[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_181":182,"_183":184},"routes/_app.x.$handle.$name",{"_98":97,"_32":45,"_106":84,"_27":107,"_13":14,"_108":-5,"_11":109,"_110":111,"_112":113,"_114":115,"_116":117,"_20":-5,"_118":119,"_120":121,"_122":25,"_123":124,"_125":25,"_126":-5,"_127":-5,"_128":129},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":-5,"_56":25,"_57":-5,"_58":-5,"_59":60,"_61":62,"_63":-5,"_64":-5,"_65":-5,"_66":-5,"_67":68,"_69":68,"_70":71,"_72":71,"_73":71,"_74":68,"_75":68,"_76":77,"_78":79,"_80":81,"_82":25},"version",8,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","019a5b0d-2fb9-710b-b322-7150a58945fd","name","fileType","interval","content","import { searchWithSerpApi } from \"https://esm.town/v/charmaine/searchWithSerpApi\";\nimport { email } from \"https://esm.town/v/std/email\";\n\n// Configuration\n// Use a BROAD search query to get more diverse results\nconst BROAD_SEARCH_QUERY = \"happy dog\"; // Broad search term\nconst SERP_API_KEY = Deno.env.get(\"SERP_API_KEY\");\n\n// These are the semantic concepts we're ACTUALLY looking for\nconst SEMANTIC_TARGET = [\n \"happy puppy\",\n \"joyful dog\",\n \"excited dog\",\n \"playful dog\",\n \"cheerful puppy\",\n \"tail wagging pup\",\n \"energetic dog\",\n \"friendly dog\",\n \"smile dog\",\n \"adorable dog\",\n \"cute puppy\",\n \"loving puppy\",\n \"affectionate puppy\",\n];\n\nconst isProd = false; // Test mode\n\n// ============ Semantic Search Functions ============\n\nfunction cosineSimilarity(vec1, vec2) {\n const dotProduct = vec1.reduce((sum, val, i) => sum + val * vec2[i], 0);\n const mag1 = Math.sqrt(vec1.reduce((sum, val) => sum + val * val, 0));\n const mag2 = Math.sqrt(vec2.reduce((sum, val) => sum + val * val, 0));\n\n if (mag1 === 0 || mag2 === 0) return 0;\n return dotProduct / (mag1 * mag2);\n}\n\nfunction createTFVector(text, vocabulary) {\n const words = text.toLowerCase().match(/\\b\\w+\\b/g) || [];\n const wordSet = new Set(words);\n\n return vocabulary.map((term) => {\n const termWords = term.toLowerCase().split(/\\s+/);\n return termWords.filter((word) => wordSet.has(word)).length;\n });\n}\n\nfunction calculateSemanticScore(postText, semanticKeywords) {\n const postVector = createTFVector(postText, semanticKeywords);\n const keywordVector = semanticKeywords.map(() => 1);\n\n return cosineSimilarity(postVector, keywordVector);\n}\n\n// Score ALL posts, don't filter any out\nfunction scorePostsBySemantic(posts) {\n const scored = posts.map((post) => {\n const text = `${post.title || \"\"} ${post.snippet || \"\"}`.toLowerCase();\n const score = calculateSemanticScore(text, SEMANTIC_TARGET);\n\n return { ...post, semanticScore: score };\n });\n\n // Sort by relevance score (highest first) but return ALL posts\n return scored.sort((a, b) => b.semanticScore - a.semanticScore);\n}\n\n// ============ Reddit Content Fetching ============\n\nasync function enrichWithRedditContent(posts) {\n console.log(`\\nFetching full content for ${posts.length} posts...`);\n\n const enriched = await Promise.all(\n posts.map(async (post, index) => {\n try {\n await new Promise((resolve) => setTimeout(resolve, index * 100));\n\n const jsonUrl = post.url + \".json\";\n const response = await fetch(jsonUrl, {\n headers: {\n \"User-Agent\": \"Mozilla/5.0 (compatible; RedditBot/1.0)\",\n },\n });\n\n if (!response.ok) {\n console.log(`Failed to fetch ${post.url}: ${response.status}`);\n return post;\n }\n\n const data = await response.json();\n const postData = data[0]?.data?.children?.[0]?.data;\n\n if (!postData) {\n return post;\n }\n\n const content = postData.selftext || postData.title || \"\";\n\n return {\n ...post,\n snippet: content.substring(0, 500),\n fullContent: content,\n score: postData.score || 0,\n num_comments: postData.num_comments || 0,\n author: postData.author,\n subreddit: postData.subreddit,\n };\n } catch (e) {\n console.error(`Error fetching ${post.url}:`, e.message);\n return post;\n }\n }),\n );\n\n console.log(\n `Successfully enriched ${enriched.filter((p) => p.snippet).length} posts\\n`,\n );\n return enriched;\n}\n\n// ============ Main Function ============\n\nexport async function redditAlert({ lastRunAt }: Interval) {\n if (!SERP_API_KEY) {\n console.error(\"Missing SERP_API_KEY. Exiting.\");\n return;\n }\n\n let timeFrame;\n let cutoffTime = null;\n\n if (isProd && lastRunAt) {\n const hoursSinceLastRun = (Date.now() - lastRunAt.getTime()) /\n (1000 * 60 * 60);\n const daysSinceLastRun = Math.max(1, Math.ceil(hoursSinceLastRun / 24));\n timeFrame = `d${daysSinceLastRun}`;\n cutoffTime = lastRunAt;\n console.log(`Last run: ${lastRunAt.toISOString()}`);\n console.log(`Hours since last run: ${hoursSinceLastRun.toFixed(1)}`);\n } else if (isProd) {\n timeFrame = \"d14\";\n } else {\n timeFrame = \"d7\";\n console.log(\"🧪 TEST MODE - Getting posts from last 7 days\");\n }\n\n try {\n console.log(`\\n=== Step 1: Broad Reddit Search ===`);\n console.log(`Search query: \"${BROAD_SEARCH_QUERY}\"`);\n console.log(`Time frame: ${timeFrame}\\n`);\n\n const response = await searchWithSerpApi({\n query: BROAD_SEARCH_QUERY,\n site: \"reddit.com\",\n apiKey: SERP_API_KEY,\n as_qdr: timeFrame,\n });\n\n console.log(`Retrieved ${response?.length || 0} Reddit posts from SerpAPI`);\n\n if (!response?.length) {\n console.log(\"No relevant Reddit posts found.\");\n return;\n }\n\n // Time filtering\n let filteredPosts;\n if (isProd && cutoffTime) {\n filteredPosts = response.filter((post) => {\n const postTime = new Date(post.date_published).getTime();\n return postTime > cutoffTime.getTime();\n });\n } else {\n filteredPosts = response;\n }\n\n console.log(`${filteredPosts.length} posts after time filtering`);\n\n if (!filteredPosts.length) {\n console.log(\"No new posts since the last run.\");\n return;\n }\n\n // Enrich with Reddit content\n console.log(`\\n=== Step 2: Fetch Full Reddit Content ===`);\n const enrichedPosts = await enrichWithRedditContent(filteredPosts);\n\n // Score ALL posts (don't filter)\n console.log(`\\n=== Step 3: Semantic Scoring ===`);\n console.log(`Target concepts: ${SEMANTIC_TARGET.join(\", \")}\\n`);\n\n const scoredPosts = scorePostsBySemantic(enrichedPosts);\n\n // Show scoring details\n console.log(`Semantic scores for all ${scoredPosts.length} posts:\\n`);\n scoredPosts.forEach((post, i) => {\n console.log(\n `${i + 1}. [${(post.semanticScore * 100).toFixed(1)}%] ${\n post.title?.substring(0, 60)\n }...`,\n );\n });\n\n // Format results - INCLUDE ALL POSTS\n const textContent = scoredPosts\n .map((post, i) =>\n `${i + 1}. [${\n (post.semanticScore * 100).toFixed(1)\n }%] ${post.title}\\n ${post.url}\\n Reddit Score: ${\n post.score || 0\n } | Comments: ${post.num_comments || 0}\\n`\n )\n .join(\"\\n\");\n\n const htmlContent = `\n

🔍 Semantic Search Results: \"${BROAD_SEARCH_QUERY}\"

Total Results: ${scoredPosts.length} posts (all ranked by semantic relevance)

Looking for: ${SEMANTIC_TARGET.join(\", \")}

\n ${p.title}
\n \n r/${p.subreddit || \"unknown\"} • \n by u/${p.author || \"unknown\"} • \n ⬆️ ${p.score || 0} • \n 💬 ${p.num_comments || 0}\n
\n \n ${(p.semanticScore * 100).toFixed(1)}% semantic match\n \n ${\n p.snippet\n ? `

${p.snippet.substring(0, 200)}${\n p.snippet.length > 200 ? \"...\" : \"\"\n }`\n : \"\"\n }\n

🧪 This is a test. Set isProd = true when ready for production.

\n `;\n\n console.log(\"\\n=== Final Results (All Posts Ranked) ===\\n\" + textContent);\n\n // Send email\n if (isProd) {\n await email({\n to: \"testingnewproducts01@gmail.com\",\n subject:\n `🔔 ${scoredPosts.length} Posts About \"${BROAD_SEARCH_QUERY}\" (Semantically Ranked)`,\n text: textContent,\n html: htmlContent,\n });\n console.log(\"\\n✓ Email sent successfully!\");\n } else {\n console.log(\"\\n⚠️ Test mode - email not sent\");\n }\n } catch (error) {\n console.error(\"❌ Error:\", error.message);\n console.error(\"Stack:\", error.stack);\n }\n}","import { searchWithSerpApi } from \"https://esm.town/v/charmaine/searchWithSerpApi\";\nimport { email } from \"https://esm.town/v/std/email\";\n\n// Configuration\n// Use a BROAD search query to get more diverse results\nconst BROAD_SEARCH_QUERY = \"happy dog\"; // Broad search term\nconst SERP_API_KEY = Deno.env.get(\"SERP_API_KEY\");\nconst EMAIL_ADDRESS = Deno.env.get(\"EMAIL_ADDRESS\");\n\n// These are the semantic concepts we're ACTUALLY looking for\nconst SEMANTIC_TARGET = [\n \"happy puppy\",\n \"joyful dog\",\n \"excited dog\",\n \"playful dog\",\n \"cheerful puppy\",\n \"tail wagging pup\",\n \"energetic dog\",\n \"friendly dog\",\n \"smile dog\",\n \"adorable dog\",\n \"cute puppy\",\n \"loving puppy\",\n \"affectionate puppy\",\n];\n\nconst isProd = true; // Test mode\n\n// ============ Semantic Search Functions ============\n\nfunction cosineSimilarity(vec1, vec2) {\n const dotProduct = vec1.reduce((sum, val, i) => sum + val * vec2[i], 0);\n const mag1 = Math.sqrt(vec1.reduce((sum, val) => sum + val * val, 0));\n const mag2 = Math.sqrt(vec2.reduce((sum, val) => sum + val * val, 0));\n\n if (mag1 === 0 || mag2 === 0) return 0;\n return dotProduct / (mag1 * mag2);\n}\n\nfunction createTFVector(text, vocabulary) {\n const words = text.toLowerCase().match(/\\b\\w+\\b/g) || [];\n const wordSet = new Set(words);\n\n return vocabulary.map((term) => {\n const termWords = term.toLowerCase().split(/\\s+/);\n return termWords.filter((word) => wordSet.has(word)).length;\n });\n}\n\nfunction calculateSemanticScore(postText, semanticKeywords) {\n const postVector = createTFVector(postText, semanticKeywords);\n const keywordVector = semanticKeywords.map(() => 1);\n\n return cosineSimilarity(postVector, keywordVector);\n}\n\n// Score ALL posts, don't filter any out\nfunction scorePostsBySemantic(posts) {\n const scored = posts.map((post) => {\n const text = `${post.title || \"\"} ${post.snippet || \"\"}`.toLowerCase();\n const score = calculateSemanticScore(text, SEMANTIC_TARGET);\n\n return { ...post, semanticScore: score };\n });\n\n // Sort by relevance score (highest first) but return ALL posts\n return scored.sort((a, b) => b.semanticScore - a.semanticScore);\n}\n\n// ============ Reddit Content Fetching ============\n\nasync function enrichWithRedditContent(posts) {\n console.log(`\\nFetching full content for ${posts.length} posts...`);\n\n const enriched = await Promise.all(\n posts.map(async (post, index) => {\n try {\n await new Promise((resolve) => setTimeout(resolve, index * 100));\n\n const jsonUrl = post.url + \".json\";\n const response = await fetch(jsonUrl, {\n headers: {\n \"User-Agent\": \"Mozilla/5.0 (compatible; RedditBot/1.0)\",\n },\n });\n\n if (!response.ok) {\n console.log(`Failed to fetch ${post.url}: ${response.status}`);\n return post;\n }\n\n const data = await response.json();\n const postData = data[0]?.data?.children?.[0]?.data;\n\n if (!postData) {\n return post;\n }\n\n const content = postData.selftext || postData.title || \"\";\n\n return {\n ...post,\n snippet: content.substring(0, 500),\n fullContent: content,\n score: postData.score || 0,\n num_comments: postData.num_comments || 0,\n author: postData.author,\n subreddit: postData.subreddit,\n };\n } catch (e) {\n console.error(`Error fetching ${post.url}:`, e.message);\n return post;\n }\n }),\n );\n\n console.log(\n `Successfully enriched ${enriched.filter((p) => p.snippet).length} posts\\n`,\n );\n return enriched;\n}\n\n// ============ Main Function ============\n\nexport async function redditAlert({ lastRunAt }: Interval) {\n if (!SERP_API_KEY) {\n console.error(\"Missing SERP_API_KEY. Exiting.\");\n return;\n }\n\n let timeFrame;\n let cutoffTime = null;\n\n if (isProd && lastRunAt) {\n const hoursSinceLastRun = (Date.now() - lastRunAt.getTime()) /\n (1000 * 60 * 60);\n const daysSinceLastRun = Math.max(1, Math.ceil(hoursSinceLastRun / 24));\n timeFrame = `d${daysSinceLastRun}`;\n cutoffTime = lastRunAt;\n console.log(`Last run: ${lastRunAt.toISOString()}`);\n console.log(`Hours since last run: ${hoursSinceLastRun.toFixed(1)}`);\n } else if (isProd) {\n timeFrame = \"d14\";\n } else {\n timeFrame = \"d7\";\n console.log(\"🧪 TEST MODE - Getting posts from last 7 days\");\n }\n\n try {\n console.log(`\\n=== Step 1: Broad Reddit Search ===`);\n console.log(`Search query: \"${BROAD_SEARCH_QUERY}\"`);\n console.log(`Time frame: ${timeFrame}\\n`);\n\n const response = await searchWithSerpApi({\n query: BROAD_SEARCH_QUERY,\n site: \"reddit.com\",\n apiKey: SERP_API_KEY,\n as_qdr: timeFrame,\n });\n\n console.log(`Retrieved ${response?.length || 0} Reddit posts from SerpAPI`);\n\n if (!response?.length) {\n console.log(\"No relevant Reddit posts found.\");\n return;\n }\n\n // Time filtering\n let filteredPosts;\n if (isProd && cutoffTime) {\n filteredPosts = response.filter((post) => {\n const postTime = new Date(post.date_published).getTime();\n return postTime > cutoffTime.getTime();\n });\n } else {\n filteredPosts = response;\n }\n\n console.log(`${filteredPosts.length} posts after time filtering`);\n\n if (!filteredPosts.length) {\n console.log(\"No new posts since the last run.\");\n return;\n }\n\n // Enrich with Reddit content\n console.log(`\\n=== Step 2: Fetch Full Reddit Content ===`);\n const enrichedPosts = await enrichWithRedditContent(filteredPosts);\n\n // Score ALL posts (don't filter)\n console.log(`\\n=== Step 3: Semantic Scoring ===`);\n console.log(`Target concepts: ${SEMANTIC_TARGET.join(\", \")}\\n`);\n\n const scoredPosts = scorePostsBySemantic(enrichedPosts);\n\n // Show scoring details\n console.log(`Semantic scores for all ${scoredPosts.length} posts:\\n`);\n scoredPosts.forEach((post, i) => {\n console.log(\n `${i + 1}. [${(post.semanticScore * 100).toFixed(1)}%] ${\n post.title?.substring(0, 60)\n }...`,\n );\n });\n\n // Format results - INCLUDE ALL POSTS\n const textContent = scoredPosts\n .map((post, i) =>\n `${i + 1}. [${\n (post.semanticScore * 100).toFixed(1)\n }%] ${post.title}\\n ${post.url}\\n Reddit Score: ${\n post.score || 0\n } | Comments: ${post.num_comments || 0}\\n`\n )\n .join(\"\\n\");\n\n const htmlContent = `\n

🔍 Semantic Search Results: \"${BROAD_SEARCH_QUERY}\"

Total Results: ${scoredPosts.length} posts (all ranked by semantic relevance)

Looking for: ${SEMANTIC_TARGET.join(\", \")}

\n ${p.title}
\n \n r/${p.subreddit || \"unknown\"} • \n by u/${p.author || \"unknown\"} • \n ⬆️ ${p.score || 0} • \n 💬 ${p.num_comments || 0}\n
\n \n ${(p.semanticScore * 100).toFixed(1)}% semantic match\n \n ${\n p.snippet\n ? `

${p.snippet.substring(0, 200)}${\n p.snippet.length > 200 ? \"...\" : \"\"\n }`\n : \"\"\n }\n

🧪 This is a test. Set isProd = true when ready for production.

\n `;\n\n console.log(\"\\n=== Final Results (All Posts Ranked) ===\\n\" + textContent);\n\n // Send email\n if (isProd) {\n await email({\n to: EMAIL_ADDRESS,\n subject:\n `🔔 ${scoredPosts.length} Posts About \"${BROAD_SEARCH_QUERY}\" (Semantically Ranked)`,\n text: textContent,\n html: htmlContent,\n });\n console.log(\"\\n✓ Email sent successfully!\");\n } else {\n console.log(\"\\n⚠️ Test mode - email not sent\");\n }\n } catch (error) {\n console.error(\"❌ Error:\", error.message);\n console.error(\"Stack:\", error.stack);\n }\n}","createdAt",["D",1762466549929],"branchVersion","branchId","c69869e2-bb56-11f0-adcd-42dde27851f2","id","c6964c02-bb56-11f0-adcd-42dde27851f2","templateRedditAlert",["D",1762464182146],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","imageUrl","019a5b0c-fa3f-75f8-a0cd-f8d4d4c0d04b","pinnedFileId","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","019a5b0c-fa40-722f-af1f-22c186c33b96","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_98":97,"_105":101},"org",{"_98":97,"_99":-5,"_100":101,"_18":54,"_102":103,"_104":25},"branch",{"_13":83,"_32":84,"_43":42,"_85":44,"_86":87,"_88":89,"_90":50,"_91":92,"_93":94,"_95":45,"_96":97},"isOwned",18,"main","projectId","forkedBranchId","0f9c20f4-1a06-11f0-b1b9-569c3dd06744","forkedBranchVersion",24,"forkedProjectPrivacy","forkedProjectId","0f9bd018-1a06-11f0-b1b9-569c3dd06744","forkedProjectOrgId","ba7bb5fb-1abb-4ffb-8f3d-7c244782e5f7","projectName","userHandle","jon0101","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzM1N2JjazJiQTZwMXVPQndWVGlIRmlFMHZxNyJ9","tier","free","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_43":44,"_32":45,"_38":177,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":-5,"_56":25,"_57":-5,"_58":-5,"_59":60,"_61":62,"_63":-5,"_64":-5,"_65":-5,"_66":-5,"_67":68,"_69":68,"_70":71,"_72":71,"_73":71,"_74":68,"_75":68,"_76":178,"_78":179,"_80":180,"_82":25},"tree",[168,169],"files",[168,169],"counts",{"_157":60,"_158":152,"_159":60,"_160":161,"_162":163},"contents",[140,141],"upstreamProject",{"_43":92,"_32":45,"_52":137,"_53":-5,"_49":50,"_76":138},"upstreamBranch",{"_43":87,"_38":130,"_131":132,"_32":84,"_85":92,"_13":133,"_86":-5,"_88":-5,"_134":135,"_47":-5,"_82":25,"_49":50,"_96":136,"_61":94,"_95":45},"emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},["D",1744727378246],"updatedAt",["D",1764618253706],25,"lockfileKey","1764618414545-esm.town_v_valdottown_templateRedditAlert@25-main","valdottown","Get notifications when specific keywords appear in Reddit posts",{"_98":136,"_105":139},"https://img.clerk.com/eyJ0eXBlIjoicHJveHkiLCJzcmMiOiJodHRwczovL2ltYWdlcy5jbGVyay5kZXYvdXBsb2FkZWQvaW1nXzJyWnQ5cFE0c3pleVRCSEVTWmxzaXJsTUlGciJ9",{"_43":149,"_32":150,"_30":151,"_143":-5,"_41":42,"_40":152,"_144":153,"_38":154,"_13":60,"_33":20,"_35":155,"_24":25,"_147":-5,"_27":156},{"_43":142,"_32":28,"_30":31,"_143":-5,"_41":42,"_40":14,"_144":25,"_38":145,"_13":146,"_33":34,"_35":37,"_24":25,"_147":-5,"_27":148},"49e41e4a-bb5c-11f0-964f-42dde27851f2","parentId","latest",["D",1762466549929],7,"label",[28],"c69a6b7a-bb56-11f0-adcd-42dde27851f2","README.md","019a5b0d-2fb9-710b-b322-6ca03c48bb2e",1,true,["D",1762464182146],"# Reddit Keyword Alerts [TEMPLATE]\n\nGet notifications when specific keywords appear in Reddit posts.\n\nThis template will help you:\n1. Search Reddit for specific keywords within a defined time range.\n2. Send notifications to your preferred platform (Discord, Slack, email, etc.)\n\nReddit does not have an API that allows users to scrape data, so we are doing this with the Google Search API, [Serp](https://serpapi.com/). \n\n---\n\n## Example\nThis val tracks mentions of \"Val Town\" and related terms on Reddit, filtering results from the last 7 days and sending alerts to a Discord webhook. \n![Screenshot 2025-01-10 at 5.13.16 PM.png](https://imagedelivery.net/iHX6Ovru0O7AjmyT5yZRoA/beecb766-824e-4672-8393-3abd2edb1c00/public)\n\n---\n\n## Set Up\n\n### 1. Fork this Val\nTo start using this template, fork this val by clicking the fork button at the top-right corner of the page.\n![Screenshot 2025-01-10 at 1.22.10 PM.png](https://imagedelivery.net/iHX6Ovru0O7AjmyT5yZRoA/c4ae349d-7e28-4378-8646-21c8958e1f00/public)\n\n---\n### 2. View Source Code\nThe `CODE` box shows you the the full source code of this val, you may need to scroll down to see it.\n![image.png](https://imagedelivery.net/iHX6Ovru0O7AjmyT5yZRoA/6a4dabb4-3b27-4cea-fce3-95a1a1c3cd00/public)\n\n---\n### 3. Get a SerpApi Key\nThis template requires a [SerpApi](https://serpapi.com/) key to search Reddit posts via Google search results.\n\n1. **Get a SerpApi key**:\n - Sign up at [SerpApi](https://serpapi.com/) to create an account.\n - Generate an API key from your account dashboard.\n\n2. **Add the SerpApi key to your environment variables**:\n - Go to your [Val Town environment variables](https://www.val.town/settings/environment-variables).\n - Add a new key:\n - Key: `SERP_API_KEY`\n - Value: Your SERP API key.\n\nWithout this key, the val will not function correctly.\n\n---\n### 4. Customize Keyword\nIn the `CODE` box below, update the terms or phrases you want to track:\n```const KEYWORDS = \"\\\"node\\\" OR \\\"node.js\\\"\";```\n\n---\n\n### 5. Set Up Your Notification Method\nThis template uses a Discord webhook for notifications. You can update this to your preferred platform:\n\n**Create a Discord webhook following [this guide](https://support.discord.com/hc/en-us/articles/228383668-Intro-to-Webhooks).**\nSave your webhook URL in your Val Town environment variables (you can find this on the left sidebar):\n- Key: `mentionsDiscord`\n- Value: Your Discord webhook URL.\nNotifications will be sent using this function:\n```\n await discordWebhook({\n url: Deno.env.get(\"mentionsDiscord\"),\n content,\n });\n```\n\nTo switch to another platform (e.g., Slack, email, or custom webhooks), replace the discordWebhook call with the appropriate integration ((e.g., [`@std/email`](https://docs.val.town/std/email/), [Slack](https://docs.val.town/integrations/slack/send-messages-to-slack/), or [anywhere else](https://docs.val.town/guides/creating-a-webhook/))\n\n---\n\n#### 🎉 Congrats! You now have a val running that ensures you never miss another Reddit mention. 🎉\n\n---\n\n### NOTE: Usage Limits\n- **SerpApi:** Free SerpApi accounts have monthly call limits.",[150],"pulls","branches","forks","envVars",{"_164":166,"_167":60},"connections",{"_164":60,"_165":60},"count","unfilledCount",2,"emptyCount",{"_43":149,"_32":150,"_30":151,"_143":-5,"_41":42,"_40":152,"_144":153,"_38":174,"_13":60,"_33":20,"_35":155,"_24":25,"_147":-5,"_171":175,"_27":176},{"_43":142,"_32":28,"_30":31,"_143":-5,"_41":42,"_40":14,"_144":25,"_38":170,"_13":146,"_33":34,"_35":37,"_24":25,"_147":-5,"_171":172,"_27":173},["D",1762466549929],"children",[],[28],["D",1762464182146],[],[150],["D",1762464182146],{"_98":97,"_105":101},{"_98":97,"_99":-5,"_100":101,"_18":54,"_102":103,"_104":25},{"_13":83,"_32":84,"_43":42,"_85":44,"_86":87,"_88":89,"_90":50,"_91":92,"_93":94,"_95":45,"_96":97},"ctx",{"_76":-5,"_127":214,"_215":216,"_217":218,"_219":25,"_220":25,"_221":25,"_222":153,"_223":-5,"_224":-7,"_225":-7,"_226":60,"_227":25,"_228":25,"_229":-5,"_230":25,"_231":25,"_232":25,"_233":-5},"ENV",{"_185":186,"_187":188,"_189":84,"_190":191,"_192":193,"_194":195,"_196":197,"_198":199,"_200":201,"_202":203,"_204":205,"_206":207,"_208":209,"_210":211,"_212":213},"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 · v8