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

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1. 

   S1 · E61 · 9 Jul 2025 · 31 min

   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, ControlNet vs instruction editing, and how licensing and C2PA provenance work now. Links More OCDevel shows - this one has siblings, each on its own subject and produced the same way Companion show. This episode is the overview. For weekly, hands-on coverage of the full image and video pipeline, from a first usable clip to scenes that cut together, listen to AI Video Generation . Try a walking desk - stay healthy & sharp while you…

2. 

   S1 · E60 · 30 May 2025 · 1 hr 6 min

   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 original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at ocdevel.com/mlg/36 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY . Thanks to T.J. Wilder from…

3. 

   S1 · E59 · 8 May 2025 · 45 min

   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 Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as…

4. 

   S1 · E58 · 7 May 2025 · 51 min

   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 learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex…

5. 

   S1 · E57 · 13 Apr 2025 · 33 min

   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, worktrees and CI, cost discipline, and where agents still fail. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way Third and last episode of the vibe-coding sequence. Vibe Coding in 2026 picked an agent; Inside a Coding Agent explained the mechanics. This one is the practice: how working engineers ship real…

6. 

   S1 · E55 · 9 Feb 2025 · 28 min

   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 pi and OpenCode, the open-weight coders, and a decision rule by constraint. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way First of three episodes on vibe coding: the field of coding agents (Claude Code, OpenAI Codex, Google Antigravity, Grok Build, pi, OpenCode) as of late 2026 and how to choose one. The…

7. 

   S1 · E54 · 9 Feb 2025 · 43 min

   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 Descript audio/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: "Attention Is All You Need" replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP)…

8. 

   S1 · E53 · 22 Jun 2022 · 26 min

   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, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at ocdevel.com/mlg/mla-21 Try a walking desk stay healthy & sharp while you learn & code Raybeam and…

9. 

   S1 · E52 · 29 Jan 2022 · 1 hr 9 min

   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 monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at ocdevel.com/mlg/mla-20 Try a walking desk stay healthy & sharp while you learn & code…

10. 

    S1 · E51 · 13 Jan 2022 · 1 hr 15 min

    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 complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at ocdevel.com/mlg/mla-19 Try a walking desk stay…