Try@DailyAi.Studio

YouTube Videos I want to try to implement!

  • New Gemini’s screen Analysis is insane for Automation



    Date: 06/25/2025

    Watch the Video

    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!

  • n8n Just Leveled Up AI Agents (Cohere Reranker)



    Date: 06/25/2025

    Watch the Video

    Okay, this video is a goldmine for anyone like me who’s knee-deep in integrating LLMs into their workflows using no-code tools like n8n. It’s all about boosting the accuracy of your AI agents by using Cohere’s re-ranker within n8n to refine the results from your vector store. The video clearly explains the value of re-ranking – that it’s a vital step to refine initial search results and how it complements vector search, and then walks you through setting it up and working around the limitations. For me, it’s exciting because it moves beyond the basic RAG implementation by incorporating hybrid search and metadata filtering.

    Why is this video so valuable? Because it directly addresses a key challenge in real-world RAG systems: getting relevant, high-quality answers. I’ve often found the initial results from vector databases to be noisy, full of irrelevant information, or just not quite what I’m looking for. Re-ranking acts like a final filter, ensuring only the most relevant content gets passed to the LLM, dramatically improving the quality of the generated responses. Think of it as upgrading from a standard search engine to one that really understands the context of your query.

    The real-world applications are huge. Imagine using this in customer support automation, internal knowledge bases, or even content generation. Instead of sifting through piles of documents or getting generic answers, you can deliver precise, context-aware information quickly. I’m personally eager to experiment with this to improve the accuracy of a document summarization workflow I’m building for a client. For me, the fact that it’s all happening within n8n, a tool I already use extensively, makes it super accessible and worth the time to implement. Seeing the practical examples with Supabase really seals the deal – it’s time to level up my RAG game!

  • I Lost $120k, Then Made $1 Million with This SaaS Idea…



    Date: 06/22/2025

    Watch the Video

    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.

  • 25 Hidden n8n Features That Save Hours of Work



    Date: 06/22/2025

    Watch the Video

    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?

  • I was wrong about Claude Code (UPDATED AI workflow tutorial)



    Date: 06/22/2025

    Watch the Video

    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?

  • I was wrong about Claude Code (UPDATED AI workflow tutorial)



    Date: 06/22/2025

    Watch the Video

    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.

  • New AI video editor, Bytedance’s VEO, new top 3D generator, new open-source AI beats DeepSeek



    Date: 06/22/2025

    Watch the Video

    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.

  • Cursor VS Claude Code: The Winner



    Date: 06/21/2025

    Watch the Video

    Okay, so this video is a head-to-head comparison of Claude Code and Cursor AI, two AI-powered tools that aim to drastically reduce the amount of traditional coding you need to do. The creator walks you through building a full-stack micro SaaS app using Claude Code and constantly compares the experience to using Cursor AI, which they use more often. It’s a practical look at how these tools can help you ship ideas faster.

    As someone diving deeper into AI coding and no-code, this video is gold. It’s not just theoretical; it shows you a real build process. We get to see the strengths and weaknesses of each platform regarding things like setup, command structures, troubleshooting, and even agentic workflows. I found the comparison especially useful because I’ve been juggling similar choices – should I stick with what I know (similar to Cursor) or invest time in learning something like Claude Code? The video also touches on the practical stuff, like costs and how to integrate these tools into your existing workflow, like setting up a Product Requirements Document (PRD) for better AI guidance.

    What makes this worth experimenting with is that it directly addresses the question, “Which of these tools will actually help me build something?” It goes beyond just demos and into real-world application. Seeing someone build a micro SaaS, showcasing how to use seed prompts, plan mode, and even leverage web searches within the workflow, gives you a concrete idea of what’s possible. Plus, the discussion around the tool’s memory and using Minimum Viable Components (MCPs) is super insightful for structuring complex projects. Honestly, it’s inspiring to see how much of the development process can be augmented, and sometimes even replaced, with these AI tools, pushing us towards faster iterations and reduced development time.

  • n8n Can Now Browse the Web Like a Human with Airtop



    Date: 06/21/2025

    Watch the Video

    Okay, this Airtop + n8n integration video is seriously inspiring! It’s all about using Airtop’s no-code web scraping, driven by plain English commands, directly within n8n workflows. Forget wrestling with complex selectors and brittle DOM structures – Airtop lets you define what you want to scrape in natural language, and then you bring that action into n8n for automation. They even have AI Agent versions of these nodes, so you could give your AI agent the power to scrape dynamic web pages. It’s a game changer for data extraction and workflow automation.

    For me, this hits the sweet spot of blending no-code ease with the power of a workflow engine. We’re talking about rapidly building integrations that used to take hours, if not days, to code manually. Think about automating lead generation by scraping social media profiles or gathering product information from e-commerce sites. Plus, the move to AI Agent tool versions means these workflows are even more adaptable and intelligent. It’s like giving my LLM projects eyes and hands to interact with the web.

    What really sells it is the idea of defining scraping actions in plain English. That’s a huge leap towards accessibility and maintainability. I’m already picturing how this could streamline some of our current data pipeline projects and potentially open up completely new automation possibilities. I’m definitely going to be experimenting with this to see how it stacks up against our current scraping solutions. The potential time savings and reduced maintenance alone make it worth the effort!

  • How to Build AI Agent Teams in Flowise (Step-by-Step)



    Date: 06/21/2025

    Watch the Video

    Okay, so this video is all about leveling up your Flowise game by building AI “teams” to tackle complex tasks. It walks you through setting up a supervisor system – think of it as a project manager AI – that coordinates specialized AI “workers,” like software engineers and code reviewers, all within Flowise. It dives deep into conditional routing, managing the flow’s state, and structuring outputs, using JSON and enums for validation. This enables your team of agents to hand off tasks and collaborate to solve bigger problems.

    Why is this inspiring? Because it’s the next step in moving beyond simple chatbot demos. For me, it’s about orchestrating multiple LLMs to handle entire development workflows. Imagine automating code generation, testing, and even deployment, all orchestrated by a Flowise supervisor. The video’s focus on structured output, conditional routing, and state management are key to building systems that are not just cool demos but are reliable and predictable, a challenge in the world of LLMs. You can take one task and break it down into a series of smaller more manageable tasks for each agent.

    Practically speaking, I can see this being used to automate a lot of tedious dev tasks. Think automated API creation, bug fixing based on error logs, or even generating documentation. The possibilities are huge, and the video gives you the tools to experiment and build something truly useful. I think it’s worth experimenting with because it showcases how to move from isolated LLM applications to orchestrated, collaborative systems. It really feels like the future of AI-assisted development.