Venelin Valkov — Channel Summaries — Page 2
AI-powered summaries of 131 videos about Venelin Valkov.
131 summaries
gpt-oss - OpenAI Open-Weight Reasoning Models | Ollama test, Benchmaxing, Safetymaxing?
OpenAI’s newly released open-weight reasoning models—GPT OSS 120B and GPT OSS 20B—sparked hype for matching closed-model performance on popular...
Build AI Agent from 0 to Production Deployment | LangChain, Ollama, MLflow & Docker (Full Tutorial)
A unit-conversion AI agent can be built end-to-end—from a single-tool loop to a streaming REST API—then packaged into a Docker container and deployed...
ML Project Template for 2025 - Build ML Pipelines with Python, uv, DVC, FastAPI, Docker
A ready-to-use machine learning project template is positioned as a 2025 blueprint for taking a model from dataset creation to a production-style...
AI Agents with LangGraph & Llama 3 | Control the Execution Flow and State of Your Agent Apps
LangGraph is positioned as a way to control both the execution order and the evolving state of agentic applications—down to loops, branching, and...
Build an AI Social Media Content Generator in 20 Minutes | AI Agents with LangGraph and Llama 3.1
A LangGraph-based agent loop can turn technical input into platform-ready social posts for both Twitter and LinkedIn—while iterating through multiple...
Build Private Chatbot wtih LangChain, Ollama and Qwen 2.5 | Local AI App with Private LLM
A fully local “private chatbot” workflow can be built by combining LangChain’s message orchestration (via LangGraph), Ollama for on-device model...
OCRFlux (3B) - Local OCR AI Model Test | Turn PDFs into Markdown
OCRFlux (3B) is a 3B-parameter visual-language OCR fine-tune aimed at turning document images (including PDFs) into structured Markdown. In local...
Gemini CLI - FREE? Claude Code by Google | First Look and NextJS RAG App Test
Gemini CLI lands as a free, open-source “developer-terminal” layer for Google’s Gemini Code Assist, pairing a ChatGPT-like coding workflow with a...
Build Production-Ready Retrieval RAG Pipeline in LangChain | Hybrid Search (BM25), Re-ranking & HyDE
A production-ready RAG pipeline needs more than embeddings: it must reliably fetch the right chunks, even when users ask for exact numbers. A simple...
Advanced AI Agents with LangGraph and Llama 3.1 | Analyze Bitcoin, Ethereum and Solana Markets
An AI agent workflow built with LangGraph can generate cryptocurrency market reports by combining three streams of evidence: cached historical price...
ToDo list Embeddings with TensorFlow in JavaScript
A practical path to “icon suggestions” for a to-do app hinges on turning short task text into numeric embeddings and then measuring similarity...
Build Local Long-Running AI Agent (Stop Your Agents from Getting Lost) | LangChain, Ollama, Pydantic
Long-running AI agents often lose their footing as tasks stretch across multiple context windows—hallucinations creep in, code can be rewritten or...
DeepSeek R1 0528 - Better Coding & Tool Calling | Is It Faster Now?
DeepSeek R1 0528’s update centers on making the model more usable for real-world coding agents by adding support for JSON output and function...
Segment Anything by Meta Research: Image Segmentation with the Largest Dataset and Model Yet!
Meta’s Segment Anything (SAM) is built to turn image segmentation into a “promptable” task: users can click, draw boxes, or provide text-like prompts...
Build Smarter AI Apps: Memory, Tools, Retrieval & Structured Output with Python, Pydantic & Ollama
AI apps become meaningfully more useful when they’re given four upgrades beyond plain text prompting: memory, structured outputs, tool use, and...
Advanced RAG Chunking: Contextual & Structural Chunking with LangChain & Ollama (100% Local)
Turning a large, converted PDF markdown into retrieval-ready chunks is often where RAG pipelines lose both speed and accuracy. The core fix here is a...
Machine Learning Engineer Mock Interview for Meta (Facebook) with ChatGPT
ChatGPT performs unevenly in a mock machine learning engineer interview for Meta: it delivers strong, technically correct answers on coding and many...
FLUX.1 Kontext [dev] Local Test - Image Generation and Edit with HuggingFace (Open Weights Model)
Black Forest WS’s FLUX.1 Context Dev (open weights) is proving it can do more than image editing: it can also generate photorealistic images from...
What is RAG? The Complete Tutorial - From Scratch to Deployed API on Production | LangChain & Ollama
Retrieval-Augmented Generation (RAG) is positioned as the practical fix for a core limitation of “just stuff everything into the prompt” approaches:...
Situational Awareness: From GPT-4 to AGI | Compute, Algorithms & Unhobbling by OpenAI Ex-Employee
The central claim is that rapid, compounding improvements in “effective compute” and model training methods could make automated AI research—and...
Deploying Local LLM but It Is Slow? Here's How to Fix It (Hopefully) | LLMOps with vLLM
Deploying a local LLM can feel painfully slow when using the default Hugging Face Transformers inference pipeline, but switching to vLLM can cut...
Build 100% Local Advanced RAG System for Financial PDFs with Qwen 3.5 | Docling, LangGraph & Ollama
A fully local “advanced RAG” stack for financial PDFs can be built end-to-end—PDF upload, parsing into citation-ready chunks, hybrid retrieval,...
Build Private AI Assistant That Actually Remembers | Chatbot Memory with Ollama, LangChain & SQLite
A fully local chatbot can keep “memory” across restarts by writing each conversation turn into a local SQL database and re-injecting that history...
Is RAG Dead in 2026? | Build Local RAG from First Principles
Retrieval-Augmented Generation (RAG) is still considered necessary in 2026—not because large language models can’t answer, but because they often...
Getting Started with LangGraph | Build Local Agentic Workflows and AI Agents with Ollama
LangGraph is presented as a practical way to turn brittle, demo-only AI prototypes into maintainable agentic systems by replacing nested if/else...
How RAG Finds Answers in Millions of Documents | Embeddings, Vector Databases, LangChain & Supabase
Retrieval in RAG hinges on one practical step: turning a user question into a vector and then finding the most semantically similar document chunks...
Llama 4 Test with Groq: Coding, Data Extraction, Data Labelling, Summarization, RAG
Meta’s Llama 4 lineup—Scout (109B), Maverick (400B), and Behemoth (2T, still training)—arrives with headline claims built around huge context windows...
LangChain Tutorial: The Core Building Blocks | LLMs, JSON output, RAGs, Tools and Observability
LangChain’s practical value comes from a small set of reusable building blocks: a unified way to call different LLM providers, structured outputs...
Gemini 2.0 Flash Thinking Test - Coding, Data Extraction, Summarization, Data Labelling, RAG
Gemini 2.0 Flash Thinking is positioned as a fast “thinking-mode” variant that exposes its internal reasoning steps, and hands-on tests suggest that...
Build Dataset For Fine-Tuning and Evaluation with LLM | Sentiment Analysis for Financial News
A practical workflow for building a sentiment-labeled dataset from financial news using a fast large language model (LLM) is the core takeaway: take...
Gemma 4 Local OCR Test with llama.cpp | How Accurate It Is for PDF Document Understanding (🔴 Live)
Gemma 4 can perform surprisingly strong document understanding for local OCR-style extraction—especially when the goal is to recover layout and...