Category: Try

Date: 02/28/2025

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Okay, so this video by Nicholas Puru looks like a goldmine for anyone like me who’s knee-deep in exploring AI agents for development. It seems to be focusing on building a data analysis agent, which is huge. We’re talking about moving beyond just writing code to actually automating complex analytical tasks, leveraging LLMs to *understand* data, and that’s a serious game changer.

What makes this video especially valuable is the practical demo and walkthrough of building the agent. Seeing how to structure the agent, define its goals, and connect it to data sources is crucial. This isn’t just theory; it’s actionable information. For us developers transitioning into AI-enhanced workflows, it bridges the gap between understanding the potential of LLMs and actually implementing them in real-world scenarios. Think about automating your QA process by having an agent analyze test results and identify patterns, or building an agent to proactively monitor application performance and flag anomalies.

Honestly, I’m excited to dive into this because it feels like a practical step toward building truly intelligent systems. It’s worth experimenting with because it allows us to go beyond basic scripting and start building autonomous tools that can really augment our development process. And honestly, if it saves me even a few hours of manual data analysis a week, it’s worth its weight in gold.

Date: 02/27/2025

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Alright, this video on building a blog-writing agent with n8n is seriously inspiring, especially for anyone like me who’s diving headfirst into AI-enhanced development. It basically shows you how to create a no-code workflow that not only generates SEO-optimized blog posts for WordPress but also improves its writing over time using an AI agent connected to Airtable for memory! Plus, it even throws in image generation using the Flux model. Seriously cool stuff.

What makes this valuable is that it tackles a real-world problem – content creation – using a fully automated, no-code solution. It’s not just about generating text; it’s about building a system that learns and adapts, leveraging LLMs *and* visual AI. Think about the possibilities: automating social media content, personalized email campaigns, even documentation! The demo showing the Telegram integration to kick off the workflow is especially compelling. You could adapt this to other trigger mechanisms as well.

I’m personally excited to try this out because it combines several technologies I’m already working with – n8n, LLMs, and WordPress. I’m thinking I can modify it to generate content for internal knowledge bases or even automate the creation of release notes. The fact that it uses Airtable for agent memory is genius! It’s a great starting point for building more complex, self-improving AI agents. Honestly, the potential time savings and scalability are worth the experimentation alone.

Date: 02/27/2025

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Okay, this video is seriously inspiring because it tackles a problem *every* developer faces: QA testing. But instead of the usual Gherkin-nightmare or endless Selenium scripts, it shows how to use AI and no-code tools like N8N and Stagehand (Playwright wrapper) to *radically* simplify the process. We’re talking AI-driven prompts replacing entire test suites. This is huge!

What makes this valuable for us, as developers transitioning into AI-enhanced workflows, is the practical application. It’s not just theory; it’s a concrete example of how to leverage LLMs to automate a critical part of the development lifecycle. Imagine using this approach for not just QA, but for things like data scraping, automated report generation, or even complex integrations with legacy systems. You could build robust automation workflows without writing mountains of code, drastically cutting down development time.

For instance, I can see adapting this to automate client onboarding. We currently spend hours manually verifying data and setting up accounts. By combining Stagehand, N8N, and some AI-powered prompts, we could automate 80% of that process. This is exactly the kind of thing I’ve been looking for to bridge the gap between traditional development and AI-powered automation. It’s definitely worth experimenting with because it promises to free up our time to focus on higher-level problem-solving and strategic development. I’m excited to see how it’ll play out in my next project!

Date: 02/27/2025

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Okay, so this video is all about migrating your community content to a new platform while preserving your SEO juice and even *growing* organic traffic. It highlights using AI to identify your top-performing posts, optimize them for search engines, and set up redirects. Sounds like a classic, necessary, and often painful task, right?

Why’s this valuable for us as devs exploring the AI/no-code space? Because it tackles a very real problem – content migration – with a modern, AI-powered twist. Instead of manually sifting through analytics and guessing which content to prioritize, you’re using AI to surface the *most* impactful pieces. Then, it’s about leveraging AI to optimize that content, not just migrating it as-is. This is gold. We can apply similar concepts to automate data transformation, content creation, and SEO optimization across various projects, using LLMs to assist with content rewriting and keyword analysis. Think of it as a blueprint for using AI to make platform migrations – or any large content handling project – far less tedious and much more effective.

For me, this video is inspiring because it takes a traditionally manual and time-consuming process and offers a streamlined, AI-driven approach. It’s not just about saving time; it’s about making data-informed decisions to actually *improve* results during a migration. I’m definitely keen to experiment with the AI tools they mention and see how I can apply these principles to my own projects, especially those involving large datasets and content repositories. It’s a prime example of how we can move beyond just building features and start building intelligent systems that handle the heavy lifting for us.

Date: 02/26/2025

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Okay, this video is seriously inspiring for anyone diving into the world of AI-assisted development! It’s all about using Cursor, that awesome AI-powered code editor, with Supabase to rapidly build apps. The creator walks through everything: generating UI instructions with Claude, creating a UI from just a screenshot (amazing!), setting up a local Supabase instance, managing the database schema, and even securing the app with Row Level Security (RLS). It’s basically a crash course in modern, AI-driven full-stack development.

What makes this valuable, especially for us devs transitioning to AI, no-code, and LLM workflows, is the practical approach. It’s not just theory; it’s showing how to *actually* use these tools together to speed up development. Think about it: being able to spin up a backend with Supabase CLI, then feeding your database schema to Cursor using something like MCP (Model Context Protocol) so the AI agent *understands* your data… that’s a game-changer. We’re talking about potentially cutting down development time from weeks to days, maybe even hours, especially for common CRUD apps.

I can already see how this applies to my projects. Imagine using Cursor to generate the initial React components and then, with a screenshot of a design, having the AI fill in the layout and styling! Then connecting that directly to a Supabase backend that’s been configured with a few AI prompts! Plus, the focus on security with RLS is crucial. I’m definitely going to experiment with the MCP integration – providing that database context to the AI agent feels like the missing link to truly intelligent code generation. It’s worth trying just for the potential time savings and the cleaner, more maintainable code that comes from having an AI assistant that *actually* understands the project’s data model.

Date: 02/25/2025

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Okay, this video on implementing long-term memory in AI agents using n8n is seriously inspiring. It walks you through building an AI assistant that remembers user preferences, just like ChatGPT. Forget stateless interactions – we’re talking persistent memory that makes your AI feel way more human.

Why is this gold for us developers diving into the AI/no-code space? Because it bridges the gap between traditional coding and LLM workflows. We’re used to managing state in our apps, but now we can offload that to a no-code platform like n8n *and* leverage LLMs to make sense of that state! The video shows you exactly how to save memories to Airtable and retrieve them to inform future interactions. Think about the possibilities: personalized customer support, dynamically tailored learning experiences, or even AI-driven project management that actually *learns* from past projects.

I am pretty excited to try this out. I can see myself using something like this to automate client onboarding, and I can’t wait to explore how this type of memory could drastically improve the user experience in our products. It’s not just about building cool AI toys, but about creating genuinely useful, adaptive applications.

Date: 02/25/2025

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This video on self-hosting Lovable with Coolify is super relevant for anyone, like myself, who’s diving into the world of AI-powered development and automation. It essentially tackles a common problem: how do you scale and control your deployments as your AI-driven projects grow, without breaking the bank with managed services? The video walks you through setting up Coolify, connecting it to your GitHub repos (including private ones!), and automating deployments with webhooks. Plus, it even covers troubleshooting common issues like bad gateway errors, which, let’s be honest, we’ve all been there!

What makes this video particularly valuable is that it demonstrates how to leverage no-code/low-code tools like Coolify to handle the deployment pipeline. This frees up time to focus on the core AI coding aspects of a project. Imagine using AI code generation to rapidly prototype a new feature, then having Coolify automatically deploy it to a self-hosted environment. Also, the promise of integrating self-hosted Supabase in part two, now that’s interesting, because that means you are in full control of your data too!

From my perspective, as someone who’s always looking for ways to streamline my workflow and reduce reliance on external services, this video is a goldmine. The ability to self-host and automate deployments allows for greater flexibility and cost control. And the peace of mind in knowing that you control your own servers, your models and your databases is a fantastic proposition. I’m definitely experimenting with Coolify this weekend!

Date: 02/23/2025

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This FlowiseAI intro video is pure gold for anyone, like me, diving headfirst into AI-powered workflows. It’s a walkthrough on setting up Flowise, an open-source, low-code platform. The video covers three different ways to set up Flowise: local installation, cloud deployment, and Flowise Cloud.

Why is this valuable? Because it bridges the gap between traditional coding and the power of AI. We can leverage visual interfaces to build AI solutions without getting bogged down in complex code, which accelerates development and opens doors to more creative AI integrations. Think about automating customer support with AI-driven chatbots, or even streamlining internal data processing with custom AI workflows.

For example, I’m currently working on a project where we’re using LLMs to analyze customer feedback. Traditionally, this would involve writing a ton of custom code. But with Flowise, I can visually connect different AI components, like language models and data connectors, to create a streamlined process. The fact that the video explores deployment options – local, cloud, and Flowise Cloud – is super practical. It gives you the flexibility to choose the setup that best fits your needs and resources. I’m particularly interested in Flowise Cloud to save myself the headache of managing infrastructure. It’s definitely worth experimenting with to see how it can speed up your AI projects and free you up to focus on the creative side of things.

Date: 02/23/2025

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This video on Supabase’s Row Level Security (RLS) is gold for anyone like me who’s been diving headfirst into AI-assisted development. It basically shows you how to enforce data access rules *right in the database* itself using PostgreSQL’s RLS policies, rather than relying solely on your application code. It walks you through using the Supabase dashboard to visually set up policies that control who can see, edit, or delete data, and shows how these rules play out across different tables and user roles. It even demos using Supabase’s AI tools to simplify policy creation.

What makes this particularly valuable is that RLS can become an essential part of your application’s security architecture. It’s all about moving security closer to the data itself. Instead of relying on potentially buggy or leaky API code to filter data, you define the rules at the database level. This means queries sent directly to the database are automatically filtered based on the user’s role and permissions. The video clearly explains how you can test these policies to ensure they work as expected for different user types. It’s a massive shift towards more robust and secure applications, especially as we start generating more code with AI. This also ties into the broader no-code/low-code movement, because Supabase AI tools are lowering the barrier to entry for complex security configurations, and they are doing it in a way that makes it auditable and repeatable in code!

Honestly, it’s worth experimenting with because it’s a fundamental piece of the puzzle when building secure, scalable applications. It’s no longer enough to just trust your API layer. With AI generating so much of our code, having that extra layer of database-level security gives you much more peace of mind. Plus, Supabase makes it surprisingly easy to get started, even if you’re not a database expert, especially using their AI tools. This is something I’m going to be incorporating into my next project!

Date: 02/22/2025

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Okay, this video looks like a goldmine for anyone, like me, who’s been diving headfirst into no-code automation! It’s all about connecting Salesforce with Google Calendar using n8n, which is a really cool, open-source alternative to Zapier. The video walks you through setting up workflows that automatically create and update Google Calendar events based on changes in Salesforce records. The best part is, it doesn’t just show you *what* to do, but *how* to do it, covering everything from setting up webhooks and outbound messages in Salesforce to using different nodes in n8n.

What makes this particularly valuable is how practical it is. As someone who’s spent years writing custom integrations, the idea of visually building these workflows with n8n is incredibly appealing. Imagine, instead of spending days coding and debugging API calls, you could achieve the same result with a drag-and-drop interface! Plus, the video also explains how to test these automations without racking up costs, which is a huge win. The video compares the approach to a similar one using Zapier, this kind of benchmarking is useful to show the trade offs in technology, in terms of cost and effort, that we need to make in evaluating these tools.

I’m itching to experiment with this! The outbound message approach, in particular, seems like a more robust and reliable trigger than some of the polling methods I’ve used in the past. Plus, I’m always looking for ways to reduce our reliance on expensive platforms and move towards more open and customizable solutions. Who knows, maybe n8n could become the new backbone of our integration strategy!