Tag: n8n

Date: 10/22/2025

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Okay, this video is gold for anyone like me who’s been straddling the line between traditional coding and the exciting world of AI agents. It tackles the core question: “n8n (no-code) or Python (code) for building AI agents?” which is exactly what I’ve been wrestling with lately. It’s not a simple answer, and the video acknowledges that, diving into the pros and cons of both approaches. For instance, n8n’s visual workflow is undeniably faster for initial prototyping, whereas Python offers that granular control that’s critical for complex logic – something I learned the hard way trying to wrangle a particularly stubborn API integration.

What makes this video super valuable is that it acknowledges the realities of modern development. We’re not strictly “code” or “no-code” anymore. It highlights a hybrid approach, leveraging the strengths of both n8n and Python. Imagine using n8n to rapidly build the basic agent structure, then dropping into Python for the intricate logic, custom integrations, or performance optimizations where n8n’s visual style might become cumbersome. I can totally see this applying to client projects where speed of deployment is key, but specific features require a more tailored solution.

Honestly, it’s inspiring because it validates the direction I’m heading. It’s a reminder that mastering AI agent development isn’t about choosing one tool, but about intelligently combining the best of both worlds. I’m itching to experiment with the hybrid approach he suggests. Maybe start by refactoring one of my existing, clunky Python scripts into a more visually manageable n8n workflow, then bolting on the custom Python bits where needed. Sounds like a perfect weekend project!

Date: 10/15/2025

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Alright, so this video dives into n8n’s new AI workflow builder, putting it through its paces with three different use cases of varying complexity. Honestly, it’s exactly the kind of content I’ve been craving as I try to ditch the old ways and fully embrace AI-assisted development. We’re talking about potentially automating tasks that used to take hours (or even days!) with traditional coding.

What makes this valuable is the real-world testing. The presenter doesn’t just take n8n’s claims at face value; they actually use it to build workflows. As someone who’s spent way too long wrestling with complex integrations in Laravel, the idea of simply describing a workflow in plain English and having the AI generate it is super appealing. Imagine using this to automate tasks like lead generation, data scraping, or even building custom APIs. The potential time savings are massive, freeing me up to focus on the more strategic and creative aspects of development.

I’m genuinely curious to see how well this AI integration handles complex logic and error handling. It’s one thing to generate a basic workflow, but can it deal with the nuances of real-world data and unexpected scenarios? Still, even if it only gets me 80% of the way there, that’s a huge win. I’m definitely adding n8n and AI-assisted workflow generation to my “must-try” list. It’s worth experimenting with just to see how much faster I can build and deploy integrations.

Date: 10/09/2025

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Okay, so this video about automating SOPs using Baserow, n8n, and AI? Seriously inspiring stuff. We’re talking about moving beyond clunky spreadsheets and into a world where your processes are dynamic, self-improving, and honestly, way less of a headache.

For those of us diving into AI-enhanced development, this is gold. It shows you how to weave together no-code tools like Baserow (an open-source database) and n8n (a workflow automation platform) with AI agents. Imagine this: you’ve got tasks automatically created and assigned, progress tracked in real-time, and then, BAM, AI swoops in to analyze feedback and tweak the workflow for optimal performance. We’ve all been there – manually adjusting processes based on gut feeling and delayed reports. This is about making data-driven decisions, automatically.

Think about applying this to client onboarding, bug tracking, or even internal code review processes. You could automate the initial steps, use AI to identify bottlenecks, and continuously improve the entire flow. For me, the real kicker is the “self-improving workflows” part. It’s not just about automation; it’s about building systems that get smarter over time. Definitely worth blocking out an hour to experiment and see how these tools can streamline our workflows.

Date: 10/02/2025

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Okay, this video on building a Hybrid RAG Search Engine in n8n is exactly the kind of thing that gets me fired up about the future of development. We’re talking about moving beyond simple vector embeddings for Retrieval Augmented Generation (RAG) and into a more robust, real-world applicable search solution. It walks you through combining dense embeddings (semantic search), sparse retrieval (BM25, lexical search), and even pattern matching within an n8n workflow using Supabase and Pinecone. The coolest part? It dynamically weights these methods based on the query type. Forget AI hallucinations!

Why is this valuable for us transitioning to AI-enhanced development? Because it addresses a very real problem: vector search alone often fails for exact matches and specific details. As someone who’s struggled with searching through piles of documentation and code using just vector databases, I can attest to that! This approach of hybrid search, especially the dynamic weighting, aligns perfectly with the kind of automation and intelligent workflows I’m aiming to build. Think about applying this to customer support bots that need to accurately find product information, or internal knowledge bases that require precise code snippet retrieval.

Seriously, the idea of programmatically shifting search strategies based on the question being asked is a game-changer. I see this as a concrete step towards building truly intelligent and adaptable AI agents. Reciprocal Rank Fusion (RRF) isn’t something I’ve used extensively, but I can already think of 10 different applications for my clients to build a better search. I’m definitely going to be experimenting with this setup – n8n, Supabase, and Pinecone are all tools I’m familiar with, so the barrier to entry is pretty low and the potential payoff is huge. It’s time to stop relying on “good enough” vector search and start building something truly intelligent!

Date: 10/02/2025

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Okay, so this n8n livestream sounds pretty awesome, especially if you’re like me and diving headfirst into AI-powered automation. Essentially, it’s a deep dive into n8n, a no-code workflow automation platform, with a focus on new features, community involvement, and real-world AI integrations. They’re even showcasing how Fireflies.AI, a meeting assistant, can be woven into n8n workflows.

Why is this valuable for us? Well, we’re trying to bridge the gap between traditional coding and these new AI/no-code tools. This livestream gives us a chance to see firsthand how to build complex automations without writing a ton of code. The Fireflies.AI integration is particularly interesting; imagine automating meeting summaries, action item extraction, and follow-ups – all within a visual workflow. It’s a fantastic example of how AI can augment our existing processes. Plus, hearing from n8n ambassadors gives insights into how others are using the platform to solve real-world problems.

I’m personally excited to see the live demos. It’s one thing to read about features, but it’s another to see them in action, especially when combined with AI tools like Fireflies.AI. I’m already brainstorming how I can adapt some of those workflows for my own projects – maybe automating client onboarding or streamlining our internal reporting. Getting a chance to ask questions and network with the n8n community makes this definitely worth blocking out time for. Seeing how others are using the platform and how these new features can be applied to real projects will provide fresh ideas and maybe even shave off hours of development time.

Date: 09/27/2025

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Okay, so this video is all about leveraging n8n (a no-code workflow automation platform) and Scrape Creators (a unified scraping API) to pull data from social media platforms. It’s essentially showing how to replace a bunch of individual, often clunky, API integrations with a single, cheaper, and more manageable solution. The demo covers scraping competitor ads and mining Reddit for content strategy, which are immediately useful use cases.

Why is this exciting for a developer like me diving into AI and no-code? Well, it’s about efficiency and shifting focus. Instead of wrestling with different APIs and writing custom scrapers, you can use n8n to orchestrate the entire process with a drag-and-drop interface, and Scrape Creators handles the actual data extraction. This frees up time to focus on what matters: analyzing the data, building AI models on top of it, or automating decisions based on insights. I’m always looking for ways to reduce boilerplate and increase the leverage I get from my code.

The real-world application is HUGE. Imagine automating market research, social listening, or lead generation with just a few clicks. You could build an AI-powered content recommendation engine using Reddit data, or monitor competitor strategies and trigger alerts when they launch new campaigns. I’m particularly interested in how this could streamline my automation workflows for client projects, saving me time and money while delivering more value. It’s absolutely worth experimenting with because it’s a tangible step towards a more AI-driven, no-code-enhanced development process!

Date: 09/25/2025

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Okay, this video on integrating knowledge graphs into n8n workflows using Graphiti MCP is seriously exciting! It’s all about augmenting Retrieval-Augmented Generation (RAG) systems – the core of many AI agents – with knowledge graphs. Essentially, instead of just relying on vector databases (which can sometimes miss contextual relationships), we’re adding a layer that lets the agent understand and reason about the relationships within the data. Think of it as giving your agent a brain that can connect the dots, not just recall information.

Why is this a game-changer for us transitioning into AI-enhanced development? Because RAG is becoming the backbone of many AI applications. We’re constantly looking for ways to make these RAG systems more robust and intelligent. This video directly addresses a limitation of traditional RAG by adding knowledge graphs on top. The ability to build AI agents that understand relationships within data is powerful. Imagine an agent that can not only find information but also reason about its implications. I can see immediately applying this to customer service bots, dynamic product recommendations, and even advanced data analysis workflows I’m building, like automating client research and identifying market opportunities. The steps provided are practical, and you can copy and paste most of it to get going right away!

Honestly, what makes this worth experimenting with is the potential to create truly intelligent automation. We’re not just scripting anymore; we’re architecting systems that understand and reason. The video provides a solid foundation and a clear path to integrate this into our existing n8n-based workflows. The n8n template that is provided for free in the video is a fantastic starting point and I plan to use it for my agentic research project.

Date: 09/22/2025

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Okay, so this video’s all about building a voice AI agent using n8n and ElevenLabs, without needing to write a ton of code. It walks you through setting up an AI that can actually talk back to you, handle calls, and respond intelligently. Pretty cool, right?

Why I think this is a valuable watch for us as we transition to AI-enhanced workflows is that it’s a perfect example of leveraging no-code tools to achieve complex automation. We can take the concepts in this video and apply them to real-world situations, like automating customer service interactions or creating personalized voice assistants for clients. Imagine using this to build a system that automatically answers FAQs or even schedules appointments through voice – think of the time saved! It’s not about replacing code entirely, but about using these tools to augment our abilities and free us up for more strategic tasks.

What makes this worth experimenting with is that it bridges the gap between the complex world of AI and our existing development skills. It’s a practical, hands-on approach to learning about AI and automation, and who knows? You might just find a new revenue stream by building and selling voice AI agents as the video suggests! I’m definitely adding this to my weekend project list!

Date: 09/22/2025

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Okay, this video is gold for any developer like me who’s diving headfirst into the world of AI-powered workflows. It’s all about scaling RAG (Retrieval Augmented Generation) systems built with n8n, a no-code automation platform. The creator shares their experience of boosting processing speed from 100 files/hour to a whopping 5,000 files/hour. They didn’t just wave a magic wand; they went through the trenches, broke things, and learned a ton about optimizing n8n, Supabase, and even dealing with Google Drive limitations at scale. Sounds familiar, right?

What makes this video a must-watch is its pragmatic approach. It’s not just theoretical fluff; it’s a deep dive into real-world challenges like bottlenecks, server crashes, and painfully slow data imports. The video provides a systematic approach for benchmarking, tuning, and scaling complex n8n workflows. They cover everything from setting up n8n workers and Redis queuing for parallel processing to building a robust orchestrator with retry logic. Plus, there’s a valuable lesson about knowing when to bypass APIs and go directly to the database. (Hello, Supabase!).

For me, the most inspiring part is the tangible impact this kind of optimization can have. Imagine automating document processing, content analysis, or even code generation at this scale. By understanding these scaling techniques, we can build more robust and efficient AI-driven solutions for clients. I can see this being super useful for automating the ingestion and processing of our documentation for the AI code generation tools we are building. It would be a time saver and a great learning experience to implement. I’m definitely eager to experiment with the concepts in the video and see how they can transform my own AI workflow integrations.

Date: 09/22/2025

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Okay, this n8n RAG scaling video is straight fire for anyone diving into AI-powered workflows! It tackles a real-world problem I’ve definitely hit: building a killer RAG system is easy with a few files, but what happens when you need to ingest thousands? This video shows you how to use n8n, Supabase, and some clever orchestrator patterns to reliably process massive amounts of data. We’re talking scaling to thousands of files per hour.

What makes this valuable is its focus on practical solutions to common AI integration bottlenecks. API rate limits, memory overloads, system instability – these are the dragons you face when moving from a PoC to a production-ready system. The video breaks down how to build an orchestrator workflow that handles parallel executions, tracks parent/child processes using Supabase, and even handles errors with automated retries. Plus, the deep dive into using webhooks instead of sub-workflows is a game-changer for performance and tracking – something I’ve been experimenting with myself lately and seen HUGE improvements from.

Imagine building a document processing pipeline for a legal firm or a content ingestion system for a large e-commerce site. This video provides a blueprint for automating those processes at scale. Honestly, the techniques for handling errors and preventing system overloads are worth the price of admission alone. I’m definitely going to be experimenting with these orchestration patterns in my next project. The layered error handling and Supabase configuration tips are gold!