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_outlineAgentic 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 ocdevel.com/mlg/mla-24
- 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
The Shift: Agentic Engineering
Andrej Karpathy transitioned from "vibe coding" in February 2025 to "agentic engineering" in February 2026. This shift represents moving from casual AI use to using agents as the primary production coding interface. The goal is to automate the software engineering lifecycle, allowing a single person to manage system design and outcomes while agents handle implementation.
Tooling and Context Efficiency
Minimize MCP servers to preserve context. 12 active servers consume 66,000 tokens, which is one-third of Claude's 200K window. Lazy-loading MCP definitions reduces usage by up to 95%.
- GitHub MCP: Accesses GitHub API for PR creation, issue management, and Actions.
- Context7: Fetches version-specific documentation to prevent hallucinations in libraries like React or Prisma.
- Sequential Thinking: Forces structured reasoning for complex architecture decisions.
- Playwright: Performs browser automation for E2E testing and UI debugging.
- Memory: Local knowledge-graph for persistent project context across sessions.
- Hooks:
PostToolUseauto-formats code via Prettier.PreToolUseblocks dangerous commands likerm -rfor writes to.env.SessionStartwith a compact matcher re-injects instructions after context compaction.
High-Impact Workflows
- Plan-First Mode: Use
Shift-Tabfor read-only exploration. Create TODOs and milestones before implementation to reduce backtracking. - Git Worktrees: Claude Code supports parallel sessions via the
--worktreeflag. This allows 3 to 5 simultaneous agents to work on different branches in a single repository. - Headless Mode: Use the
--printflag and JSON formatting to script Claude into external automation or CI/CD pipelines.
The Automated Engineering Pipeline
- Trigger: Issues are filed or labels like
claude-autofixare applied. Tools like n8n or OpenClaw can also trigger sessions via webhooks or Slack. - Implementation: Claude plans, implements changes, and writes tests in an isolated worktree.
- Self-Review: The code-review plugin runs four parallel agents to score changes for correctness and security.
- CI and Auto-Fix: Claude monitors CI status, auto-fixes failures, and merges PRs to staging via squash once checks pass.
- Human Gate: The engineer reviews the accumulated changes in the staging branch before merging to main for production deployment.
Career Transition
The role of the engineer moves from writing code to acting as an engineering operator. Daily work involves triaging issues, making architectural judgment calls, and optimizing the automation system. Maintaining a CLAUDE.md file under 100 lines ensures maximum token efficiency and performance for the agentic team.