AI & LLMs

93 articles

Jun 9, 2026

Claude Fable 5: What It Is, Benchmarks, Pricing, and Why It Matters

Claude Fable 5 brings Anthropic's Mythos-class capability to general availability, with top benchmark scores, strict safeguards, 1M-token context, and a new safety tradeoff for frontier AI users.

AI & LLMs

Claude Fable 5: What It Is, Benchmarks, Pricing, and Why It Matters

Capstone Project: Private Local AI Platform

Module 1 Lesson 1: What Local LLMs Are

Module 1 Lesson 2: Local vs Cloud-Based Models

Module 1 Lesson 3: Privacy, Cost, and Control

Module 1 Lesson 4: Hardware Requirements

Module 1 Lesson 5: CPU vs GPU vs Apple Silicon

Module 1 Lesson 6: Memory and Storage Considerations

Module 1 Wrap-up: Inspecting Your Resources

Module 10 Lesson 1: Why RAG Is Important for Local Models

Module 10 Lesson 2: Embeddings with Local Models

Module 10 Lesson 3: Vector Stores

Module 10 Lesson 4: Chunking Strategies

Module 10 Lesson 5: Retrieval Pipelines

Module 10 Wrap-up: Building Your Local Q&A System

Module 11 Lesson 1: When Fine-Tuning Is Needed

Module 11 Lesson 2: LoRA and Adapter-Based Training

Module 11 Lesson 3: Training Data Preparation

Module 11 Lesson 4: Training Tooling Overview

Module 11 Lesson 5: Loading Adapters in Ollama

Module 11 Wrap-up: The Fine-Tuning Journey

Module 12 Lesson 1: Local AI Security Model

Module 12 Lesson 2: Data Privacy and Anonymization

Module 12 Lesson 3: Auditing Ollama Interactions

Module 12 Lesson 4: Running in Air-Gapped Environments

Module 12 Lesson 5: Compliance Standards

Module 12 Wrap-up: Hardening Your Local AI

Module 13 Lesson 1: Running Ollama in Docker

Module 13 Lesson 2: Multi-GPU Support

Module 13 Lesson 3: Concurrency and Parallelism

Module 13 Lesson 4: Load Balancing Local AI

Module 13 Lesson 5: Monitoring Performance Metrics

Module 13 Wrap-up: Your High-Performance Stack

Module 14 Lesson 1: Setting up a Remote Ollama Server

Module 14 Lesson 2: Hardware Selection for Production

Module 14 Lesson 3: Continuous Deployment (CD) for Models

Module 14 Lesson 4: Cost Management and ROI

Module 14 Lesson 5: Backup and Recovery Strategies

Module 14 Wrap-up: The Full-Scale Transition

Module 2 Lesson 1: What Ollama Is

Module 2 Lesson 2: Ollama Architecture

Module 2 Lesson 3: Supported Operating Systems

Module 2 Lesson 4: Installing Ollama

Module 2 Lesson 5: Ollama CLI Basics

Module 2 Lesson 6: Ollama Server and API Overview

Module 2 Wrap-up: Your First Local Chat

Module 3 Lesson 1: Ollama Model Registry

Module 3 Lesson 2: Model Naming and Tags

Module 3 Lesson 3: Popular Ollama Models

Module 3 Lesson 4: Model Sizes and Variants

Module 3 Lesson 5: Prompting Local Models

Module 3 Lesson 6: Streaming Responses

Module 3 Wrap-up: The Model Comparison Challenge

Module 4 Lesson 1: Transformer Architecture Overview

Module 4 Lesson 2: Quantization Concepts

Module 4 Lesson 3: GGUF Model Format

Module 4 Lesson 4: Context Length and Tokens

Module 4 Lesson 5: Tokenization

Module 4 Lesson 6: Performance Trade-offs

Module 4 Wrap-up: Measuring Model Efficiency

Module 5 Lesson 1: What a Modelfile Is

Module 5 Lesson 2: Modelfile Syntax

Module 5 Lesson 3: System Prompts

Module 5 Lesson 4: Runtime Parameters

Module 5 Lesson 5: Model Inheritance

Module 5 Lesson 6: Versioning and Reproducibility

Module 5 Wrap-up: Engineering Your Custom Bot

Module 6 Lesson 1: Hugging Face Model Ecosystem

Module 6 Lesson 2: Model Types and Licenses

Module 6 Lesson 3: Supported Architectures

Module 6 Lesson 4: Converting Models to GGUF

Module 6 Lesson 5: Quantization Options

Module 6 Lesson 6: Compatibility Validation

Module 6 Wrap-up: Bringing Hugging Face Home

Module 7 Lesson 1: Model Caching

Module 7 Lesson 2: Disk Space Management

Module 7 Lesson 3: RAM and VRAM Optimization

Module 7 Lesson 4: Context Window Tuning

Module 7 Lesson 5: Batch Inference