YouTube Videos I want to try to implement!

Date: 03/05/2025

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Okay, this video sounds right up my alley! It’s all about using LLMs and Cursor (the IDE) to streamline the creation of Model Context Protocol (MCP) components, leveraging project-specific rules, GitHub’s MCP workflow, and standard git flow. It touches on using tools like `chunkify-openapi.lovable.app` for managing OpenAPI specs, which is a common pain point. Basically, it’s a practical demonstration of how to use AI to automate the creation of reusable, context-aware components.

For someone like me, knee-deep in the transition to AI-assisted coding, this is gold. It directly addresses the challenge of integrating LLMs into existing development workflows. The use of Cursor rules, as shared by @BMadCode, adds a layer of automation that goes beyond simple code completion. It’s about enforcing project standards *while* leveraging AI, and that’s huge. Seeing the MCP workflow and git flow integrated with AI coding is also key, maintaining version control and collaboration while ramping up your automated code creation.

The real-world application is clear: faster development cycles, more consistent code, and less time spent on boilerplate. The example of chunking OpenAPI specs highlights a very practical use case. Imagine using this approach to generate API clients, documentation, or even test cases – all driven by the spec and LLMs. I’m particularly excited to experiment with integrating these techniques into my Laravel projects. Defining project rules and then letting the LLM assist with component creation, seems like it could drastically cut down on development time. Definitely worth a try!

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?

Date: 03/05/2025

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Okay, this video looks *incredibly* useful for anyone, like me, diving headfirst into AI-powered workflows. It’s about building an AI chatbot that can answer questions about data from a Google Sheet, but instead of the typical vector database approach, it uses PostgreSQL and dynamic SQL queries. This is huge because, as the video points out, vector databases aren’t always the best for numerical analysis. Think of it as moving from “fuzzy matching” to precise calculations – a real game-changer for structured data!

What’s exciting is that this workflow can be a real-world problem solver. Imagine using it to automate financial reporting, inventory management, or even customer analytics dashboards. Instead of manually querying databases and generating reports, an AI assistant can do it for you on demand. The video even touches on system prompting, which is key to making AI generate accurate and relevant SQL. I can immediately see how this applies to my clients, who are always asking how to turn raw data into actionable insights, faster.

Honestly, the fact that this is a “work in progress” makes it even more appealing. It’s not a polished, “magic bullet” solution, but a foundation you can build upon. The creator admits there’s room for improvement, especially in database updates, which is a great opportunity to experiment and contribute. This is exactly the kind of hands-on, practical example that motivates me to ditch my old habits and start leveraging AI to build smarter, more efficient applications. I’m definitely checking this out and plan to adapt it in the coming days.

Date: 03/05/2025

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Okay, so this video dives into Microsoft’s new Phi-4 family, specifically the Mini and the multimodal 5.6B model. It’s not just another model announcement; the video gets practical, demonstrating function calling, quantized model deployment, and even a multimodal demo. For someone like me, actively integrating AI into existing Laravel/PHP workflows, this is gold. We’re talking about moving beyond simple text generation to building applications that can *reason* and *interact* with the real world via images.

Why is this valuable? Because it showcases how these smaller, specialized models are becoming increasingly powerful and accessible. The Phi-4 family isn’t just another LLM; it’s designed for efficiency and targeted tasks. The video shows how to deploy these models, potentially on lower-powered hardware, which is a huge win for cost-effective solutions. Plus, the multimodal aspect means we can start building truly integrated applications that can “see” and “understand” images alongside text – imagine automating content moderation or enhancing e-commerce experiences with image analysis, right within our existing applications!

Honestly, the function calling demo alone is worth the watch. It’s the key to building agents that can interact with APIs and external tools. This kind of practical example bridges the gap between theoretical AI and real-world application development. I’m definitely going to experiment with the quantized deployment techniques; that could be a game-changer for performance in our production environments. It’s all about finding the right tool for the job and Phi-4 looks like a serious contender for many AI-powered features we’re looking to add.

Date: 03/05/2025

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Alright, so this video is pure gold for anyone trying to blend traditional dev with this new wave of AI tools. It’s all about using Flowise, a low-code platform, to build chat widgets powered by LLMs, specifically for RAG systems. The real kicker, though, is the deep dive into fixing those dreaded CORS errors when you’re trying to deploy these widgets. We’ve *all* been there, right? You’ve got your awesome widget all set, then BAM! Cross-Origin Request Blocked. Nightmare.

What makes this video inspiring is its practical approach. It’s not just theory; it’s a real-world solution using Coolify and a Docker proxy to bypass those CORS restrictions. You could even use Nginx. This is huge because it demonstrates how to take a powerful tool like Flowise and actually get it working in a production environment. Plus, the video highlights Flowise’s features like starter prompts, speech-to-text, and even file uploads, which really levels up the chat experience and ties back to some key features of a RAG system. I am a big proponent of N8N, but even I can see the simplicity in this approach.

For me, this is more than just a tutorial; it’s a roadmap for leveraging no-code tools without sacrificing control and customization. The video even touches on self-hosting and cost savings by moving from platforms like Digital Ocean to Hetzner, which aligns perfectly with the lean, efficient workflows I’m always striving for. It’s definitely got me thinking about how I can incorporate Flowise and Coolify into my projects to streamline the creation of AI-powered chat interfaces. I’m particularly excited about the potential for automating customer support and lead generation, and the CORS fix alone is worth its weight in gold. Time to experiment!

Date: 03/04/2025

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Okay, so this Fireship video on 10 unusual uses for PostgreSQL? It’s gold for us right now. As developers diving into AI-assisted workflows, we’re not just trying to automate code generation; we’re aiming for *smarter* solutions. This video basically shows you how Postgres can be way more than just a place to store data. We’re talking about using it for things like background jobs, message queues, and even simple APIs – stuff you might traditionally reach for Redis or dedicated services for.

Why is this so valuable? Because understanding these advanced Postgres capabilities opens doors for more efficient automation. Imagine using LLMs to generate SQL queries that leverage these unusual features. You could build complex data pipelines or event-driven systems with significantly less custom code. The video’s “build a fullstack app with Postgres” section is particularly interesting. By deeply understanding SQL and database-level logic, we can train AI models to generate optimized database interactions, abstracting away boilerplate and improving application performance.

For me, this video is inspiring because it challenges the traditional “code first, database later” mindset. It hints at a future where the database itself becomes a more active participant in application logic. It’s worth experimenting with because, even if you don’t use *all* 10 tips, mastering even a few can unlock new possibilities for AI-driven development and automation, making our workflows leaner and more powerful. Plus, Neon’s free tier making it easier than ever to spin up Postgres databases is a total win!

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.

Date: 03/04/2025

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Okay, this video showcasing Simon’s “Founder Stack” is super relevant to where a lot of us are headed. Essentially, he’s built a comprehensive software portfolio using open-source and self-hosted tools like Strapi, NocoDB, Plane, and n8n, glued together with a bit of AI from Deepseek and Hugging Face. It’s about owning your data and infrastructure while still leveraging powerful AI capabilities – a sweet spot for developers like me who are tired of vendor lock-in but also want to automate everything.

The value here is seeing how these different pieces can fit together in a real-world SaaS context. We’re talking about a complete system, from project management with Plane to data visualization with Grafana, all underpinned by scalable, self-hosted solutions. For someone transitioning to AI coding, the integration of Deepseek for AI tasks is particularly interesting. Imagine automating code reviews, generating documentation, or even building out entire features using AI models trained on your own data within this stack. That’s powerful stuff!

This video is definitely worth a look because it provides a tangible blueprint. It’s not just about individual tools, but about a holistic approach to building and managing a SaaS business. I’m personally keen to experiment with the Deepseek integration. I envision using it to automate repetitive coding tasks and free up my time for more creative problem-solving. Plus, the self-hosted aspect gives you full control and avoids those pesky monthly subscription fees that can quickly add up. It’s a playground for AI-enhanced automation and well worth exploring.

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!

Date: 03/03/2025

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Okay, so this video walks you through getting Corcelio/mobius running locally to generate images from text. Why is that cool for us as PHP/Laravel devs starting to dabble in AI? Well, think about it: we’re always looking for ways to automate content creation, right? Imagine building a feature where your Laravel app automatically generates product images based on descriptions or creates unique blog post headers on the fly. That’s the kind of automation we’re aiming for!

The real value here isn’t just generating cool pictures. It’s about understanding how to integrate these AI models into our existing workflows. Getting hands-on with local installations lets us tweak parameters, experiment with prompts, and truly understand the possibilities (and limitations) of these tools. Plus, local control means no reliance on external APIs for basic tasks, giving you data privacy.

Honestly, it’s worth checking out just to demystify the process. Even if you don’t plan on becoming a full-time AI artist, understanding how these models work under the hood will give you a huge leg up in finding creative automation solutions for your projects. I am personally going to dive in and try to create automated blog post images and unique feature snippets based on user inputted text. That’s the future, and we need to be ready for it!