Tag: n8n

Date: 08/25/2025

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Okay, this n8n AI Assistant Builder video is seriously inspiring for anyone diving into AI-assisted automation. It showcases how you can literally type a prompt and n8n spins up a workflow, handling everything from architecture to node wiring. Think Calendly leads going to GoHighLevel, analyzed by OpenAI, and then personalized follow-ups. I’ve been wrestling with similar pipelines using a mix of custom PHP and brittle API integrations for years. This video shows a path to build the same, or better, in minutes, not days.

What’s really got me excited is the LangChain multi-agent example. I’ve been experimenting with autonomous agents, but the configuration overhead is a killer. The video demonstrates how n8n streamlines this, letting you define a “content swarm” where agents collaborate, using each other as tools. Imagine the possibilities for content creation, data enrichment, or even complex business logic, all orchestrated with a simple prompt. The presenter also shares how to fix common errors and iterate quickly using the sidebar co-pilot, which is golden. For me, it’s a clear signal that the future of development is about orchestrating AI, not writing endless lines of code. I’m definitely spinning up n8n and giving this a shot. That promise of going from idea to working automation without the usual JSON spaghetti is too good to ignore.

Date: 08/15/2025

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Okay, this video from Nate Herk is gold for devs like us who are diving headfirst into AI-powered workflows. Essentially, he’s giving away the blueprint for an “Ultimate Media AI Agent” built on n8n, a no-code automation platform. This agent isn’t just a simple task runner; it’s a full-fledged digital assistant that handles everything from email management and content creation (images, videos!) to social media posting and even web scraping for research. What’s really exciting is the activity logging – crucial for debugging and understanding how your AI is actually performing.

Why is this valuable? Because it’s a practical, real-world example of integrating AI into everyday tasks. We’re talking about automating content creation, social media management, and research—things that traditionally eat up a ton of developer time or require a dedicated team. It’s not just theory; Nate breaks down the cost and provides templates and workflows to get you started. Think about using this to automatically generate marketing materials for a new feature release or to monitor competitor activity and compile reports. The possibilities are huge!

Honestly, I’m pumped to experiment with this. The promise of automating social media content creation and research alone makes it worth exploring. Plus, seeing a concrete implementation of n8n with AI capabilities is super inspiring. The best part is the head start of getting templates. I have been looking into setting up n8n so this may be the final push I need to get going.

Date: 08/13/2025

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Okay, so this video is pure gold for us devs looking to level up our workflow with AI and automation. Basically, it’s a walkthrough of an n8n automation that lets you blast content to all your social media platforms—Instagram, TikTok, X, LinkedIn, Facebook, you name it—from one single spot. No more jumping between apps or wrestling with APIs! You dump your content into a Google Sheet, add a caption, connect your accounts via Blotato, and boom, you’re posting everywhere.

Why is this awesome? Because it’s a perfect example of how we can ditch tedious, repetitive tasks with no-code tools and automation. We can use this as a base, then pair it with an LLM-powered content creation workflow. Imagine: an AI drafts social media posts based on a topic you give it, then this n8n workflow automatically publishes it across all your channels. Think about the time that would save, and how much more effectively we can manage marketing for a client. It’s one of those things that really hits home to someone who’s written hundreds of http requests and OAuth integrations themselves.

Honestly, it’s worth checking out just to see how easily you can string together these powerful tools. The fact that the creator gives away the n8n template free is just icing on the cake. It’s a tangible, real-world example of how AI coding and no-code platforms can come together to seriously streamline your processes and boost your output. I’m already thinking about how I can adapt this for a client who is struggling with social media consistency.

Date: 08/06/2025

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Okay, this video on OpenAI’s new open-source model, GPT-OSS, is exactly the kind of thing I’ve been diving into lately! It’s all about setting up and using this powerful model locally with Ollama, and also exploring the free Groq cloud alternative—and then tying it all together with N8N for automation. Forget those crazy API costs!

Why is this cool? Well, for one, we’re talking about running models comparable to early frontier models locally. No more constant API calls! The video demonstrates how to integrate both local and cloud (Groq) options into N8N workflows, which is perfect for building AI agents with custom knowledge bases and tool calling. Think about automating document processing, sentiment analysis, or even basic code generation – all without racking up a huge bill. The video even tests reasoning capabilities against the paid OpenAI models! I’m already imagining using this setup to enhance our internal tooling and streamline some of our client onboarding processes.

Frankly, the biggest win here is the democratization of access to powerful AI. The ability to experiment with these models without the constant fear of API costs is massive, especially for learning and prototyping. Plus, the N8N integration makes it practical for real-world automation. It’s definitely worth setting aside an afternoon to experiment with. I’m particularly excited about the Groq integration – blazing fast inference speed combined with N8N could be a game-changer for certain real-time applications we’re developing.

Date: 07/29/2025

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Okay, this video on integrating n8n with Supabase for file uploads is seriously inspiring, and here’s why. It’s all about automating file management with a focus on the practical details that often get overlooked. The video dives deep into using n8n’s HTTP node to upload files to Supabase Storage, handling everything from authentication to generating signed URLs and dealing with errors. Crucially, it covers both public and private buckets, which is essential for any real-world app dealing with different levels of data sensitivity.

Why is this valuable for us as developers shifting to AI and no-code? Well, think about it: a huge part of AI workflows involves handling data, often files like images or documents. This video shows you how to build a robust, automated pipeline for managing that data in Supabase. It’s not just theory; it walks through the tricky parts, like dealing with binary data and setting up the HTTP node correctly. Plus, the examples of connecting Supabase real-time events to n8n for triggering automations? Gold! Imagine automatically kicking off an image processing workflow in response to a new file upload – that’s a game changer for efficiency.

For me, the most exciting part is the potential for real-world application. The video touches on use cases with mobile apps, web interfaces, and even image-to-insight AI workflows. I can immediately see how this could streamline data ingestion and processing in a ton of projects. I’m definitely going to experiment with hooking up n8n to a Supabase-backed app for automated image analysis. Being able to secure files while triggering automations? Sign me up!

Date: 07/28/2025

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Okay, so this video’s all about “Top 10 n8n Tools for 2025.” It gives a rundown of new nodes and apps to supercharge your n8n workflows, with a focus on AI tools. We’re talking things like integrating Google Gemini, AI voice with ElevenLabs, web scraping with Apify, and even AI-powered search using Perplexity. I’m seeing a lot of LLM integration, with things like Mistral and DeepSeek making an appearance too.

Why’s it interesting for us? Because it’s a direct look at how AI is being plugged into no-code platforms like n8n. Instead of building everything from scratch in Laravel or PHP, you’re orchestrating these AI services. I can immediately see using this to automate marketing content generation, improve data enrichment processes, or even build more intelligent customer support flows. Think about it: automating lead qualification using AI to analyze social media profiles scraped with Apify, then generating personalized outreach emails using an LLM through n8n. Boom!

I think what makes this video particularly worth checking out is how practical it is. It’s not just about the “what,” but the “how” of integrating these AI tools into your existing workflow. Seeing someone demonstrate how to connect these services in n8n sparks ideas for how I could apply them to projects I’m working on right now. Definitely giving this a watch and experimenting!

Date: 07/23/2025

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Okay, so this video is all about turbocharging your RAG (Retrieval Augmented Generation) agents in n8n using a deceptively simple trick: proper markdown chunking. Instead of just splitting text willy-nilly by characters, it guides you on structuring your data by markdown headings before you vectorize it. Turns out, the default settings in n8n can be misleading and cause your chunks to be garbage. It also covers converting various formats like Google Docs, PDFs, and HTML into markdown so that you can process them.

For someone like me, neck-deep in the AI coding revolution, this is gold. I’ve been wrestling with getting my LLM-powered workflows to produce actually relevant and coherent results. The video highlights how crucial it is to feed your LLMs well-structured information. The markdown chunking approach ensures that the context stays intact, which directly translates to better answers from my AI agents. I can immediately see this applying to things like document summarization, chatbot knowledge bases, and even code generation tasks where preserving the logical structure is paramount. Imagine using this for auto-generating API documentation from a codebase!

Honestly, the fact that a 10-second fix can dramatically improve RAG performance is incredibly inspiring. It’s a reminder that even in the age of complex AI models, the fundamentals – like data preparation – still reign supreme. I’m definitely diving in and experimenting with this; even if it saves me from one instance of debugging nonsensical LLM output, it’ll be worth it!

Date: 07/14/2025

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Okay, this video on using Zep memory with AI agents in n8n is seriously inspiring for anyone looking to move beyond basic LLM integrations. It’s about giving your AI agents actual long-term memory using a relational graph database (that’s Zep), which means they can understand relationships between entities, users, and events. Think of it: no more just relying on the immediate context window!

The real value here isn’t just about the cool tech, but about the practical strategies the video shares. It highlights the potential cost explosion you can face by blindly implementing long-term memory, and then dives into token reduction techniques in n8n. This is critical because, while giving an AI agent a memory of all past conversations or user interactions sounds great, it becomes a nightmare when you’re paying by the token. The video shows how to intelligently combine short-term and long-term memory, using session IDs, and other methods so that we can reduce cost without sacrificing performance.

For me, this video represents a key evolution in how I’m approaching AI-powered automation. No-code tools like n8n, combined with services like Zep that provide memory, offer a powerful way to build sophisticated AI agents. I’m already imagining how I could adapt this to create more personalized customer support bots or even intelligent internal knowledge management systems. It’s one thing to connect an LLM to an API, and it’s another to create systems that truly learn and evolve over time. This video has actionable strategies for that. I am going to sign up for n8n using the link the video provides.

Date: 07/14/2025

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Okay, this video is exactly the kind of content that gets me fired up about the future of development. It’s all about building smarter AI agents with n8n that can actually understand and query structured data, like spreadsheets, using a hybrid RAG (Retrieval-Augmented Generation) approach. We’re talking about giving our agents the ability to not just semantically search, but to do things like sum columns, filter rows, and perform real SQL queries through natural language!

Why is this valuable? Well, how many times have you built a clunky interface just to let a user run a simple report on some data? This video shows you how to use an AI agent to interpret a user’s natural language request (“What were the total sales in France last month?”) and translate it into an actual SQL query against a Supabase database. The magic is in how the data is ingested and managed – storing structured data in a flexible JSONB column, so you don’t need a rigid schema upfront. Plus, it smartly combines vector search for unstructured data with SQL queries for the structured stuff – the agent decides which to use. It walks through a complete data pipeline, too, covering things like handling data changes in Google Drive and keeping everything synced. No-code is cool and all, but the real power comes when you can seamlessly blend it with robust backend logic.

For me, the most exciting thing is the shift from building rigid UIs and APIs to crafting intelligent agents that can adapt to changing data and user needs. Imagine the possibilities for automating reporting, data analysis, and even complex business workflows! I’m already brainstorming ways to apply this to a reporting project for a client. I’m thinking by setting up a system like this, we can drastically cut down the time spent manually building reports and dashboards. It’s worth experimenting with, as I see it lowering dev time by potentially 50%!