Category: Try

Date: 08/12/2025

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Okay, as someone knee-deep in integrating AI into my Laravel workflow, this video immediately caught my attention. It’s all about turning Claude Code into an MCP (Model Context Protocol) server and then letting GPT-5 use Claude Code’s coding tools (file editing, bash commands, etc.) to build a React to-do app. In essence, you’re giving GPT-5 the brain and Claude Code the hands. The video also shows how to set up FlowiseAI as an MCP client for cross-model tool sharing.

Why is this valuable? Well, we’re moving beyond just using one AI model in isolation. This video demonstrates how to orchestrate different AI models, leveraging their strengths. For example, GPT-5 might be better at reasoning and planning the React app’s architecture, while Claude Code excels at the actual code generation and execution. I can see this applying to real-world scenarios where I need one model to handle complex logic and another to deal with specific coding tasks within a Laravel project. Think of using a model specializing in database schema design collaborating with a model that’s a wizard at crafting Eloquent queries.

What makes this experiment inspiring is the potential for creating more robust and efficient AI-driven workflows. The idea of mixing and matching AI capabilities opens doors for automating complex development tasks that would otherwise require significant manual effort. It’s definitely worth experimenting with because it could lead to a future where AI agents work together seamlessly to accelerate development cycles and improve code quality. I’m eager to try this out, specifically for automating the creation of complex database migrations and API endpoints in my Laravel projects.

Date: 08/12/2025

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Alright, buckle up fellow devs, because this video about Open-SWE is seriously inspiring! It’s all about a free and open-source alternative to tools like Jules, which, let’s face it, can get pricey. Open-SWE leverages LangGraph to function as an asynchronous AI coding agent. That means it can dive deep into your codebase, plan out solutions, write, edit, and even test code, and automatically submit pull requests, all without you having to constantly babysit it. You can run it locally or in the cloud and connect it to your API key, even free APIs like OpenRouter or locally with Ollama.

Why is this a game-changer for those of us exploring AI-enhanced workflows? Well, first off, the “free” part is music to my ears. More importantly, it demonstrates how we can integrate AI agents into our existing development pipelines without being locked into proprietary systems. Think about automating those tedious tasks like bug fixing, writing unit tests, or even refactoring larger codebases. Imagine setting it off to run and self-review, while you get back to designing new features!

From my perspective, what makes Open-SWE worth experimenting with is that it empowers us to build genuinely custom AI assistants tailored to our specific project needs. I could see this being useful for automating repetitive tasks, freeing me up to tackle more complex challenges. It’s about adding another AI engineer to your team but without that monthly bill. Plus, the fact that it’s open-source means the community can contribute, evolve, and improve it. I’m already thinking about how I can integrate this into my workflow and automate some of the more mundane aspects of my projects. The flexibility to use it locally with models hosted on Ollama is really interesting and a big win. I’d recommend giving it a whirl if you have any interest at all in AI assisted coding and have looked into tools like Jules!

Date: 08/11/2025

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Okay, so this video is all about using Eddie AI, a virtual assistant editor, to streamline video production, specifically for filmmakers. It demonstrates how AI can automate tedious tasks like logging footage, organizing media, and even creating rough cuts. It’s basically showing how to use AI to massively speed up the editing workflow.

This is gold for someone like me (and maybe you!) who’s diving into AI coding and no-code solutions because it’s a concrete example of AI tackling a real-world creative problem. We’re always looking for ways to automate the boring stuff so we can focus on the actual development, right? Well, imagine applying these AI-powered transcription and organization techniques to code documentation, bug reporting, or even generating initial code structures from project descriptions. Think about feeding meeting recordings into an AI to automatically generate action items and code changes!

What really makes this video worth checking out is seeing Eddie AI in action, especially the rough cut mode. It provides a glimpse into how LLMs can assist creative processes, not just replace them. Plus, the video acknowledges the limitations, which is crucial. It’s not about blindly trusting the AI, but about leveraging it as a powerful assistant. I am all in to test this in my personal video editing projects and see where it fits in my workflow!

Date: 08/06/2025

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Okay, these videos on building an AI agent that queries relational databases with natural language are seriously cool and super relevant to what I’ve been diving into lately. Forget those basic “AI can write a simple query” demos – this goes deep into understanding database structure, preventing SQL injection, and deploying it all securely.

The real value, for me, is how they tackle the challenge of connecting LLMs to complex data. They explore different ways to give the AI the context it needs: dynamic schema retrieval, optimized views, and even pre-prepared queries for max security. That’s key because, in the real world, you’re not dealing with toy databases. You’re wrestling with legacy schemas, complex relationships, and the constant threat of someone trying to break your system. Plus, the section on combining relational querying with RAG? Game-changer! Imagine being able to query both structured data and unstructured text with the same agent.

Honestly, this is exactly the kind of workflow I’m aiming for – moving away from writing endless lines of code and towards orchestrating AI to handle the heavy lifting. Setting up some protected views to prevent SQL injection sounds like a much better security measure than anything I could write by hand. It’s inspiring because it shows how we can leverage AI to build truly intelligent and secure data-driven applications. Definitely worth experimenting with!

Date: 08/03/2025

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Okay, so this video is a rapid-fire rundown of some seriously cool AI advancements – everything from Tencent’s Hunyuan World (a generative world model!) to GLM-4.5, which boasts improvements over GPT-4, and even AI-powered motion graphics tools. It’s basically a buffet of what’s new and shiny in the AI space.

Why is this useful for us, moving towards AI-enhanced development? Well, first, it’s about awareness. We need to know what’s possible. Seeing things like X-Omni (for AI-driven UI/UX) and the FLUX Krea dev tool (AI-powered image generation) immediately sparks ideas about how we can automate front-end tasks, create dynamic content, or even rapidly prototype interfaces. Imagine using something like Hunyuan World to generate realistic test environments for our applications. The key is to keep our minds open to how these tools could be integrated into our existing workflows, potentially saving us hours on design, testing, and even initial coding.

Honestly, staying on top of this stuff can feel like drinking from a firehose, but that’s why these curated news roundups are so valuable. It’s worth experimenting with a couple of these tools – maybe that Hera motion graphics tool for spicing up our UI or diving into GLM-4.5 to see if it can streamline our code generation. The goal isn’t to replace ourselves with AI, but to find those 20% of tasks that AI can handle, freeing us up to focus on the higher-level problem-solving and architecture that makes development truly rewarding. Plus, keeping our skills current means we can deliver more value to clients and stay ahead of the curve.

Date: 08/01/2025

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This video showcasing Ollama’s new ChatGPT-style interface is incredibly inspiring because it directly addresses a pain point I’ve been wrestling with: simplifying local AI model interaction. We’re talking about ditching the terminal for a proper UI to download, run, and chat with models like Llama 3 and DeepSeek R1 – all locally and securely. Forget wrestling with command-line arguments just to experiment with different LLMs! The ability to upload documents, analyze them, and even create custom AI characters with personalized prompts opens up so many possibilities for automation and tailored workflows.

Think about it: I could use this to build a local AI assistant specifically trained on our company’s documentation, providing instant answers to common developer questions without exposing sensitive data to external APIs. Or maybe prototype a personalized code reviewer that understands our team’s coding style and preferences. Plus, the video touches on optimizing context length, which is crucial for efficient document processing. For anyone who, like me, is trying to move from traditional coding to leveraging local AI, this is a game-changer.

It’s not just about ease of use, though that’s a huge plus. It’s about having complete control over your data and AI models, experimenting without limitations, and truly understanding how these technologies work under the hood. The video makes it seem genuinely straightforward to set up and start playing with, which is why I’m adding it to my “must-try” list this week. I’m especially keen on testing DeepSeek R1’s reasoning capabilities and exploring how custom system prompts can fine-tune models for very specific tasks. This could seriously accelerate our internal tool development!

Date: 07/31/2025

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Okay, this video on building AI agent teams with Claude and a multi-agent framework? Seriously inspiring stuff for anyone like us diving headfirst into AI-enhanced development.

Here’s the gist: it’s not just about firing off prompts to an LLM anymore. The video shows how to use Claude Projects (from Anthropic) alongside a multi-agent framework to create a team of AI agents that tackle complex tasks collaboratively. We’re talking about automating everything from social media content creation (with tailored mentions!) and lead qualification right out of Gmail, to even designing thumbnails. And the coolest part? It connects directly to Zapier, unlocking a world of integrations. Imagine your agents updating databases, sending emails, triggering other automations – all on their own.

Why is it valuable? Because it gives us a glimpse into a future where we’re orchestrating AI, not just coding every single line ourselves. Instead of spending hours on repetitive tasks, we could define the high-level goals, set up the agent team, and let them handle the grunt work. Think about applying this to automating API integrations, generating documentation, or even testing. This isn’t about AI taking our jobs; it’s about AI amplifying our abilities. I’m definitely experimenting with this; the idea of having AI agents handle tedious tasks while I focus on the bigger architectural challenges? Sign me up.