AI agents within deterministic workflows
Using LLMs and agents for research, classification, generation, decision support, memory, and human handoffs inside n8n systems.
34%
Best tweets about n8n
50 Best Tweets About n8n (2026)
Browse the best tweets about n8n, featuring automation workflows, AI agents, integrations, self-hosting, templates, and production lessons.
Real n8n workflows, nodes, integrations, self-hosting, agent automation, reliability, and measurable operating outcomes.
- Creators
- 40
- Updated
Original Xholic analysis
What 50 top n8n posts reveal
The conversation often presents n8n as an orchestration layer for AI-enabled systems: workflows can handle repeatable steps while AI is used for tasks such as research, classification, and decision support. Posts also stress that production use requires workflow design, data foundations, and operational visibility; several examples report claimed time, cost, or operational benefits.
- Dominant tone
- Positive
- Median score
- 12.3
- Leading format
- Case Study
- Recent posts
- 42%
86% of posts
All-time engagement
32% of posts
Published in 90 days
Conversation map
The themes creators return to
Workflow design and maintainability
Mapping business logic before building, avoiding visual-workflow sprawl, using reusable workflows, state-machine patterns, and code where appropriate.
32%
Agent-built workflows and MCP
MCP-enabled agents creating, modifying, deploying, and operating n8n workflows from natural-language goals.
24%
GTM, sales, and lead automation
Lead scraping, enrichment, qualification, outbound sequencing, CRM routing, and connected revenue-stack workflows.
22%
Reliability, observability, and governance
Retries, idempotency, error handling, monitoring, OpenTelemetry, logs, approval gates, credential isolation, and safe production operations.
20%
Business process automation
Automating repetitive operational work such as invoice handling, subscriptions, inventory, CRM updates, and access management.
16%
Content, research, and media pipelines
Automated content repurposing, social research, reporting, publishing, second-brain ingestion, and video production workflows.
14%
Self-hosting and open-source stacks
Running n8n locally or on lightweight infrastructure, using Docker and private AI components, and comparing open automation platforms.
14%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 30
- Median reposts
- 4
- Median replies
- 7
- Median views
- 3.3K
Posts with media make up 64% of this collection. Their median all-time score is 12.3, compared with 13.6 for text-only posts.
Format mix
- Case Study 32% · score 9.46
- Opinion 20% · score 9.07
- Announcement 18% · score 21.7
- Tutorial 16% · score 13.6
Consensus and debate
Where creators agree, and where they do not
Shared view
Agents can sit within structured workflows
Posts describe using n8n workflows, webhooks, schedules, scripts, and defined paths around agent capabilities. They argue that repeatable work can be made explicit while agents handle less deterministic tasks.
Shared view
Business examples focus on repetitive operations
Examples describe invoice processing, subscription-access management, inventory tracking, and lead workflows. The cited authors frame these as recurring tasks where automation may reduce manual work.
Shared view
Map the process before implementation
Posts recommend understanding the business process, mapping decisions and data flows, and defining handoffs before building in the visual editor. One post describes graph-style workflows as explicit routes and checkpoints for repeated work.
Shared view
Reliability and visibility are recurring production concerns
Posts call out retries, idempotency, error handling, alerts, logging, approval steps, credential handling, and observability as considerations beyond a successful test run.
Open debate
AI-built workflows: capability claims versus operational prerequisites
MCP-related posts claim that agents can create or modify n8n workflows from natural-language requests. Other posts argue that production systems still require reliable connectors, current data, memory, logs, and other operational foundations.
Open debate
Visual workflows versus code
One post presents n8n’s visual builder as approachable for a non-engineering team, while other posts criticize visual-workflow sprawl or describe moving some workflows to custom code.
Open debate
Self-hosting appeal and licensing limits
Posts promote lightweight and local self-hosted n8n setups. A licensing comparison states that n8n’s Sustainable Use License restricts hosting n8n as a paid-access service.
What performs
Patterns behind standout posts
Agent-building and MCP was the highest-scoring theme
Agent-built workflows and MCP had the highest theme median all-time score at 38.64. The first three listed score outliers were posts about native MCP support or an n8n-MCP repository.
Lists led the format benchmarks
Lists had the highest format median all-time score at 59.359, ahead of announcements, tutorials, case studies, and opinions.
Media was prevalent but had a lower median score than text
Media appeared in 32 posts, representing 64% of the set. Its median all-time score was 12.32, compared with 13.59 for text posts.
Case studies were the most common format
Case studies accounted for 16 posts, or 32% of the set. Cited examples pair workflow descriptions with author-reported commercial, cost, or time-saving outcomes.
Statistical standouts
- View standout post 1 Score 464.4 · 37.7× median
- View standout post 2 Score 306.0 · 24.84× median
- View standout post 3 Score 303.4 · 24.63× median
- View standout post 4 Score 143.8 · 11.67× median
- View standout post 5 Score 132.9 · 10.79× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Avi Chawla
@_avichawla
2 posts
-
2. Cody Schneider
@codyschneider
2 posts
-
3. Dan Rosenthal
@dan__rosenthal
2 posts
-
4. Machina
@EXM7777
2 posts
-
5. Aaron
@IAmAaronWill
2 posts
-
6. Harsh Makadia
@MakadiaHarsh
2 posts
Machina led the supplied top-voice median scores
Machina posted twice and had the highest supplied top-voice median all-time score, 385.2. Both cited posts discuss MCP-enabled agents creating or managing automations.
Dan Rosenthal described an operations-oriented stack
Dan Rosenthal’s two cited posts describe a GTM and company-operations stack involving n8n, agents, human review, guardrails, and migration of some workflows to custom code.
Mike Scully used an explicit cost comparison
Mike Scully’s cited posts describe the same farm-shop invoice workflow and compare a €12,000 implementation with the author’s stated estimate of €27,593 in annual full-time labor at Ireland’s minimum wage assumptions.
Since the previous snapshot
What changed since Aug 12, 2026
- 60% of the selected posts remained.
- The creator count changed by +6.
- The leading sentiment remained stable.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best n8n tweets
Top n8n tweets from 40 creators
Ranked 01–50
- 01
@EXM7777 ·
n8n shipped native MCP support and nobody's connecting what this actually means... your AI agents can now CREATE and MODIFY n8n automations programmatically the setup: > install the n8n-mcp server > add it to your Claude Code config > Claude now has access to all 1,396 n8n nodes (812 core + 584 community) what used to happen: > open n8n dashboard > manually drag nodes > configure each one > test, debug, repeat > 45 minutes per workflow what happens now: > "build me an automation that monitors my competitors' pricing pages every 6 hours and sends a Slack alert when anything changes" > Claude builds the entire n8n workflow > 3 minutes, zero mistakes the real implication: your agents can now build their own automations not "trigger existing workflows"... literally construct new ones from scratch based on what they need this is the beginning of self-automating systems
- 02
@EXM7777 ·
something is happening in agent infrastructure that's bigger than any single tool release... in the last 7 days: > n8n shipped native MCP - agents can now build their own automations > Stripe Projects launched - agents can provision databases, auth, hosting from CLI > Open WebUI turned MCP servers into visual apps inside chat > Ruflo hit GitHub - 60 simultaneous Claude agents with self-improving hierarchy > VS Code positioned itself as "the home for multi-agent development" > Copilot Swarm Orchestrator got plugin system + MCP > OpenAI invested in Isara - a startup JUST for multi-agent orchestration every single one of these is about the same thing: agents that use other tools are being replaced by agents that build and manage other agents the stack is flipping: > 2024: human builds workflow, AI executes steps > 2025: human describes goal, AI builds the workflow > 2026: AI identifies the goal, builds the workflow, monitors results, and improves itself if your AI setup still requires you to manually configure every tool and connection... you're about to be lapped by people whose agents set up their own infrastructure the companies building orchestration layers (not just agents) are the ones that will crush it in 12 months
Watch video
- 03
@techxutkarsh ·
Best GitHub repos for Claude Code that will 10x your next project in 2026 1. Claude Mem https://t.co/LkOYuP33uq Persistent memory across sessions — stop re-teaching Claude your codebase 2. UI UX Pro Max https://t.co/ljrCxRKSjM 50+ styles, 161 color palettes, 99 UX guidelines — Claude stops building ugly UIs 3. n8n-MCP https://t.co/ljrCxRKSjM Connect Claude Code to 400+ n8n integrations via MCP 4. LightRAG https://t.co/mv8zvqydym Graph + vector RAG — lets Claude understand large codebases structurally 5. Everything Claude Code https://t.co/JMxCkTjHP7 Skills, instincts, security scanning, multi-language coverage — full agent harness 6. Awesome Claude Code https://t.co/Ka0cFmpEue Community bible — curated skills, hooks, slash commands, orchestrators 7. Superpowers https://t.co/ejNfA5fYax Forces structured thinking before writing a single line of code 8. Claude Code Ultimate Guide https://t.co/bbRtaEgEzQ 23K+ lines of docs, 219 templates, 271 quizzes — beginner to power user 9. Antigravity Awesome Skills https://t.co/Ka0cFmpEue 1,200+ ready-to-use skills — one of the largest collections 10. Claude Agent Blueprints https://t.co/H8OqMJrUb0 75+ agent workspace templates beyond coding 11. VoiceMode MCP https://t.co/AHpDtODmXT Natural voice conversations with Claude Code via Whisper + Kokoro 12. Awesome Claude Plugins https://t.co/OEORrsNhzi 9,000+ repos indexed with adoption metrics — find what people actually install Bookmark this before your next build.
- 04
@HamelHusain ·
Ya'll worried about AI Coding slop, when there as an entire army of n8n experts who are installing unmaintainable visual workflow spaghetti in small/medium sized businesses at scale Literal merchants of complexity. Its so much worse than using claude code. It's an artifact of being stuck 6 months in the past and n8n is all you know.
- 05
@Mike_Scully_ ·
We signed a €12,000 contract with a farm shop. Not a tech company. Not a SaaS startup. A physical farm shop processing 7,000 invoices a year manually. Here's what we're building and why the ROI was a complete no-brainer for them. The problem was simple. Every invoice came in via email as a PDF attachment. Someone would open it, read it, and manually enter the data into their CRM. 7,000 times a year. Every. Single. One. Here's what we're doing to fix it: Emails land in their inbox → n8n detects the attachment automatically → the PDF gets parsed and the data extracted → it gets cleaned and structured → entered directly into their ERP without a single human touching it. The whole workflow runs silently in the background 24/7. No mistakes. No delays. No manual entry. On the sales call I asked one question that closed the deal: "If you don't solve this with automation, what does that look like?" He said: "We hire someone full time." In Ireland minimum wage is €14.15/hr. Average work week is 37.5 hours. That's €27,593 a year spent entirely on someone opening emails and typing numbers into a system. Or they pay €12,000 once. And it's done. First instalment of €4,000 just landed. Two more to go. The ROI conversation ended there. Now here's the part I want you to pay attention to: We are not software engineers. We didn't write a single line of complex code. n8n has a visual workflow builder where you connect the logic like building blocks. The tools exist. The infrastructure is already there. All we did was understand the client's problem and know which tools to connect together. That's the opportunity right now. There are thousands of businesses like this farm shop. Completely offline. Drowning in manual processes. No idea that a solution exists let alone that it costs a fraction of a full time hire. You don't need to be a developer to sell this. You need to understand the problem, know the tools, and be able to show the ROI. That's it.
- 06
@dan__rosenthal ·
We’re currently scaling our company to 8-figures with AI. Our GTM automation layer runs on 10 tools: 1. n8n handles most automation use-cases (now transitioning to code) 2. Claude Code powers our company OS and AI agents 3. Supabase stores our internal datasets (companies, contacts, campaigns, posts) 4. Vector stores winning examples as RAG for AI agents. 5. Clay is where we execute our GTM workflows. 6. HubSpot is our central brain for everything revenue. 7. Instantly is where we automate e-mail campaings. 8. HeyReach for LinkedIn campaigns. 9. Nooks to parallel dial. 10. Ergo to update the CRM while we close. Lot’s of data providers mixed in there but this is a good piece of the stack..
- 07
@dan__rosenthal ·
The way I operate my agency wouldn’t have been possible 1 year ago. But we predicted this... And made a big bet on AI-Native Services. Claude Code is now our most used interface. We uniquely blend software and human expertise. We call it “AI-Native Services”. Here’s how it works operationally: 1️⃣ Company OS in git Our entire company dataset lives in one GitHub repo called Company OS. What's inside: • company/ - team, voice guide, design system, industry intel • wiki/ - SOPs, playbooks, campaign guides • clients/ - per-client context files • raw/ - client calls, market research, competitor data • plugin/ - 26 agents, 23 commands, hooks • skills/ - 79 Claude skills Data is constantly flowing in to keep it up-to-date. 2️⃣ Client repos Every client gets their own private repo. Same engineering pattern as the Company OS, just personalized to their account. What's inside: • Their ICP, voice guide, brand assets • Historical campaigns and what worked • Onboarding form data and deep research • Slack threads, call transcripts, GDrive changes • API/MCP connections to their revenue stack Result: every team member has full client + company in every session. 3️⃣ Human interaction layer We still log into some SaaS UIs, but Claude is slowly taking over. Across 20 team members, the efficiency gain has been massive. We try to automate as much of the admin as possible: • Client onboarding • Content research and ideation • Skill tuning from team feedback • Reply triage + sentiment routing • Campaign launch pre-flight checks So AI does the legwork, but humans ship. So we can spend more time on strategy + creative GTM. 4️⃣ MCP + CLI engine MCPs + CLIs let Claude act across our stack vs. just advise. Some of our favorite MCPs/CLIs: • GitHub - Company OS + client repos • Findymail - email verification waterfall • Google Workspace - client docs • Airtable - automation backend • InstantlyAI - email campaigns • Slack - team + client comms • Apolloio - list + enrichment • Notion - internal wiki + PJM • HeyReach - DM sequences Plus HubSpot, Browserbase, Supabase, Vercel, Figma, Stripe, Pinecone, Clay, Apify, Firecrawl, and more. We're also migrating a ton of workflows to custom code. 5️⃣ Operationalize As an AI-native services company, we're constantly optimizing how we work with AI and software. Built into the system: • Guardrails: safety hooks gate 94+ risky operations. • PR-based governance: anyone on the team can propose a new skill, agent, or tweak as a branch. • Workflows-engineering plugin: 26 agents, 79 skills, 23 commands auto-propagated. Agent swarms split tasks into 5-20 sub-agents. • Self-improvement loop: n8n syncs tech stack data back into the Company OS. Pinecone stores past content + performance metrics for skills to query. Human corrections feed back in. There isn't ever going to be a finish line, so we're building like it's a marathon.
- 08
@codyschneider ·
apify APIs + claude code actually so OP kind of crazy tbh been testing n8n flow does apify youtube channel email scraper scrapes channel emails for your niche keywords, validate emails with millionverifier, send email instantly ai, instantly ai cold emails them asking for pricing for 3 video package, they respond back, gets added to a spreadsheet, agent compares costs looking for people underpricing their work, agent closes creator deals in followup email, circles in human if needed
- 09
@KanikaBK ·
I found 5 AI agent GitHub repos that most developers have never heard of. COMBINED STARS: 750,000+. ALL OPEN SOURCE. ALL FREE. Here is exactly what each one does. 1. n8n: https://t.co/y5MH1YnDFm ↳ 180,000+ stars and still climbing ↳ Open source alternative to Zapier but built for serious technical teams ↳ 400+ integrations, visual workflow builder, and native AI agent nodes ↳ Self-host it and get unlimited automations for free with zero per-task fees 2. Dify: https://t.co/Sww2R8KVT5 ↳ 136,000+ stars ↳ Build, deploy, and manage AI applications without drowning in boilerplate code ↳ Comes with RAG pipelines, prompt orchestration, model management, and usage monitoring built in ↳ Supports OpenAI, Anthropic, Ollama, and 100+ other model providers out of the box 3. Langflow: https://t.co/3cBWhbnHSv ↳ 146,000+ stars ↳ Drag and drop visual builder for AI agents and RAG workflows ↳ Shortens the path from idea to working AI pipeline without writing extensive code ↳ Used by data scientists and engineers who want to prototype and ship fast 4. Open WebUI: https://t.co/ISqMHwCnyd ↳ 124,000+ stars and 282 million Docker downloads ↳ Self-hosted ChatGPT-style interface that runs completely offline ↳ Connects to Ollama and any OpenAI-compatible API with one pip command ↳ Built-in RAG, voice features, custom agent builder, and enterprise SSO 5. Ollama: https://t.co/nnQaDjLa1T ↳ 165,000+ stars ↳ Run Llama, Mistral, Gemma, and DeepSeek entirely on your own hardware ↳ No data sent to external servers, no API fees, no cloud dependency ↳ The backbone of almost every serious self-hosted AI stack running today Most developers are paying per token for things they could be running locally for free. These five repos are why that excuse is running out.
- 10
- 11
@_avichawla ·
Developers just shipped a new class of AI Agents! To understand why it matters, you need to see where it sits. Level 1: Prompt → Response Each call is stateless. The model can use tools/APIs within a single request but nothing persists. Most production LLM apps are sophisticated Level 1 wrappers. Level 2: Interactive assistant The platform handles persistence for you with memory, tools, files, connectors. ChatGPT and Claude live here. These are capable, but entirely reactive. Level 3: Delegated execution You define the goal and the agent owns the execution. Claude Code, Codex, and deep research operate here. Your task keeps running when you walk away, but the agent won't start new work on its own. Level 4: Autonomous scheduled operation The agent runs on its own clock using cron, webhooks, or event triggers with persistent state across runs. OpenClaw with heartbeat, n8n with AI nodes, or the custom stacks devs stitch together. Level 5: Self-building systems Tools like Lovable and Bolt already go from prompt to deployed app. But the output is a web app that sits there until someone interacts with it. Level 5 is different. You can say "monitor my competitors' blogs, store new posts in a table, and Slack me when they launch a product" and then the agent creates the database schema, connects the integrations, sets the schedule, and deploys a workflow that runs every morning on its own. No one needs to be present. The workflow it just created runs on a schedule, maintains persistent state, and acts without human initiation. Those are the exact characteristics of Level 4. So the output of a Level 5 agent is itself a Level 4 agent. If you want to see this in practice, @simdotai shipped Mothership as an early implementation of this. You can describe what you need and it creates tables, wires workflow blocks, configures integrations, and sets the schedule. It's fully open-source (27k+ GitHub stars, Apache 2.0) so you can easily self-host it and see the full implementation on GitHub. I have shared the GitHub repo in the replies!
- 12
@_avichawla ·
16 powerful Agent skills for AI Engineers: Claude Code’s .claude/ skills system lets you package reusable instructions, workflows, and tool configurations into portable folders that any agent session can pick up. Here are 16 powerful Agent skills for AI engineers: - Superpowers: A structured dev workflow that forces Claude to brainstorm, plan, and test before writing any code. Useful when you want rigor over speed. - InsForge: Semantic backend layer that exposes auth, database, storage, and functions through one agent-friendly API. Think of it as a unified backend for agents. - Bright Data Skills: Teaches Claude to orchestrate 60+ MCP tools for web scraping and structured data extraction. Handles the messy parts of live web access. - Context7: MCP server that feeds live, version-specific library docs directly into Claude’s context. No more hallucinated APIs from outdated training data. - Claude-Mem: Persistent memory plugin that auto-captures sessions and reinjects relevant context into future ones. Solves the “Claude forgot everything” problem between sessions. - Everything Claude Code: Curated skills and rules collection with smart token-saving compaction at logical breakpoints. A good starting point if you’re building your own .claude/ setup. - Planning with Files: Persistent markdown files for planning, progress tracking, and knowledge storage across sessions. Simple approach, surprisingly effective for multi-session projects. - Sentry Security Review: Security review skill built on 15 years of real Sentry patches and Django ORM pitfalls. Catches the kind of bugs that only show up in production. - Frontend Design: Official Anthropic skill for distinctive, non-generic UI output with bold design choices. Ships with Claude Code and pushes past the default “looks like every other AI-generated UI” problem. - Web Quality Skills: Lighthouse and Core Web Vitals optimization for performance, accessibility, and SEO. Bakes web quality checks directly into the agent loop. - n8n-MCP: MCP server with docs and schemas for all 1,396 n8n automation nodes. If you’re building automations with n8n, this gives Claude full visibility into the node catalog. - Claude-Reflect: Captures your repeated corrections and turns them into reusable commands with human review. The agent learns your preferences over time instead of making the same mistakes. - cc-DevOps Skills: Generator and validator loops for Terraform, Kubernetes, Docker, and CI/CD configs. Generates infra code, then validates it before you apply. - Agent Sandbox: Isolated E2B cloud sandboxes for building, hosting, and testing apps without touching local files. Good for when you want the agent to experiment freely without risk. - Agile Workflow: Full agile delivery pipeline with multi-model parallel review via Codex and Gemini agents. Brings structured software delivery practices into the agent workflow. - Claude Code Plugins+: Plugin directory with a CLI package manager for searching and installing niche skills. Think npm but for Claude Code skills. The .claude/ skills folder is becoming the package manager layer for agent behavior. Each of these skills is a self-contained instruction set that shapes how Claude approaches a specific type of work. The interesting thing to note here is that skills aren’t just prompts. They combine instructions, file templates, tool configurations, and validation loops into composable units. The best ones encode real practitioner knowledge (like Sentry’s 15 years of security patches) into something an agent can apply consistently. 👉 Over to you: Have you built any custom ones for your workflow? ____ Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.
- 13
@alex_prompter ·
If I had 6 months to go from average AI user to AI operator, this is the path. Built for one person who wants the output of a ten-person team. Stage 1: Thinking Before Prompting Problem decomposition, first-principles framing, defining the output before you write the input, judging good results from bad. Stage 2: Prompt Architecture Context engineering, role-task-constraints structure, few-shot examples, structured output, tight iteration loops. Stage 3: Model Fluency Claude, ChatGPT, Gemini, Grok, Perplexity. Matching the model to the job, knowing where each one breaks, switching mid-task when it pays. Stage 4: Standing Systems System prompts, custom instructions, Projects, reusable context. Moving from one-off prompts to setups that persist. Stage 5: Knowledge and Memory Project files, retrieval (RAG), persistent memory, a knowledge base the model pulls from so you stop re-explaining yourself. Stage 6: Multi-Step Workflows Chaining prompts, splitting a job into stages, model-to-model handoffs, checkpoints you can inspect between steps. Stage 7: Automation n8n, Make, Zapier. Triggers, connecting AI to the tools you already use, pulling yourself out of the repetitive middle. Stage 8: Agents and Tools AI agents, tool calling, MCP servers. Giving the model the ability to act on your behalf, not just answer. Stage 9: Judgment and Quality Control Verification, catching hallucinations, fact-checking, editing, taste. The human layer that makes the output safe to ship. Stage 10: Operationalize SOPs, repeatable systems, delegating the work to AI, scaling output without scaling your hours. Most people collect prompts and stay stuck. Operators build systems and stop competing. LLMs don't think; you do. (Bookmark this.)
- 14
@Aje_Dynamicz ·
Vibe Coding has officially arrived inside n8n and it's going to change how a lot of freelancers and developers build automations... Instead of spending time searching for the right node, connecting everything manually, and configuring each step one by one, you can now describe the workflow you want in plain language and let AI build the foundation for you. For freelancers, this means: Faster delivery times More projects completed in less time Reduced setup fatigue Lower barrier to offering automation services For developers, it means... Rapid prototyping Less time on repetitive workflow setup More focus on logic, optimization, and business outcomes Easier collaboration with non-technical teams The biggest win is that automation becomes accessible to more people. A marketer, operations manager, or business owner can explain a process in natural language and get a working workflow without needing deep technical knowledge. That doesn’t replace developers; it makes developers more productive. The real value shifts from dragging nodes around to designing better systems, solving complex problems, and refining workflows that create measurable results. AI-generated workflows won’t eliminate expertise. They’ll simply remove a lot of the repetitive work that slows experts down. The future of automation isn’t just no-code. It’s intent-driven building where ideas turn into workflows in minutes instead of hours...
- 15
Just wrapped up a project that turned a 571,000-customer spreadsheet into something that actually works. The client runs a subscription box business. Their problem? Every time someone cancelled or paused their subscription, someone on the team had to check when to remove them from the community manually. With over half a million customers, you can imagine how that was going. Here's what I built: - 5 automated workflows that now handle everything - from sending community invites to new subscribers, tracking cancellations and pauses, updating billing dates, and moving expired subscriptions to a removal queue. All without anyone touching a spreadsheet. - The system categorizes people automatically. If you only paid for the trial, you get 14 days of access. If you've been a paying customer, you get 30 days from your last billing date. Fair is fair. - Everything flows into https://t.co/2NkBV5KuyB so the team can see exactly who needs to be removed and when. No more calculations or missed removals. No more customers keeping access they haven't paid for. - We also synced 73 historical cancellations from their old spreadsheet into the new system. The result? The team saves 10+ hours every week. More importantly, they're not worrying about whether they missed someone or gave the wrong person access. This is what automation should do; handle the repetitive stuff that eats up your time so you can focus on actually growing the business. If your team is drowning in manual subscription management, billing tracking, or any process that involves "checking spreadsheets daily," send me a DM. #Automation #n8n #BusinessAutomation #SubscriptionManagement #NoCode
- 16
@alexabelonix ·
Build in public > what this girl is doing in build in public? 😂 > 3h exploring sales funnels and sales flow automation > planing to have a consultation about n8n sales automation > assuming that can be expensive > in my mind flow can be like that: DM → auto-reply / AI assistant or scripted bot → qualification questions → ICP / hot lead detection → request email / WhatsApp if needed → send data to CRM → send asset / trial / payment link → update CRM status → follow-up reminders → human handoff if needed > build a product is complicated (even with vibe coding tools. hey, debugging) > fking distribution is far more complicated
- 17
@n8n_io ·
RaspberryTips published a new guide on running n8n on a Raspberry Pi. It covers Docker setup, key nodes, real project ideas, and common issues you’ll run into. Good reference for lightweight, self-hosted automation setups. Builders teaching builders. We love that. 🙌 https://t.co/Bh2kyjw8yP
- 18
@itsalexvacca ·
We've built GTM systems for 267+ B2B companies. Most were running 20+ tools. They didn't need half of them. The teams that close the most deals have the leanest stack. Here's what actually works across every layer of the revenue stack in 2026: > CRM: We started on HubSpot. Data lived in 3 places. Nothing talked to each other. Switched to Attio. > If you're under 100 people, you don't need a $50K/year CRM with a 6-month implementation. > Data Enrichment: Used to pay ZoomInfo $30K/year and still manually verify half the data. Clay replaced all of that. > One workflow in Clay, 50+ data sources, FullEnrich for phone numbers. We used to hire VAs to write the first line of every cold email by hand. Clay killed that overnight. > Outreach: This is where most teams burn money. In 2021, 500+ emails a day was standard. Bottom 10% send the most volume for the fewest replies. Top 10% send micro-campaigns with the highest positive reply rates. > 50 targeted emails beat 500 spray-and-pray every time. > Intent Signals: Barely existed 3 years ago. Now it's the backbone of ColdIQ. > We don't prospect cold anymore. We stack signals: recent hires, GTM engineer postings, headcount growing 20% in 6 months, fresh funding rounds. > We use RB2B to ID anonymous website visitors, push them into Clay, and trigger outreach automatically. Cold outbound is dead. > CPQ: Salesforce CPQ hit end-of-sale March 2025. Most of our clients were quoting with spreadsheets and PDFs anyway. Hyperline now connects CRM to billing natively. > Billing: 67% of SaaS companies use usage-based pricing. Legacy billing tools can't handle hybrid pricing in one system. Hyperline does. > AI & Automation: In 2021, "AI in sales" meant lead scoring nobody trusted. Now AI runs the infrastructure. At ColdIQ, one GTM engineer manages 10 clients. Three years ago that number was 3. > Claude Code builds our internal tools. Clay orchestrates the data layer. n8n connects every workflow in between. The 2021 approach was bolting together 20 tools and hoping the data syncs. The 2026 approach is a connected revenue system where every layer feeds the next.
- 19
@sentient_agency ·
10 FREE TOOLS THAT LET YOU BUILD YOUR OWN AI AGENTS WITHOUT WRITING A SINGLE LINE OF CODE Bookmark every single one. In 2026, you no longer need to be a developer to build an AI that works for you. You drag, you drop, you connect. Here's where to start. 1. https://t.co/MgNHdcMCR6 The one everyone's using right now. Drag visual blocks onto a canvas to build an agent that reads your email, researches a lead, updates your CRM, and pings you on Slack, all on its own. The AI Agent node connects to any model. Free forever when you self-host, and it's a top-50 project on all of GitHub for a reason. 2. https://t.co/UULgbO9ggl Build AI agents and chatbots by connecting nodes on a visual board, no code at all. Point it at your own documents and it answers questions from them. Free and open-source to self-host. The fastest way to turn a pile of PDFs into an assistant that actually knows your stuff. 3. https://t.co/S3A65MCUGa A full open-source platform for building AI apps and agents through a visual interface. Workflows, knowledge bases, and multi-step agents, all drag-and-drop. Free to self-host with a generous free cloud tier to start. One of the most complete no-code AI builders that exists in 2026. 4. https://t.co/vn4BLaA27N Lay out your agent's logic visually, like a flowchart, and watch it run. Built on the same ideas powering serious AI apps, but with zero code required. Free and open-source. The tool that makes "how does an AI agent actually think" finally click, because you can see every step. 5. https://t.co/GWNC2Yag9i and https://t.co/6futH5rZIE Open-source, no-code automation built for the AI era. Connect your apps and drop an AI step into any workflow to make it smart. Free to self-host. The open answer to the expensive automation tools, with AI baked in from the start. 6. https://t.co/3KGkT77G0o A visual automation canvas with a free tier and built-in AI modules. Connect thousands of apps and let an AI agent decide what happens next. More beginner-friendly than the code-leaning tools, and you can build a working agent in an afternoon without touching a terminal. 7. https://t.co/rQJzWLcBA4 Build conversational AI agents that talk to customers, qualify leads, and answer support questions, all through a visual editor with a free tier. The no-code way to put an actual working agent on your website or WhatsApp this week. 8. https://t.co/V7rHHsyoJt Design AI agents that hold real conversations, by mapping out the flow visually. Popular with teams building customer-facing assistants. Free tier to start. You draw the conversation like a map, and the agent follows it. 9. https://t.co/Ids0WSOgzQ (Gemini in Workspace Studio) Google now lets you build automated AI workflows right inside Gmail, Sheets, Drive, and Chat using plain-language instructions. Describe what you want in a sentence and Gemini wires it up. The agent builder hiding inside tools you already pay nothing extra for. 10. n8n-io/self-hosted-ai-starter-kit on GitHub The shortcut. n8n's official starter kit spins up a complete local AI setup, the builder, a local model through Ollama, and a vector store, with one command. Everything runs on your own machine, fully private, completely free. The fastest path from zero to your first working agent. One honest note for 2026: the builders are free, but the AI thinking still costs pennies per run if you use a paid model like GPT or Claude. Run a local model through Ollama and even that drops to zero. Most people are still just chatting with AI. The ones paying attention are building little workers that run on their own. The barrier used to be code. In 2026, it's gone.
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@boxmining ·
Hermes Agent (@NousResearch) just made MCP setup way easier. The new MCP catalog lets you install approved servers like Linear and n8n without digging through GitHub repos, copying configs, or guessing what’s safe. This is how agent tooling gets mainstream.
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@NoahEpstein_ ·
the difference between a 4-figure and 5-figure automation isn't complexity it's visibility most people build workflows that run in the background client has no idea if it's working. no idea if it broke. no idea what it's doing. they paid for magic they can't see. that's a 4-figure automation. 5-figure automations let them FEEL it working. click a button → watch the flow run → see the dashboard update that's the difference between "i think it's working" and "i just watched it work" here's how i pitch it now: "you'll click one button, watch the system process everything, and see your dashboard update in real time. you'll know it's working because you'll see it working." same backend. same logic. same n8n nodes. but now they can see the value. if they can't see it, it doesn't count. build the interface. charge the premium.
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@zaimiri ·
My client used to have 2 Virtual Assistants. Now everything runs automatically with AI systems I built. > Fully automatic Inventory Tracker > Pulls data from Dropshipping platform > Cleans up the data + formats in n8n > Saving them 4 hours/week of manual tracking > $300/week saved > $1200/month Cost to implement: $2000 After 6 weeks = full profit for client.
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@IAmAaronWill ·
Here's how to repurpose content automatically with n8n: 1. Open Notion 2. Create a content bank 3. Use apify to scrape your tweets 4. Open n8n 5. Trigger when a post is "published" 6. Feed to Claude 7. Have it analyse the tweet 8. Return 1 newsletter, video and article 9. Have it text you the ideas on telegram 10. Schedule the approved posts 1 post. 3 formats. 0 manual work.
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@RoundtableSpace ·
A TRADER BUILT A SECOND BRAIN IN OBSIDIAN THAT WAKES HIM UP WITH 3 TRADING IDEAS EVERY MORNING AND MADE $180,000 IN 6 MONTHS No Bloomberg terminal, no analytics desk, no team. Just a Mac Mini, an iPhone and six N8N pipelines pulling every article, podcast and voice note into a local vault 24/7. Every night a neural network reads across 4,000 connected notes and finds where fresh information lines up with old theses. The system only wakes him up when something contradicts an active position or an idea breaks 90% confidence. $120 a month in API costs against roughly $30,000 a month in returns.
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@zaimiri ·
I found a GitHub repo that turns n8n into an automation app store. Not a course. Not a SaaS bundle. Just 280+ ready-to-import workflows for: - Email automation - AI chatbots - Lead workflows - Document processing - Social media ops - Research systems The first version of your back office is becoming a JSON file.
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@Toriva_ ·
Hey everyone — quick update we're excited about. There are a lot of automation platforms out there — Zapier, n8n, Make, you name it — but they all have the same problem: you have to build the automation. You sit there picking triggers, dragging actions, writing prompts, figuring out what to connect to what. It’s powerful but it’s manual, and if you’re not already comfortable with that kind of tooling, you bounce. We’ve seen this with our own users — automations work great once they’re set up, but two things keep tripping people up: 1. Writing the agent prompt properly is hard if you’re not used to it 2. A lot of you have the gut feeling that something in your week could be automated but aren’t sure what So we built a Setup Helper — a chat that sits next to the new-automation form and does both for you. It has two modes: “I know what I want to build” — describe it in plain English (“every Monday email me last week’s revenue”) and it fills in the form: schedule, instructions, connectors, the lot. You just hit Save. “Help me figure out what to automate” — it reads your business profile, role-plays a typical week for someone in your shoes, and proposes 2–3 specific ideas tied to your actual operations. No generic “weekly Gmail digest” — it’ll suggest things like a 6am prep alert for a restaurant or a Sunday-evening pipeline brief for a SaaS founder. Video below shows both flows end-to-end. Try it on /automations/new and let me know how it feels — especially if the discovery proposals miss the mark for your business.
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@InduTripat82427 ·
This Chinese guy built a Second Brain in Obsidian and now wakes up to 3 AI-generated trading ideas every morning. In 6 months, it made him over $180K. The setup is wild: • N8N workflows pulling articles, podcasts, bookmarks, and voice notes into Obsidian • AI connecting old insights with fresh information overnight • Daily brief at 6 AM with the top 3 market opportunities • Telegram bot to capture random thoughts on the go • Entire thing running locally on a Mac Mini No Bloomberg. No analyst team. No expensive quant setup. Just one vault with 4,000 connected notes and an AI system constantly spotting patterns humans would miss. The craziest part? The whole stack costs him around $120/month to run.
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@MakadiaHarsh ·
If I had 3 months to break into AI Automation, here's exactly how I'd do it: Stage 1: API fundamentals. REST, webhooks, auth, rate limits, error handling. Stage 2: LLM basics. Tokens, context windows, when to use the $0.25 model vs the $15 one. Cost intuition is a skill. Stage 3: Prompting for systems. System prompts, few-shot examples, structured JSON outputs with validation. Stage 4: The glue layer. This is the actual job: connecting Gmail, Sheets, Slack, CRMs, payment tools. Master one orchestrator (n8n, Activepieces, Make) AND know when to drop into code. Stage 5: Orchestration patterns. Tool calling, retries, idempotency, human-in-the-loop approvals. The difference between a demo and a system a business trusts. Stage 6: RAG - the light version. Chunking, one vector DB, hybrid search. Most client work needs "answers from our docs," not a research pipeline. Stage 7: Evals and reliability. Test sets, accuracy scoring, regression checks. This is what separates $2K projects from $20K retainers. Stage 8: Security. Prompt injection defense, PII handling, output filtering. Non-negotiable the moment your agent touches email or customer data. Stage 9: Deploy boring. Docker, one cloud, logging you can actually read. Nobody pays extra for exotic infra. Stage 10: Ship publicly. Build 3 real automations for real problems. Document them. Post the numbers. The roadmap isn't the hard part. 3 months of actually building is.
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@heynavtoor ·
If I had to run a viral faceless content channel without a team, here are the 10 GitHub repos I would build my entire stack on. 1. openai-whisper Auto-transcribes any podcast or video with 99% accuracy. Where every content pipeline starts. Repo → https://t.co/blaJ4i4MnH 2. Coqui TTS 1,100+ voices, 16 languages, natural output. Community-maintained fork. Kills ElevenLabs at $22 a month. Repo → https://t.co/F4mVET6L3G 3. AI Faceless Video Generator Script, voiceover, visuals, and final video from a single topic. Working alternative to Pictory and InVideo. Repo → https://t.co/qy9DbH5Ky3 4. Remotion Program video in React. Data-driven graphics, animations, transitions. The reason serious creators stopped using After Effects. Repo → https://t.co/N05SGxFxj0 5. FFmpeg The invisible tool that powers every video on the internet. Cuts, converts, compresses anything. Repo → https://t.co/rqDACw36Xu 6. auto-editor Removes silence, filler words, and jump cuts automatically. Takes a 60-minute recording down to 10 in one command. Repo → https://t.co/Y4AsH6x83b 7. WhisperX Word-level timestamps for perfect subtitles. Runs 70x faster than base Whisper. Repo → https://t.co/IMAz4c2a12 8. Postiz Schedule to X, LinkedIn, Instagram, YouTube, TikTok, and 8 more platforms. Kills Buffer at $180 a year. Repo → https://t.co/Ai0woV7L5b 9. Karakeep Save every video, article, thread, and image that inspires you. AI auto-tags your idea database. 28,000+ stars. Repo → https://t.co/IZ96g5duzC 10. n8n Ties every step above into one workflow. Trending topic in, finished video out. Kills Zapier at $240 a year. Repo → https://t.co/hdycABGGc1
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@codyschneider ·
so you tried vibe marketing but you had all these issues with actually getting it to run it works great in the chat window. you paste in some ad data, it writes you a killer analysis, you screenshot it, everybody claps then you try to make it a real thing that runs every day and the whole thing falls over you export a csv from ads manager. paste it into claude. copy the answer into a google doc. do it again tomorrow. congrats you're the api now so you go build it in n8n. 40 nodes later you're debugging a json parsing error in a little box instead of just writing the ten lines of python you already knew how to write. and the meta api rate limits you at 3am and the whole chain silently returns null and nobody finds out until thursday no memory it has no idea what it did yesterday so it "discovers" the same insight every morning no data you're feeding it a 500 row csv sample of a 50k row account and asking it to tell you what to turn off no logs you cannot answer the question "why did it pause that campaign" which means you will never let it touch a live account everybody keeps optimizing the prompt. the prompt is not the problem. claude and gpt are already smarter than most media buyers a marketing agent is like 10% prompt and 90% plumbing. you need somewhere the data lands. you need it to refresh on a schedule. you need connectors that don't break when meta changes a field name. you need a warehouse the agent can actually query instead of guessing off a sample. you need to see what it did that's the boring part and it's the entire job the people shipping agents that actually run production accounts aren't better at prompting than you. they just built the bottom of the stack first go build the bottom
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@MusawirRaji ·
Spent 2 weeks building a system that researches companies, finds decision makers, and writes personalized cold emails while I sleep. Job boards → research → proposal. Fully automated. Found that https://t.co/aZIsvDDxRI was founded in Rotterdam in 2021 by someone who also runs a 100-client Webflow agency. That level of detail used to cost me an hour per lead. Stack: Next.js, n8n, Claude, Gemini, Serper, Supabase. Drafter drops soon.
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@jspeiser ·
A founder in hampton produces 300+ YouTube videos per month for clients. To keep up, he built an Ai system (Claude + n8n) that tracks every social platform over 28 days and generates one report: "what performed and where." Saves his team 30+ minutes of manual work before every meeting. humans still add the "why" and "how to fix it." Ai just eliminates the grunt work of compiling data across each platform.
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@SimonHoiberg ·
One thing I think most people get wrong with agents: They keep trying to make the agent smarter. I mostly try to make everything around it more predictable. That is why OpenClaw works so well for me. OpenClaw sits in the middle, but I have a bunch of very boring infrastructure wrapped around it: n8n workflows. Webhooks. Cron jobs. Reusable scripts. Markdown docs. Runbooks. RAG + vector DB. Nothing fancy. Just the stuff that turns random one-off requests into repeatable systems. If I ask the agent to do something twice, that is usually a sign it should become a workflow. If I explain a process once, it should probably be documented. If something needs to happen every Monday, I should not be remembering it. That is where the leverage comes from. The agent uses fewer tokens because it does not need the same context explained 50 times. The output gets more predictable because the paths are already defined. And the whole setup gets better every time we turn a messy repeated task into a script, doc, or workflow. A lot of people are still using AI like a browser tab. I think the real unlock is when the agent becomes the interface to the systems you already run.
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@n8n_io ·
n8n now supports OpenTelemetry natively. 🔍 Your workflows aren't side projects anymore, they're production systems running payments, support, CRM syncs, and AI agents. When something breaks, you deserve the same visibility you'd have for any other service. Read the full blog post here: https://t.co/QBJZfwjiOR
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@Mike_Scully_ ·
How to find a $10,000 automation in any business in one conversation. You're not looking for something clever. You're looking for something boring and repeated. Ask the owner three questions: What does someone on your team do every day that they hate? How long does it take, and how often? If nobody did it, what would you do instead? That third answer is your price. A farm shop processed 7,000 invoices a year by hand. Someone opened a PDF, read it, typed the numbers in. 7,000 times. I asked what happens if they don't fix it. "We hire someone full time." That's €27k a year in Ireland. So €12k once was a no-brainer. You didn't need to be a developer to see that. You needed to spot the boring repeated task and put a number on the pain. n8n connects the rest like building blocks. The businesses are everywhere. They just don't know a fix exists.
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@thisdudelikesAI ·
A dev in Amman, Jordan built a free open-source Zapier that ships 300+ integrations, all available as MCP servers out of the box. Your AI agent can already talk to Gmail, Slack, Notion, Airtable, Stripe, GitHub, HubSpot, and hundreds of other tools. No glue code. Zapier Starter is $29.99 a month for 750 tasks. Zapier Team is $103.50 a month for 2,000 tasks. To connect your AI to the tools you already pay for. It's called Activepieces. 22,450 stars. MIT core. Pushed yesterday. Activepieces vs Zapier vs n8n: - Price: Zapier $29 to $103+ a month → n8n free → Activepieces free - Task limits: Zapier caps at 750 or 2,000 → n8n unlimited → Activepieces unlimited - License: Zapier closed → n8n Sustainable Use → Activepieces MIT core - AI-native: Zapier bolted on → n8n bolted on → Activepieces built around MCP Here is the wildest part. n8n's Sustainable Use License explicitly forbids "hosting n8n and charging people money to access it" ([n8n docs](https://t.co/2MibbC1BHf)). Activepieces core is MIT. You can resell it. SaaS-host it. White-label it. The license does not bite back. Honest flag: code under packages/ee/ is enterprise-licensed. For 95% of dev use cases this does not matter. Mo AbuAboud built Activepieces from Amman, Jordan. YC-backed, but he still ships commits as a top contributor. Zapier charges $1,242 a year on Team and caps you at 2,000 tasks. Activepieces does the same job, unlimited, on a $5 VPS. This is what workflow automation looks like when the dev who built it is also the dev who would refuse to pay Zapier. (Link in the comments)
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@AlexEngineerAI ·
n8n shipped MCP support this week and the implications are flying under the radar... your AI can now WRITE and DEPLOY n8n workflows without you touching the editor getting it running: > spin up the n8n-mcp server > drop it into your Claude Code config > Claude has direct access to 1,396 n8n nodes (812 built-in + 584 community) how things worked before: > open n8n > manually wire up each node > fill in every setting by hand > test it, watch it fail, repeat > 45 minutes per automation how things work now: > "every 6 hours, scrape our top 5 competitors' checkout pages and drop a Slack alert the moment any price shifts" > Claude maps out the full workflow and wires it up > under 4 minutes, zero manual config what this actually unlocks: AI that designs its own pipelines, not just runs them not "trigger a premade workflow"... spin up entirely new ones on demand self-building automation is no longer theoretical
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@MakadiaHarsh ·
HubSpot charges $800/month for its marketing automation suite. A client was using maybe 20% of it. Lead capture. Email sequences. Basic reporting. I replaced it with a $32/month stack: Tally for forms (free) → Airtable as the CRM ($20/month) → Resend for email sequences ($12/month) → a simple n8n workflow connecting them. Same lead capture. Same email sequences. Same reporting. $768/month saved. She asked me - "Why did nobody tell me this before?" Because the person selling HubSpot makes a commission. The person auditing your tools makes you money. Know the difference.
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@suraj_sharma14 ·
Reallocate your weekend schedule to advance your professional development, particularly if you are employed in the marketing sector. It is imperative to address the following priorities: - Configure Claude Code, ensuring the activation of advanced capabilities, permission overrides & Obsidian integration. - Develop a comprehensive brand foundation document detailing voice, tone, target audience & prohibited messaging. - Map existing workflows to identify opportunities for integrating AI automations. - Establish marketing skill sets, either by sourcing them from GitHub or developing them independently. - Implement Wispr Flow to enable voice-to-text functionality across all applications. - Construct an n8n content repurposing pipeline to distribute a single post across multiple platforms. - Utilize Clay for AI-powered lead generation and data enrichment. - Engineer a multi-agent content system comprising roles for researcher, writer, editor & publisher. - Evaluate Perplexity Computer to establish a dedicated marketing agent team within the environment. - Complete the Anthropic Skilljar course to obtain Claude certification. - Assess Perplexity Pro source mapping capabilities for competitive research. - Develop an outbound pipeline integrating Apify, Claude Code, and n8n to execute scraping, enrichment & email outreach. - Deploy AI-powered advertising dashboards capable of real-time updates. - Implement a targeting strategy utilizing LinkedIn scraping, email enrichment & Facebook custom audiences. - Study Go-To-Market (GTM) engineering, recognizing that the interval between product shipment and user acquisition represents the primary value driver. - Acquire proficiency in prompt engineering as input quality directly dictates output results. The present moment is critical for securing these advancements.
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@shannholmberg ·
what's the difference between a loop and a graph? (marketing edition) both are ways to run an agent, the difference is who decides the path, the agent or you. a loop still starts with you. you set the goal, the brief, and the bar it has to clear. what the agent owns is the path. take writing an SEO article: hand it the brief and it drafts, reads the draft back against that brief, rewrites the weak parts, checks again, and keeps circling until it clears the bar. the one thing you did not write is the step-by-step it took to get there. a graph is you drawing the steps and the routes between them ahead of time. same article, but now you set the map: research the keyword and the competitors ranking for it. draft from what you find. score that draft against your rubric. if it clears, add the internal links and publish. if it misses, back to the draft. the agent still decides how to handle each step, it just travels the routes you laid down. the shape of this has a name, a state machine. every node is a state the work can be in, and a check at each one decides where it goes next, forward when it clears or back to an earlier node when it misses. if you have built a workflow in n8n, you have already drawn one. nodes wired together, branches that fire on a condition, a step that loops until it clears, that picture is a graph. an agent graph is the same shape, the nodes hold agents doing the work instead of single api calls. the way I think about it, a graph is a map of loops and checkpoints. some nodes run once, others are their own loop where the agent works something out, and the checkpoints between them read the result and route the work. you keep laying down nodes and checkpoints until the map reliably gives you the output you want. the vault accelerator I run at my agency is one of these maps, 3 sessions that hand off in a fixed order: > research session: reads our company brain and past campaign results, pulls in competitor and market context, and builds the cohort we go after > landing page session: takes that research and builds the page from it > content session: uses the research and the page to write the copy, illustrations, and slides for the live sessions we run inside the content session runs a loop, a critic scores each draft against a rubric and sends it back until it clears the bar. that is one node on the map, the checkpoints between the sessions carry the work from one to the next a graph earns its extra setup on anything you run every week: > validation gates the work cannot skip > a fixed set of routes the job can take > a clear failure point, you see the exact step something broke on a loop on its own is enough for the work you only do once, where you don't know the path yet, let the agent find it. graphs earn their place on the jobs you repeat, the content pipeline, the SEO and AEO funnel step by step, the vault accelerator once the map works you reuse it, feed it the next cohort and the whole pipeline runs again past the loop, the next thing you design is the map it runs inside.
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@SimonHoiberg ·
OpenClaw + n8n. This is an extremely powerful combination. People thought OpenClaw (or Claude Code) would replace n8n, but here's what they're missing. n8n - as an application layer between the agent and the tool - still makes perfect sense for at least 3 reasons. 1️⃣ Observability OpenClaw can write its own skills. But instead of letting it script away, ask it to create n8n workflows for itself to use. It is much easier for you to investigate and see what the agent built for itself in n8n, rather than looking through 100 ugly written JavaScripts. 2️⃣ Security After the agent is done creating its n8n workflow, you can lock in place. Making it read-only from that point. Instead of adding API keys to .env.local for the agent to use (and abuse) in any way it likes, you can now add the credentials securely to n8n. From here, you can also easily add any additional safeguarding step, making sure your agent doesn't make a mess by mistake. 3️⃣ Performance An agent adds value when it needs to make decisions. But a lot of work is still predictable and deterministic. Turn it into a workflow. It's faster and you save tokens. 🔁 The flow - The agent needs access to an API. - It writes an n8n workflow with incoming webhook. - You lock the workflow and add the API key. - You add extra safeguarding steps. The agent now proxies all calls through n8n. It never sees the API key. It's prevented from making crucial mistakes. I know... It feels addictive to let OpenClaw do everything. But this thing is a beast! Use it responsibly.
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@asaio87 ·
I don't think Claude will replace make or n8n. Because AI will give you different outputs for the same prompts. Whenever you need something done in a certain way, then you need fixed systems Its a much better idea to integreate Claude in certain steps of a workflow. Like use n8n, to build a workflow, where you use claude to create content and use @schedpilot api to post on social media as agentic social media workflow. thats just an example
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@frog_omo ·
someone built an AI receptionist for coaches and consultants using n8n + GPT-4o + supabase. the goal: replace the "fill out this form before we talk" experience with a chat that collects lead info naturally. what it does: → receives messages from a website chat widget → identifies whether the visitor is new or returning using session-based memory → stores conversation history in postgresql (supabase) → extracts names and email addresses from natural conversations → updates existing contact records instead of creating duplicates → generates human-like responses using GPT-4o mini → maintains conversation context across messages the key insight: most visitors don't provide their email in the first message. the initial approach was to use email as the primary identifier. but that forces a form before the conversation starts. the fix: use session IDs as the primary identifier. update the contact record when the AI extracts a name or email later in the conversation. the result: visitors can start chatting immediately instead of being forced to fill out a form. why this matters for lead qualification: traditional chat widgets: "enter your email to continue" → friction → bounce. this approach: start the conversation → build rapport → extract info naturally → qualify. the form still gets filled. it just happens inside the conversation instead of before it. my take: this is the right architecture for any high-touch service business (coaches, consultants, agencies). the session-first approach is underrated. most chat implementations still gate on email upfront because it's easier to track in the CRM. but it kills conversion. one thing i'd add: intent classification before the first response. some visitors are ready to book. some are just browsing. the AI should route differently based on that. github repo included https://t.co/9CjhfDzslD — via r/n8n
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@digital_ab98389 ·
Everyone is talking about AI automation. It reminds me of when affiliate marketing was the hottest trend. Everyone wanted to get in, but only a few were creating real results. The same thing is happening with n8n automation, AI agents, and workflow automation. Thousands of people are teaching AI automation, but far fewer are using n8n, APIs, and AI to solve real business problems. That has created a huge gap in the industry. Many people think that once they learn how to connect a few nodes in n8n, they can call themselves an AI Automation Specialist, Automation Engineer, or Automation Consultant. That's not how it works. The tool is only a small part of the job. Before you can confidently build workflow automation, CRM automation, or business process automation, you must first understand the business you're building for. You need to identify bottlenecks, analyze workflows, understand how data moves between systems, ask the right questions, and know why a process exists before trying to automate it. You might say, "It's not my business, so how can I understand it?" Simple. Research the company. Study the job description. Understand how they generate revenue. Map their current workflow. Ask smart questions. Identify where time, money, or leads are being lost. That research gives you an edge and helps you design automation that actually delivers results. My biggest advice? Build on paper first. Forget n8n for a moment. Use the most powerful node you have—your brain. Draw the workflow. Map every decision. Think through the business logic. Understand how every API, CRM, database, AI model, and user interacts before you open your automation platform. Once you've solved the problem on paper, building it in n8n becomes the easy part. Don't start by connecting nodes. Start by understanding the business. Research first. Ask questions. Design the workflow. Then build the automation. That's what separates someone who knows n8n from someone who solves business problems with AI automation, workflow automation, AI agents, API integrations, and scalable business systems. If you're looking for practical insights on n8n, AI automation, workflow design, CRM automation, AI agents, and building systems that help businesses scale, follow Agbaje Olarewaju. Portfolio: https://t.co/Uy2Q6TzQDc
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@cem_hasoglu ·
I had a call last week with a founder who paid $12,000 for a "lead qualification system" I asked him to show it to me it was 4 nodes in n8n a webhook that catches form submissions, an AI node that scores them, a filter that splits hot and cold leads, and a Slack notification 45 minutes to build, maybe an hour if you're slow he wasn't ripped off because the agency was malicious he was ripped off because he didn't know what was possible that gap between what automation can do and what most business owners know exists is massive right now if you actually understand this stuff you're sitting on something real
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@comfortfajugbag ·
A workflow that works once is not automation yet One thing I wish beginners heard earlier: If your n8n workflow works in a test run, that doesn’t mean it’s ready. The real questions are: what if the API fails? what if the same item arrives twice? what if one field is missing? what if the token expires? what if it runs 100 times instead of once? The first time I understood automation, it was when I stopped asking “did it run?” and started asking “will it keep running?” My biggest lesson: The 'Error Trigger' node is just as important as the actual business logic. Beginners build for the happy path. Engineers build for the failure modes. If your workflow doesn’t have an automatic retry strategy on HTTP requests, a sub-workflow to catch unhandled errors, and a clear alerting mechanism (like a Discord/Telegram webhook for failures), you haven't built an automation. You've just built a script that requires manual monitoring. Design for the crash, not just the success. What was the first reliability lesson you learned in n8n?
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