid}`,\n { headers: X_HEADERS },\n );\n if (!res.ok) {\n console.warn(`[trends ${regionName}] HTTP ${res.status}`);\n return [];\n }\n const data = await res.json();\n const list = Array.isArray(data) ? data[0]?.trends ?? [] : [];\n return list\n .slice(0, 25)\n .map((tr: any, i: number) => {\n const name = String(tr?.name ?? \"\").trim();\n const vol = Number(tr?.tweet_volume ?? 0);\n return {\n topic: name,\n category: classifyTopic(name),\n volume: vol > 0\n ? `${vol.toLocaleString(\"en-IN\")} tweets`\n : `rank #${i + 1}`,\n rank: i + 1,\n region: regionName,\n tweetVolume: vol,\n };\n })\n .filter((t) => t.topic);\n } catch (err) {\n console.warn(`[trends ${regionName}] failed:`, (err as Error).message);\n return [];\n }\n}\n\n// ─── 2. Search X for a topic + build long article ───────────────\nasync function searchTopic(\n spec: TopicSpec,\n): Promise<\n {\n name: string;\n emoji: string;\n section: string;\n tweets: Tweet[];\n article: Article;\n }\n> {\n const url = `https://x.com/i/api/graphql/${SEARCH_QUERY_ID}/SearchTimeline`;\n\n const body = {\n variables: {\n rawQuery: spec.query,\n count: 30,\n querySource: \"typed_query\",\n product: \"Latest\",\n withDownvotePerspective: false,\n withReactionsMetadata: false,\n withReactionsPerspective: false,\n },\n features: SEARCH_FEATURES,\n fieldToggles: { withArticleRichContentState: false },\n };\n\n let tweets: Tweet[] = [];\n\n try {\n const res = await fetch(url, {\n method: \"POST\",\n headers: { ...X_HEADERS, \"content-type\": \"application/json\" },\n body: JSON.stringify(body),\n });\n\n if (!res.ok) {\n console.warn(`[search ${spec.name}] HTTP ${res.status}`);\n } else {\n const data = await res.json();\n const instructions =\n data?.data?.search_by_raw_query?.search_timeline?.timeline\n ?.instructions ??\n [];\n const collected: Tweet[] = [];\n for (const inst of instructions) {\n const entries = inst.entries ?? [];\n for (const entry of entries) {\n const result = entry?.content?.itemContent?.tweet_results?.result;\n if (!result) continue;\n const tweet = result.tweet ?? result;\n const legacy = tweet?.legacy;\n const user = tweet?.core?.user_results?.result?.legacy;\n if (!legacy?.full_text || !user?.screen_name) continue;\n\n collected.push({\n text: legacy.full_text,\n author: user.name ?? user.screen_name,\n handle: user.screen_name,\n likes: Number(legacy.favorite_count ?? 0),\n retweets: Number(legacy.retweet_count ?? 0),\n url: `https://x.com/${user.screen_name}/status/${legacy.id_str}`,\n });\n if (collected.length >= 30) break;\n }\n if (collected.length >= 30) break;\n }\n tweets = collected\n .filter((t) => t.text.length > 20 && !t.text.startsWith(\"RT @\"))\n .sort((a, b) => b.likes - a.likes)\n .slice(0, 10);\n }\n } catch (err) {\n console.warn(`[search ${spec.name}] failed:`, (err as Error).message);\n }\n\n const article = await buildLongArticle(spec, tweets);\n return {\n name: spec.name,\n emoji: spec.emoji,\n section: spec.section,\n tweets,\n article,\n };\n}\n\n// ─── 3. ntfy push ───────────────────────────────────────────────\nasync function pushCard(title: string, message: string, tags: string[]) {\n return await fetch(\"https://ntfy.sh\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n topic: NTFY_TOPIC,\n title: title.slice(0, 200),\n message,\n priority: 4,\n tags,\n markdown: false,\n }),\n });\n}\n\nasync function pushBlock(name: string, body: string, tags: string[]) {\n const chunks = chunkText(body);\n for (let i = 0; i < chunks.length; i++) {\n const label = chunks.length > 1 ? ` [${i + 1}/${chunks.length}]` : \"\";\n await pushCard(`${name}${label}`, chunks[i], tags);\n await new Promise((r) => setTimeout(r, 700));\n }\n}\n\n// ─── 4. Core agent ──────────────────────────────────────────────\nasync function runAgent() {\n const runAtIst = istNow();\n\n const [trends, ...topicResults] = await Promise.all([\n Promise.all(CITIES.map((c) => fetchTrendsForRegion(c.woeid, c.name))),\n ...TOPICS.map((t) => searchTopic(t)),\n ]);\n\n const national: Trend[] = [];\n const regional = new Map();\n trends.forEach((list, i) => {\n const city = CITIES[i];\n if (city.name === \"India\") national.push(...list);\n else regional.set(city.name, list);\n });\n\n const seen = new Set();\n const nationalUnique = national\n .filter((t) => {\n const k = t.topic.toLowerCase();\n if (seen.has(k)) return false;\n seen.add(k);\n return true;\n })\n .sort((a, b) => b.tweetVolume - a.tweetVolume)\n .slice(0, 25);\n\n const result = {\n run_at_ist: runAtIst,\n national: nationalUnique,\n regional: Object.fromEntries(\n Array.from(regional.entries()).map((\n [city, list],\n ) => [city, list.slice(0, 8)]),\n ),\n topics: topicResults,\n };\n\n try {\n await blob.setJSON(STORE_KEY, result);\n } catch { /* ignore */ }\n\n return result;\n}\n\n// ─── 5. Push everything ─────────────────────────────────────────\nasync function pushAll(result: Awaited>) {\n const llmCount =\n result.topics.filter((t) => t.article.sourceKind === \"llm\").length;\n const totalWords = result.topics.reduce((s, t) => s + t.article.wordCount, 0);\n\n const frontHeads = result.topics\n .filter((t) => t.article.headline)\n .map((t) => `${t.article.section}: ${t.article.headline}`);\n await pushBlock(\n \"📰 THE INDIA BRIEF — Front Page\",\n `Edition: ${result.run_at_ist}\\nSections: ${result.topics.length} · AI-written: ${llmCount} · Total words: ${totalWords}\\n\\n${\n frontHeads.join(\"\\n\\n\")\n }`,\n [\"newspaper\"],\n );\n\n for (const topic of result.topics) {\n const a = topic.article;\n const sourceLine = a.sources.length\n ? `\\n\\nSources: ${a.sources.join(\", \")}`\n : \"\";\n const story =\n `${a.section} · ${a.wordCount} words\\n\\n${a.headline}\\n\\n${a.body}${sourceLine}`;\n await pushBlock(`${topic.emoji} ${topic.name}`, story, [\"newspaper\"]);\n await new Promise((r) => setTimeout(r, 900));\n }\n\n await pushCard(\n \"✅ End of Edition\",\n `Compiled at ${istNow()}\\nSections: ${result.topics.length} · Total: ${totalWords} words`,\n [\"white_check_mark\"],\n );\n}\n\n// ─── 6. 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