YouTube Videos I want to try to implement!

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.

Date: 11/14/2025

Watch the Video

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.

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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.

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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.

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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.

 

Date: 11/14/2025

Watch the Video

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:

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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.

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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.

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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.

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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.

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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.

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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.