Tag: n8n

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%!

Date: 07/04/2025

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Okay, this video on automating QA with n8n and Browser Use is seriously inspiring, and here’s why it hits home for me. It’s all about taking the pain out of deployments. We’ve all been there, right? You push code, hold your breath, and pray nothing breaks. This video shows how to use n8n, a no-code automation tool, combined with Browser Use, to automatically trigger tests using natural language prompts. Think about it: you deploy, n8n kicks off tests based on simple instructions, and you get instant feedback. No more manual clicking and hoping for the best!

What makes this valuable is that it directly addresses the transition from traditional development to AI-enhanced workflows. I’ve been exploring LLM-based workflows myself to streamline deployments, and this is another piece of the puzzle. Imagine setting up a workflow that not only runs tests but also uses an LLM to analyze the results and identify potential issues based on the error messages. That’s real automation, saving time and giving you confidence.

For me, the real appeal is the blend of no-code and AI. It’s about empowering developers to build robust, automated systems without getting bogged down in complex scripting. It’s definitely worth experimenting with to see how it can integrate into your existing CI/CD pipeline. I can already see how this approach could be adapted to automate other tedious tasks like data validation, performance monitoring, and even security checks. It’s time to ditch the deployment anxiety and embrace automated QA.

Date: 06/29/2025

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Okay, so this video is all about the Bambu Lab A1 mini, a $250 3D printer, and whether it’s actually any good. As someone who is knee-deep in automating everything from code deployment to client report generation, the promise of accessible 3D printing immediately piqued my interest. Why? Because rapid prototyping and custom hardware solutions can be a huge bottleneck, and a cheap, reliable printer could seriously cut down development time.

What makes this video valuable for us is the exploration of how accessible tech empowers faster iteration. Think about it: we’re constantly leveraging AI to generate code snippets or using no-code platforms to build UIs, but sometimes we need a physical component to tie it all together. Imagine using an LLM to design a custom enclosure for a Raspberry Pi project, then printing it out in a matter of hours. That kind of speed and agility is game-changing. We could go from a conceptual idea to a functional prototype in a single day.

Ultimately, the potential for integrating affordable 3D printing into our development workflows is huge. Whether it’s creating custom jigs for electronics projects, printing replacement parts for existing equipment, or even prototyping new product concepts, the possibilities are endless. I am now thinking about picking one up. It’s a small investment with potentially massive returns in terms of time saved and innovation unlocked.

Date: 06/22/2025

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Okay, so this video is basically a treasure trove of n8n tips and tricks from someone who’s clearly been in the trenches with it for a year. It’s like getting insider knowledge straight from a seasoned user, covering everything from basic efficiency hacks to more advanced automation techniques. Think of it as a “level up your n8n game” guide.

Why’s it valuable for us? Because as we’re shifting towards AI-enhanced development, tools like n8n are becoming essential for orchestrating workflows between different services and LLMs. We can use this to build custom AI agents, connect them to our Laravel apps, automate tedious tasks, and basically glue everything together without writing a ton of code. The video’s progression, starting with simple tips and moving to advanced ones, acknowledges that learning curve we’re all facing.

Imagine this: using these n8n tricks to automate the process of training a custom LLM on new data, then deploying it to a Laravel API endpoint. Or even simpler, automating lead generation and follow-up sequences based on specific triggers in our applications. Honestly, what makes this worth experimenting with is the potential time saved and the ability to focus on higher-level logic instead of getting bogged down in the nitty-gritty details of workflow construction. It’s about working smarter, not harder, which is the whole point of embracing AI in our workflow, right?