Try@DailyAi.Studio

Category: Try

  • Stop Using RAG for Spreadsheets — Use This Instead (n8n)



    Date: 07/14/2025

    Watch the Video

    Okay, this video is exactly the kind of content that gets me fired up about the future of development. It’s all about building smarter AI agents with n8n that can actually understand and query structured data, like spreadsheets, using a hybrid RAG (Retrieval-Augmented Generation) approach. We’re talking about giving our agents the ability to not just semantically search, but to do things like sum columns, filter rows, and perform real SQL queries through natural language!

    Why is this valuable? Well, how many times have you built a clunky interface just to let a user run a simple report on some data? This video shows you how to use an AI agent to interpret a user’s natural language request (“What were the total sales in France last month?”) and translate it into an actual SQL query against a Supabase database. The magic is in how the data is ingested and managed – storing structured data in a flexible JSONB column, so you don’t need a rigid schema upfront. Plus, it smartly combines vector search for unstructured data with SQL queries for the structured stuff – the agent decides which to use. It walks through a complete data pipeline, too, covering things like handling data changes in Google Drive and keeping everything synced. No-code is cool and all, but the real power comes when you can seamlessly blend it with robust backend logic.

    For me, the most exciting thing is the shift from building rigid UIs and APIs to crafting intelligent agents that can adapt to changing data and user needs. Imagine the possibilities for automating reporting, data analysis, and even complex business workflows! I’m already brainstorming ways to apply this to a reporting project for a client. I’m thinking by setting up a system like this, we can drastically cut down the time spent manually building reports and dashboards. It’s worth experimenting with, as I see it lowering dev time by potentially 50%!

  • Earn your first $100 on the App Store in 30 days (even if you’re a terrible coder)



    Date: 07/14/2025

    Watch the Video

    Okay, so this video by Adam Lyttle is all about building simple iOS apps, even if you think you’re a terrible coder, and making your first $100 on the App Store in 30 days. Sounds like a classic “build in public” journey, which I’m always a sucker for. He focuses on simple app ideas and fast development strategies, and shares the resources he used to get started.

    What makes this valuable for us, as developers transitioning to AI-assisted workflows, is the mindset shift. It’s not about being a perfect coder anymore. It’s about quickly iterating and validating ideas. We can use AI tools to rapidly prototype these simple apps, generate boilerplate code, or even debug issues. Astro for keyword research, mentioned in the video, is a great example of leveraging a tool to identify market opportunities and use LLMs and no-code tools to get an app to market quickly.

    Imagine using an LLM to generate a basic framework for one of these simple app ideas, then using a no-code platform to flesh out the UI and user flow. We could even use AI to write the app store description and generate marketing materials. This video is an inspiration to embrace the “fail fast, learn faster” approach, and these tools can help us validate ideas quicker than ever. I’m adding this one to my watchlist – it’s time to experiment and see what simple apps we can launch in the next month!

  • Kimi K2- The FREE AI Model That Killed Claude Code??



    Date: 07/13/2025

    Watch the Video

    Okay, this video about Kimi K2 looks super interesting, especially if you’re like me and constantly searching for better, faster, and cheaper AI coding assistants. The presenter walks you through setting up and using Kimi K2, highlighting its potential to shake up the AI industry. What caught my eye is the promise of using it to code, potentially even a whole 3D first-person shooter game in ThreeJS – for free! That’s a bold claim, but the benchmarks mentioned in the video make me want to dive in and see how it stacks up against other models.

    For those of us neck-deep in the transition to AI-enhanced workflows, this is a potential game-changer. Imagine being able to quickly prototype ideas, automate repetitive coding tasks, or even generate entire modules with a tool like this. A 3D FPS game is a good example because it’s complex enough to really put the AI through its paces. If Kimi K2 can actually deliver usable code, it could drastically reduce development time and allow us to focus on the more creative and strategic aspects of our projects.

    Honestly, even if it doesn’t perfectly generate the entire game, the potential time savings in boilerplate code and initial setup are huge. I’m thinking about how this could be applied to rapidly prototyping different UI components or even automating API integrations in Laravel. The fact that it’s potentially free to try makes it a no-brainer. I’m definitely going to experiment with this, especially with its single-file output, which is perfect for proof-of-concept projects.

  • Kimi K2: BEST Opensource Model! BEATS SONNET 4! Powerful, Fast, & Cheap! (Fully Tested)



    Date: 07/12/2025

    Watch the Video

    Okay, this video on Moonshot’s Kimi K2 looks like a game-changer, and here’s why I’m excited. It’s about a new open-source LLM with a massive 1 trillion parameters, specifically designed for coding and agentic tasks. The video dives into how Kimi K2 stacks up against the big boys like GPT-4.1 and Claude Sonnet 4, showing benchmark results and real-world coding tests. The fact that it’s outperforming or matching those models and it’s open-source is huge.

    As someone knee-deep in exploring AI-driven development, this is exactly the kind of thing I’m looking for. We’re talking about a potentially powerful tool for automating code generation, reasoning, and even complex agent workflows, and also a cheap API. Imagine integrating this into a Laravel application to automatically generate API endpoints based on database schema changes, or building a custom CI/CD pipeline that leverages Kimi K2 to identify and fix code vulnerabilities. We’re talking about streamlining development tasks that used to take hours – or even days – into something that can be done in minutes.

    Honestly, the fact that Moonshot has open-sourced both a base model and an instruction-tuned version, Kimi-K2-Base and Kimi-K2-Instruct, means we can actually experiment with fine-tuning and customizing the model to our specific needs. Forget about being locked into proprietary APIs with limited control. This video is a call to arms to dive in, get our hands dirty, and start building the future of AI-powered development. I know I’m going to!

  • OpenCode: FASTEST AI Coder + Opensource! BYE Gemini CLI & ClaudeCode!



    Date: 07/11/2025

    Watch the Video

    This video’s about OpenCode, a new open-source AI coding agent that’s aiming to be the go-to CLI tool for developers. It boasts speed, a slick terminal UI, multi-agent support, and compatibility with a ton of LLMs (including local models!). The presenter dives into why it’s potentially better than existing options like Gemini CLI and ClaudeCode.

    As someone knee-deep in exploring AI-assisted development, this video is pure gold. I’ve been experimenting with different LLMs and code generation tools, and the promise of a fast, flexible CLI agent that plays well with multiple LLM providers is incredibly appealing. The multi-agent support is especially interesting – imagine farming out different parts of a task to specialized AI agents, all orchestrated from your terminal! Plus, the fact that it’s open-source means we can tweak and extend it to fit our specific needs.

    Think about it: you could use OpenCode to automate tedious tasks like generating boilerplate code, refactoring legacy systems, or even debugging complex algorithms. The ability to share sessions for real-time collaboration could revolutionize how teams work together on code. Honestly, the potential time savings and productivity gains are huge. I’m definitely going to spin this up and see how it stacks up against my current workflow. The promise of a more efficient, AI-powered coding experience is too good to pass up.

  • Refact.ai: NEW FULLY FREE AI Software Engineer Is Insane! RIP Cursor & Github Copilot!



    Date: 07/10/2025

    Watch the Video

    Okay, this Refact.ai video looks seriously compelling, especially for where I’m trying to take my development workflow. The gist is that it’s showcasing a fully free, self-hosted, open-source AI coding agent that’s gunning for the top spot currently held by tools like Copilot and Cursor. The video highlights its features, like autonomous coding, IDE integration, codebase fine-tuning, and its impressive #1 ranking on the SWE-bench Verified leaderboard.

    Why is this exciting? Well, I’ve been deep-diving into AI-assisted coding and LLM-based automation, and the idea of a self-hosted, open-source alternative is huge. I’ve been experimenting with Copilot and other tools, but the “black box” nature and the vendor lock-in always felt a bit limiting. Refact.ai promises more control and transparency, which is critical for understanding how the AI is making decisions and tailoring it to specific project needs. Plus, the video emphasizes seamless integration and context-awareness, which are key for real-world applications. Imagine being able to fine-tune an AI agent to your specific Laravel project, and it just gets the nuances of your architecture. That could shave off hours of debugging and boilerplate coding!

    Honestly, the SWE-bench Verified ranking alone is enough to pique my interest. Seeing it plan, execute, and deploy code is far beyond simple autocompletion. It means this tool is potentially useful in creating more complex automated workflows. I’m already thinking about how I could use something like this to automate repetitive tasks like API integrations, database migrations, or even generating basic CRUD interfaces in Laravel. For me, the fact that it’s free and open-source makes it a must-try. I’m itching to set it up and put it through its paces on a real project. Who knows, this could be the key to unlocking a whole new level of development efficiency!

  • Veo-3 Gets a BIG Upgrade & Moonvalley First Look!



    Date: 07/09/2025

    Watch the Video

    Okay, so this video is basically a double-shot espresso for developers like us who are knee-deep in the AI revolution. It’s all about Google’s VEO-3 unleashing image-to-video with audio and a first look at MoonValley, a new AI video generator geared towards professionals. We’re talking practical tips on using VEO-3, exploring its cost, and a solid dive into MoonValley’s text-to-video, image-to-video, and video-to-video capabilities. Plus, it shares a free prompt builder, which is gold!

    Why is this valuable? Because it bridges the gap between traditional dev and the AI-powered future. Imagine automating marketing video creation, generating realistic product demos from simple images, or even creating interactive training materials without needing a full-blown film crew. The video’s exploration of these tools, along with the discussion of prompt engineering, helps us understand how to translate ideas into effective instructions for AI. That’s huge for anyone looking to integrate LLMs and no-code platforms into their workflows!

    I’m personally stoked about the video-to-video features mentioned. Think about feeding in a basic wireframe animation and using AI to flesh it out with realistic textures, lighting, and effects. It’s like having a virtual assistant that understands both code and creative vision. The discussion around MoonValley and its copyright-free model is also crucial because it addresses a major hurdle in using AI for commercial projects. It’s definitely worth experimenting with to see how we can leverage these tools to build more engaging and efficient applications.

  • SuperClaude: SUPERCHARGE Claude Code – BEST AI Coder! BYE Gemini CLI & OpenCode!



    Date: 07/07/2025

    Watch the Video

    Okay, this video on “SuperClaude” is seriously exciting for anyone looking to level up their AI-assisted coding. It’s all about a framework that turbocharges Anthropic’s Claude Code, making it way more powerful and customizable right in your terminal. Think custom personas, new slash commands, and generally faster workflows – basically, taking Claude from a helpful assistant to a full-blown AI coding powerhouse.

    As someone who’s been diving deep into LLM-based workflows, the idea of a modular framework like SuperClaude is incredibly appealing. We’re talking about the ability to tailor the AI’s behavior, integrate custom commands, and automate complex tasks in ways that weren’t easily possible before. Imagine creating personas that understand your project’s specific coding style, or using custom commands to automate repetitive tasks – that’s a huge win for productivity. This isn’t just about writing code faster; it’s about streamlining the entire development process.

    What makes it worth experimenting with? The potential for real-world impact. Think about automating complex deployments, generating documentation on the fly, or even refactoring legacy code with specific guidelines, all driven by a highly customized AI assistant. Plus, the video claims it’s free and easy to integrate, which means less time wrestling with setup and more time exploring its capabilities. I’m already brainstorming how to incorporate this into my Laravel projects to speed up boilerplate generation and even help with debugging. Seriously, this looks like a game-changer for AI-assisted development.

  • I made the PC I couldn’t buy



    Date: 07/05/2025

    Watch the Video

    Okay, this video documenting a PC build inside a SAMA IM-01 case, inspired by the Mac Pro XDR, is surprisingly relevant to us as we transition to AI-enhanced development. On the surface, it’s a standard build log, but think about it – it’s a perfect microcosm of automation and customization, core tenets of the AI/no-code world.

    The builder 3D-printed a custom front panel. Now, imagine using an LLM like GPT-4 to generate the initial design for that front panel based on a text prompt like, “Create a minimalist front panel for a SAMA IM-01 case, inspired by the Mac Pro XDR, with improved airflow.” We could then feed that design into a no-code CAD tool, further refine it visually, and then send it directly to the 3D printer. Suddenly, a task that would have taken hours of manual design and iteration becomes a streamlined process, freeing us to focus on higher-level concerns.

    This video is inspiring because it highlights the intersection of physical creation and digital design. While it’s “just” a PC build, the same principles apply to building entire software architectures. We can use AI to generate boilerplate code, design UI elements, and even automate deployment pipelines. The key is to see beyond the hardware and recognize the underlying potential for automation and customization that these tools unlock. Time to fire up Fusion 360 and see what kind of AI-assisted case mods we can cook up!

  • QA Automation UsWork + N8N + BrowserUse



    Date: 07/04/2025

    Watch the Video

    Okay, this video on automating QA with n8n and Browser Use is seriously inspiring, and here’s why it hits home for me. It’s all about taking the pain out of deployments. We’ve all been there, right? You push code, hold your breath, and pray nothing breaks. This video shows how to use n8n, a no-code automation tool, combined with Browser Use, to automatically trigger tests using natural language prompts. Think about it: you deploy, n8n kicks off tests based on simple instructions, and you get instant feedback. No more manual clicking and hoping for the best!

    What makes this valuable is that it directly addresses the transition from traditional development to AI-enhanced workflows. I’ve been exploring LLM-based workflows myself to streamline deployments, and this is another piece of the puzzle. Imagine setting up a workflow that not only runs tests but also uses an LLM to analyze the results and identify potential issues based on the error messages. That’s real automation, saving time and giving you confidence.

    For me, the real appeal is the blend of no-code and AI. It’s about empowering developers to build robust, automated systems without getting bogged down in complex scripting. It’s definitely worth experimenting with to see how it can integrate into your existing CI/CD pipeline. I can already see how this approach could be adapted to automate other tedious tasks like data validation, performance monitoring, and even security checks. It’s time to ditch the deployment anxiety and embrace automated QA.