YouTube Videos I want to try to implement!

Date: 04/03/2025

Watch the Video

Okay, so this video is all about Augment Agent, an open-source AI pair programmer that’s aiming to be smarter and faster than tools like Cursor and Windsurf. It’s particularly interesting because it claims to deeply understand your codebase, learn your style, and even evolve with you. Plus, it seems to handle large projects without choking on context limits, a common pain point with other AI coding assistants.

Why is this relevant for us as we transition into AI-enhanced development? Well, the promise of a truly agentic AI that can genuinely understand and assist with complex projects is HUGE. Think about spending less time wrestling with boilerplate, debugging repetitive tasks, or even just getting a second opinion on architectural decisions. The video highlights features like multi-component prompting (MCP) allowing integration with APIs, SQL, and CLI tools, which speaks directly to the need to integrate AI into existing workflows, not replace them.

Honestly, what really sells it for me is the open-source nature and the SWE-bench verification. The fact that you can dig into the code, contribute, and see how it performs on standard benchmarks is incredibly valuable. Imagine finally having an AI assistant that truly understands your codebase and contributes meaningfully to your projects – automating the mundane and freeing you to focus on the creative, problem-solving aspects of development. Seems worth a shot to me!

Date: 04/02/2025

Watch the Video

Okay, this Supabase Edge Functions update is seriously interesting, especially with Deno 2.1 and full Node.js compatibility. In essence, the video (and accompanying blog post) highlight how you can now build and deploy serverless functions directly from the Supabase dashboard, using either Deno or Node.js. The big deal? No more messing with complex configurations; you can just write your code and ship it, leveraging the power of serverless without the usual setup headaches. They’ve even baked in seamless package management, which is huge for dependency wrangling.

For a developer like me, constantly exploring AI coding and no-code/low-code solutions, this is valuable because it streamlines a crucial part of the development workflow: the backend. Think about it: instead of spending hours configuring servers and deployment pipelines, I can focus on the AI-powered logic and user experience, letting Supabase handle the infrastructure. For example, I’ve been experimenting with using LLMs to generate code for specific API endpoints. With these enhanced Edge Functions, I could deploy those AI-generated endpoints directly from the Supabase dashboard with very little setup. That’s a massive productivity booster and means the time from “AI generated code” to “deployed feature” is drastically reduced.

The potential applications are vast. Imagine automating complex data transformations, integrating third-party services, or building custom authentication flows all with code deployable with one click. It lets you focus on the unique value you bring to a project. It’s worth experimenting with because it aligns perfectly with the direction I’m heading: leveraging powerful tools to abstract away complexity and focus on building intelligent, automated solutions. Plus, the ability to migrate existing Node.js apps with minimal changes? Yes, please!

Date: 04/02/2025

Watch the Video

Okay, this Supabase update on “Broadcast from Database” is seriously interesting, especially if you’re like me and trying to leverage AI and no-code for faster, smarter development. Essentially, it’s about getting real-time database updates directly to your client-side applications with much more control. Instead of relying on something like Postgres Changes which can be a bit of a firehose, this lets you define exactly what data you want to broadcast and when, using Postgres triggers. Think about it: no more over-fetching data, cleaner payloads, and you can even perform joins within the trigger itself, eliminating extra queries!

Why is this valuable in our new AI-driven world? Because it provides the precise, structured data that LLMs crave for analysis, automation, and intelligent application features. Imagine building a real-time dashboard that’s not only responsive but also feeds specific data points into an LLM to trigger automated alerts or workflows. Or a collaborative app where AI can analyze user interactions as they happen and suggest improvements – all powered by this finely tuned real-time stream. Instead of feeding raw data to an LLM, this approach ensures that the AI has access to pre-processed and relevant information, leading to improved accuracy and faster decision-making.

For me, the power of shaping the payload is the real game-changer. If I was building a new feature based on real-time analytics, by using AI tools such as Cursor, Github Copilot or even Phind, I could write the trigger function to optimize the payload and immediately test it. This approach not only reduces bandwidth and client-side processing, but it also lowers the risk of exposing sensitive data and optimizes the data for AI analysis. It feels like a perfect bridge between backend database logic and the intelligent front-end experiences we’re all aiming to create. Definitely worth experimenting with!

Date: 04/01/2025

Watch the Video

Okay, so this video is gold for us devs diving into the AI space. It’s all about Cache-Augmented Generation (CAG), which is like RAG’s smarter, faster cousin. Instead of hitting the database every time, it leverages server-side memory from the big players like OpenAI, Anthropic, and Google Gemini. The video then pits CAG against traditional RAG in a head-to-head comparison focusing on speed, cost, and accuracy. It demos the implementation using n8n, showing how to set up workflows with different LLMs and how to upload documents to Gemini’s cache. Super practical stuff.

Why’s it valuable? Well, as we’re transitioning into AI-enhanced workflows, RAG is becoming a foundational piece for building AI tools that actually know something beyond their training data. This video takes it a step further. The comparison between CAG and RAG is key – it helps us understand when it’s worth investing in a more sophisticated caching mechanism. Plus, the n8n demo is killer because it provides a tangible, no-code approach to integrating these techniques. Instead of abstract theory, you see real workflows.

Think about it: We’re building more and more complex applications that rely on LLMs. The ability to reduce latency and lower costs while maintaining (or even improving) accuracy is HUGE. Imagine using CAG for customer support chatbots, internal knowledge bases, or even code generation tools that need to quickly access and recall vast amounts of information. Honestly, what I find most inspiring is the practical, hands-on approach. It’s not just about the “what,” but the “how.” I’m definitely eager to experiment with CAG to see how it stacks up against our current RAG implementations. Plus, n8n makes it super easy to prototype and test these ideas, so why not give it a shot?

Date: 04/01/2025

Watch the Video

Okay, this Supabase UI Library video is exactly the kind of thing I’m geeking out about these days. It’s all about pre-built UI components – authentication, realtime collaboration, file uploads, even AI-powered coding rules integrated directly into your Supabase workflow. Forget spending hours building basic UI elements from scratch; this library lets you drag-and-drop your way to a functional app, which frees up time to focus on the actual innovative parts of your project. As someone knee-deep in AI coding and no-code solutions, this resonates big time!

Why is this valuable for developers moving into the AI/no-code space? Well, think about it: we’re trying to offload the repetitive tasks to AI and automation so we can focus on architectural design and complex logic. This library does the same thing for the front-end. For instance, instead of hand-coding a file upload feature, you drop in a pre-built component and spend your time integrating it with, say, an LLM to automatically tag and categorize the uploaded files. That’s real-world automation powered by AI, and this UI library is the perfect jumping-off point.

Honestly, the AI Rules feature alone makes this worth experimenting with. The video hints at using AI to guide code quality, which is HUGE. Imagine integrating that with existing LLM workflows to generate code that’s not only functional but also adheres to best practices. This is the sweet spot where AI enhances, not replaces, our coding, and it’s why I’m planning to spend some serious time playing with this Supabase UI Library. Plus, anything that helps me “ship faster” gets a gold star in my book!

Date: 03/27/2025

Watch the Video

Okay, this video is gold for anyone trying to bridge the gap between traditional coding and AI-powered automation. It’s all about using Claude 3.7 to generate n8n workflows, JSON templates, and even sticky notes directly from screenshots or YouTube transcripts. Forget manually building everything from scratch – this video shows you how to literally “show” the AI what you want, and it generates the necessary code and documentation. Pretty wild, right?

Why is this a game-changer? Well, for me, it’s about speed and accessibility. I’ve spent countless hours tweaking n8n workflows, and the idea of just uploading a screenshot and getting a functional template in return is mind-blowing. Plus, the video highlights Claude’s “Extended Thinking” capabilities, which means the AI isn’t just mindlessly converting images to code; it’s actually understanding the logic and optimizing it. Imagine grabbing a workflow from a YouTube tutorial, pasting the transcript, and having Claude not only generate the workflow but also add helpful notes explaining each step. This is HUGE for learning and customization.

The practical applications are endless. Think onboarding new team members, rapidly prototyping automation ideas, or even reverse-engineering complex workflows you find online. It’s like having an AI coding assistant dedicated to streamlining your automation efforts. I’m definitely experimenting with this. I’m eager to see how it handles some of the more complex workflows I’ve built and how much time I can save on future projects. The potential to create custom templates without having to pay a fortune is seriously tempting.

Date: 03/26/2025

Watch the Video

Alright, this video is pure gold for anyone transitioning into AI-enhanced development. It’s all about setting up Anything LLM locally and building custom agents. We’re talking about running different LLMs, even optimizing for RTX GPUs, and diving into the world of private AI interaction. The video goes through step-by-step, showing how to configure custom agents and utilize their skills. Plus, it touches on the community hub and other useful tools.

Why is this valuable? Well, for us developers, local LLM setups mean data privacy and control, which is huge for sensitive projects. Building custom agents opens doors to automating complex tasks that previously required tons of manual coding. Imagine creating agents specialized for code review, documentation, or even refactoring. This aligns perfectly with incorporating AI into our workflows, streamlining development, and boosting productivity.

This kind of hands-on approach is inspiring because it bridges the gap between theoretical AI and practical application. The idea of running these tools locally, experimenting with different models, and tailoring agents to specific tasks? That’s something worth sinking your teeth into. It’s about taking control of the AI, making it work for you, and ultimately, building smarter, more efficient solutions. Definitely worth experimenting with to see what it can bring to your workflow.

Date: 03/26/2025

Watch the Video

Okay, this video on building a restaurant reservation system with N8N and VAPI is seriously cool and right up our alley! It’s basically about creating an AI voice receptionist using no-code tools. Think about it: instead of a human answering the phone, an AI handles booking reservations, potentially managing multiple calls simultaneously.

For us devs diving into AI and no-code, this is gold. The video breaks down how to build the entire workflow in N8N, from setting up the initial call flow to extracting reservation details using VAPI. It’s not just theoretical; it walks you through creating the tools, testing the process, and even talks about enhancements. It is incredibly powerful to extract structured data using AI instead of Regex. This is a must have to be able to connect LLMs to databases. Imagine automating all those tedious tasks with AI.

What makes this worth experimenting with is the tangible application. We can apply these concepts to automate customer support, appointment scheduling, or even lead qualification processes. Plus, the potential cost savings and efficiency gains are huge. I am excited to try out building my own AI powered voice assistant for my web apps. It’s a great way to see how these new tools can revolutionize how we build and deploy solutions.

Date: 03/25/2025

Watch the Video

Okay, this Perplexica video looks seriously cool. It’s basically about an open-source, AI-powered search engine inspired by Perplexity AI, but you can self-host it! It uses things like similarity searching and embeddings, pulls results from SearxNG (privacy-focused!), and can even run local LLMs like Llama3 or Mixtral via Ollama. Plus, it has different “focus modes” for writing, academic search, YouTube, Wolfram Alpha, and even Reddit.

Why am I excited? Because this screams custom workflow potential. We’ve been hacking together similar stuff using the OpenAI API, but the thought of a self-hosted, focused search engine that I can integrate directly into our Laravel apps or no-code workflows is huge. Imagine a Laravel Nova panel where content creators can research articles by running Perplexica’s “writing assistant” mode, then import the results into their CMS. Or an internal knowledge base that leverages the “academic search” mode to keep employees up-to-date with the latest research. The privacy aspect is also a big win for clients who are sensitive about data.

Honestly, the biggest appeal is the control and customization. I’m already brainstorming how we could tweak the focus modes and integrate them with our existing LLM chains for even more targeted automation. The fact that it’s open source and supports local LLMs means we aren’t just relying on closed APIs anymore. I’m definitely earmarking some time this week to spin up a Perplexica instance and see how we can make it sing. Imagine the possibilities!

Date: 03/24/2025

Watch the Video

Okay, so this video is basically about building and monetizing a software portfolio, specifically using AI agents. Simon’s selling access to his FounderStack portfolio as a one-time purchase, and it looks like a great example of leveraging AI to create and launch multiple SaaS projects.

For someone like me diving into AI coding, no-code, and LLM workflows, this is gold. It’s inspiring because it showcases how we can shift from building one huge app to creating a suite of smaller, specialized tools. Think about it: using AI to rapidly prototype and launch mini-SaaS products that address niche needs. We could build AI-powered content generators, or specialized data analysis tools tailored to specific industries, and bundle them up in a portfolio.

The real-world application is huge. Instead of spending months on a single project, we could use LLMs to generate the boilerplate code, AI agents to automate testing and deployment, and no-code tools for the UI. This accelerates the entire development lifecycle. It’s worth experimenting with because it could dramatically reduce development costs and time to market, while also diversifying your income streams. I’m definitely grabbing FounderStack; seeing how Simon structures his portfolio and uses AI is a powerful motivator.