Try@DailyAi.Studio

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  • Build an Open AG-UI Canvas with CopilotKit + Mastra



    Date: 09/23/2025

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    Okay, this video on integrating Mastra with CopilotKit and AG-UI is seriously inspiring! It walks through building a real-time interactive UI powered by LLM agents. Basically, Mastra handles the heavy lifting – reasoning, managing multiple LLMs, workflows, and RAG – while CopilotKit and AG-UI take that agent output and turn it into a dynamic interface.

    Why’s it valuable? Because it showcases a practical way to orchestrate complex LLM interactions and present them in a user-friendly way. We’re talking about moving beyond simple chatbots and into building full-fledged AI-powered applications. Think about automating complex workflows with a visual interface, allowing users to guide and refine the process in real-time. It gets us closer to building real AI assistants that truly augment user capabilities.

    This video’s a must-watch because it’s not just theory. It’s a tangible example of how we can leverage LLMs, no-code UI components, and AI orchestration tools to build genuinely useful applications. I’m excited to experiment with this stack and see how it can streamline my development process and unlock new possibilities for client projects. Anything that makes this process easier is gold!

  • The Future of AI and SaaS is Agentic Experiences (Here’s How to Build Them)



    Date: 09/23/2025

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    Okay, this video is seriously inspiring because it’s all about moving beyond the “AI agent as a standalone product” hype and integrating agents directly into our existing applications. We’re talking about making AI a seamless part of the user experience, and AG-UI is the protocol that standardizes how AI agents connect to apps. Think of it as the common language that lets different AI frameworks and frontends (like CopilotKit and Pydantic AI) talk to each other.

    For someone like me who’s been diving headfirst into AI-enhanced workflows, this is HUGE. I’m tired of the “AI bolted on as an afterthought” approach. This video shows how to embed AI deeply into your application’s DNA. The video demonstrates a practical tech stack: AG-UI, Pydantic AI, and CopilotKit, showing how they work together to build agentic experiences. Plus, the presenter shares links to a GitHub repo, AG-UI demos, and Pydantic AI docs, which means you have everything you need to replicate the project.

    The idea of building a RAG agent app with AG-UI, as shown in the video, really resonates. Imagine being able to add intelligent, context-aware features to your Laravel app without completely rewriting everything. That’s the promise here. The section on the principles of agentic experiences (14:34) is also a must-watch. I’m definitely going to be experimenting with this stack; the potential to create truly intelligent and user-friendly applications is too exciting to ignore! Plus, standardizing agent integration with AG-UI feels like a critical step toward maintainable and scalable AI-powered applications.

  • Notion Agent Just Changed Notion Forever. Hello AgentOS!



    Date: 09/22/2025

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    Okay, so this video is all about Notion 3.0 and “Notion Agents,” which the creator, Simon, believes will fundamentally change how we use Notion. He demos his “AgentOS” template, showing how to personalize and use these agents, and teases the new features announced at “Make With Notion 2025.” Essentially, it’s about leveraging AI inside Notion to automate knowledge work.

    As someone knee-deep in exploring AI-powered workflows, this is super valuable. Think about it: we’re constantly trying to bridge the gap between our code, our data, and our documentation. Notion Agents could be the glue, allowing us to build LLM-driven workflows directly within our knowledge base. Imagine automating documentation updates based on code changes, or using AI to synthesize meeting notes into actionable tasks that then trigger scripts via a no-code platform. He also has links to free templates and demos to get you started.

    The most exciting part? It feels like a playground for experimentation. We can start small, automating simple tasks, and gradually build more complex, integrated systems. It’s not just about replacing tasks, but about augmenting our abilities and freeing us to focus on the higher-level strategic aspects of development. I’m keen to dive in and see how I can connect these Agents to my Laravel projects via APIs and webhooks – the potential for automation is huge!

  • This Voice AI Agent Can Handle EVERYTHING | n8n + ElevenLabs (FREE Template)



    Date: 09/22/2025

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    Okay, so this video’s all about building a voice AI agent using n8n and ElevenLabs, without needing to write a ton of code. It walks you through setting up an AI that can actually talk back to you, handle calls, and respond intelligently. Pretty cool, right?

    Why I think this is a valuable watch for us as we transition to AI-enhanced workflows is that it’s a perfect example of leveraging no-code tools to achieve complex automation. We can take the concepts in this video and apply them to real-world situations, like automating customer service interactions or creating personalized voice assistants for clients. Imagine using this to build a system that automatically answers FAQs or even schedules appointments through voice – think of the time saved! It’s not about replacing code entirely, but about using these tools to augment our abilities and free us up for more strategic tasks.

    What makes this worth experimenting with is that it bridges the gap between the complex world of AI and our existing development skills. It’s a practical, hands-on approach to learning about AI and automation, and who knows? You might just find a new revenue stream by building and selling voice AI agents as the video suggests! I’m definitely adding this to my weekend project list!

  • To Scale our RAG Agent (5,000 Files per/hr)



    Date: 09/22/2025

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    Okay, this video is gold for any developer like me who’s diving headfirst into the world of AI-powered workflows. It’s all about scaling RAG (Retrieval Augmented Generation) systems built with n8n, a no-code automation platform. The creator shares their experience of boosting processing speed from 100 files/hour to a whopping 5,000 files/hour. They didn’t just wave a magic wand; they went through the trenches, broke things, and learned a ton about optimizing n8n, Supabase, and even dealing with Google Drive limitations at scale. Sounds familiar, right?

    What makes this video a must-watch is its pragmatic approach. It’s not just theoretical fluff; it’s a deep dive into real-world challenges like bottlenecks, server crashes, and painfully slow data imports. The video provides a systematic approach for benchmarking, tuning, and scaling complex n8n workflows. They cover everything from setting up n8n workers and Redis queuing for parallel processing to building a robust orchestrator with retry logic. Plus, there’s a valuable lesson about knowing when to bypass APIs and go directly to the database. (Hello, Supabase!).

    For me, the most inspiring part is the tangible impact this kind of optimization can have. Imagine automating document processing, content analysis, or even code generation at this scale. By understanding these scaling techniques, we can build more robust and efficient AI-driven solutions for clients. I can see this being super useful for automating the ingestion and processing of our documentation for the AI code generation tools we are building. It would be a time saver and a great learning experience to implement. I’m definitely eager to experiment with the concepts in the video and see how they can transform my own AI workflow integrations.

  • The KEY to Infinitely Scale Your n8n RAG Agents



    Date: 09/22/2025

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    Okay, this n8n RAG scaling video is straight fire for anyone diving into AI-powered workflows! It tackles a real-world problem I’ve definitely hit: building a killer RAG system is easy with a few files, but what happens when you need to ingest thousands? This video shows you how to use n8n, Supabase, and some clever orchestrator patterns to reliably process massive amounts of data. We’re talking scaling to thousands of files per hour.

    What makes this valuable is its focus on practical solutions to common AI integration bottlenecks. API rate limits, memory overloads, system instability – these are the dragons you face when moving from a PoC to a production-ready system. The video breaks down how to build an orchestrator workflow that handles parallel executions, tracks parent/child processes using Supabase, and even handles errors with automated retries. Plus, the deep dive into using webhooks instead of sub-workflows is a game-changer for performance and tracking – something I’ve been experimenting with myself lately and seen HUGE improvements from.

    Imagine building a document processing pipeline for a legal firm or a content ingestion system for a large e-commerce site. This video provides a blueprint for automating those processes at scale. Honestly, the techniques for handling errors and preventing system overloads are worth the price of admission alone. I’m definitely going to be experimenting with these orchestration patterns in my next project. The layered error handling and Supabase configuration tips are gold!

  • Windows 11 Users Need This File Explorer Replacement



    Date: 09/22/2025

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    Alright, this video is about switching from the clunky default Windows File Explorer to a modern alternative called “Files.” This tool promises easier file management, a better UI, and more customization. Why does this matter for us, the AI-driven developers? Think about it: we’re automating everything else, but are we still dealing with a dated file system?

    This is important because, as we bring in AI coding and no-code tools, efficient file management becomes crucial. We’re handling more files, more code snippets, more AI-generated assets, and we need a system that can keep up. Picture this: you use an LLM to generate hundreds of image variations for a marketing campaign. “Files” could help you organize, tag, and quickly access those assets in a way the default explorer just can’t. Plus, if the UI is genuinely more intuitive, that means less time searching and more time coding.

    Honestly, it’s worth trying out because it’s a small change that could significantly impact your daily workflow. We’re already embracing automation in our code; why not extend that to how we manage the very foundation of our projects—the files themselves? It makes sense to see if “Files” can boost our productivity and make our lives a bit easier, one file at a time.

  • This AI Changes Film, Games, and 3D Forever (and you can use it today for Free)



    Date: 09/19/2025

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    Okay, this video on World Labs’ Marble model is seriously inspiring, especially for us devs exploring the AI frontier! It’s all about creating interactive 3D environments from single images, letting you “walk around” inside them. Think of it: instead of painstakingly modeling everything from scratch, you’re using AI to build a world.

    What makes this valuable is how it bridges the gap between traditional content creation and AI-powered workflows. The video walks through creating a short film entirely within World Labs, using tools like Reve for AI clean-up, VEO 3 for animation, and even integrating it into Premiere Pro for post-production. This shows that you don’t need to abandon your existing skills; you augment them with AI.

    Imagine automating environment design for games or creating immersive VR experiences with minimal modeling. This isn’t just theoretical; the video shows it in action. For me, the idea of rapidly prototyping interactive environments and then refining them with familiar tools is a game-changer. It’s definitely worth experimenting with because it provides a glimpse into a future where creativity is amplified, not replaced, by AI. The friction is gasoline for creativity, as the author puts it.

  • Ai Home Datacenter Build (part 1)



    Date: 09/16/2025

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    This video showcases a homelab datacenter rebuild, focusing on upgrading to new racks (APC AR3150) and incorporating servers (Dell R730xd, R930) and JBODs (NetApp DS4246/DE6600) for optimized storage performance. It’s all about building a robust, high-performance home datacenter, which is super relevant for us as we explore AI-driven workflows.

    Why’s this valuable? Because as we integrate AI coding and LLMs into our development lifecycle, we’re increasingly dealing with data-intensive tasks: training models, managing large datasets, automating testing. This video highlights the importance of a solid infrastructure to support those workloads. Thinking about how to scale and optimize our local development environments – maybe even building a homelab like this – lets us prototype and test AI-powered features more effectively. Plus, understanding hardware limitations helps us write more efficient code and design better solutions when deploying to the cloud.

    Imagine using no-code tools to automate the monitoring and management of this homelab, or even leveraging LLMs to predict storage needs and optimize data placement. It’s all about taking that deep understanding of infrastructure and automating it! Seeing someone build this from the ground up is inspiring. It’s a reminder that understanding the foundations empowers us to build better, more scalable AI-driven applications, and it’s got me thinking about finally upgrading my own dev environment. Definitely worth a watch!

  • QWEN3 NEXT 80B A3B the Next BIG Local Ai Model!



    Date: 09/14/2025

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    This video is all about Qwen3 Next, a new LLM architecture emphasizing speed and efficiency for local AI inference. It leverages “super sparse activations,” a technique that dramatically reduces the computational load. While there are currently some quirks with running it locally with vllm and RAM offloading, the video highlights upcoming support for llama.cpp, unsloth, lmstudio, and ollama, making it much more accessible.

    Why is this exciting for us as we transition to AI-enhanced development? Well, the promise of faster local AI inference is HUGE. Think about the possibilities: real-time code completion suggestions, rapid prototyping of AI-driven features without relying on cloud APIs, and the ability to run complex LLM-based workflows directly on our machines. We’re talking about a potential paradigm shift where the latency of interacting with AI goes way down, opening up new avenues for creative coding and automation.

    The potential applications are endless. Imagine integrating Qwen3 Next into a local development environment to automatically generate documentation, refactor code, or even create entire microservices from natural language prompts. The fact that it’s designed for local inference means more privacy and control, which is crucial for sensitive projects. I’m particularly keen to experiment with using it for automated testing and bug fixing – imagine an AI that can understand your codebase and proactively identify potential issues! This is worth experimenting with, not just to stay ahead of the curve, but to fundamentally change how we build software, making the development process more intuitive, efficient, and dare I say, fun!