Machine Learning Guide

MLA 030 AI and Programming Jobs: What Happened and How to Position

The aggregate job market held, the entry-level door narrowed, and software postings sit a quarter below pre-pandemic. Why cheap implementation made specification, verific

MLA 029 OpenClaw and Personal Agents

OpenClaw as the worked example of the always-on personal agent: gateway, markdown memory, heartbeats, skills, coding agents from your phone, hosted vs local models, and t

MLA 028 AI Agents: Loops, Tools, Memory, Protocols, and Evaluation

What an AI agent actually is, why coding agents got good first, how memory really works, what MCP and A2A standardize, which SDKs are alive, how to evaluate on trajectori

MLA 027 The AI Media Pipeline: Voice, Music, ComfyUI, APIs, and Finishing

How to automate AI media end to end: clone your own voice on open TTS, pick music that's actually licensed, run ComfyUI graphs headless, design around fal, Replicate and

MLA 026 AI Video Generation 2026: Veo, Gemini, Kling, Runway, MiniMax, Sora

Sora is shut down, Google runs two video models, Kling 3 does lip-synced dialogue, and open-weight MiniMax H3 is what you can actually fine-tune. What a usable clip costs

MLA 025 AI Image Generation 2026: GPT Image, Nano Banana, Midjourney, Flux

Editing replaced generation as the core task. How GPT Image 2.5, Google's Nano Banana line, Midjourney V8.2 and Flux 2 differ, what open weights and LoRAs buy you, Contro

MLG 036 Autoencoders

Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing orig

MLG 035 Large Language Models 2

At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with R

MLG 034 Large Language Models 1

Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context le

MLA 024 Agentic Software Engineering: Specs, Verification, and the Review Loop

  How working engineers ship with coding agents: issues an agent can verify, plan mode before code, a verification loop with a browser in it, agent review of agent code,

MLA 023 Inside a Coding Agent: Context, Instructions, Hooks, Skills, MCP

A coding agent is a loop appending to a context window, and the window is what runs out. Instruction files, permissions, hooks, skills, MCP, subagents and memory explaine

MLA 022 Vibe Coding: Codex vs Claude Code vs Antigravity vs Grok

What a coding agent actually is, and how Claude Code, OpenAI Codex, Google Antigravity and xAI's Grok Build differ in philosophy rather than features. Plus open harnesses

MLG 033 Transformers

Links: Notes and resources at ocdevel.com/mlg/33 3Blue1Brown videos: https://3blue1brown.com/ Try a walking desk stay healthy & sharp while you learn & code Try Descrip

MLA 021 Databricks: Cloud Analytics and MLOps

Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, D

MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes

Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monito

MLA 019 Cloud, DevOps & Architecture

The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be com

MLA 017 AWS Local Development Environment

AWS development environments for local and cloud deployment can differ significantly, leading to extra complexity and setup during cloud migration. By developing directly

MLA 016 AWS SageMaker MLOps 2

SageMaker streamlines machine learning workflows by enabling integrated model training, tuning, deployment, monitoring, and pipeline automation within the AWS ecosystem,

MLA 015 AWS SageMaker MLOps 1

SageMaker is an end-to-end machine learning platform on AWS that covers every stage of the ML lifecycle, including data ingestion, preparation, training, deployment, moni

MLA 014 Hosting and Deploying ML: Managed APIs, Serverless GPUs, Self-Hosting

Where the model behind your product should run in 2026: managed APIs vs open weights, AWS native vs Modal, RunPod and Cloud Run GPU, vLLM and SGLang, quantized CPU infere