YouTube Videos I want to try to implement!

Date: 06/27/2025

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Okay, so Runway just dropped an AI Game Engine, and honestly, it’s got me buzzing. This video is a walkthrough of their new “Game World” feature, letting you build and play text-based adventures using AI. Think Zork meets cutting-edge generative AI. You can create characters, navigate environments, and even generate images within the game, all driven by AI. The video highlights a pretty wild example – surviving a monster outbreak in a warehouse while fulfilling delivery orders! It’s a creative explosion waiting to happen.

For us developers diving into AI coding and no-code tools, this is huge. It’s a playground for LLM-based workflows. We can see how AI interprets prompts, generates narratives, and handles dynamic scenarios in real-time. Imagine using these principles to prototype interactive training simulations, automate customer service flows with dynamically generated content, or even build AI-powered storyboarding tools for filmmaking. The video specifically calls out the potential for making films from games which is cool.

What makes this video worth experimenting with? Simple: it’s tangible. It’s not just theory; it’s a real-world application of AI that sparks creativity. I’m already brainstorming how I could adapt this for generating interactive documentation or even prototyping game mechanics before diving into full code. Plus, the “Overnight Delivery” example alone is enough to get anyone’s creative juices flowing! I’m diving in and I suggest you do as well!

Date: 06/27/2025

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Okay, this video on Hybrid RAG is seriously inspiring stuff and totally worth checking out, especially if you’re like me and trying to level up your AI game. Basically, it dives into how to combine semantic (vector) search with keyword (sparse) search to build smarter, more accurate RAG (Retrieval-Augmented Generation) systems. Think about it – you’ve probably noticed that semantic search alone can stumble when you throw specific terms like “SKU-42” or a weird acronym at it. This video nails that pain point and shows you how to fix it!

The real value for us, the AI-curious developers, is in the practical implementations. The video walks you through setting up Hybrid RAG using both Supabase and Pinecone, and then integrates it all into an N8N workflow. That’s huge! Imagine building a customer support bot that can actually understand and retrieve the right information about specific products or technical issues because it’s not just relying on semantic similarity but also nailing those exact keyword matches.

I’m already thinking about how I can apply this to a project where we’re building an internal knowledge base. Before, we were struggling to get precise results for document retrieval based on specific software versions or error codes. With Hybrid RAG, we could finally get the best of both worlds – semantic understanding for general queries and keyword precision for those critical details. I am excited to try this because it makes the promise of AI-driven automation actually useful. Definitely adding this to my “to-experiment-with” list!

Date: 06/26/2025

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Okay, this video is gold for anyone like me who’s been knee-deep in trying to get AI agents to do some serious data fetching. It cuts right to the chase: your basic search tools inside these AI platforms? They’re kinda lame when it comes to actual web scraping. We’re talking simple Google searches, not real content extraction.

What makes this inspiring is the Bright Data MCP server and how it’s implemented inside Flowise. The video shows you exactly how to get past all the typical web scraping headaches—IP blocks, captchas, the works—and pull real-time data from anywhere. Think live product data from Amazon or snagging the latest OpenAI news. It’s not just about getting some data, it’s about getting the right data, reliably.

I can already see this being huge for automating things like competitive pricing analysis, real-time market research, and even dynamic content generation. Imagine feeding your AI agent live data and watching it adapt on the fly! It’s not just theory either, they show how to actually get it working in Flowise with live examples. Honestly, anything that can take the pain out of web scraping and pump data directly into my AI workflows is worth experimenting with. I’m adding this to my weekend project list right now!

Date: 06/25/2025

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Okay, this video is seriously inspiring if you’re like me and constantly looking for ways to level up your dev game with AI. In a nutshell, it shows how Gemini Pro 2.5 can analyze a video of you performing a task, then generate a script for Nanobrowser to automate that task in your browser. Think of it as turning your screen recording into a mini-automation engine.

The real value here, especially for those of us diving into AI-assisted workflows, is the low barrier to entry. Forget wrestling with complex no-code platforms like n8n or Make (which, don’t get me wrong, are powerful, but can be overkill sometimes). If you can record a video, you can potentially automate a process. Imagine onboarding new team members: instead of writing lengthy documentation, just record yourself going through the steps, and boom, an automated workflow is ready to go. Or think about automating repetitive tasks in your CMS, like content updates or image optimization.

Honestly, the “record and automate” concept is just too good to pass up. The idea of building automations from simple screen recordings, analyzed and scripted by Gemini, then executed inside the browser via Nanobrowser – it’s a workflow revolution. I’m already brainstorming how to use this for client demos, internal tool configurations, and even creating personalized training modules. Definitely worth setting aside an afternoon to experiment and see what’s possible!

Date: 06/22/2025

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Okay, so this video is about someone who initially threw a ton of money, $120k to be exact, at a new software idea, which ultimately didn’t pan out. But here’s the kicker – they learned from that experience, applied a bootstrapped, lean approach to their next SaaS idea, and ended up making over $1 million. That’s the kind of real-world lesson that resonates.

Why is this valuable for us as we’re diving into AI coding and no-code? Because it’s a reminder that technology isn’t a magic bullet. Sometimes, having all the fancy tools (or a huge budget) can distract you from the core problem you’re trying to solve. This video highlights the importance of starting small, validating your ideas, and iterating quickly – all things that are amplified when you leverage AI for rapid prototyping and development. Imagine using LLMs to generate initial code snippets, no-code tools to build out UIs rapidly, and then focusing your energy on fine-tuning and iterating based on real user feedback. We can avoid the trap of over-investing upfront in features nobody wants.

Think about it: Instead of sinking $120k into a fully-fledged, unvalidated product, imagine using AI to build a minimal viable product (MVP) for a fraction of the cost and time. You get to test your core assumptions, gather feedback, and pivot as needed. The video’s message of bootstrapping and learning from failure aligns perfectly with the iterative nature of AI-assisted development. It’s a worthwhile watch because it underscores the importance of smart experimentation and resourcefulness, which are even more critical in this rapidly evolving landscape. I am going to watch to find out what that first failed idea was, and what he did differently the second time.

Date: 06/22/2025

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Okay, so Chris is building productivity apps like Mogul, Ellie, Luna, and Lily, and in this video, he’s diving deep into his updated AI coding workflow using Claude Code. He explains why he switched from Cursor and shares his thoughts on the whole AI coding landscape. Crucially, he claims this new setup makes him 20x faster as a developer.

For those of us transitioning into AI-assisted development, this is gold! Chris outlines his 9-step Claude Code workflow and even provides concrete examples where Claude Code outperformed Cursor’s agents. He gets into the nitty-gritty of which model he’s using and explores the downsides of Claude Code – it’s not all sunshine and roses, apparently. He caps it off with who he thinks should be using it. The fact that he switched from one AI tool to another and provides a clear, step-by-step breakdown of his reasoning and workflow is super valuable.

This isn’t just theoretical; Chris is building real productivity apps! Imagine applying his workflow to automate tedious tasks in Laravel, generate boilerplate code, or even refactor legacy code. He’s essentially showing us how to leverage LLMs for a significant productivity boost. Honestly, the potential to 20x your output is reason enough to experiment! I’m eager to see how this integrates with my existing Laravel projects, especially with the promise of such a dramatic speed increase. Worth a try, right?

Date: 06/22/2025

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Okay, this video by Chris about his updated AI coding workflow using Claude Code is seriously inspiring, and here’s why. As someone neck-deep in transitioning to AI-enhanced development, seeing a fellow indie developer go all-in and achieve a “20x faster” speed boost is hard to ignore. The video dives into his 9-step workflow using Claude Code, explaining why he switched from Cursor, and highlighting instances where Claude Code outshone Cursor agents. We are talking real-world comparisons between different AI tools in a coder’s real workflow.

The real value lies in Chris’s practical approach. He doesn’t just hype up AI; he breaks down his exact workflow. The examples provided and the time-stamps make it easy to drill down into the most important sections. For someone like me, who’s actively looking for ways to integrate LLMs into Laravel and PHP projects, this is gold. Imagine automating the generation of complex Eloquent queries or scaffolding entire API endpoints with a few well-crafted prompts. I’m really interested in testing some of Chris’s examples in my day to day.

Ultimately, what makes this worth experimenting with is the promise of tangible productivity gains. Chris is upfront about the downsides, which keeps it real. It’s not about replacing developers, but about augmenting our abilities. The video is not just about coding, but about building apps and increasing productivity. Now, if I can carve out a couple of hours this week, I will definitely dive into the same approach.

Date: 06/22/2025

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Okay, so this video is a rapid-fire roundup of some seriously cool AI advancements. We’re talking about everything from 3D model generation (Hunyuan 3D 2.1, PartTracker) to video creation (Midjourney V1) and even AI that can understand and interact with humans in a more nuanced way (InterActHuman, POLARIS). There’s also some interesting stuff on prompt engineering and model editing (LoraEdit). It’s a lot to take in, but that’s what makes it so inspiring.

For a developer like me, who’s been diving headfirst into AI-assisted workflows, this video is gold. It’s not just about flashy demos; it’s about seeing practical applications of these tools that could revolutionize how we build software. Imagine using Hunyuan 3D 2.1 to rapidly prototype 3D assets for a game, or leveraging LoraEdit to fine-tune a model for a specific client’s needs without retraining from scratch. And Midjourney V1 video? Think about creating engaging marketing materials or explainer videos in a fraction of the time. The possibilities for automation and faster development cycles are huge.

Honestly, what makes this video worth experimenting with is the sheer breadth of tools presented. It’s a reminder that the AI landscape is evolving at warp speed. While I might not use every single tool showcased, it’s crucial to stay informed and explore how these advancements can be integrated into my existing Laravel and PHP projects. Plus, the resources and links provided offer a solid starting point for hands-on experimentation. Definitely adding a few of these to my “try this next” list.