Agentic Workflow Design
AI agents embedded in n8n workflows, including LLM routing, memory, RAG, approvals, and human handoffs.
46.3%
Best tweets about n8n
41 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
- 33
- Updated
Original Xholic analysis
What 41 top n8n posts reveal
The n8n conversation is predominantly supportive and operational, with posts covering workflow-backed agents, GTM and back-office use cases, and self-reported operating outcomes. MCP and programmatic workflow creation lead the supplied theme-level engagement data. The evidence also includes cautionary views on maintainability, security, and the reliability work required beyond a successful test run.
- Dominant tone
- Positive
- Median score
- 10.3
- Leading format
- Case Study
- Recent posts
- 39%
78% of posts
All-time engagement
34.1% of posts
Published in 90 days
Conversation map
The themes creators return to
Architecture & Tool Selection
Choosing n8n versus custom code or other automation tools, and combining deterministic workflows with AI components.
24.4%
GTM & Sales Automation
Lead capture, enrichment, qualification, CRM routing, outbound campaigns, and revenue-stack orchestration.
24.4%
Business Process Automation
Concrete n8n automations for documents, subscriptions, inventory, monitoring, content, research, and internal operations.
22%
Reliability, Observability & Security
Production-grade workflow practices: retries, idempotency, error handling, observability, alerts, security, and guardrails.
19.5%
MCP & Programmatic Workflows
MCP-based agent access to n8n, enabling Claude and other agents to create, edit, deploy, and consume workflows.
17.1%
Automation Services & ROI
Automation consulting and productized services for SMBs, framed through labor savings, ROI, and vertical solutions.
9.8%
Self-Hosting & Operations
Self-hosting, migration, deployment, credentials, and operating n8n infrastructure.
7.3%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 29
- Median reposts
- 2
- Median replies
- 5
- Median views
- 2.8K
Posts with media make up 68.3% of this collection. Their median all-time score is 10.0, compared with 10.3 for text-only posts.
Format mix
- Case Study 34.1% · score 10.0
- List 22% · score 23.9
- Tutorial 17.1% · score 5.55
- Announcement 14.6% · score 14.6
Consensus and debate
Where creators agree, and where they do not
Shared view
Automation is applied to recurring operations work
Examples focus on repetitive operational work, including invoice entry, subscription access, and inventory tracking. Their authors frame time savings, reduced manual work, or avoided hiring as the business case.
Shared view
Agents are paired with deterministic workflows
Several posts recommend using agents for decisions while moving repeatable tasks into n8n workflows. They cite predefined paths, lower token use, easier inspection, and credential controls as benefits of this split.
Shared view
Reliability practices distinguish demos from operating systems
Posts about production workflows emphasize retries, idempotency, error handling, alerting, logging, evaluations, and security. n8n’s native OpenTelemetry announcement is presented as added visibility for workflows used in production contexts.
Open debate
Visual flexibility versus maintainability
One post characterizes n8n deployments as unmaintainable “workflow spaghetti.” Another recommends n8n for frequently changing or understandable workflows, while favoring custom code for one-off work and very high-volume processing.
Open debate
Will agents displace workflow builders?
One post calls managed agents a direct threat to node-based workflow tools. Other posts argue that fixed workflows remain useful when teams need predictable outputs, observability, safeguards, and protected credentials, with AI used in selected steps.
What performs
Patterns behind standout posts
Outliers pair a concrete mechanism with a broad implication
Four of the five analytics outliers combine broad business or product implications with a tangible mechanism: a vertical automation-service proposition, MCP-enabled workflow creation, an official connector, or an invoice-processing workflow.
Practical formats lead the conversation
Case studies are the largest format group in the supplied analytics. Tutorial examples include lead scraping and content repurposing, while the dataset also contains high-scoring list and announcement formats.
MCP is the highest-engagement theme
MCP and programmatic workflows have the highest median all-time score of the supplied themes. Referenced posts cover native MCP support, the official Claude Code connector, and n8n-MCP visibility into the node catalog.
Statistical standouts
- View standout post 1 Score 1992.1 · 192.51× median
- View standout post 2 Score 464.4 · 44.88× median
- View standout post 3 Score 321.3 · 31.05× median
- View standout post 4 Score 143.8 · 13.89× median
- View standout post 5 Score 132.9 · 12.84× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 24.4% of the selected posts.
-
1. Dan Rosenthal
@dan__rosenthal
2 posts
-
2. Aaron
@IAmAaronWill
2 posts
-
3. Alex Vacca
@itsalexvacca
2 posts
-
4. Harsh Makadia
@MakadiaHarsh
2 posts
-
5. n8n.io
@n8n_io
2 posts
-
6. Pe:p Laja
@peeplaja
2 posts
Dan Rosenthal: orchestration within a governed stack
Dan Rosenthal describes n8n as one component of a wider GTM and agency operating stack, while also saying that many workflows are moving to custom code and that guardrails are used for risky actions.
Aaron: concise workflow recipes
Aaron shares concise workflow recipes: Apify-to-Notion lead collection and a Notion, Apify, and Claude content-repurposing flow coordinated in n8n.
n8n.io: programmatic building and visibility
n8n.io highlights an official Claude Code connector for creating and editing workflows and native OpenTelemetry support for workflow visibility.
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 41-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 33 creators
Ranked 01–41
- 01
@gregisenberg ·
startup idea for you use postiz (20k+ github stars project) to sell AI social media content/management to 1 niche of SMBs. what's postiz? it's an open source social media scheduler with AI built in. basically buffer + AI and free to download. 1. self-host postiz. use codex/claude code to help you figure this out in an afternoon. 2. pick one niche. dentists, realtors, lawyers. can even go a subniche like orthodentists vs dentists. family law over of lawyers. 2. wrap it in their language. "AI social media for dental practices" 3. add "we write your captions with AI" as the hook. that's what they're actually paying for. 4. plug it into n8n, make, or zapier so posting, scheduling, and approvals run on autopilot. the client approves with one tap. everything else is handled. 5. charge $50/mo-$100 per seat. that's nothing to a business paying $2,000/mo for a social media freelancer. you're 25x cheaper and 10x more reliable because the system runs whether you're awake or not. win-win for everyone. 6. build one landing page. run one onboarding call. that's the whole sales motion. 7. build media to attract customers. post tips for that niche on X, tiktok, youtube. become the "social media for dentists" person. 8. reinvest profits to build other tools that serve that same niche. scheduling, reviews, patient intake. build those tools or plug in more open source projects. now you own the vertical. these businesses KNOW they need to post. they hate doing it. they will never find postiz on github. they will google "someone please handle my social media." that's you open source is the new wholesale. the code is free. the customer relationship is where the margin lives. you can do this as one person. you can do this as a two person team. you don't need funding. you don't need an office. you need a laptop, a niche, and the willingness to start. someone is going to do this. might as well be you.
- 02
@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
- 03
@n8n_io ·
n8n's official Claude Code connector can now create and edit workflows! This goes way beyond plugging an API into MCP. It's purpose built for LLMs. Includes a new workflow TypeScript SDK so workflows are written as code instead of JSON, with more reliable validation. Works anywhere MCPs are supported (n8n 2.18.5+). 🔗 Full video: https://t.co/9D8H2VgHaY
Watch video
- 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
- 09
@_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.
- 10
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
- 11
@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
- 12
@DivyanshT91162 ·
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.
- 13
@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.
- 14
@PentesterLab ·
𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗪𝗼𝗿𝘁𝗵 𝗥𝗲𝗮𝗱𝗶𝗻𝗴 - 𝗪𝗲𝗲𝗸 𝟳, 𝟮𝟬𝟮𝟲 Parser Differential, TypeScript and AI ⨐ 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗗𝗼𝘄𝗻 𝗖𝗩𝗘-𝟮𝟬𝟮𝟲-𝟮𝟱𝟬𝟰𝟵: 𝗛𝗼𝘄 𝗧𝘆𝗽𝗲𝗦𝗰𝗿𝗶𝗽𝘁 𝗧𝘆𝗽𝗲𝘀 𝗙𝗮𝗶𝗹𝗲𝗱 𝗻𝟴𝗻'𝘀 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 A great explanation of the recent vulnerabilities impacting n8n. If you are working in security on TypeScript projects, it's a must read. https://t.co/9cqTpEAluI. ⚒️ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗔𝘂𝗴𝘂𝘀𝘁𝘂𝘀: 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗟𝗟𝗠 𝗣𝗿𝗼𝗺𝗽𝘁 𝗜𝗻𝗷𝗲𝗰𝘁𝗶𝗼𝗻 𝗧𝗼𝗼𝗹 Praetorian is back with another tool (one of their 12 Caesars): Augustus... Make sure you check it out! https://t.co/kIgkNwiNMM. 🤺 𝗪𝗵𝗲𝗻 𝗧𝘄𝗼 𝗣𝗮𝗿𝘀𝗲𝗿𝘀 𝗗𝗶𝘀𝗮𝗴𝗿𝗲𝗲: 𝗘𝘅𝗽𝗹𝗼𝗶𝘁𝗶𝗻𝗴 𝗤𝘂𝗲𝗿𝘆 𝗦𝘁𝗿𝗶𝗻𝗴 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹𝘀 𝗳𝗼𝗿 𝗫𝗦𝗦 If you enjoy parser differential issues, you are going to love this exploit. Don't think "It's a CTF challenge" or "It's just an XSS", read between the lines to find the real gold. https://t.co/rwf2BGnerh. 🤖 𝗥𝗖𝗘 𝗶𝗻 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗔𝗜 𝗰𝗼𝗱𝗲 𝗲𝗱𝗶𝘁𝗼𝗿 𝗔𝗻𝘁𝗶𝗴𝗿𝗮𝘃𝗶𝘁𝘆 - $𝟭𝟬𝟬𝟬𝟬 𝗕𝗼𝘂𝗻𝘁𝘆 A detailed blog post on hacking Antigravity with a lot of interesting details on its inner workings. https://t.co/3ExHVYrf9X. 🚛 𝗧𝗿𝗮𝗶𝗹𝗶𝗻𝗴 𝗗𝗮𝗻𝗴𝗲𝗿: 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗛𝗧𝗧𝗣 𝗧𝗿𝗮𝗶𝗹𝗲𝗿 𝗽𝗮𝗿𝘀𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗿𝗲𝗽𝗮𝗻𝗰𝗶𝗲𝘀 Probably one of the lesser-known features of HTTP... Trailers. This post provides details on the feature and explains how they can be leveraged to find security issues. https://t.co/I2J8yvXdpa.
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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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@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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@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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@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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@Jacobsklug ·
After building a $2.5M AI software agency, I’ve learned that anyone who says there's a "magic AI bullet" is lying to you. You don't win with one secret tool. You win by stacking the right ones and making them work together. Here's the full stack I'd start with today: 1/ Claude Code The agent runtime. The thing that actually does the work. Give it the task and it builds, edits, and ships across your whole project, completion and all. 2/ @Lovable Where the product takes shape. Describe the app in plain English and Lovable ships the front end for you. The point isn't the speed. It's that you no longer need a dev team to do it. 3/ Supabase This gives your app a spine. Auth, APIs, user data, database. Lovable connects to it natively, so the backend stops being the hard part. 4/ Zapier and n8n The automation layer. These trigger the repetitive work and connect your app to everything else. CRM, email, third-party tools. The stuff that should run without you. 5/ Github Memory and version control. Every change tracked, nothing lost, easy to roll back when an agent goes sideways. The tools aren't the moat. Anyone can list them. The skill is connecting them into one system that earns while you sleep. Get that right and you can turn almost any service offer into a product. Save this for when you're ready to build.
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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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@alphabatcher ·
5 ways you can use AI to solve key business needs (automation guide) I talked to dozens of small business owners over the past year Most of them were losing 15-20 hours a week to tasks that could run on autopilot Not because of bad strategy. because of repetitive tasks no one ever automated Here are 5 real business problems AI solves right now 👇 1. Losing leads because you respond too slow the average business responds to a new inquiry in 2-3 hours but studies show that if you don't respond within 5 minutes, your chances of closing drop by 80% the fix: - AI responds to every new inquiry instantly, 24/7 - qualifies the lead with 2-3 questions - books a call or sends an offer automatically - logs everything into your CRM without anyone touching it tools: Manychat, Tidio, n8n + your CRM of choice 2. Team wastes hours answering the same questions the average support agent spends 4-5 hours a day answering questions that never change that's more than half the workday gone on copy-paste replies "what's the price", "how does it work", "what's the timeline", "do you have X" the fix: - AI handles all repetitive questions instantly - complex or sensitive cases get escalated to a human with full context already attached - customer gets an answer in seconds, not hours - team focuses only on conversations that actually need them tools: Intercom, Tidio, custom GPT connected to your docs 3. Data lives in five different places and nobody looks at it most small businesses collect data, but almost nobody actually uses it decisions get made on gut feeling because the numbers are never ready in time because pulling it together takes so long that by the time the report is ready, it's already outdated the fix: - connect all your tools once (CRM, ads, sales, ops) - AI pulls the data automatically on a schedule - you get a clean summary delivered to your Slack or email every Monday morning - no spreadsheets, no manual work, no outdated numbers tools: n8n or Make + Airtable + your data sources 4. Onboarding is inconsistent and nobody notices until it's too late the first 7 days after someone buys or joins your team are the most important but in most companies, no one actually checks whether the person received everything they needed the fix: - new client or employee triggers an automatic sequence - day 1: welcome + what to expect - day 3: useful resource or check-in - day 7: quick feedback question - all of it runs without anyone managing it tools: n8n or Make + ActiveCampaign or MailerLite + Notion or Google Docs (for resource delivery) 5. Content either doesn't happen or happens in chaos businesses that post consistently get 3x more inbound leads than those that don't but most small businesses either don't post at all or post randomly when someone finds 20 minutes between meetings the fix: - you prepare content once a week - AI generates post variations for each platform - you pick one, make small edits, approve - everything goes out on schedule automatically tools: Make or n8n + Claude or ChatGPT + Buffer or Publer CONCLUSION: Most small businesses already have the problems listed above They just don't know they can be solved without hiring more people Pick one problem from this list. Build one solution. Deliver one result That's how you start. That's also how you scale The demand is real. The tools are accessible. The businesses that move first will have a serious edge over everyone still doing it manually
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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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@peeplaja ·
We gave every person at Wynter 1 week to build their own AI agent. Budget of about 4 hours each. Today was demo day. Everyone presented what they built. Most had never built anything like this before. Some had zero technical background. Didn't matter. The combo of Claude for teaching and n8n for agent building made it possible for everyone. The average build time was about 3 hours. Here's what the team shipped: → Automated cold email mailbox manager that handles 726 sender mailboxes across domains. Used to take hours of spreadsheet work, now it's one click. → Customer onboarding tracker that monitors new pro customers through HubSpot, flags accounts needing follow-up every morning at 7am. → Renewals management assistant hooked into HubSpot and Gmail. Tracks status of every renewal convo, suggests when to check in. → Error notification workflow that triages support tickets, extracts key info, and determines if it's a bug automatically. → GitHub Actions for always-on development. A "file diet" that breaks down large code files, plus a daily test improver. → Automated unit test generator that writes tests for every new code change, re-reviews when the PR gets updated. → AI-powered test recommendation engine that analyzes patterns from past tests and suggests the next highest-impact test with methodology and ICP. → Interview process automation that creates ClickUp tasks and sends welcome emails the moment a new sale closes. → Copy variation generator that creates headline variants using different persuasive techniques, then lets you run a Wynter preference test with one click. → Support ticket triage bot. Enter a ticket number, get an instant summary with key details extracted. → Next test suggestion model that reads customer data and recommends three test ideas with methodology and hypotheses. → Stale PR notifier that pings Slack every Monday with the top 5 oldest unmerged pull requests, tagging the responsible people. → Competitive intelligence scanner that monitors daily articles about competitors and tracks homepage copy changes for positioning changes. I built my v1 in 90 minutes. Another hour for v2. The biggest takeaway across the board was the same. People realized they can actually do this. No deep technical skills needed. Just ask the right questions. One person said they completely changed their approach after 16 failed attempts and then it just worked. Another built their first AI automation in 2 hours having never done anything like it. That's the whole point. The fear of building AI tools is the main blocker for most people. Once you push through it once, you start seeing automation opportunities everywhere. Every team at Wynter now builds their own agents. Because they saw what's possible and want more.
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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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@itsalexvacca ·
"Target people who hate your competitors." We saw 15-25% reply rates with this strategy for one of our clients. But the question is: How do you do it? ↳ Visit G2, Capterra, and Trustpilot. These platforms are full of people actively complaining about tools and looking for a better product. But you don't have to do it manually. Here's what to do instead: 1️⃣ Review Scraping Start by scraping negative reviews from 2-3 core competitors: 1. n8n (starts at $20/mo): Build custom scraping workflows for G2, Capterra, and Trustpilot. We use this to power the majority of our review mining workflows. b. Apify (starts at $49/mo): Pre-built scrapers for review platforms when you don't want to build from scratch. 2️⃣ Pain Point Analysis This is where it gets awesome. Compile all these complaints into specific themes: feature gaps, terrible support, UX disasters, security issues, pricing problems. ↳ Use ChatGPT, Claude, or Perplexity for this. Now you have a database of what your prospects hate about their current tools (matters the most for "targeted" messaging). 3️⃣ Positioning Alignment Map those complaints to your actual strengths. If reviews mention "hidden fees" and your pricing is transparent, that's your angle. ↳ Google Sheets (free) / Airtable ($20/mo) for mapping pain points to your positioning. Remember that instead of just pitching features, you're solving their frustrations. 4️⃣ Data Enrichment & Targeting Once you know who's unhappy and why, find decision makers at companies using tools like: ↳ Clay($149/mo) or FullEnrich ($29/mo) Since the messaging angle is already pre-validated based on your prospect's frustrations, they're a warm lead. 5️⃣ Outbound Execution Once the data part is covered, the next step is to engage your leads and ensure your message delivers: 1. Instantly (starts at $37/mo) for unlimited email outreach 2. lemlist (starts at $69/mo) for multi-channel outreach or HeyReach for LinkedIn-focused campaigns 6️⃣ System Automation This is where the actual game comes in. A good strategy comes first, but automation helps you scale things up. We built this using n8n (scraping) + ChatGPT (clustering complaints) + Google Sheets (inputs) + Clay + Instantly (execution). The entire system runs on autopilot.
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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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@MakadiaHarsh ·
When a client asks "should we use n8n or build custom?" — I use this decision tree: Will this automation change frequently? → n8n Visual editor means the client or a junior dev can adjust workflows without touching code. Does it need to process more than 50,000 events/day? → Custom n8n handles volume well but at true scale, a dedicated service with proper queuing is more reliable. Does the business need to own and understand the system? → n8n Non-technical founders can look at the workflow and understand what’s happening. Try that with a Python codebase. Does it involve complex AI agent logic with multiple models? → n8n with LangChain nodes Persistent memory. Vector DB support. Better than most custom implementations. Is this a one-off data job that runs once? → Custom script Don't over-engineer a workflow for something you'll run once and delete.
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@JulianGoldieSEO ·
𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗖𝗹𝗮𝘂𝗱𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀 𝘀𝗼 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗯𝘂𝗶𝗹𝗱 𝗲𝗻𝘁𝗶𝗿𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗲𝗮𝗺𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗻𝘀𝗼𝗹𝗲. Deep researcher. Support agent. Field monitor. Pick a template or build from scratch in plain language. Here's how it works: → Go to Claude Console. Click Quick Start. Pick a template or type exactly what you want your agent to do. → Configure the environment. Set network access. Add credentials for any tools you want to connect. → Run agents without MCP servers for the simplest setup. Schedule background tasks that run automatically with no external connections needed. → Ask your deep research agent a question. Watch it decompose the task, run web research across multiple steps, and come back with sourced results. → Switch between agent versions like checkpoints. Edit the system prompt anytime. Change the model speed between fast and deep thinking. This is a direct threat to tools like n8n. That required setting up individual nodes and navigating intimidating flow diagrams. Claude Managed Agents just lets you type what you want. One agent ran a full April AI news breakdown and caught the Claude Mythos leak. Not a default template. Real live research done autonomously. It's still in public beta and OAuth setup takes some work. But the agent builder itself is the most accessible version of this I've seen.
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@sachinrekhi ·
What's the right AI tool for the job? The answer has gotten far more complicated today with the proliferation of tools. To deal with this, I've developed the following mental model around AI interfaces and their applicability to particular tasks. Chatbots - This is the category that started it all. ChatGPT, Claude, Gemini. Simple question & answers end up being useful for a whole host of exploratory questions. And this remains my daily personal driver. Copilots - Copilots then emerged as a way to use AI to manipulate artifacts. Code was the original artifact with tools like Cursor. But copilots expanded beyond code to documents with AI tooling in Notion, Google Docs, Google Sheets, and more. Whenever I want to manipulate an artifact, I reach for the appropriate copilot. Agents - Agents have emerged as fully autonomous AIs designed to execute workflows all on their own. On one side you have the coding agents like Claude Code and Codex. On the other end you have workflow automation tools like Zapier, Relay, and n8n. Whenever I want to create a fully autonomous workflow that I can repeatedly run, I reach for an agent. So now every time I think about using AI, I ask myself whether I should reach for a chatbot, a copilot, or an agent for the task.
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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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