Automate AI newsletter with n8n template: ingest, select, format, and publish

Automate your AI newsletter — from raw markdown and tweets to a polished, Slack-reviewed edition. Built with n8n, this end-to-end template ingests .md files and tweet objects from an R2/S3 “data-ingestion” bucket, uses LLM chains to pick and write the top stories in an Axios-like style, generates subject lines and preheaders, resolves and aggregates source content, and routes everything through a Slack approval loop before producing the final markdown file. Key highlights: - Ingest: Search and download markdown + tweet objects for a given date from S3/R2. - Curate: LLM-driven story selection with structured outputs (4 top stories + chain-of-thought). - Compose: Per-story content generation (headline, 3 bullet “Unpacked” points, and a 2-sentence “Bottom line”). - Source resolution: Resolve identifiers, download segment content, and optionally scrape external sources for deeper context. - Review loop: Post selections and drafts to Slack, capture approvals/feedback, and support targeted edits. - Delivery: Assemble the full newsletter as clean markdown, convert to a file, and upload/post to Slack. Why it matters: - Produces consistent, repeatable newsletter issues while cutting manual research and drafting time. - Keeps a human-in-the-loop review step so editors retain control over tone and sources. - Configurable for different LLM providers, storage backends, and internal APIs — plug it into your existing stack. Quick setup notes: you’ll need R2/S3 credentials, the internal API HTTP auth, LLM credentials (Google Gemini / Anthropic or others), and a Slack OAuth app/channel configured. Curious to see it in action or adapt it for your team? I’d love to hear how you’d customize the prompts or integrations — link to the template is in the first comment below. Template link in the comments section. #n8n #WorkflowAutomation #NoCodeAutomation #AI #LangChain #LLM #AINewsletter #NewsletterAutomation #EmailMarketing #MarketingAutomation #SlackAutomation #AWSS3 #CloudflareR2 #WebScraping #GoogleGemini #Anthropic #ClaudeAI #ContentAutomation #MarTech #ContentCuration

Want to automate your AI newsletter end-to-end? This n8n template turns raw markdown and tweets into a polished, Axios-style email — complete with story selection, section writing, subject-line generation, and a Slack approval loop. How it helps: - Ingests .md files and tweet objects from an S3/R2 “data-ingestion” bucket and extracts text - Uses LangChain LLM chains (configurable models) to pick top stories and write newsletter sections with strict, reproducible prompts - Resolves content identifiers via an internal API, downloads source files, and optionally scrapes external links for verification - Builds intro, story segments, and a “Shortlist,” generates subject line + preheader, then assembles the final markdown and posts/uploads to Slack Why it’s useful: it saves hours of manual research and editing, enforces consistent formatting and sourcing, and gives an approval loop for editors via Slack. It’s also configurable — swap LLMs, update prompts, change S3/R2 and Slack credentials, or reuse parts of the pipeline in other automations. If you run a content team, produce a regular briefing, or want to prototype a reproducible editorial workflow for AI updates, this template is a fast jumpstart. Try it as-is or fork and tailor prompts, model creds, and channels to your stack. Template link in the comments section. #n8n #Automation #WorkflowAutomation #NoCode #AI #GenerativeAI #LLM #LangChain #NewsletterAutomation #EmailMarketing #ContentMarketing #ContentAutomation #AINewsletter #WebScraping #Slack #AWS #CloudflareR2 #MarketingAutomation #GoogleGemini #Anthropic

Building a weekly AI newsletter shouldn’t mean juggling files, Slack threads, and last-minute edits. I put together an end‑to‑end n8n template that turns raw markdown and tweets into a polished, Slack‑ready newsletter — from story selection to subject line, editor feedback, and final markdown export. What it automates: - Ingest .md files and tweet objects from an S3/R2 “data-ingestion” bucket for a target date - Run a LangChain-driven selection step to surface the top four stories (with identifiers and reasoning) - Resolve story content, scrape linked sources, and aggregate text + images - Use LLM chains (Google Gemini / Anthropic) to write Axios-style sections, intro, and a subject line - Support an editor approval loop via Slack and assemble the final markdown file for distribution Why it helps content teams: - Turns a multi-hour manual process into a repeatable workflow you can run daily or weekly - Keeps source identifiers, external links, and image assets traceable for audits and follow-ups - Makes it easy to swap LLMs, storage, or the Slack channel to fit your stack If you run a newsletter, editorial pipeline, or content ops team — this can be a fast way to prototype automation and scale production. Link to the template in the comment below — would love to hear how you’d adapt it for your workflow. Template link in the comments section. #AI #GenerativeAI #Automation #NewsletterAutomation #EmailMarketing #Newsletter #ContentMarketing #ContentOps #WorkflowAutomation #n8n #LangChain #LLM #PromptEngineering #WebScraping #Slack #AmazonS3 #CloudflareR2 #MarketingAutomation #NoCode #GoogleGemini

Tired of manually turning customer questions into content? I built an n8n template — “VOC Data into Blogs” — that automates turning question-style Reddit posts into quick blog drafts and stores them in Google Sheets. What it does: - Fetches newest posts from a subreddit and filters titles that read like questions. - Rephrases each question consistently using OpenAI (via LangChain). - Generates a URL-friendly slug plus a short intro, a detailed step-by-step section, and a concise conclusion. - Saves the full blog scaffold back to a Google Sheet for easy review and export. Why it’s useful: it turns voice-of-customer signals into publishable scaffolds in minutes — ideal for content teams, product managers, and support teams who want a repeatable pipeline for blog ideas and FAQs. Quick customization tips: change the subreddit, tweak prompts, swap the model, or point the results to a CMS instead of Google Sheets. The template uses a manual trigger and is inactive by default, so you can test safely. If you want a hand tailoring prompts, connecting a different data source, or adapting this to another channel (Twitter, forums, Intercom), I’m happy to help — leave a comment or DM me. Template link in the comments section. #n8n #WorkflowAutomation #ContentAutomation #Reddit #BlogAutomation #SEOBlogging #ContentMarketing #AIAgents #LangChain #OpenAI #GPT4o #GoogleSheets #NoCode #LowCode #GenerativeAI #MarketingOps #VoiceOfCustomer #DataAutomation #AIContent #CreatorTools

Ship a polished AI newsletter from raw markdown and tweets — without rebuilding your content pipeline. I built an n8n template that automates the full editorial flow: ingest files and tweets from an S3/R2 bucket, pick the top stories with structured LLM prompts, resolve each story’s source material, generate Axios-style sections, create a subject line + preheader, and run an approval loop via Slack — then export a ready-to-send Markdown file. Key capabilities: - Ingest: date-based .md and tweet objects from an S3/R2 “data-ingestion” bucket - Story selection: LangChain + LLM chain returns 4 structured top stories with identifiers and chain-of-thought - Per-story assembly: fetch content, optional external-source scraping, aggregate sources and images - Writing: dedicated LLM prompts produce "The Recap" intro, Axios-like segments, and a short "Other Top Stories" section - Subject lines & preheaders: automated generation with approval/edit loop - Delivery: assemble full markdown, convert to file and post to Slack Who this helps: newsletter editors, content ops teams, growth and product teams who want repeatable, auditable newsletter production with minimal manual drafting. Why it matters: removes tedious copy-paste work, centralizes source tracking (identifiers + external links), and enforces consistent voice and formatting via prompts — so you can focus on editorial judgement instead of plumbing. Want to try it or adapt it to your stack (different storage, different LLMs, or a different delivery target)? I’d love to hear how you’d customize it — drop a comment or DM and I’ll share tips. Template link in the comments section. #n8n #WorkflowAutomation #NewsletterAutomation #EmailMarketing #ContentMarketing #AIAutomation #GenerativeAI #LangChain #LLM #PromptEngineering #SlackAutomation #AmazonS3 #CloudflareR2 #WebScraping #MarTech #ContentCreation #NoCode #LowCode #GoogleGemini #Anthropic

Building a weekly AI newsletter shouldn’t be a full-time engineering project. I built an n8n template — “Content - Newsletter Agent” — that automates the entire pipeline from raw markdown and tweets to a ready-to-send newsletter draft and Slack-ready file. What it does - Ingests .md content and tweets for a given date from an S3/R2 bucket - Uses LLM chains to pick top stories, write Axios-style newsletter sections, and craft subject + preheader text - Resolves content identifiers, fetches external sources, and scrapes linked pages when needed - Supports an editor approval loop via Slack, then outputs a markdown file and uploads it to a channel Why this helps teams - Saves hours of manual compilation and editing by turning content collection + writing into a repeatable workflow - Keeps editorial control with Slack-based review and structured prompts for consistent tone - Easy to adapt: swap LLMs, customize prompts, or change storage endpoints without rewriting the pipeline How it’s structured (high level) - Trigger: simple form (Date + optional previous newsletter) - Content discovery: S3/R2 search for .md and tweet objects, plus optional scraping of external links - LLM layers: story selection -> segment writing -> intro + shortlist -> subject line generation - Delivery: assemble markdown, convert to file, and post to Slack for distribution or review Who should use it - Newsletter editors, product/content teams, AI-focused publishers, and growth teams who want fast, repeatable editions Quick setup pointers - Add your S3/R2 and internal API credentials, connect Gemini/Anthropic (or swap providers), and point Slack OAuth to your review channel - Tweak the prompts and output schemas if you want a different voice or section structure I’ve added the template link in the first comment if you want to clone it and start testing. Template link in the comments section. #n8n #LangChain #AI #GenerativeAI #LLM #NewsletterAutomation #EmailNewsletter #EmailMarketing #ContentAutomation #MarketingAutomation #WorkflowAutomation #NoCode #LowCode #SlackAutomation #AmazonS3 #CloudflareR2 #WebScraping #GoogleGemini #Anthropic #AIContent

Turn raw markdown and tweets into a polished AI newsletter — automatically. I just finished an n8n template called “Content - Newsletter Agent” that does the heavy lifting for creators and product teams who publish AI newsletters. Feed it your daily .md files and tweets, and it: - Finds and aggregates source material from your S3/R2 bucket and tweet archive - Uses LLM chains to pick the top 4 stories and generate Axios-style sections (Recap, Unpacked, Bottom line) - Produces subject lines + preheader text and supports an editor approval loop via Slack - Resolves content identifiers, scrapes external sources when available, and bundles images - Is configurable — swap prompts, LLM credentials (Gemini/Anthropic), S3/R2 settings, or the internal API auth Why it matters: it turns a multi-hour manual workflow into a repeatable automation that keeps newsletters timely, consistent, and easy to edit. If you run a newsletter, manage content ops, or want to prototype LLM-driven publishing workflows — this template can save you hours each edition. I’ll drop the template link in the first comment — try it and tell me what you build. Template link in the comments section. #n8n #workflowautomation #aiautomation #newsletterautomation #emailmarketing #emailnewsletter #contentautomation #contentmarketing #langchain #llm #generativeai #slack #aws #awss3 #webscraping #apisintegration #lowcode #nocode #martech #automation

I recently finished a small system that makes a website contact form a bit smarter. Instead of just collecting messages, it uses AI to read what people write, identify the type of message (lead, question, feedback), generate a short natural reply, and send everything directly to where I work — Discord, Slack, or email. It’s built entirely with APIs: OpenAI for categorization and replies, Supabase for storage, and Next.js for the frontend. The article explains the full setup and how you can build something similar for your own site. https://lnkd.in/dCzjScrf

Tired of manually syncing external data to Notion? Here’s a ready-made n8n workflow that automates Notion updates and gives your data a searchable, context-aware layer. What it does - Listens for incoming POST requests via a webhook and ingests the payload - Splits long text into chunks and generates embeddings with OpenAI (text-embedding-3-small) - Stores embeddings in Supabase for fast vector search - Uses a RAG agent + chat model to provide context-aware responses and drive the update logic - Appends results to a Google Sheet for an audit trail - Sends Slack alerts on errors so nothing slips through the cracks Why it helps - Turns raw incoming data into a searchable knowledge layer you can query and reuse - Adds intelligent, retrieval-augmented logic so updates are context-aware instead of blind writes - Keeps an auditable log and proactive alerts for reliability - Low-code: import the template, add credentials, and connect the pieces Quick setup tips - Import the template into n8n and configure OpenAI, Supabase, Anthropic, Google Sheets, and Slack credentials - Replace the SHEET_ID and any environment values for your Notion integration - Test with a sample POST to the webhook and watch the pipeline store embeddings and produce RAG-driven responses Try it out and tell me what you automate—I’d love to see how teams use this to keep Notion up to date and context-rich. Template link in the comments section. #n8n #Notion #NotionAPI #Automation #NoCode #APIAutomation #Webhooks #OpenAI #Embeddings #Supabase #VectorDatabase #VectorSearch #RAG #RetrievalAugmentedGeneration #LLM #GenerativeAI #GoogleSheets #Slack #WorkflowAutomation #KnowledgeManagement

🚀 Beginner-friendly automation project alert! Just dropped a super fun and actually useful project built with n8n — perfect if you’re just starting your automation journey or want to add a bit of AI magic to your workflow ✨ 🔧 Project: Lead Qualification Automation with n8n What it does 👇 📬 Watches your Gmail → grabs incoming lead emails 🧠 Uses OpenAI (GPT-4) to rate their intent (High / Medium / Low) 📈 If it’s High Intent → logs it in Google Sheets + pings your team on Slack 💌 If it’s not → adds it to “All Leads” + auto-sends a friendly follow-up All this → zero code, just smart automation with Gmail + Sheets + Slack. 🔗 Check it out on GitHub → https://lnkd.in/gJNF-G2U 🧭 Why it’s a W for beginners: ✨ Learn real automation blocks — Gmail triggers, branching logic, AI classification ⚡ Plug-and-play JSON workflows (no need to build from scratch) 💼 Real use-case: automating actual lead triage, not just a “hello world” flow 🤖 Bonus: you’ll see how AI + automation can work together 💬 Uses everyday tools (Gmail, Sheets, Slack) — all free/freemium 💡 How to try it out: 1️⃣ Fork/download the repo & import both JSON workflows into n8n 2️⃣ Add your creds (Gmail, Google Sheets, Slack, OpenAI API key) 3️⃣ Create two tabs in Sheets: High Intent + All Leads 4️⃣ Update sheet IDs / Slack channel IDs 5️⃣ Hit Activate → send yourself a test email → boom 💥 automation! Once you’ve got it running, remix it: → Add your CRM → Create extra intent levels → Drop custom AI prompts 🔥 Pro tip: Share your “before vs after” stats — show how much manual work you saved. That’s what makes a killer post. #n8n #automation #nocode #openai #workflowautomation #ai #genztech #productivityhack