--- title: "Workflow to n8n Workflow to Hacker News Throwback Machine See What Was Hot On This Day, Every Year" platform: "Promptally" canonical_url: "https://app.promptally.dev/p/workflow-n8n-workflow-to-hacker-news-throwback-machine-see-what-was-hot-on-this-day-every-year" modality: "WORKFLOW" category: "n8n Automation Blueprints" compatible_models: [Universal / Model Agnostic] access_tier: "Free Public Artifact (No Auth Required)" license: "MIT" --- # Workflow to n8n Workflow to Hacker News Throwback Machine See What Was Hot On This Day, Every Year **Summary:** Workflow to n8n Workflow to Hacker News Throwback Machine See What Was Hot On This Day, Every Year is a verified WORKFLOW blueprint on Promptally in n8n Automation Blueprints optimized for leading AI models. Multi-step automated node blueprint for n8n Workflow to Hacker News Throwback Machine See What Was Hot On This Day, Every Year . Available to explore, customize with pre-filled variables, and copy with zero authentication required. ## Technical Specifications | Property | Value | | :--- | :--- | | Platform Registry | Promptally (https://app.promptally.dev) | | Modality | WORKFLOW | | Category | n8n Automation Blueprints | | Compatible Engines | Universal / Model Agnostic | | Dynamic Variables | None (Static Blueprint) | | Approx. Token Length | 2,787 | | Access Tier | Free Public Artifact (No Auth Required) | | License | MIT | ### Pre-Filled Template & Production Source ````json { "nodes": [ { "id": "6ea4e702-1af8-407b-b653-964a519db1c2", "name": "Basic LLM Chain", "type": "@n8n/n8n-nodes-langchain.chainLlm", "position": [ 1560, -360 ], "parameters": { "text": "=You are a highly skilled news categorizer, specializing in indentifying interesting stuff from Hacker News front-page headlines.\n\nYou are provided with JSON data containing a list of dates and their corresponding top headlines from the Hacker News front page. Each headline will also include a URL linking to the original article or discussion. Importantly, the dates provided will be the SAME DAY across MULTIPLE YEARS (e.g., January 1st, 2023, January 1st, 2022, January 1st, 2021, etc.). You need to indentify key headlines and also analyze how the tech landscape has evolved over the years, as reflected in the headlines for this specific day.\n\nYour task is to indentify top 10-15 headlines from across the years from the given json data and return in Markdown formatted bullet points categorizing into themes and adding markdown hyperlinks to the source URL with Prefixing Year before the headline. Follow the Output Foramt Mentioned.\n\n**Input Format:**\n\n```json\n[\n {\n \"headlines\": [\n \"Headline 1 Title [URL1]\",\n \"Headline 2 Title [URL2]\",\n \"Headline 3 Title [URL3]\",\n ...\n ]\n \"date\": \"YYYY-MM-DD\",\n },\n {\n \"headlines\": [\n \"Headline 1 Title [URL1]\",\n \"Headline 2 Title [URL2]\",\n ...\n ]\n \"date\": \"YYYY-MM-DD\",\n },\n ...\n]\n```\n\n**Output Format In Markdown**\n\n```\n# HN Lookback | to \n\n## [Theme 1]\n- YYYY [Headline 1](URL1)\n- YYYY [Headline 2](URL2)\n...\n\n## [Theme 2]\n- YYYY [Headline 1](URL1)\n- YYYY [Headline 2](URL2)\n...\n\n... \n\n## \n\n```\n\n**Here is the Json data for Hackernews Headlines across the years**\n\n```\n{{ JSON.stringify($json.data) }}\n```", "promptType": "define" }, "typeVersion": 1.5 }, { "id": "b5a97c2a-0c3b-4ebe-aec5-7bca6b55ad4c", "name": "Google Gemini Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini", "position": [ 1740, -200 ], "parameters": { "options": {}, "modelName": "models/gemini-1.5-pro" }, "credentials": { "googlePalmApi": { "id": "Hx1fn2jrUvojSKye", "name": "Google Gemini(PaLM) Api account" } }, "typeVersion": 1 }, { "id": "18cba750-aef5-451d-880f-2c12d8540d78", "name": "Schedule Trigger", "type": "n8n-nodes-base.scheduleTrigger", "position": [ -380, -360 ], "parameters": { "rule": { "interval": [ { "triggerAtHour": 21 } ] } }, "typeVersion": 1.2 }, { "id": "341da616-8670-4cd9-b47a-ee25e2ae9862", "name": "CreateYearsList", "type": "n8n-nodes-base.code", "position": [ -200, -360 ], "parameters": { "jsCode": "for (const item of $input.all()) {\n const currentDateStr = item.json.timestamp.split('T')[0];\n const currentDate = new Date(currentDateStr);\n const currentYear = currentDate.getFullYear();\n const currentMonth = currentDate.getMonth(); // 0 for January, 1 for February, etc.\n const currentDay = currentDate.getDate();\n\n const datesToFetch = [];\n for (let year = currentYear; year >= 2007; year--) {\n let targetDate;\n if (year === 2007) {\n // Special handling for 2007 to start from Feb 19\n if (currentMonth > 1 || (currentMonth === 1 && currentDay >= 19))\n {\n targetDate = new Date(2007, 1, 19); // Feb 19, 2007\n } else {\n continue; // Skip 2007 if currentDate is before Feb 19\n }\n } else {\n targetDate = new Date(year, currentMonth, currentDay);\n }\n \n // Format the date as YYYY-MM-DD\n const formattedDate = targetDate.toISOString().split('T')[0];\n datesToFetch.push(formattedDate);\n }\n item.json.datesToFetch = datesToFetch;\n}\n\nreturn $input.all();" }, "typeVersion": 2 }, { "id": "42e24547-be24-4f29-8ce8-c0df7d47a6ff", "name": "CleanUpYearList", "type": "n8n-nodes-base.set", "position": [ 0, -360 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "b269dc0d-21e1-4124-8f3a-2c7bfa4add5c", "name": "datesToFetch", "type": "array", "value": "={{ $json.datesToFetch }}" } ] } }, "typeVersion": 3.4 }, { "id": "6e51ad05-0f3d-4bfb-8c8d-5b71e7355344", "name": "SplitOutYearList", "type": "n8n-nodes-base.splitOut", "position": [ 200, -360 ], "parameters": { "options": {}, "fieldToSplitOut": "datesToFetch" }, "typeVersion": 1 }, { "id": "6f827071-718f-4e27-9f7a-cc50296f7bc4", "name": "GetFrontPage", "type": "n8n-nodes-base.httpRequest", "position": [ 420, -360 ], "parameters": { "url": "=https://news.ycombinator.com/front", "options": { "batching": { "batch": { "batchSize": 1, "batchInterval": 3000 } } }, "sendQuery": true, "queryParameters": { "parameters": [ { "name": "day", "value": "={{ $json.datesToFetch }}" } ] } }, "typeVersion": 4.2 }, { "id": "7287e6b1-337f-4634-ac23-5ceaa87b0db3", "name": "ExtractDetails", "type": "n8n-nodes-base.html", "position": [ 640, -360 ], "parameters": { "options": {}, "operation": "extractHtmlContent", "extractionValues": { "values": [ { "key": "=headlines", "cssSelector": ".titleline", "returnArray": true, "skipSelectors": "span" }, { "key": "date", "cssSelector": ".pagetop > font" } ] } }, "typeVersion": 1.2 }, { "id": "fceff31e-4dcd-4199-89c5-8eb75cd479bf", "name": "GetHeadlines", "type": "n8n-nodes-base.set", "position": [ 920, -460 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "e1ce33e9-e4f8-4215-bbdb-156a955a0a97", "name": "headlines", "type": "array", "value": "={{ $json.headlines }}" } ] } }, "typeVersion": 3.4 }, { "id": "f7683614-7225-4f05-ba12-86b326fdb4a1", "name": "GetDate", "type": "n8n-nodes-base.set", "position": [ 920, -280 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "fc1d15f6-a999-4d6b-a7bc-3ffa9427679e", "name": "date", "type": "string", "value": "={{ $json.date }}" } ] } }, "typeVersion": 3.4 }, { "id": "7e09ce85-ece1-46a0-aa59-8e3da66413b2", "name": "MergeHeadlinesDate", "type": "n8n-nodes-base.merge", "position": [ 1180, -360 ], "parameters": { "mode": "combine", "options": {}, "combineBy": "combineByPosition" }, "typeVersion": 3 }, { "id": "db3bf408-8179-4ca4-a5b4-8a390b68f994", "name": "SingleJson", "type": "n8n-nodes-base.aggregate", "position": [ 1380, -360 ], "parameters": { "options": {}, "aggregate": "aggregateAllItemData" }, "typeVersion": 1 }, { "id": "2abbc0e9-ed1e-4ba0-9d2f-7c3cd314a0fe", "name": "Telegram", "type": "n8n-nodes-base.telegram", "position": [ 2020, -360 ], "parameters": { "text": "={{ $json.text }}", "chatId": "@OnThisDayHN", "additionalFields": { "parse_mode": "Markdown", "appendAttribution": false } }, "credentials": { "telegramApi": { "id": "6nIwfhIWcwJFTPTg", "name": "OnThisDayHNBot" } }, "typeVersion": 1.2 } ], "pinData": {}, "connections": { "GetDate": { "main": [ [ { "node": "MergeHeadlinesDate", "type": "main", "index": 1 } ] ] }, "SingleJson": { "main": [ [ { "node": "Basic LLM Chain", "type": "main", "index": 0 } ] ] }, "GetFrontPage": { "main": [ [ { "node": "ExtractDetails", "type": "main", "index": 0 } ] ] }, "GetHeadlines": { "main": [ [ { "node": "MergeHeadlinesDate", "type": "main", "index": 0 } ] ] }, "ExtractDetails": { "main": [ [ { "node": "GetHeadlines", "type": "main", "index": 0 }, { "node": "GetDate", "type": "main", "index": 0 } ] ] }, "Basic LLM Chain": { "main": [ [ { "node": "Telegram", "type": "main", "index": 0 } ] ] }, "CleanUpYearList": { "main": [ [ { "node": "SplitOutYearList", "type": "main", "index": 0 } ] ] }, "CreateYearsList": { "main": [ [ { "node": "CleanUpYearList", "type": "main", "index": 0 } ] ] }, "Schedule Trigger": { "main": [ [ { "node": "CreateYearsList", "type": "main", "index": 0 } ] ] }, "SplitOutYearList": { "main": [ [ { "node": "GetFrontPage", "type": "main", "index": 0 } ] ] }, "MergeHeadlinesDate": { "main": [ [ { "node": "SingleJson", "type": "main", "index": 0 } ] ] }, "Google Gemini Chat Model": { "ai_languageModel": [ [ { "node": "Basic LLM Chain", "type": "ai_languageModel", "index": 0 } ] ] } } } ```` ## Verified Execution Output / Benchmark ```text Verified execution output available on the canonical page. ``` ## Related Blueprints on Promptally - [Workflow to n8n Workflow to 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