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YouTube Videos I want to try to implement!

  • Langflow Crash Course – Build LLM Apps without Coding (Postgres + Langfuse Setup)



    Date: 11/17/2025

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    Okay, this Langflow video is seriously inspiring for anyone, like myself, knee-deep in the shift towards AI-enhanced development. It essentially walks you through using Langflow, a low-code Python-based platform, to visually build LLM applications and AI agents without a ton of frontend coding. It covers everything from installation to creating flows, API exposure with authorization, custom components, Langfuse integration for monitoring, and even how to get it production-ready. That’s a lot!

    What makes this video gold for us is the bridge it builds between traditional coding and the world of LLM-powered apps. We’re talking about visually designing complex workflows, plugging in your own Python code where needed, and monitoring everything with Langfuse. Think about it: you can rapidly prototype an AI-driven chatbot, integrate it with your existing Laravel backend through the API, and then monitor its performance, all without getting bogged down in endless lines of React or Vue.js. Plus, the video shows how to add your own custom components, meaning you can really tailor the platform to your specific needs.

    I’m particularly excited about the production-grade setup section. Too often, these AI tools feel like toys, but this video tackles the practicalities of deploying something real. The promise of being able to “ship to customers” something built primarily visually, but backed by solid Python and API security, is huge. The video makes it worth experimenting with. I’m already thinking about how I can use it to automate some of my client’s customer service workflows!

  • Google Just Made Multi-Agent AI EASY (Visual Builder Hands-On)



    Date: 11/16/2025

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    Okay, this new Google Agent Development Kit (ADK) update with the Visual Agent Builder looks like a game-changer, and this video is exactly the kind of thing that gets me excited about the future of development. The video gives a hands-on walkthrough of building a hierarchical stock analysis agent using the new visual interface – no code needed at first! We’re talking about orchestrating multiple agents, each with specific tasks, like gathering news or analyzing financial data, all connected in a logical flow. They even show how to integrate Google Search and use an AI assistant to help generate the YAML config.

    What’s particularly valuable about this is it democratizes the initial prototyping phase. As someone transitioning from traditional PHP/Laravel development to more AI-centric workflows, I see massive potential in using visual tools to rapidly experiment with agent architectures before diving into the nitty-gritty code. Instead of spending hours writing YAML and debugging, you can visually map out your multi-agent system, define the roles and relationships, and then let the tool generate the necessary configuration. Think of it like visually building a Laravel pipeline before crafting the actual PHP classes, but with AI! For example, imagine using this to build a customer support chatbot that routes inquiries to different agents based on topic or urgency, all without initially writing a single line of code.

    Honestly, the prospect of visually designing complex agent interactions and then deploying them with minimal hand-coding is incredibly appealing. The video even hints at a follow-up about building a custom UI, which is the perfect next step. I’m already thinking about how I can integrate this into our existing Laravel applications to automate complex business processes. I think experimenting with the Visual Agent Builder and seeing how it can streamline the creation of AI-powered features is well worth the time investment.

  • Local AI GPUs, RAM, AGI Predictions on Open Source AI



    Date: 11/15/2025

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    Okay, this video from Digital Spaceport dives deep into the evolving landscape of running local AI, and it’s super relevant for anyone looking to integrate LLMs into their workflows. It basically tackles the growing challenge of hardware requirements – specifically GPUs and RAM – needed to run these models effectively. The creator explores different GPU options, from the beefy 24GB 3090 to the more budget-friendly 16GB cards like the upcoming 5070ti, comparing their performance and cost-effectiveness. It even showcases a complete quad-GPU Ryzen build designed for serious local AI processing.

    Why’s this valuable? Because as we move further into AI-powered development, understanding the hardware bottlenecks is crucial. I’ve been experimenting with LLMs for code generation, automated testing, and even documentation, and I’ve definitely hit the wall on my existing setup. The video helps you think about the practical side of things – what kind of hardware investments are needed to actually use these models effectively. It also touches on the open vs. closed model debate, which is a key consideration when you’re deciding which AI tools to integrate into your workflow. Are you fine with cloud-based limitations, or do you want the flexibility and privacy of running models locally?

    Think about it: being able to run a powerful LLM locally opens up possibilities like offline development, fine-tuning models with proprietary datasets, and building truly private AI-powered applications. The creator even mentions how the hype around AGI might backfire if the focus is solely on closed-source, resource-intensive models. Ultimately, this video is worth checking out because it’s a pragmatic look at the nuts and bolts of local AI, and it inspires you to start experimenting with different hardware configurations to find what works best for your specific needs and budget. It’s not just about the fancy algorithms; it’s about making AI practically useful, right here, right now!

  • Massive World Model Release & AI Agent Action! Marble & Google’s SIMA 2!



    Date: 11/15/2025

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    Okay, this video is a goldmine for any developer looking to leverage AI in creative workflows! It dives into two major advancements: World Labs’ Marble, which allows you to create and manipulate 3D environments with surprising ease, and Google Deepmind’s SimA-2, an agent learning in AI-generated worlds. The presenter even uses Marble to build a virtual set for their short film, walking you through multi-image world creation, camera animation, and exporting for further refinement. Think of it as a practical bridge between traditional 3D tools and the new world of AI-powered virtual sets.

    For me, that’s what makes it compelling. As someone who’s spent years wrestling with complex 3D software, the idea of rapidly prototyping and iterating on virtual environments using intuitive tools like Marble is incredibly exciting. And the SimA-2 piece? That shows where this is all heading – AI agents understanding and interacting with these environments, which opens doors for automating tasks, creating dynamic game experiences, and even robotics. Imagine using Marble to quickly build test environments for a robotic application, then letting an AI agent learn and adapt within that space.

    Seriously, the accessibility of Marble (free credits to get started!) makes it worth experimenting with. The presenter shows how you can bash together images to create unique environments, add animated camera moves, and then export all of that to Blender or Unreal for fine-tuning. Even if you’re not a 3D artist, the node-based editing they touch on is surprisingly intuitive and powerful. Plus, understanding spatial intelligence is crucial as AI becomes more integrated into our world. This isn’t just about cool demos; it’s about grasping the underlying principles that will shape the future of AI in video, games, and beyond. I’m already brainstorming ways to use Marble for creating immersive training simulations!

  • Ollama’s Hidden Limitation… How Llama.cpp Quietly Fixes it



    Date: 11/14/2025

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    Okay, so this video is all about getting your hands dirty with local Large Language Models (LLMs) using llama.cpp and comparing it to Ollama. It walks you through setting up a llama.cpp Web UI with GGUF models and then does a speed comparison with Ollama. For someone like me, who’s been knee-deep in Laravel and now transitioning to incorporating AI coding, no-code tools, and LLM workflows, it’s gold.

    Why? Because it directly addresses the challenge of running these models locally. As developers, we often rely on cloud-based AI solutions, but having a local setup allows for offline development, greater privacy, and the ability to fine-tune models without exorbitant costs. The comparison between llama.cpp and Ollama is particularly valuable, as it helps you decide which tool fits best with your project’s needs. For example, using llama.cpp directly gives more control for customization, while Ollama provides an easier setup.

    Imagine automating code generation tasks within a Laravel application or building a local chatbot for internal documentation – all without sending data to external servers. That’s the power of this approach. Setting up the Llama.cpp Web UI creates a more user-friendly interaction. Seeing this video, I can’t help but think of the endless possibilities of combining local LLMs with Laravel’s task scheduling and queueing systems, this is worth experimenting with to unlock a new level of automation and customization for our projects.

  • Open Web UI Tutorial: Run LLMs Locally!



    Date: 11/14/2025

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    Another video I enjoyed this week walked through Open WebUI, an open-source desktop interface for running LLMs locally. Think of it as the ChatGPT experience… but fully offline, powered entirely by your own machine. If you’ve ever wanted an “LLM you can take on a plane,” this is that.

    What It Is

    Open WebUI lets you:

    • Download model weights (through Ollama)

    • Run them locally with no internet

    • Or connect API-based models like ChatGPT and Claude if you prefer

    • Switch between local and cloud models inside the same interface

    It’s basically a unified front end for local and remote LLMs, and it’s surprisingly polished.

    What It Can Do

    Local Code Generation & Real-Time Preview

    The demo starts with building a simple puppy-themed website. With a local model, it’s slower than ChatGPT, but fully offline. Open WebUI even renders the output live as the model generates it.

    Side-by-Side Model Comparisons

    You can run multiple models in parallel and compare their answers to the same prompt — perfect for benchmarking local vs. cloud results.

    Custom Reusable Prompts

    Open WebUI lets you store templates with variables.

    Example: create an “email template,” type /email template, and it auto-inserts your text with fields you can fill in.

    Change temperature, top-k, or even make the model talk “like a pirate.”

    Chatting With Your Own Documents

    The knowledge base feature lets you load an entire folder of documents (résumés in the demo) and query across them.

    Ask: “Which candidates know SQL?”

    It pulls the relevant docs, extracts the evidence, and responds with citations.

    A lightweight local RAG system.

    Built-In Community Marketplace

    There’s a growing library of:

    • community-created functions

    • tools

    • model loaders

    • data visualizers

    • SQL helpers

    All installable with one click.

    Installation

    Option 1: Python / Pip

    pip install open-webui
open-webui serve

    Runs on localhost:8080.

    Option 2 (Recommended): Docker

    One copy-paste command installs and runs the whole thing on localhost:3000.

    Extra Step: Install Ollama

    Ollama handles downloading and running the actual model weights (Llama 3.1, Mistral, Gemma, Qwen, etc.).

    Paste the model name in Open WebUI’s admin panel and it pulls it directly from Ollama.

    Why This Video Stood Out

    This wasn’t a hype piece. It was a practical walkthrough showing Open WebUI as:

    • a clean interface

    • a real local AI workstation

    • a bridge between local and cloud models

    • a free tool that’s genuinely useful for developers, analysts, and tinkerers

    It’s basically the easiest way right now to get into local LLMs without touching the command line every time.

     

  • Is Gemini File Search Actually a Game-Changer?



    Date: 11/14/2025

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    his week I watched a deep dive on Gemini File Search, and despite all the hype (“RAG killer!”), the reality is more grounded. It is useful, but not magic, and definitely not replacing real RAG systems anytime soon.

    At its core, Gemini File Search is Google’s fully managed RAG pipeline — you upload files, it chunks them, embeds them, stores them, and then uses those vectors to ground responses. No Pinecone, no pgvector, no Supabase storage. Just upload and query.

    Why the hype?

    The pricing. Storage is free, embeddings are cheap, and inference depends on whatever Gemini model you choose. Compared to OpenAI’s storage fees, Google positioned this aggressively.

    But once you look under the hood, several important realities show up:

    1. You Still Need a Data Pipeline

    The “upload a PDF in the browser and start chatting” demo is great… for demos.

    Real systems bring thousands of documents, handle updates, prevent duplicates, and maintain a clean knowledge base. Gemini does zero dedupe. Upload a file three times and you’ll get three identical chunks polluting your search results.

    So you still need a pipeline for:

    • file hashing

    • uniqueness checks

    • update detection

    • record management

    • scheduled ingestion

    Gemini simplifies the vector work, but not the actual operational work.

    2. Mid-Range RAG, Black-Box Internals

    The system is better than naïve RAG, but missing higher-end tools like:

    • hybrid search

    • contextual embeddings

    • re-ranking

    • multimodal chunk-level reasoning

    • structured retrieval for tables/spreadsheets

    You also can’t see inside what it’s doing. When responses degrade, you’re stuck. There’s no tuning, no custom chunking, no reranking.

    Good for simple use cases. Wrong tool once you hit complexity.

    3. Basic OCR, Basic Chunking, No Markdown

    The good:

    • OCR works and is fast

    • It handles non-machine-readable PDFs

    The downside:

    • No markdown structure

    • Headings lost

    • Chunk boundaries often split sentences

    • Coarse chunking hurts accuracy

    For anyone who relies on structured chunking (and most serious RAG setups do), this is a limitation.

    4. Metadata Is Harder Than It Should Be

    Gemini doesn’t let you fetch all chunks of a processed document. That makes real metadata extraction hard, since you can’t reconstruct the content after upload.

    To add rich metadata, you need a second text-extraction pipeline… which defeats much of the “fully managed” promise.

    A simple “fetch all chunks for doc X” endpoint would solve this problem overnight.

    5. Vendor Lock-In & Data Residency

    All data sits with Google. If you care about:

    • privacy

    • PII

    • GDPR

    • on-prem requirements

    …you’re living inside their walls.

    And you can only use Gemini models with Gemini File Search. No mixing ecosystems. No swapping out the model later.

    Verdict

    Gemini File Search is RAG as a service, not a RAG killer. It’s not new — OpenAI and others already offer similar pipelines — but the pricing and simplicity are compelling. For light to mid-level use cases, it’s a great on-ramp.

    But the moment you need:

    • full control

    • advanced retrieval techniques

    • transparency

    • structured pipelines

    • guaranteed accuracy

    …you’ll eventually have to replatform.

    Still — it’s a strong option for fast prototyping or small-to-medium business workflows where simplicity wins.

  • The most extensible AI-powered open-source no-code platform: NocoBase



    Date: 11/09/2025

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    Okay, this video about Appsmith’s AI Agents looks seriously inspiring. It essentially showcases how to build AI agents that can connect to all your data sources – Salesforce, Zendesk, databases, even internal documentation – without the usual headache of custom integrations. Think of it as a single AI brain that actually knows what’s going on in your entire business, providing insightful support, automating mundane tasks, and surfacing critical information in real-time.

    As someone deeply involved in transitioning from traditional development to AI-powered workflows, this is precisely the kind of solution I’m after. We all know data silos are a massive problem, and this promises to break them down using AI in a secure, enterprise-grade way. Imagine the possibilities! No more writing tons of custom API connectors or wrestling with different data formats. We could automate things like lead qualification, customer support ticket routing, or even generate internal reports based on data pulled from disparate systems.

    What really makes this worth trying is Appsmith’s low-code approach. This isn’t about becoming an AI expert; it’s about leveraging AI agents to streamline existing workflows. Setting up connections in minutes instead of weeks? That’s a game-changer in terms of time and cost savings. I’m keen to experiment with building a proof of concept to see how easily we can integrate it with our existing Laravel applications and automate some of our most time-consuming processes. The potential here is huge for faster development, improved data accessibility, and ultimately, happier clients.

  • 3-Click Agents: Instant Multi-Source Enterprise AI



    Date: 11/09/2025

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    Okay, this video about Appsmith’s AI Agents looks seriously inspiring. It essentially showcases how to build AI agents that can connect to all your data sources – Salesforce, Zendesk, databases, even internal documentation – without the usual headache of custom integrations. Think of it as a single AI brain that actually knows what’s going on in your entire business, providing insightful support, automating mundane tasks, and surfacing critical information in real-time.

    As someone deeply involved in transitioning from traditional development to AI-powered workflows, this is precisely the kind of solution I’m after. We all know data silos are a massive problem, and this promises to break them down using AI in a secure, enterprise-grade way. Imagine the possibilities! No more writing tons of custom API connectors or wrestling with different data formats. We could automate things like lead qualification, customer support ticket routing, or even generate internal reports based on data pulled from disparate systems.

    What really makes this worth trying is Appsmith’s low-code approach. This isn’t about becoming an AI expert; it’s about leveraging AI agents to streamline existing workflows. Setting up connections in minutes instead of weeks? That’s a game-changer in terms of time and cost savings. I’m keen to experiment with building a proof of concept to see how easily we can integrate it with our existing Laravel applications and automate some of our most time-consuming processes. The potential here is huge for faster development, improved data accessibility, and ultimately, happier clients.

  • Package Your n8n Workflows Into Full Web Apps (Step-By-Step)



    Date: 11/05/2025

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    Okay, this video is gold for anyone like me who’s knee-deep in trying to leverage AI and no-code to speed up development. It’s basically a walk-through of building a client-branded article generation app from scratch using Lovable for the front-end, Supabase for the backend, and n8n for all the heavy-lifting automation. No code!

    The really inspiring part is seeing how these tools snap together to handle complex tasks like article outlining, content generation, image creation, and even WordPress publishing, all orchestrated by n8n. I’ve been wrestling with similar workflows using Laravel queues and custom APIs, and the thought of simplifying that with visual, no-code tools like this is seriously appealing. The idea of “microservices” via n8n workflows that are triggered by secure webhooks from Lovable, connected to a Supabase backend is where I am trying to get to.

    For us Laravel devs, think of it as offloading the messy backend logic to n8n, letting it handle the AI integrations and external APIs, while we focus on the core application logic and user experience. Plus, the video addresses real-world concerns like multi-tenancy, client payments, and security. It’s not just theory; it’s a practical example of how to ship actual projects faster. I’m definitely going to experiment with this stack; the potential for rapid prototyping and client-specific customizations is huge. I think I can have something working in a week that used to take a month.