Category: Try

Date: 04/12/2025

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Okay, so this video dives into setting up a local Supabase environment, running migrations, and connecting it to a Next.js frontend. Sounds pretty standard, right? But what makes it super relevant for us—developers looking to leverage AI and no-code—is that it streamlines the backend setup. Think about it: less time wrestling with infrastructure means more time experimenting with AI-powered features and LLM integrations in our applications. We can offload a lot of the traditional backend drudgery and focus on the cool, innovative stuff.

Imagine using this setup as a playground for testing AI-driven data transformations triggered by Supabase database changes. Or, picture building a no-code interface on top of this Supabase backend, letting non-technical team members manage data and trigger AI workflows. This video essentially gives you a quick way to build a robust backend scaffolding, allowing you to focus on your AI coding and LLM workflows.

For me, the appeal is in its practicality. You can get a local Supabase instance up and running quickly, which is ideal for rapid prototyping and experimenting with new ideas. Rather than spending a ton of time on infrastructure, you can immediately start wiring up AI services, testing LLM prompts, and exploring no-code automation. It’s all about lowering the barrier to entry for AI-enhanced development, and this video provides a solid first step. I’m definitely adding this to my list of weekend experiments.

Date: 04/12/2025

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Okay, this video is exactly what I’ve been looking for! It tackles a pain point I’ve definitely felt: n8n’s built-in chat interface for AI agents is…basic. It’s fine for quick tests, but falls apart when you need history, customization, or a more user-friendly experience. The video shows how to hook up your n8n AI agents to Open WebUI, giving you a full ChatGPT-like interface with persistent conversations and a slick frontend – something that significantly elevates the end-user experience.

What makes this valuable is the bridge it builds between low-code automation (n8n) and a more sophisticated UI. Think about it: We can build complex workflows and AI agents in n8n, then provide a real conversational interface to our clients or internal users via Open WebUI. Imagine building a lead qualification agent, and giving your sales team a dedicated, branded chat interface to interact with it. Or think about a customer service bot that runs in n8n but presents a familiar chat experience. This video basically gives you the keys to creating these kinds of polished, production-ready AI applications, and it looks relatively straightforward to implement.

I’m particularly excited about the “n8n Agent Template” and “Open WebUI + n8n Pipeline” resources. Having those pre-built starting points drastically reduces the ramp-up time. I’m definitely going to experiment with this over the next few days. The idea of packaging powerful n8n agents with a user-friendly chat interface? That’s a huge win for both internal automation and client-facing applications! Plus, the video addresses security, which is always top-of-mind when dealing with webhooks and external services. Worth a watch and a weekend project for sure!

Date: 04/10/2025

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Okay, this video on n8n’s new MCP (Model Context Protocol) support is seriously exciting and a total game-changer for how we integrate AI into our workflows. Basically, it shows you how to build custom AI tools that directly hook into things like Claude and Cursor, using n8n’s no-code platform as the glue. Think to-do list management, email handling, or even content generation, all powered by AI and automated without writing a single line of code.

For someone like me who’s been diving headfirst into AI-enhanced development, this is gold. Instead of wrestling with APIs and SDKs, we can leverage n8n to create MCP servers and clients, effectively building custom AI tools tailored to our specific needs. The video walks you through setting up the server, integrating it with AI apps, and even using n8n as an MCP client to access external services. Imagine automating the tedious parts of your development lifecycle with custom AI agents responding to your instructions in Claude or Cursor.

The real kicker is the potential for practical applications. We could build automated testing workflows, generate documentation from code comments, or even create AI-powered code review assistants. The video touches on connecting to-do lists and other services, which is just scratching the surface. And let’s be real, the thought of creating these kinds of custom integrations without getting bogged down in code is incredibly appealing and efficient. I’m particularly intrigued by the MCP client node. It basically unlocks a whole new level of automation. I’m already thinking of how I can use this to connect my internal tools with LLMs, and honestly, that’s an experiment worth diving into.

Date: 04/09/2025

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Okay, this video looks like gold for where I’m trying to go with my workflow! It’s all about building a local AI agent using n8n for automation, Ollama for the LLM, and PostgreSQL for vector storage. The beauty is that it’s entirely self-hosted, which means no hefty API bills or privacy concerns. The video walks you through the entire process, from setting up Ollama and PostgreSQL to orchestrating everything within n8n. They even tackle common troubleshooting issues.

This is exactly the kind of thing I need to dive deeper into. For the past year, I have been looking at self-hosted AI for cost reasons and privacy, but found it daunting to integrate it into actual workflows. Right now, I still use OpenAI for all my jobs, but it would be great to use this at least for local testing or for clients who have compliance issues. It seems possible I could create a RAG workflow that does not leave the customer premises. Imagine automating report generation, content summarization, or even personalized customer service bots, all running locally!

The video shows how to add RAG (Retrieval Augmented Generation) and tools into the workflow, which opens up huge possibilities for automating complex tasks. It’s worth experimenting with because it gives you a practical, hands-on approach to building AI solutions without being locked into external services. I’m always looking for ways to streamline development and cut costs, and this seems like a very promising avenue to explore.

Date: 04/09/2025

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Okay, this video on using Cursor AI with Claude 3.5 Sonnet for rapid prototyping? It’s exactly the kind of thing I’m geeking out on right now. The video dives into using AI-powered tools to take inspiration from places like Dribbble and Pinterest, then quickly generate functional UI components. It even touches on integrating tools like Shadcn UI, which I’ve found to be a massive time-saver. It’s not just theory; it’s about practical application. I’m finding more and more that these AI dev tools are helping me go from idea to initial project structure in record time.

What makes it valuable is its focus on real-world workflows. Copying designs, working within context windows, and iterating rapidly – these are the daily realities of development. The presenter highlights the importance of frequent commits, which is a great reminder in this fast-paced environment. Plus, seeing how tools like Cursor AI can be used alongside LLMs like Claude 3.5 Sonnet for code generation and understanding the “why” behind design decisions is pretty cool. I could see using this same workflow to automate the creation of admin panels, dashboards, or even complex forms based on user input – think generating a whole Laravel CRUD interface from a simple description.

Honestly, the part that gets me excited is the potential for experimentation. The video highlights that these tips apply to similar AI tools like Windsurf AI, Cline, GitHub Copilot, and V0 from Vercel, so it’s an invitation to explore the rapidly changing landscape of AI-assisted development. I am going to block out an afternoon this week and play around with one of my old projects to see how much faster I can iterate with these tools. It feels like we’re finally at a point where AI isn’t just a helper but a true partner in the development process!

Date: 04/09/2025

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Okay, this video is a goldmine for anyone knee-deep in Supabase and itching to automate their workflow. It tackles a real pain: keeping Supabase instances in sync using migrations. No more clunky manual backups and restores – the video shows you how to leverage the Supabase CLI to streamline the process.

As someone who’s been transitioning to more AI-assisted coding and no-code solutions, this resonates big time. Imagine integrating this workflow into a CI/CD pipeline, or even better, having an AI agent manage these migrations based on changes detected in your schema. It’s all about automating the tedious parts of development. For instance, I’ve been experimenting with using LLMs to generate migration files based on schema diffs. This video provides the foundational knowledge to then connect those AI-powered tools into a fully automated deployment pipeline.

The practical implications are huge. Think about staging environments, disaster recovery, or even just replicating your production database for local development. This video isn’t just about Supabase; it’s about embracing infrastructure-as-code and applying that philosophy to your database. Definitely worth checking out and experimenting with! I’m already brainstorming how to use this to simplify our team’s workflow.

Date: 04/08/2025

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Okay, so this video is all about exploring Gemini 2.5 Pro within Cursor AI, and the presenter dives right into building two apps without writing code. Yes, you read that right. One’s a helicopter game and the other is a Notion clone, both leveraging NeonDB for the database. What really grabbed me was the “one-shot prompt DB setup” – the presenter’s reaction says it all: “Mind Blown!”.

Why is this inspiring? Because it’s a practical demonstration of what we’re aiming for: shifting from writing every line of code to orchestrating AI to build entire applications. As someone with 25 years of development experience, the idea of generating a database setup with a single prompt? That’s game-changing. This is especially appealing because it brings together Cursor AI, Gemini 2.5 Pro, and NeonDB. I’m already using Cursor for code generation. The idea of integrating it directly with an LLM like Gemini to generate full-fledged apps and databases is a huge step forward in automation.

Imagine using this approach to rapidly prototype new features or even build entire microservices. Instead of spending days setting up databases and writing boilerplate code, you could potentially have a functional prototype in hours. Plus, the video showcases the latest Cursor AI version and some pre-built “MCPs” (not fully explained in the description, but I’d guess they’re like code templates or macros) – hinting at more advanced features that could streamline development further. I’m definitely going to experiment with this – even if it’s not perfect yet, seeing the potential to build complex apps with minimal coding is incredibly exciting.

Date: 04/08/2025

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Okay, this Databutton and Aqua integration video is seriously inspiring for anyone looking to bridge the gap between traditional coding and AI-powered workflows. Basically, it shows how you can use natural language prompts with Aqua to build a simple MCP (Monitoring and Control Panel) server on Databutton. It connects that server to both YouTube (for live data) and Slack (for notifications), and then uses Claude (via Aqua) to analyze YouTube videos and send updates directly to Slack. Think of it as a low-code way to build intelligent monitoring and alerting systems.

Why is this cool for us? Because it demonstrates how we can offload tedious boilerplate code to AI. Instead of hand-coding API integrations with YouTube and Slack, you’re describing what you want to happen, and the tools handle the rest. Imagine using this to automate anomaly detection in server logs or track customer sentiment on social media. We could build custom dashboards that react in real-time to events, all without writing thousands of lines of code. It’s all about leveraging LLMs to abstract away complexity and accelerate development.

It’s definitely worth experimenting with because it hints at a future where development is more about orchestrating AI agents than writing code line-by-line. The video highlights the potential for faster prototyping, easier maintenance, and more accessible development for non-technical team members. And honestly, the speed at which they built that integration – just a few minutes! – that alone is a huge productivity boost compared to building everything from scratch. I am pretty happy I stumbled across this and can’t wait to find some spare time to check it out.

Date: 04/06/2025

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This video is incredibly inspiring because it showcases a real-world transition from traditional web development to an AI-powered workflow using tools like Cursor AI, Next.js, and Tailwind CSS. The creator demonstrates how AI can drastically speed up the prototyping and MVP creation process, claiming a 10x faster development cycle. It really hits home for me, as I’ve been experimenting with similar AI-driven tools to automate repetitive tasks and generate boilerplate code, freeing up my time to focus on the more complex aspects of projects.

What makes this valuable is the hands-on approach. The video dives into practical examples like setting up email forms with Resend, using MCP search, and even generating a logo with ChatGPT. This isn’t just theoretical; it’s a look at how these AI tools can directly impact your daily tasks. Imagine building a landing page in a fraction of the time, handling deployment with AI assistance, and quickly iterating on designs. It also brings up the important step of reviewing the AI generated code. It’s a great way to stay in control, especially when learning new processes.

I’m particularly excited about experimenting with the MCP (Meta-Cognitive Programming) tools mentioned, despite the security warnings. The idea of leveraging these AI-powered components to enhance development workflows is super intriguing. The video provides a glimpse into how AI can truly augment our abilities as developers, making it well worth the time to check out and experiment with these new workflows.

Date: 04/06/2025

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Okay, this video on using Gemini 2.5 Pro for audio transcription and analysis is definitely something to check out! It basically walks you through leveraging Google’s latest LLM to transcribe audio and, more importantly, analyze it. As someone knee-deep in automating workflows, the audio diarization process alone (mentioned around 6:43) is super intriguing. Think about automatically creating meeting summaries, extracting key insights from customer calls, or even generating transcripts for educational content – all without manually typing a single word.

Why is this valuable for us? Well, we’re moving beyond just writing code. We’re integrating AI to understand data, and audio is a huge part of that. Imagine piping call center recordings through Gemini 2.5 Pro, identifying customer pain points, and automatically triggering support tickets. Or, think about transcribing and summarizing technical interviews to quickly assess candidates. The possibilities are endless. The video also mentions the specifics like pricing and audio formats, which is great for getting a handle on the practical side of things.

Honestly, the ability to analyze audio effectively opens up a whole new realm of automation. Instead of spending hours manually reviewing audio files, we can let the LLM do the heavy lifting. I’m already thinking about how to integrate this into a project I’m working on that involves customer feedback analysis. The Colab demo (around 5:25) is a perfect starting point for experimentation. Definitely worth a look!