Analyze Reddit posts with AI to identify business opportunities
Use case Manually monitoring Reddit for viable business ideas is time-consuming and inconsistent. This workflow automatically analyzes trending Reddit discussions using AI to surface high-potential opportunities, filter irrelevant content, and generate actionable insights - saving entrepreneurs 10+ hours weekly in market research. What this workflow does This AI-powered workflow automatically collects trending Reddit discussions, analyzes posts for viable business opportunities using GPT-4, applies smart filters to exclude low-value content, and generates scored opportunity reports with market insights. It identifies unmet customer needs through sentiment analysis, prioritizes high-potential ideas using custom criteria, and outputs structured data to Google Sheets for actionable decision-making. Setup Add Reddit,Google and OpenAI credentials Configure target subreddits in Subreddit node Test workflow by testing workflow Review generated opportunity report in Google Sheets How to adjust this template Change data sources: Replace Reddit trigger with Twitter/X or Hacker News API Modify criteria: Adjust scoring thresholds in Opportunity Calculator node Add integrations: Create automatic Slack alerts for urgent opportunities Generate draft business plans using AI Document Writer
By Alex HuangGet first and last names from Facebook Graph API
Companion workflow for Facebook node docs
By amudhanConvert RSS news to AI avatar videos with Heygen & GPT-4o
🎬 Automated News-to-Video Workflow (n8n + Heygen + GPT-4o) 📄 Overview: This n8n workflow turns news from an RSS feed (e.g., CNN) into short, AI-generated avatar videos using Heygen. It: Fetches news from an RSS feed. Logs headlines to Google Sheets. Uses GPT-4o or Google Gemini to generate a 30–60 sec script. Sends the script to Heygen to create an avatar video. Monitors and retrieves the final video. Logs video metadata (title, link, etc.) to Google Sheets. 🎯 Ideal for content creators, marketers, or media pages repurposing written news into video content at scale. --- ⚙️ Setup Guide (No Sensitive Info) 🔑 1. Heygen API Paid Heygen plan required. Add your API key in the Setup Heygen node: json "heygenapikey": "yourkeyhere" Optional: Set "avatarid" and "voiceid" as desired. 💡 2. AI Model: GPT-4o or Gemini GPT-4o: Use OpenAI’s node or HTTP request with your API key. Gemini: Link your Google Cloud project and connect the Gemini node using OAuth2 credentials. 📥 3. RSS Feed Add an RSS node (e.g., CNN). Extract title, link, and content. 📊 4. Google Sheets + Drive Connect via OAuth2: "Google Sheets account 2" "Google Drive account 2" Replace sheet IDs in: Log news to sheets Log video URL and title to sheets 📹 5. Create Video (Heygen) Send a POST request to Heygen's API using the generated script, avatar, and voice ID. ⏳ 6. Monitor Status Poll the status endpoint until video is ready. Capture the download link. 🧾 7. Log Final Output Save video metadata to a Google Sheet for publishing or archiving. Set up video: Link in Workflow ---
By David OlusolaMessage buffer system with Redis for efficient processing
🚀 Message-Batching Buffer Workflow (n8n) This workflow implements a lightweight message-batching buffer using Redis for temporary storage and a JavaScript consolidation function to merge messages. It collects incoming user messages per session, waits for a configurable inactivity window or batch size threshold, consolidates buffered messages via custom code, then clears the buffer and returns the combined response—all without external LLM calls. --- 🔑 Key Features Redis-backed buffer queues incoming messages per context_id. Centralized Config Parameters node to adjust thresholds and timeouts in one place. Dynamic wait time based on message length (configurable minWords, waitLong, waitShort). Batch trigger fires on inactivity timeout or when buffer_count ≥ batchThreshold. Zero-cost consolidation via built-in JavaScript Function (consolidate buffer)—no GPT-4 or external API required. --- ⚙️ Setup Instructions Extract Session & Message Trigger: When chat message received (webhook) or When clicking ‘Test workflow’ (manual). Map inputs: set variables context_id and message into a Set node named Mock input data (for testing) or a proper mapping node in production. Config Parameters Add a Set node Config Parameters with: minWords: 3 Word threshold waitLong: 10 Timeout (s) for long messages waitShort: 20 Timeout (s) for short messages batchThreshold: 3 Messages to trigger batch early All downstream nodes reference these JSON values dynamically. Determine Wait Time Node: get wait seconds (Code) JS code: js const msg = $json.message || ''; const wordCount = msg.split(/\s+/).filter(w => w).length; const { minWords, waitLong, waitShort } = items[0].json; const waitSeconds = wordCount < minWords ? waitShort : waitLong; return [{ json: { contextid: $json.contextid, message: msg, waitSeconds } }]; Buffer Message in Redis Buffer messages: LPUSH bufferin:{{$json.contextid}} with payload {text, timestamp}. Set buffer\count increment: INCR buffercount:{{$json.context_id}} with TTL {{$json.waitSeconds + 60}}. Set last\seen: record lastseen:{{$json.context_id}} timestamp with same TTL. Check & Set Waiting Flag Get waiting\reply: if null, Set waiting\reply to true with TTL {{$json.waitSeconds}}; else exit. Wait for Inactivity WaitSeconds (webhook): pauses for {{$json.waitSeconds}} seconds before batch evaluation. Check Batch Trigger Get last\seen and Get buffer\count. IF (now - last_seen) ≥ waitSeconds 1000 OR buffer_count ≥ batchThreshold, proceed; else use Wait node to retry. Consolidate Buffer consolidate buffer (Code): js const j = items[0].json; const raw = Array.isArray(j.buffer) ? j.buffer : []; const buffer = raw.map(x => { try { return typeof x === 'string' ? JSON.parse(x) : x; } catch { return null; } }).filter(Boolean); buffer.sort((a, b) => new Date(a.timestamp) - new Date(b.timestamp)); const texts = buffer.map(e => e.text?.trim()).filter(Boolean); const unique = [...new Set(texts)]; const message = unique.join(' '); return [{ json: { contextid: j.contextid, message } }]; Cleanup & Respond Delete Redis keys: bufferin, buffercount, waitingreply, lastseen (for the context_id). Return consolidated message to the user via your chat integration. --- 🛠 Customization Guidance Adjust thresholds by editing the Config Parameters node. Change concatenation (e.g., line breaks) by modifying the join separator in the consolidation code. Add filters (e.g., ignore empty or system messages) inside the consolidation Function. Monitor performance: for very high volume, consider sharding Redis keys by date or user segments. --- © 2025 Innovatex • Automation & AI Solutions • innovatexiot.carrd.co • LinkedIn
By Edisson GarciaProcess multiple prompts in parallel with Azure OpenAI Batch API
Process Multiple Prompts in Parallel with Azure OpenAI Batch API Who is this for? This workflow is designed for developers and data scientists who want to efficiently send multiple prompts to the Azure OpenAI Batch API and retrieve responses in a single batch process. It is particularly useful for applications that require processing large volumes of text data, such as chatbots, content generation, or data analysis. What problem is this workflow solving? Sending multiple prompts to the Azure OpenAI API can be time-consuming and inefficient if done sequentially. This workflow automates the process of batching requests, allowing users to submit multiple prompts at once and retrieve the results in a streamlined manner. This not only saves time but also optimizes resource usage. What this workflow does This workflow: Accepts an array of requests, each containing a prompt and associated parameters. Converts the requests into a JSONL format suitable for batch processing. Uploads the batch file to the Azure OpenAI API. Creates a batch job to process the prompts. Polls for the job status and retrieves the output once processing is complete. Parses the output and returns the results. Key Features of Azure OpenAI Batch API The Azure OpenAI Batch API is designed to handle large-scale and high-volume processing tasks efficiently. Key features include: Asynchronous Processing: Handle groups of requests with separate quotas, targeting a 24-hour turnaround at 50% less cost than global standards. Batch Requests: Send a large number of requests in a single file, avoiding disruption to online workloads. Key Use Cases Large-Scale Data Processing: Quickly analyze extensive datasets in parallel. Content Generation: Create large volumes of text, such as product descriptions or articles. Document Review and Summarization: Automate the review and summarization of lengthy documents. Customer Support Automation: Handle numerous queries simultaneously for faster responses. Data Extraction and Analysis: Extract and analyze information from vast amounts of unstructured data. Natural Language Processing (NLP) Tasks: Perform tasks like sentiment analysis or translation on large datasets. Marketing and Personalization: Generate personalized content and recommendations at scale. Setup Azure OpenAI Credentials: Ensure you have your Azure OpenAI API credentials set up in n8n. Configure the Workflow: Set the azopenaiendpoint in the "Setup defaults" node to your Azure OpenAI endpoint. Adjust the api-version in the "Set desired 'api-version'" node if necessary. Run the Workflow: Trigger the workflow using the "Run example" node to see it in action. How to customize this workflow to your needs Modify Prompts: Change the prompts in the "One query example" node to suit your application. Adjust Parameters: Update the parameters in the requests to customize the behavior of the OpenAI model. Add More Requests: You can add more requests in the input array to process additional prompts. Example Input json [ { "api-version": "2025-03-01-preview", "requests": [ { "custom_id": "first-prompt-in-my-batch", "params": { "messages": [ { "content": "Hey ChatGPT, tell me a short fun fact about cats!", "role": "user" } ] } }, { "custom_id": "second-prompt-in-my-batch", "params": { "messages": [ { "content": "Hey ChatGPT, tell me a short fun fact about bees!", "role": "user" } ] } } ] } ] Example Output json [ { "custom_id": "first-prompt-in-my-batch", "response": { "body": { "choices": [ { "message": { "content": "Did you know that cats can make over 100 different sounds?" } } ] } } }, { "custom_id": "second-prompt-in-my-batch", "response": { "body": { "choices": [ { "message": { "content": "Bees communicate through a unique dance called the 'waggle dance'." } } ] } } } ] Additional Notes Job Management: You can cancel a job at any time, and any remaining work will be canceled while already completed work is returned. You will be charged for any completed work. Data Residency: Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. Exponential Backoff: If your batch jobs are large and hitting the enqueued token limit, certain regions support queuing multiple batch jobs with exponential backoff. This template provides a comprehensive solution for efficiently processing multiple prompts using the Azure OpenAI Batch API, making it a valuable tool for developers and data scientists alike.
By Greg EvseevSmartLead Sheet Sync (Airtable Edition)
Auto-capture client inquiries straight into Airtable — no coding, just plug & play. Running a service business, agency, or funnel? Stop manually copying leads from your form to your CRM. This simple automation does it for you. ✅ What It Does: Instantly captures form submissions via Webhook Formats the data (name, email, project info, etc.) Adds each new lead into your Airtable base — like magic 💼 Perfect For: Freelancers & coaches Agencies & consultants Creators with landing pages or service funnels 💻 You’ll Get: A pre-built n8n workflow (Webhook → Code → Airtable) Plug & play setup instructions (takes 5–10 mins) Clean, editable JavaScript code to match your form fields Thumbnail for marketing or reselling 🛠 Tools Used: n8n (Free automation platform) Airtable (Free/paid CRM & spreadsheet tool) 🔒 No fees. No recurring tools. No code headaches. 💸 Built to deliver massive value for just $8.
By David Olusola