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Tag: n8n

  • Will CAG replace RAG in N8N? Gemini, OpenAI & Claude TESTED



    Date: 04/01/2025

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    Okay, so this video is gold for us devs diving into the AI space. It’s all about Cache-Augmented Generation (CAG), which is like RAG’s smarter, faster cousin. Instead of hitting the database every time, it leverages server-side memory from the big players like OpenAI, Anthropic, and Google Gemini. The video then pits CAG against traditional RAG in a head-to-head comparison focusing on speed, cost, and accuracy. It demos the implementation using n8n, showing how to set up workflows with different LLMs and how to upload documents to Gemini’s cache. Super practical stuff.

    Why’s it valuable? Well, as we’re transitioning into AI-enhanced workflows, RAG is becoming a foundational piece for building AI tools that actually know something beyond their training data. This video takes it a step further. The comparison between CAG and RAG is key – it helps us understand when it’s worth investing in a more sophisticated caching mechanism. Plus, the n8n demo is killer because it provides a tangible, no-code approach to integrating these techniques. Instead of abstract theory, you see real workflows.

    Think about it: We’re building more and more complex applications that rely on LLMs. The ability to reduce latency and lower costs while maintaining (or even improving) accuracy is HUGE. Imagine using CAG for customer support chatbots, internal knowledge bases, or even code generation tools that need to quickly access and recall vast amounts of information. Honestly, what I find most inspiring is the practical, hands-on approach. It’s not just about the “what,” but the “how.” I’m definitely eager to experiment with CAG to see how it stacks up against our current RAG implementations. Plus, n8n makes it super easy to prototype and test these ideas, so why not give it a shot?

  • How to Use Voice AI Tool Calling with Vapi & n8n (Step-By-Step, No Code)



    Date: 03/26/2025

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    Okay, this video on building a restaurant reservation system with N8N and VAPI is seriously cool and right up our alley! It’s basically about creating an AI voice receptionist using no-code tools. Think about it: instead of a human answering the phone, an AI handles booking reservations, potentially managing multiple calls simultaneously.

    For us devs diving into AI and no-code, this is gold. The video breaks down how to build the entire workflow in N8N, from setting up the initial call flow to extracting reservation details using VAPI. It’s not just theoretical; it walks you through creating the tools, testing the process, and even talks about enhancements. It is incredibly powerful to extract structured data using AI instead of Regex. This is a must have to be able to connect LLMs to databases. Imagine automating all those tedious tasks with AI.

    What makes this worth experimenting with is the tangible application. We can apply these concepts to automate customer support, appointment scheduling, or even lead qualification processes. Plus, the potential cost savings and efficiency gains are huge. I am excited to try out building my own AI powered voice assistant for my web apps. It’s a great way to see how these new tools can revolutionize how we build and deploy solutions.

  • How I Use N8N to Fine-Tune a Model



    Date: 03/14/2025

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    Okay, this video on fine-tuning LLMs with N8N is right up my alley! It essentially walks you through building automated workflows to prepare data and then fine-tune an LLM, specifically using OpenAI’s API, but with considerations for local LLMs too. The value here for developers making the leap into AI is huge. We’re not just talking about *using* LLMs, but *customizing* them to our specific needs – think consistent tone, domain-specific knowledge, or project-specific requirements.

    Why is this valuable? Because fine-tuning bridges the gap between generic LLM outputs and truly production-ready AI. Imagine automating content generation that perfectly matches your brand’s voice, or having an AI assistant that *really* understands your project’s codebase. The video tackles a real-world case study, RecallsNow, and provides N8N workflows for data extraction, prompt engineering, and formatting the output into the required JSON Lines format for the fine-tuning API. It even touches on the crucial aspect of testing the newly fine-tuned model.

    For me, what makes this worth experimenting with is the potential for serious time savings and improved results. Instead of constantly tweaking prompts, you’re molding the LLM to your needs. Plus, the provided N8N workflows are a fantastic starting point. I can already see adapting these to automate documentation generation, code reviews, or even custom API integrations tailored to specific client requirements. Time to roll up my sleeves and start fine-tuning!

  • Is MCP the Future of N8N AI Agents? (Fully Tested!)



    Date: 03/13/2025

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    Okay, so this video on MCP (Model Context Protocol) is seriously intriguing, especially for us devs diving headfirst into AI-powered workflows. Basically, it’s pitching MCP as a universal translator for AI agents, like a “USB-C for AI Models”. Imagine your AI agent being able to plug-and-play with tools like Brave Search, GitHub, Puppeteer, etc., without needing a ton of custom code for each. The video demos this inside N8N, which is awesome because N8N is a fantastic low-code automation platform that I’ve been experimenting with myself.

    The real value here is the potential for huge time savings and increased flexibility. Instead of wrestling with individual APIs and complex integrations, MCP offers a standardized way for AI agents to interact with different services. Think about it: building an automated content scraper that uses AI to analyze the data, then automatically commits changes to a GitHub repo – all orchestrated without writing mountains of bespoke code. The video’s use case of connecting AI agents within N8N really highlights how you can visually map out and automate these complex tasks.

    Honestly, the promise of a plug-and-play standard for AI agent interactions is a game-changer. It aligns perfectly with my journey of leveraging AI to automate tedious development tasks and streamline workflows. I’m definitely going to check out the N8N MCP Community Module on GitHub and see how I can integrate this into some of my projects. It’s worth experimenting with because if MCP takes off, it could drastically reduce the development overhead for AI-driven automations and open up a whole new world of possibilities.

  • How Does AI Effortlessly Generate High Quality Articles For WordPress?



    Date: 03/13/2025

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    Okay, this video on automating WordPress content creation with n8n, Airtable, and RankMath is *exactly* the kind of thing I’m diving into right now. Basically, it shows you how to build a workflow where Airtable acts as your content calendar, n8n orchestrates the AI content creation process (likely leveraging something like GPT-4 or Claude), and then automatically publishes to WordPress while optimizing for SEO using RankMath. No more manual copy-pasting or fiddling with SEO settings – the AI does it all!

    Why is this so valuable? Well, as I transition more into AI-enhanced development, I’m constantly looking for ways to automate repetitive tasks. This video provides a blueprint for doing just that with content generation – a task that can be incredibly time-consuming. Think about it: you could use this same structure for automating other types of content, like product descriptions for an e-commerce site, or even documentation for a software project! The integration aspect is key. If I can set up a system where data flows seamlessly between different platforms and AI models, that’s a huge win in terms of efficiency and scalability.

    Honestly, what makes this video worth experimenting with is the sheer potential for time savings. If I can shave off even a few hours a week by automating my content workflow, that frees me up to focus on more strategic development tasks. Plus, the fact that it’s all built using no-code tools like n8n makes it accessible even to developers who aren’t AI/ML experts. It’s a practical, real-world example of how AI and no-code can come together to create something really powerful. I’m definitely grabbing that 3-day trial and diving in!

  • I Built a FULL Web App with n8n, Lovable & Supabase (NO CODE!)



    Date: 03/05/2025

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    Okay, so this video promises to show you how to build a full web application using n8n, Lovable, and Supabase *without* writing a single line of code? Sign me up! As someone neck-deep in transitioning to AI-enhanced workflows, this is incredibly inspiring. We’re talking about visually building complex applications, including AI Agents using no-code. This approach lets you focus on the *logic* and *outcomes* rather than wrestling with syntax and debugging hell.

    What makes this video particularly valuable is the combination of tools. n8n, a workflow automation platform, gives you the backbone for orchestrating tasks. Supabase provides a robust, open-source alternative to Firebase for your database needs. Then Lovable is on top for AI agent interactions. Together, they present a powerful stack. Imagine automating lead qualification, content creation, or even customer support—all driven by AI agents you build visually.

    I’m personally excited to experiment with this because it drastically reduces the barrier to entry for complex AI-powered applications. We can quickly prototype and test ideas, iterating faster and delivering value sooner. While I’m not abandoning code entirely, this opens up possibilities for delegating simpler tasks to no-code solutions, freeing me up to focus on the more challenging and strategic aspects of development. It’s worth a shot, right?

  • How to Run an AI Browser Agent with Make.com and n8n (No-code)



    Date: 03/04/2025

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    Okay, this video on integrating AI-powered browser agents with N8N and Make.com is seriously inspiring and relevant to what I’ve been digging into lately. Essentially, it shows you how to automate web browsing, scraping, and data extraction using platforms like Browser Use, and then integrate them into your workflows using no-code tools. Think of it as “headless browser” automation amped up with AI to make decisions and handle dynamic content.

    For someone like me who’s been knee-deep in traditional PHP and Laravel development for years, this opens up a new realm of possibilities. Imagine automating competitor research, gathering real-time data for dynamic dashboards, or even orchestrating complex user journeys for testing purposes. The fact that it leverages no-code platforms like N8N and Make.com means I can quickly prototype and deploy these automations without getting bogged down in custom coding for every single interaction. We are talking about a shift where you could focus on the logic and outcomes rather than the low level implementation details.

    What makes this video particularly appealing is the promise of pre-built blueprints and templates. Jumping straight into something and modifying it has proven a great way to learn new AI tools. I’m keen to experiment with it because it offers a tangible way to bridge the gap between my existing coding skills and the burgeoning world of AI-driven automation. Plus, the practical demonstrations of what works (and, importantly, what *doesn’t*) provide a realistic perspective on the current state of the technology.

  • N8N + Postgres Event-Driven Workflows 🚀



    Date: 03/03/2025

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    Okay, this “Postgres & n8n Event-Driven Workflows” video is exactly the kind of thing that gets me fired up! It’s about ditching the old-school, resource-hogging polling method for database updates and embracing real-time automation using Postgres triggers and n8n. Instead of constantly asking “Did anything change? Did anything change?”, we’re letting the database tell us when something *actually* happens. This paradigm shift—moving from polling to event-driven—is where it’s at.

    For developers like us who are diving into AI coding and no-code tools, this is gold. Imagine automating tasks based on specific database changes without writing complex polling scripts or hammering your database with unnecessary queries. The video shows you how to listen for database events using Postgres triggers, filter updates to trigger workflows only when *meaningful* data changes, and even migrate your setup to production using Prisma. I can already see how I could use this for things like triggering AI model retraining when new data is added, automatically updating reports based on sales data changes, or even sending notifications when a user’s status changes in the database.

    What I find most appealing is the potential for cleaner, more efficient, and more scalable workflows. The presenter touches upon risks and debugging tips, providing guidance on migrating to production, and keeping workflows modular and easy to test! Plus, the code examples in the Gist make it super practical. I think experimenting with this setup could really boost our productivity and let us build smarter automations that react in real-time. Definitely worth blocking out some time to dive in!

  • n8n Ai Agent: Build a Blog Writing Agent! (n8n tutorial)



    Date: 02/27/2025

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    Alright, this video on building a blog-writing agent with n8n is seriously inspiring, especially for anyone like me who’s diving headfirst into AI-enhanced development. It basically shows you how to create a no-code workflow that not only generates SEO-optimized blog posts for WordPress but also improves its writing over time using an AI agent connected to Airtable for memory! Plus, it even throws in image generation using the Flux model. Seriously cool stuff.

    What makes this valuable is that it tackles a real-world problem – content creation – using a fully automated, no-code solution. It’s not just about generating text; it’s about building a system that learns and adapts, leveraging LLMs *and* visual AI. Think about the possibilities: automating social media content, personalized email campaigns, even documentation! The demo showing the Telegram integration to kick off the workflow is especially compelling. You could adapt this to other trigger mechanisms as well.

    I’m personally excited to try this out because it combines several technologies I’m already working with – n8n, LLMs, and WordPress. I’m thinking I can modify it to generate content for internal knowledge bases or even automate the creation of release notes. The fact that it uses Airtable for agent memory is genius! It’s a great starting point for building more complex, self-improving AI agents. Honestly, the potential time savings and scalability are worth the experimentation alone.

  • Build an AI Agent That Actually Remembers You (n8n Tutorial)



    Date: 02/25/2025

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    Okay, this video on implementing long-term memory in AI agents using n8n is seriously inspiring. It walks you through building an AI assistant that remembers user preferences, just like ChatGPT. Forget stateless interactions – we’re talking persistent memory that makes your AI feel way more human.

    Why is this gold for us developers diving into the AI/no-code space? Because it bridges the gap between traditional coding and LLM workflows. We’re used to managing state in our apps, but now we can offload that to a no-code platform like n8n *and* leverage LLMs to make sense of that state! The video shows you exactly how to save memories to Airtable and retrieve them to inform future interactions. Think about the possibilities: personalized customer support, dynamically tailored learning experiences, or even AI-driven project management that actually *learns* from past projects.

    I am pretty excited to try this out. I can see myself using something like this to automate client onboarding, and I can’t wait to explore how this type of memory could drastically improve the user experience in our products. It’s not just about building cool AI toys, but about creating genuinely useful, adaptive applications.