Try@DailyAi.Studio

Category: Try

  • VEO-3 has a Secret Super Power!



    Date: 07/29/2025

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    Okay, so this video is all about leveling up your AI video game with visual prompting and Runway’s new “Aleph” model. Basically, instead of just typing prompts, you’re drawing and annotating directly on images to guide AI video generation. Think adding speech bubbles to characters or drawing motion paths for dragons to follow. It also gives you a sneak peek at Runway’s new in-context video model, Aleph, for video-to-video edits and object removal.

    This is gold for us developers diving into AI-enhanced workflows! We’re always looking for ways to get more granular control over AI tools, and visual prompting seems like a natural evolution. Imagine using this to prototype animations for a client, rapidly iterating on camera angles, or even generating complex visual effects with more precision.

    The coolest part? The video shows how to get started with free tools like Adobe Express. It’s a practical guide to experimenting with these cutting-edge techniques today. I’m particularly excited to explore how visual prompting can streamline the creation of marketing materials for my own projects, and even integrate it into some of the no-code automation workflows I’ve been building with tools like Zapier. Definetly time to start experimenting with visual prompting!

  • The BEST 10 n8n Apps Released in 2025 (I Wish I Knew Sooner)



    Date: 07/28/2025

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    Okay, so this video’s all about “Top 10 n8n Tools for 2025.” It gives a rundown of new nodes and apps to supercharge your n8n workflows, with a focus on AI tools. We’re talking things like integrating Google Gemini, AI voice with ElevenLabs, web scraping with Apify, and even AI-powered search using Perplexity. I’m seeing a lot of LLM integration, with things like Mistral and DeepSeek making an appearance too.

    Why’s it interesting for us? Because it’s a direct look at how AI is being plugged into no-code platforms like n8n. Instead of building everything from scratch in Laravel or PHP, you’re orchestrating these AI services. I can immediately see using this to automate marketing content generation, improve data enrichment processes, or even build more intelligent customer support flows. Think about it: automating lead qualification using AI to analyze social media profiles scraped with Apify, then generating personalized outreach emails using an LLM through n8n. Boom!

    I think what makes this video particularly worth checking out is how practical it is. It’s not just about the “what,” but the “how” of integrating these AI tools into your existing workflow. Seeing someone demonstrate how to connect these services in n8n sparks ideas for how I could apply them to projects I’m working on right now. Definitely giving this a watch and experimenting!

  • Building an AI Agent Swarm in n8n Just Got So Easy



    Date: 07/27/2025

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    Okay, this video is seriously inspiring because it tackles a challenge I’ve been wrestling with: how to build truly intelligent AI systems without getting bogged down in code. The creator demonstrates how to build an AI agent swarm using n8n, a no-code automation platform. The key is modularity. Instead of one giant, complex AI, you have a “parent” agent delegating tasks to specialized “sub-agents.” Think of it like a team of experts focused on their specific domains, all coordinated to solve a bigger problem.

    For developers like us transitioning into AI-enhanced workflows, this is gold! We’re constantly looking for ways to streamline development and improve accuracy. Agent swarms address both. By breaking down complex tasks, we reduce prompt bloat and increase context accuracy, which are major headaches when dealing with LLMs. Plus, the video highlights how n8n’s visual workflow makes debugging and iteration much faster. It really resonated with me; managing sprawling if/else trees in code feels like ancient history compared to this!

    The potential applications are huge. Imagine automating complex customer support flows, building sophisticated data analysis pipelines, or even creating self-optimizing marketing campaigns. What I find super exciting is that this isn’t just theory. The video provides resources to download and experiment with. I’m already thinking about how I can adapt this approach to my current project, which involves orchestrating multiple LLM calls for content generation. It’s definitely worth carving out some time to dive in and see how agent swarms can up our game.

  • Claude Code Agents: The Feature That Changes Everything



    Date: 07/26/2025

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    Okay, so this video about Claude Code’s new agents feature is seriously exciting for anyone diving into AI-enhanced workflows. Basically, it’s a deep dive into how you can build custom AI agents (think souped-up GPTs) within Claude Code and chain them together to automate complex tasks. The video shows you how to build one from scratch with the dice roller and then it ramps up. I am now using that YouTube outline workflow myself!

    Why is this valuable? Well, for me, the biggest draw is the ability to automate multi-step processes. Instead of just using an LLM for a single task, you’re creating mini-AI workflows that pass information between each other. The video nails the importance of clear descriptions for agents. It’s so true–the more precise you are, the better the agent will perform. This directly translates into real-world scenarios like automating code reviews, generating documentation, or even building CI/CD pipelines where each agent handles a specific stage.

    Honestly, what makes this video worth checking out is the practical, hands-on approach. Seeing the presenter build an agent from scratch and then apply it to something completely outside of coding (like video outlining) is inspiring. It highlights the versatility of these AI tools and hints at the potential for truly transforming how we work. I’m going to explore how I can use these agents to help automate new feature implementations and it will be a game changer.

  • Google Just Released an AI App Builder (No Code)



    Date: 07/25/2025

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    Okay, so this video is packed with exactly the kind of stuff I’ve been geeking out over: rapid AI app development, smarter AI agents, and AI integration within creative workflows. Basically, it’s a rundown of how Google’s Opal lets you whip up mini AI apps using natural language – like, describing an AI thumbnail maker and bam, it exists! Plus, the video dives into how ChatGPT Agents can actually find practical solutions, like scoring cheaper flights (seriously, $1100 savings!). And Adobe Firefly becoming this AI-powered creative hub? Yes, please!

    Why is this gold for a developer transitioning to AI? Because it showcases tangible examples of how we can drastically cut down development time and leverage AI for problem-solving. Imagine automating routine tasks or creating internal tools without writing mountains of code. The idea of building a YouTube-to-blog post converter with Opal in minutes? That’s the kind of automation that could free up serious time for more complex challenges. It’s not about replacing code, it’s about augmenting it.

    What really makes this worth a shot is the sheer speed and accessibility demonstrated. The old way of doing things involved weeks of coding, testing, and debugging. Now, we’re talking about creating functional apps in the time it takes to grab a coffee. This is about rapid prototyping, fast iteration, and empowering anyone to build AI-driven solutions. It’s inspiring and something I will be exploring myself.

  • We made Supabase Auth way faster!



    Date: 07/25/2025

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    Okay, this video on Supabase JWT signing keys is definitely worth checking out, especially if you’re like me and trying to level up your development game with AI and automation. In a nutshell, it shows how to switch your Supabase project to use asymmetric JWTs with signing keys, letting you validate user JWTs client-side instead of hitting the Supabase Auth server every time. The demo uses a Next.js app as an example, refactoring the code to use getClaims instead of getUser and walking through enabling the feature and migrating API keys. It also touches on key rotation and revocation.

    Why is this so relevant for us? Well, imagine you’re building an AI-powered app that relies heavily on user authentication. Validating JWTs server-side becomes a bottleneck, impacting performance. This video provides a clear path to eliminating that bottleneck. We can use this approach not only for web apps but also adapt it for serverless functions or even integrate it into our AI agents to verify user identity and permissions locally. It will help improve performance and reduce dependence on external services, and in turn that will speed up our entire development/deployment cycles.

    What I find particularly exciting is the potential for automation. The video mentions a single command to bootstrap a Next.js app with JWT signing keys. Think about integrating this into your CI/CD pipeline or using an LLM to generate the necessary code snippets for other frameworks. Faster authentication means faster feedback loops for users, and less dependency on external validation. It’s a small change that can yield huge performance and efficiency gains, and that makes it absolutely worth experimenting with.

  • ChatGPT Agent Just Went Public—Here’s My Honest Reaction



    Date: 07/25/2025

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    Okay, this ChatGPT Agent video is a must-watch if you’re trying to figure out how to integrate AI into your development workflow. The presenter puts the new Agent through a real-world gauntlet of tasks—from researching projectors to planning trips and even curating a movie newsletter. It’s a fantastic overview of what’s possible (and what isn’t) with this new tool.

    What makes this so valuable is seeing the ChatGPT Agent tackle problems that many of us face daily. Think about automating research for project requirements, generating initial drafts of documentation, or even scripting out basic user flows. Watching the Agent struggle with some tasks while excelling at others gives you a realistic expectation of what it can do. We could potentially use this for automating API research or generating boilerplate code based on specific requirements.

    What really excites me is the potential for no-code/low-code integrations using the Agent. Imagine feeding it user stories and having it generate a basic prototype in a tool like Bubble or Webflow. The possibilities are endless, but it’s crucial to understand its limitations, which this video clearly highlights. I’m definitely going to experiment with this—if nothing else, to save myself a few hours of tedious research each week!

  • This One Fix Made Our RAG Agents 10x Better (n8n)



    Date: 07/23/2025

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    Okay, so this video is all about turbocharging your RAG (Retrieval Augmented Generation) agents in n8n using a deceptively simple trick: proper markdown chunking. Instead of just splitting text willy-nilly by characters, it guides you on structuring your data by markdown headings before you vectorize it. Turns out, the default settings in n8n can be misleading and cause your chunks to be garbage. It also covers converting various formats like Google Docs, PDFs, and HTML into markdown so that you can process them.

    For someone like me, neck-deep in the AI coding revolution, this is gold. I’ve been wrestling with getting my LLM-powered workflows to produce actually relevant and coherent results. The video highlights how crucial it is to feed your LLMs well-structured information. The markdown chunking approach ensures that the context stays intact, which directly translates to better answers from my AI agents. I can immediately see this applying to things like document summarization, chatbot knowledge bases, and even code generation tasks where preserving the logical structure is paramount. Imagine using this for auto-generating API documentation from a codebase!

    Honestly, the fact that a 10-second fix can dramatically improve RAG performance is incredibly inspiring. It’s a reminder that even in the age of complex AI models, the fundamentals – like data preparation – still reign supreme. I’m definitely diving in and experimenting with this; even if it saves me from one instance of debugging nonsensical LLM output, it’ll be worth it!

  • Qwen 3 2507: NEW Opensource LLM KING! NEW CODER! Beats Opus 4, Kimi K2, and GPT-4.1 (Fully Tested)



    Date: 07/22/2025

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    Alright, so this video is all about Alibaba’s new open-source LLM, Qwen 3-235B-A22B-2507. It’s a massive model with 235 billion parameters, and the video pits it against some heavy hitters like GPT-4.1, Claude Opus, and Kimi K2, focusing on its agentic capabilities and long-context handling. Think of it as a deep dive into the current state of the art in open-source LLMs.

    For someone like me, who’s knee-deep in exploring AI-powered workflows, this video is gold. It’s not just about the hype; it’s about seeing how these models perform in practical scenarios like tool use, reasoning, and planning—all crucial for building truly automated systems. Plus, the video touches on the removal of “hybrid thinking mode,” which is fascinating because it highlights the trade-offs and challenges in designing these complex AI systems. Knowing Qwen handles a 256K token context is a game changer when thinking about the possibilities around document processing and advanced AI workflows.

    What makes it worth experimenting with? Well, the fact that you can try it out on Hugging Face or even run it locally is huge. This isn’t just theoretical; we can get our hands dirty and see how it performs in our own projects, maybe integrate it into a Laravel application or use it to automate some of those tedious tasks we’ve been putting off. For example, could it write better tests that I keep putting off or, even better, is it capable of self-debugging and auto-fixing things? I’m definitely going to be diving into this one.

  • Google Veo 3 For AI Filmmaking – Consistent Characters, Environments And Dialogue



    Date: 07/21/2025

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    Okay, this VEO 3 video looks incredibly inspiring for anyone diving into AI-powered development, especially if you’re like me and exploring the convergence of AI coding, no-code tools, and LLM-based workflows. It basically unlocks the ability to create short films with custom characters that speaks custom dialogue, leveraging Google’s new image-to-video tech to bring still images to life with lip-synced audio and sound effects. Talk about a game changer!

    The video is valuable because it’s not just a dry tutorial; it demonstrates a whole AI filmmaking process. It goes deep on how to use VEO 3’s new features, but also showcases how to pair it with other AI tools like Runway References for visual consistency, Elevenlabs for voice control (I have been struggling to find a good tool), Heygen for translation, Suno for soundtracks, and even Kling for VFX. The presenter also gives great prompting tips and also some cost savings ideas (a big deal!). This multi-tool approach is exactly where I see the future of development and automation going. It is about combining the best of breed tools to create new workflows and save time and money.

    For example, imagine using VEO 3 and Elevenlabs to quickly prototype interactive training modules with personalized character dialogues. Or, think about automating marketing video creation by generating visuals with VEO 3, sound effects with Elevenlabs and translating them into multiple languages. What I found to be very interesting is how it can be used to create story boarding content quickly. The possibilities are endless! I’m genuinely excited to experiment with this workflow because it bridges the gap between traditional filmmaking and AI-driven content creation. I am especially interested to see how the presenter created the short film, Hotrod. I want to see if I can create something similar.