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YouTube Videos I want to try to implement!

  • I Replaced My Content Team With These SEO AI Agents (n8n, OpenAI, Aidbase)



    Date: 05/07/2025

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    Okay, this video by Simon Lüthi is straight up inspiring for anyone like me who’s diving headfirst into the world of AI-powered development. Basically, he walks through building an entire AI-driven SEO workflow using n8n (the no-code workflow automation platform), Aidbase (an AI knowledge base), OpenAI, and Replicate. He goes from topic generation and research, all the way to writing the blog post, generating a thumbnail, and then publishing and sharing it. All automated!

    What’s so valuable here is seeing how these different tools can be orchestrated to achieve a complete task that traditionally required hours of manual work. For instance, Aidbase acts as a kind of “internal knowledge” store for the AI, feeding it the right context for better content generation. That’s killer for keeping the AI on-brand and factually accurate. You can envision taking the same approach, but for tasks like automated code documentation, intelligent issue triaging in Jira, or even dynamic API integrations based on LLM prompts. The video shows a real-world example, not just theoretical possibilities.

    Honestly, this video makes me want to jump right in and start experimenting. Building complex, automated workflows used to mean writing a ton of custom code. Now, with tools like n8n and the ability to leverage LLMs for specific tasks, you can visually build something powerful in a fraction of the time. The thought of freeing up that much time to focus on more strategic initiatives? That’s what makes this worth checking out. I’m already brainstorming how to apply this exact workflow to automating some client reporting tasks I’ve been putting off.

  • I Built the Ultimate RAG MCP Server for AI Coding (Better than Context7)



    Date: 05/05/2025

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    Okay, this video is definitely inspiring, and a great next step for anyone diving into AI-enhanced development. The core problem it tackles is something I’ve run into constantly: AI coding assistants are amazing, but they can absolutely “hallucinate” when dealing with specific frameworks or niche tools. This video introduces an open-source MCP server (Crawl4AI RAG) built to address this head-on by creating your own RAG (Retrieval-Augmented Generation) knowledge base from crawled websites, all stored in Supabase. Think of it as building a private, ultra-focused documentation library that your AI assistant can actually rely on.

    What makes this video valuable is that it moves beyond the “black box” approach of tools like Context7 (which, let’s be honest, can feel messy and not truly open-source). It empowers you to build your own RAG system tailored to your specific tech stack. Imagine feeding it all the documentation for your favorite Laravel packages, specific internal company documentation, or even blog posts related to your project. Now your AI assistant has a highly relevant and accurate context, drastically reducing those frustrating hallucinations and speeding up development. The video also touches on integrating this with Archon, an AI agent builder, which opens doors to automating even more complex tasks.

    The most inspiring part? This is a tangible, ready-to-use solution. The video provides the GitHub link for the Crawl4AI RAG MCP server, so you can install it and start building your knowledge base today. For me, the thought of having AI agents and coding assistants with reliable, project-specific context is a game-changer. I’m already envisioning how I can use this to streamline onboarding new developers, automate code reviews, and even generate custom documentation on the fly. It’s absolutely worth experimenting with because it puts the power of custom AI knowledge right in our hands, shifting us from passive users to active architects of AI-driven workflows.

  • How to Build an AI SQL Agent with n8n to Query Databases Effortlessly



    Date: 05/05/2025

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    Okay, this n8n tutorial on building an AI-powered SQL agent? Seriously inspiring stuff and right up my alley! It walks you through creating a chatbot that translates natural language questions into SQL queries, hitting a Postgres database (Supabase in this case). You’re essentially building a smarter, conversational interface to your data.

    Why is this valuable for us devs diving into AI and no-code? Because it’s a tangible example of how to bridge the gap between human language and database logic. Forget painstakingly crafting SQL queries; this shows you how to leverage AI to automate that. The video uses n8n, a no-code workflow automation tool, to orchestrate the entire process, making it accessible even if you’re not an AI/ML expert. It tests the agent with scenarios like “find the most expensive equipment” or “calculate averages,” which are real-world use cases we encounter all the time.

    Think about it: imagine building internal tools that let non-technical team members easily query data without needing to understand SQL. Or automating report generation based on complex, natural language requests. It’s all about boosting efficiency and empowering everyone on the team. For me, the appeal is the blend of traditional DB knowledge with cutting-edge AI. This looks like a fun weekend project and potentially game changing. I’m definitely going to play around with this.

  • NEW DeepAgent: The First-Ever GOD-TIER AI Agent! Automate and Build Anything! (UPDATE)



    Date: 05/04/2025

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    This video showcasing the upgraded DeepAgent from Abacus AI is seriously compelling. It’s all about an AI agent that can not only research and code but also generate dashboards, presentations, and automate workflows across platforms like Slack and Gmail. What really grabs my attention is the Pro Tier’s database support, custom domains, and integrations. Imagine building real, data-driven apps with persistent storage, deploying them under your own domain, and having them seamlessly integrate with your team’s existing tools. That’s a game changer for quickly prototyping and even deploying internal tools without needing to write tons of boilerplate code or manage complex infrastructure.

    Why is this valuable? Because it directly addresses the pain points of transitioning to AI-enhanced development. It’s not just about AI spitting out code snippets; it’s about a comprehensive system that handles the entire lifecycle from idea to deployment. The ability to build AI-powered apps with persistent data opens up possibilities for automating business processes that were previously out of reach. Think of automated reporting systems, intelligent customer support bots, or even dynamic dashboards driven by real-time data – all built with minimal traditional coding.

    For me, the appeal lies in the potential for rapid iteration and experimentation. The video claims you can build “insane workflows” in minutes, and if that’s even remotely true, it’s worth exploring. I’m keen to see how DeepAgent can be integrated into existing Laravel projects, perhaps by automating the creation of API endpoints or generating admin panels. I would really like to see if it could automate some of the more tedious parts of maintaining legacy applications as well. Plus, the fact that you get three free tasks to try before upgrading makes it a no-brainer to check out!

  • I Built an MCP Server in 18 Minutes (FULL Cursor Tutorial)



    Date: 05/03/2025

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    Okay, so this video dives into building an “MCP (Modal Context Protocol) Server,” which sounds super geeky but is actually about creating a central hub to manage different AI interactions and contexts. Instead of having your AI tools scattered and siloed, the MCP server lets you orchestrate them, feeding information between them in a structured way.

    Why’s this valuable for us Laravel devs moving into the AI/no-code space? Because it’s about control and automation. We’re used to building complex systems, and this video shows you how to apply that same mindset to AI. It’s about not just using AI, but orchestrating it to do exactly what you need. Instead of relying on pre-built integrations, you can build your own custom workflows, tailored to your specific business logic and data. For example, imagine using an MCP server to connect a sentiment analysis tool, a content generation AI, and a social media posting scheduler to automatically create and publish engaging content based on real-time feedback.

    Honestly, what makes this worth experimenting with is the potential for hyper-automation. We’re talking about building systems that can adapt and evolve based on the context they’re operating in. It’s about unlocking a new level of efficiency and innovation, and that’s something I’m definitely keen to explore further in my own projects.

  • I gave AI full control over my database (postgres.new)



    Date: 05/03/2025

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    Okay, this database.build (formerly postgres.new) video is seriously inspiring for anyone diving into AI-assisted development. It’s essentially a fully functional Postgres sandbox right in your browser, complete with AI smarts to help you generate SQL, build database diagrams, and even import CSVs to create tables on the fly. Think about it: no more local setup headaches, just instant database prototyping!

    Why is this a big deal for us? Well, imagine quickly mocking up a data model for a new Laravel feature without firing up Docker or dealing with migrations manually. The AI assistance could be a huge time-saver for generating boilerplate SQL or even suggesting schema optimizations. Plus, the built-in charting and reporting features could be invaluable for rapidly visualizing data and presenting insights to clients before even writing a single line of PHP. This kind of rapid prototyping and iteration is exactly where I see the biggest wins with AI and no-code tools.

    Frankly, the idea of spinning up a database, generating a data model, and visualizing some key metrics all within a browser in a matter of minutes is incredibly powerful. It’s like having a supercharged scratchpad for database design. I’m definitely experimenting with using this to brainstorm new application features and generate initial database schemas way faster than I could before. Definitely worth a look!

  • We improved Supabase AI … A lot!



    Date: 05/02/2025

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    Okay, so this video with the Supabase AI assistant, where “John” builds a Slack clone using only AI prompts, is seriously inspiring. It’s a clear demonstration of how far AI-assisted development has come. We’re talking about things like schema generation, SQL debugging, bulk updates, even charting – all driven by natural language. For someone like me who’s been wrestling with SQL and database design for ages, the idea of offloading that work to an AI while I focus on the higher-level logic is a game-changer.

    What really stands out is seeing these AI tools applied to a practical scenario. Instead of just theoretical possibilities, you’re watching someone build something real – a Slack clone. Think about the implications: instead of spending hours crafting complex SQL queries for data migrations, you could describe the desired transformation in plain English and let the AI handle the syntax. Or imagine generating different chart types to visualize database performance with a single prompt! This isn’t just about saving time; it’s about unlocking a level of agility and experimentation that was previously out of reach.

    Honestly, seeing this makes me want to dive in and experiment with Supabase’s AI assistant ASAP. I can envision using it to rapidly prototype new features, explore different data models, and even automate tedious database administration tasks. Plus, debugging SQL is one of those tasks that every developer loves to hate. I really recommend giving it a try, because you’ll start to notice other tasks you could offload. It feels like we’re finally getting to a point where AI isn’t just a buzzword, but a genuine force multiplier for developers.

  • 3 new things you can do with SupaCharged Edge Functions



    Date: 05/02/2025

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    Okay, this Supabase Functions v3 video is seriously inspiring for anyone diving into AI-powered development, especially with LLMs. It’s not just about “new features,” it’s about unlocking practical workflows. The demo shows how to proxy WebSocket connections through a Supabase Edge Function to OpenAI’s Realtime API (key protection!), and how to handle large file uploads using temporary storage with background processing. Imagine zipping up a bunch of vector embeddings and sending them off for processing.

    Why is this gold for us? Well, think about securing API keys when integrating with LLMs – the WebSocket proxy is a game-changer. It’s all about building secure, scalable AI-driven features without exposing sensitive credentials directly in the client-side code. Plus, offloading heavy tasks like processing large files (mentioned in the video) to background tasks is crucial for maintaining a responsive user experience. This helps when dealing with massive datasets for training or fine-tuning models. It’s literally the type of thing that helps to scale.

    The potential here is huge. Imagine building a real-time translation app powered by OpenAI, or an automated document processing pipeline that extracts key information and stores it in your database, triggered by a file upload. Supabase is leveling up its functions to compete with the big players. It’s time to get our hands dirty experimenting with these features – the combination of secure API access, background tasks, and temporary storage feels like a major step forward in building robust AI applications that are both secure and scalable. I am now adding “rebuild my OpenAI Slack bot using Supabase Functions v3” to my project list.

  • Manage secrets and query third-party APIs from Postgres



    Date: 05/02/2025

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    This Supabase video about Foreign Data Wrappers (FDW) is a game-changer for any developer looking to streamline their data workflows. In essence, it shows you how to directly query live Stripe data from your Supabase Postgres database using FDWs and securely manage your Stripe API keys using Supabase Vault. Why is this so cool? Imagine being able to run SQL aggregates directly on your Stripe data without having to build and maintain separate ETL pipelines!

    For someone like me who’s been diving deep into AI-enhanced workflows, this video is pure gold. It bridges the gap between complex data silos and gives you the power to access and manipulate that data right within your existing database environment. Think about the possibilities for building automated reporting dashboards, triggering custom logic based on real-time Stripe events, or even training machine learning models with up-to-date financial data. Plus, the integration with Supabase Vault ensures that your API keys are securely managed, which is paramount in any data-driven application.

    This approach could revolutionize how we handle real-world development and automation tasks. Instead of writing custom code to fetch and process data from external APIs, you can simply use SQL. And, let’s be honest, who doesn’t love writing SQL? I’m definitely going to experiment with this. The time saved by not having to build separate data integration pipelines and increased agility from having direct access to Stripe data within Postgres are huge wins!

  • Suna: FULLY FREE Manus Alternative with UI! Generalist AI Agent! (Opensource)



    Date: 05/01/2025

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    Okay, so this video introduces Suna AI, which is pitched as an open-source, fully local AI agent. It’s positioned as a direct competitor to commercial offerings like Manus and GenSpark AI, but with the significant advantages of being free and having a clean, ready-to-use UI. The video walks through setting it up with Docker, Supabase (for the backend), and integrating LLM APIs like Anthropic Claude via LiteLLM. It even covers how to use Daytona for easier environment provisioning, which is super helpful.

    Why is this interesting for us as developers moving into AI-enhanced workflows? Well, the promise of a powerful, fully local AI agent is huge. I’ve been increasingly focused on bringing AI capabilities closer to the metal for better control, privacy, and cost efficiency. Suna AI seems to tick all those boxes. Imagine having an AI assistant that you can tweak, customize, and integrate deeply into your existing systems without relying on external APIs or worrying about data privacy. Plus, the video highlights real-world use cases like data analysis and research, which are exactly the kind of tasks I’m looking to automate and improve.

    For me, the biggest draw is the control and flexibility. I’m tired of being locked into proprietary platforms with limited customization options. The idea of having a fully local, open-source AI agent that I can mold to my specific needs is incredibly appealing. Experimenting with Suna could lead to creating custom tools for code generation, automated testing, or even client communication. It’s definitely worth checking out and seeing how it can fit into my AI-enhanced development workflow.