MLA 030 AI Job Displacement & ML Careers
Machine Learning Guide
ML engineering demand remains high with a 3.2 to 1 job-to-candidate ratio, but entry-level hiring is collapsing as AI automates routine programming and data tasks. Career longevity requires shifting from model training to production operations, deep domain expertise, and mastering AI-augmented workflows before standard implementation becomes a commodity. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want Market Data and Displacement ML engineering demand rose 89% in early 2025....
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MLA 029 OpenClaw
Machine Learning Guide
OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want OpenClaw is a self-hosted AI agent daemon (Node.js, port 18789) that executes autonomous tasks via messaging apps like WhatsApp or Telegram. Developed by Peter...
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MLA 028 AI Agents
Machine Learning Guide
AI agents differ from chatbots by pursuing autonomous goals through the ReACT loop rather than responding to turn-based prompts. While coding agents are currently the most reliable due to verifiable feedback loops, the market is expanding into desktop and browser automation via tools like Claude co-work and open claw. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want Fundamental Definitions Agent vs. Chatbot: Chatbots are turn-based and human-driven. Agents receive...
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MLA 027 AI Video End-to-End Workflow
Machine Learning Guide
How to maintain character consistency, style consistency, etc in an AI video. Prosumers can use Google Veo 3’s "High-Quality Chaining" for fast social media content. Indie filmmakers can achieve narrative consistency by combining Midjourney V7 for style, Kling for lip-synced dialogue, and Runway Gen-4 for camera control, while professional studios gain full control with a layered ComfyUI pipeline to output multi-layer EXR files for standard VFX compositing. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI...
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MLA 026 AI Video Generation: Veo 3 vs Sora, Kling, Runway, Stable Video Diffusion
Machine Learning Guide
Google Veo leads the generative video market with superior 4K photorealism and integrated audio, an advantage derived from its YouTube training data. OpenAI Sora is the top tool for narrative storytelling, while Kuaishou Kling excels at animating static images with realistic, high-speed motion. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want S-Tier: Google Veo The market leader due to superior visual quality, physics simulation, 4K resolution, and , which removes...
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MLA 025 AI Image Generation: Midjourney vs Stable Diffusion, GPT-4o, Imagen & Firefly
Machine Learning Guide
The AI image market has split: Midjourney creates the highest quality artistic images but fails at text and precision. For business use, OpenAI's GPT-4o offers the best conversational control, while Adobe Firefly provides the strongest commercial safety from its exclusively licensed training data. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want The 2025 generative AI image market is defined by a split between two types of tools. "Artists" like Midjourney excel at creating...
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MLG 036 Autoencoders
Machine Learning Guide
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 - stay healthy & sharp while you learn & code Build the future of multi-agent software with . Thanks to from for recording...
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MLG 035 Large Language Models 2
Machine Learning Guide
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...
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MLG 034 Large Language Models 1
Machine Learning Guide
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...
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MLA 024 Agentic Software Engineering
Machine Learning Guide
Agentic engineering shifts the developer role from manual coding to orchestrating AI agents that automate the full software lifecycle from ticket to deployment. Using Claude Code with MCP servers and git worktrees allows a single person to manage the output and quality of an entire engineering organization. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want The Shift: Agentic Engineering Andrej Karpathy transitioned from "vibe coding" in February 2025 to "agentic engineering" in...
info_outlineClaude Code distinguishes itself through a deterministic hook system and model-invoked skills that maintain project consistency better than visual-first tools like Cursor. Its multi-surface architecture allows developers to move sessions between CLI, web sandboxes, and mobile while maintaining persistent context.
Links
- Notes and resources at ocdevel.com/mlg/mla-23
- Try a walking desk - stay healthy & sharp while you learn & code
- Generate a podcast - use my voice to listen to any AI generated content you want
Agent Comparison
- Cursor: VS Code fork. Uses visual interactions (Cmd+K, Composer mode), multi-line tab completion, and background cloud agents. Credit-based billing ($20 to $200).
- Codex CLI: Terminal-first Rust agent. Uses GPT-5.3-Codex. Features three autonomy modes (Suggest, Auto-approve, Full Auto). Included in $20 ChatGPT Plus.
- Antigravity: Agent-first interface using Gemini 3 Pro. Manager View orchestrates parallel agents that produce verifiable task lists and recordings.
- Claude Code: Terminal, IDE, and mobile sessions. Uses Sonnet/Opus 4.5/4.6. Differentiates via deep composability and cross-surface persistence.
Persistent Memory and Skills
- CLAUDE.md: 4-tier hierarchy (Enterprise, Project, User, Local). Loads recursively, enabling monorepo support where child directories load lazily. Imports use
@syntax. - Skills: Model-invoked capability folders. Three-stage loading (metadata, instructions, supporting resources) minimizes context use. Claude triggers them based on description fields.
- Commands: User-triggered slash commands.
/compactpreserves topics while trimming history,/initgenerates memory files, and/checkpointmanages rollbacks.
Enforcement and Integration
- Hooks: Deterministic shell commands or LLM prompts. Fired at 10 events, including
PreToolUse(blocking),PostToolUse(formatting), andStop(self-correction). Exit code 2 blocks actions, code 0 allows. - MCP: Standard for connecting external tools (PostgreSQL, GitHub, Sentry). Tool Search activates when metadata exceeds 10% context window. Claude Code can serve its own tools via MCP.
- Subagents: Isolated context workers.
Exploreuses Haiku for discovery,Planuses Sonnet for research.isolation: worktreeprovides filesystem-level separation. - Agent Teams: Persistent multi-pane coordination via tmux. Modes: Hub-and-Spoke, Task Queue, Pipeline, Competitive, and Watchdog.
Operations and Security
- Checkpoints: Granular undo allows independent rollback of code changes or conversation history.
- Thinking Triggers: Keywords
ThinktoUltrathinkadjust reasoning compute allocation. - Headless:
--printor--headlessflags enable CI/CD. GitHub Action uses four parallel agents to score review findings above 80% confidence. - Sandboxing: Uses Apple Seatbelt (macOS) or Bubblewrap (Linux). Restricts filesystem and network access, reducing permission prompts by 84%.
- Output Styles: Modifies system prompts for
Default,Explanatory, orLearningpersonas.