YouTube Videos I want to try to implement!

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?

Date: 04/01/2025

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Okay, this Supabase UI Library video is exactly the kind of thing I’m geeking out about these days. It’s all about pre-built UI components – authentication, realtime collaboration, file uploads, even AI-powered coding rules integrated directly into your Supabase workflow. Forget spending hours building basic UI elements from scratch; this library lets you drag-and-drop your way to a functional app, which frees up time to focus on the actual innovative parts of your project. As someone knee-deep in AI coding and no-code solutions, this resonates big time!

Why is this valuable for developers moving into the AI/no-code space? Well, think about it: we’re trying to offload the repetitive tasks to AI and automation so we can focus on architectural design and complex logic. This library does the same thing for the front-end. For instance, instead of hand-coding a file upload feature, you drop in a pre-built component and spend your time integrating it with, say, an LLM to automatically tag and categorize the uploaded files. That’s real-world automation powered by AI, and this UI library is the perfect jumping-off point.

Honestly, the AI Rules feature alone makes this worth experimenting with. The video hints at using AI to guide code quality, which is HUGE. Imagine integrating that with existing LLM workflows to generate code that’s not only functional but also adheres to best practices. This is the sweet spot where AI enhances, not replaces, our coding, and it’s why I’m planning to spend some serious time playing with this Supabase UI Library. Plus, anything that helps me “ship faster” gets a gold star in my book!

Date: 03/27/2025

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Okay, this video is gold for anyone trying to bridge the gap between traditional coding and AI-powered automation. It’s all about using Claude 3.7 to generate n8n workflows, JSON templates, and even sticky notes directly from screenshots or YouTube transcripts. Forget manually building everything from scratch – this video shows you how to literally “show” the AI what you want, and it generates the necessary code and documentation. Pretty wild, right?

Why is this a game-changer? Well, for me, it’s about speed and accessibility. I’ve spent countless hours tweaking n8n workflows, and the idea of just uploading a screenshot and getting a functional template in return is mind-blowing. Plus, the video highlights Claude’s “Extended Thinking” capabilities, which means the AI isn’t just mindlessly converting images to code; it’s actually understanding the logic and optimizing it. Imagine grabbing a workflow from a YouTube tutorial, pasting the transcript, and having Claude not only generate the workflow but also add helpful notes explaining each step. This is HUGE for learning and customization.

The practical applications are endless. Think onboarding new team members, rapidly prototyping automation ideas, or even reverse-engineering complex workflows you find online. It’s like having an AI coding assistant dedicated to streamlining your automation efforts. I’m definitely experimenting with this. I’m eager to see how it handles some of the more complex workflows I’ve built and how much time I can save on future projects. The potential to create custom templates without having to pay a fortune is seriously tempting.

Date: 03/26/2025

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Alright, this video is pure gold for anyone transitioning into AI-enhanced development. It’s all about setting up Anything LLM locally and building custom agents. We’re talking about running different LLMs, even optimizing for RTX GPUs, and diving into the world of private AI interaction. The video goes through step-by-step, showing how to configure custom agents and utilize their skills. Plus, it touches on the community hub and other useful tools.

Why is this valuable? Well, for us developers, local LLM setups mean data privacy and control, which is huge for sensitive projects. Building custom agents opens doors to automating complex tasks that previously required tons of manual coding. Imagine creating agents specialized for code review, documentation, or even refactoring. This aligns perfectly with incorporating AI into our workflows, streamlining development, and boosting productivity.

This kind of hands-on approach is inspiring because it bridges the gap between theoretical AI and practical application. The idea of running these tools locally, experimenting with different models, and tailoring agents to specific tasks? That’s something worth sinking your teeth into. It’s about taking control of the AI, making it work for you, and ultimately, building smarter, more efficient solutions. Definitely worth experimenting with to see what it can bring to your workflow.

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.

Date: 03/25/2025

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Okay, this Perplexica video looks seriously cool. It’s basically about an open-source, AI-powered search engine inspired by Perplexity AI, but you can self-host it! It uses things like similarity searching and embeddings, pulls results from SearxNG (privacy-focused!), and can even run local LLMs like Llama3 or Mixtral via Ollama. Plus, it has different “focus modes” for writing, academic search, YouTube, Wolfram Alpha, and even Reddit.

Why am I excited? Because this screams custom workflow potential. We’ve been hacking together similar stuff using the OpenAI API, but the thought of a self-hosted, focused search engine that I can integrate directly into our Laravel apps or no-code workflows is huge. Imagine a Laravel Nova panel where content creators can research articles by running Perplexica’s “writing assistant” mode, then import the results into their CMS. Or an internal knowledge base that leverages the “academic search” mode to keep employees up-to-date with the latest research. The privacy aspect is also a big win for clients who are sensitive about data.

Honestly, the biggest appeal is the control and customization. I’m already brainstorming how we could tweak the focus modes and integrate them with our existing LLM chains for even more targeted automation. The fact that it’s open source and supports local LLMs means we aren’t just relying on closed APIs anymore. I’m definitely earmarking some time this week to spin up a Perplexica instance and see how we can make it sing. Imagine the possibilities!

Date: 03/24/2025

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Okay, so this video is basically about building and monetizing a software portfolio, specifically using AI agents. Simon’s selling access to his FounderStack portfolio as a one-time purchase, and it looks like a great example of leveraging AI to create and launch multiple SaaS projects.

For someone like me diving into AI coding, no-code, and LLM workflows, this is gold. It’s inspiring because it showcases how we can shift from building one huge app to creating a suite of smaller, specialized tools. Think about it: using AI to rapidly prototype and launch mini-SaaS products that address niche needs. We could build AI-powered content generators, or specialized data analysis tools tailored to specific industries, and bundle them up in a portfolio.

The real-world application is huge. Instead of spending months on a single project, we could use LLMs to generate the boilerplate code, AI agents to automate testing and deployment, and no-code tools for the UI. This accelerates the entire development lifecycle. It’s worth experimenting with because it could dramatically reduce development costs and time to market, while also diversifying your income streams. I’m definitely grabbing FounderStack; seeing how Simon structures his portfolio and uses AI is a powerful motivator.

Date: 03/19/2025

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Okay, so this video by Jason Zhou showcases how to use Claude 3.7 (the new hotness!) to design beautiful UI, and then quickly translates that into a Next.js app. That’s exactly the kind of workflow I’ve been chasing lately. We’re talking about going from a conceptual design to a functional prototype with AI handling the heavy lifting on the UI code. Forget endless tweaking of CSS – imagine just describing what you want and having an LLM spit out something visually appealing and functional.

Why’s this valuable? Because it bridges the gap between the design phase and development. I’ve been using LLMs to generate API endpoints and backend logic, but the front-end has always been a bottleneck. If Claude 3.7 can genuinely generate clean, usable UI code based on simple prompts, that’s a massive time-saver. We can then spend less time on tedious front-end work and more time on the core business logic and user experience, which actually makes a difference.

Imagine using this for rapid prototyping. A client needs a dashboard? Instead of spending days wireframing and coding, you can use Claude to generate a few different UI options instantly. Then, iterate based on their feedback. Frankly, even if it only gets you 80% of the way there, that’s still a huge win. I’m going to give this a try myself; it aligns perfectly with my goals of integrating AI deeper into my development workflow. It might be the key to unlocking even faster development cycles and delivering more value, more quickly to my clients.

Date: 03/18/2025

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Okay, this video on SmolDocling is seriously inspiring, especially for someone like me who’s knee-deep in finding ways to blend AI into our Laravel development workflows. It’s essentially a deep dive into a new OCR model that promises to be more efficient and potentially more accurate than existing solutions. The video not only introduces the model but also links to the research paper, Hugging Face model, and a live demo.

What makes this valuable is its potential to automate document processing, a task that often bogs down many projects. Imagine being able to seamlessly extract data from invoices, contracts, or even scanned receipts directly into your Laravel applications. This could drastically reduce manual data entry and free up time for more complex tasks. For example, we could build an automated invoice processing system that uses SmolDocling to read invoices, and then automatically creates accounting records in our Laravel application.

It’s worth experimenting with because it seems to bridge the gap between cutting-edge AI and practical application. The demo allows for quick testing, and the provided resources give developers a solid foundation for integrating SmolDocling into their projects. Plus, exploring these kinds of tools could open up entirely new avenues for automation and efficiency gains. I’m personally excited to see how it stacks up against other OCR solutions and what kind of custom workflows we can build around it.

Date: 03/18/2025

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Okay, so this video is all about leveling up your AI-assisted coding with Cursor, focusing on how to effectively manage context and rules. It dives into setting up project-specific MCP (Model Context Protocol) servers and using nested rules to keep things organized and context-aware. Think of it as giving your AI a super-focused brain for each project.

Why is this valuable? As someone knee-deep in integrating AI into my workflow, the biggest pain point is always context. Generic AI assistance is okay, but project-specific knowledge is where the real magic happens. This video shows you how to segment your rules so that only the relevant ones load when you need them, saving valuable context window space. It also touches on generating a whole software development plan from a PRD (Product Requirements Document), which is HUGE for automation. I’ve been experimenting with similar workflows using other LLMs, and the ability to generate detailed plans from high-level requirements is a game-changer.

Imagine being able to spin up a new Laravel project and have Cursor automatically configure itself with all the necessary database connections, code style preferences, and even generate initial models and migrations based on your PRD. The video also mentions AquaVoice for dictation, further streamlining input, which, let’s be honest, is a task we all want to speed up. I’m going to give this a shot because the idea of having my AI coding assistant actually understand the nuances of each project is incredibly appealing. The GitHub repo provides the templates, making it a no-brainer to experiment with and customize to my own workflows. Worth a look!