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YouTube Videos I want to try to implement!

  • 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.

  • AI Tools Are Outpacing How We Build Software



    Date: 11/04/2025

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    Okay, this video on Codex Cloud’s “Apply” feature is seriously hitting home. It’s essentially showing how AI-powered tools like Codex and Claude are outpacing our traditional development workflows. Imagine this: you ask AI to improve an animation, and suddenly you’re drowning in parallel builds, variant branches, and a PR nightmare across GitHub, Cursor, Netlify, Vercel. The core issue isn’t the AI or the code, it’s that our SDLC wasn’t designed for this hyper-speed creation.

    The real value for someone like me, who’s been diving deep into AI coding and no-code tools, is that it highlights a critical bottleneck. We’re automating code generation, but the deployment and management processes are stuck in the past. The video walks through a concrete example of how this “Apply” pattern exposes the cracks in our workflows. AI can create branches and PRs, but managing them in GitHub becomes a whole other beast.

    What’s inspiring about this is the call to rethink the entire “building software” process. It’s not just about writing code anymore; it’s about orchestrating AI-generated code, managing parallel changes, and streamlining deployment. The idea of potentially bypassing the desktop entirely for certain tasks (as teased in the video) is incredibly enticing. I’m definitely going to experiment with Codex Cloud to see how it can help bridge this gap and bring my workflow up to AI speed. It’s time we started building processes for AI, not just with AI.

  • you need to learn MCP RIGHT NOW!! (Model Context Protocol)



    Date: 11/03/2025

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    Okay, this video on the Model Context Protocol (MCP) looks like a game-changer! In a nutshell, it’s about enabling LLMs like Claude and ChatGPT to interact with real-world tools and APIs through Docker, instead of being stuck with just GUIs. The video walks you through setting up MCP servers, connecting them to different clients (Claude, LM Studio, Cursor IDE), and even shows how to build your own custom servers, including a Kali Linux hacking example. Seriously cool stuff!

    Why is this valuable for someone like me—and probably you, too—who’s diving into AI-enhanced development? Because MCP bridges the gap between the powerful potential of LLMs and our existing workflows. No more copy-pasting code snippets or relying on limited chatbot interfaces. We can now build intelligent, automated systems that leverage AI to interact directly with our code, tools, and environments. Think automated security testing in Kali via AI, or seamlessly integrating AI-powered code completion and refactoring into VS Code.

    For me, the real inspiration is the potential for automating tasks that I used to dread. Imagine using an LLM, via an MCP server in a Docker container, to automatically document a legacy codebase or even generate tests! Being able to build custom MCP servers to connect AI to any application is pure gold. I am keen to experiment with this. The Kali Linux demo alone makes it worth checking out – a fun, real-world application of this tech. The fact that Docker simplifies the deployment and management of MCP servers is just icing on the cake.

  • GitHub Trending monthly #1: nanochat, DeepSeek-OCR, TOON, AI-Trader, Superpowers, BentoPDF, Dexter



    Date: 11/02/2025

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    Okay, so this video is essentially a rapid-fire showcase of 20 trending open-source GitHub projects from the past month, covering everything from AI-powered chatbots (nanochat) and OCR solutions (DeepSeek-OCR) to AI Trading tools (AI-Trader) and developer utilities like Networking Toolbox. It’s like a buffet of cool new tech!

    Why is it gold for a developer like me (and maybe you) who’s diving headfirst into AI coding and no-code? Because it’s a curated snapshot of what’s buzzing in the open-source community right now. We’re not talking about theoretical possibilities; these are real, actively developed projects tackling real-world problems. Imagine using something like “Open Agent Builder” to automate client onboarding, or “Paper2Video” to generate marketing materials, or using “DeepSeek-OCR” to automate processing client documents, that kind of innovation is a game changer.

    Honestly, what gets me excited is the sheer breadth of innovation. You can see tangible applications of LLMs and AI in areas you might not have even considered. It’s a great way to spark ideas for automation, workflow optimization, and even entirely new product offerings. I’m particularly interested in diving deeper into projects like “Open Agent Builder” and seeing how I can integrate it with our existing Laravel applications. Experimenting with these trending repos is how we stay ahead of the curve and build truly next-generation solutions.