Machine Learning Guide – Podcastlijstje

Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

Afleveringen

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

26 februari 2026 · 36 min
Afspelen

The aggregate job market held, the entry-level door narrowed, and software postings sit a quarter below pre-pandemic. Why cheap implementation made specification, verification and domain scarce, how ML roles split five ways, and how to position. Lees meer

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

What coding agents did to programming and machine learning jobs by late 2026: the labor data, the mechanism behind it, how the ML career splintered into five roles, and a concrete positioning plan. Companion to the vibe coding trio (Vibe Coding in 2026, Inside a Coding Agent, Agentic Software Engineering) and the agents pair (AI Agents in 2026, OpenClaw and the Personal Agent).

Displacement vs task change

Two different claims hide inside "AI is taking programming jobs": displacement (the role disappears, nobody is rehired) and task change (the role stays, the work shifts). They appear in different data. Displacement shows in unemployment and layoff reports; task change shows in what postings ask for and how teams are shaped. The aggregate evidence is mostly task change with one real pocket of displacement at the entry level, which sets up offensive advice for most listeners and defensive advice for new entrants.

The evidence

Aggregate. The BLS Employment Situation has unemployment at 4.1% with payrolls beating forecasts, far from the 10-20% Dario Amodei floated in the Axios "white-collar bloodbath" interview. Both he and Sam Altman have since softened the timeline; Altman said he was "delighted to be wrong" (Fortune, Time). The Yale Budget Lab tracker finds no discernible disruption. Goldman Sachs Research estimates a net drag of about 16,000 jobs a month across 800+ occupations, with a long-run baseline of 6-7% of workers displaced.

Entry level. Stanford's Canaries in the Coal Mine (August 2026 paper, dashboard) puts 22-25 year olds in AI-exposed occup…
MLA 029 OpenClaw and Personal Agents

22 februari 2026 · 37 min
Afspelen

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 the 2026 security record (exposed instances, two critical CVEs, ClawHavoc) with the posture that makes it survivable. Lees meer

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

Second and last episode of the agents pair. AI Agents in 2026 covered the theory: loops, tools, memory, protocols, SDKs, evaluation. This one takes a single category, the always-on personal agent, and its most-cloned instance, OpenClaw, all the way down to the security posture required before it touches your inbox.

What a personal agent is

A personal agent is the agent loop with three additions: it runs continuously on a machine you control and can wake itself; its interface is messaging (WhatsApp, Telegram, Signal, iMessage, Slack) rather than a chat tab; and it holds your files, shell, browser, calendar and, if you allow it, email. Messaging removes the gap between having a thought and delegating it, and a chat thread is a natural home for asynchronous work that reports back later. The access that makes it useful is also the entire security problem.

OpenClaw today

OpenClaw is an MIT-licensed, self-hosted TypeScript agent created by Peter Steinberger in late 2025. It was released as Warelay, passed through claw-themed names, hit an Anthropic trademark complaint, and settled on OpenClaw at the end of January 2026 per Wikipedia; the org remains openclaw/openclaw. Steinberger joined OpenAI in February 2026 and stewardship moved to the OpenClaw Foundation, a US 501(c)(3) chaired by Dave Morin with a small full-time staff and donors including OpenAI, GitHub, Nvidia and Microsoft; the repo's README says OpenAI is a donor, not an owner, and there is no paid tier or hosted service. The project ships near-weekly CalVer re…
MLA 028 AI Agents: Loops, Tools, Memory, Protocols, and Evaluation

22 februari 2026 · 34 min
Afspelen

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 trajectories, and where the products stand after browser agents contracted. Lees meer

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 two episodes on AI agents. This one is the architecture: the loop, tools and verifiable feedback, memory, the protocols (MCP, A2A, computer use), the SDK landscape, evaluation and observability, the product map, and when multiple agents help. The next episode, OpenClaw and the Personal Agent, applies it to one always-on assistant with security as the centerpiece. Coding-agent products and mechanics live in the vibe coding sequence starting at MLA 22.

Agent vs workflow vs chat: the loop

A chat model returns a message; a workflow is your code calling a model at fixed steps; an agent is a model that owns the control flow, choosing its next action from what it observes. That puts systems on a spectrum (chat, chat plus tools, workflows, agents) rather than in a binary, the framing Anthropic's Building Effective Agents uses. The loop itself is ReAct (Yao et al.): thought, action, observation, repeat, with the reasoning trace letting the model track and update a plan. What changed by 2026 is not the loop but the infrastructure around it, and every part of that infrastructure is an attack on per-step error compounding.

Tools, function calling, and verifiable feedback

Function calling: you describe tools as schemas, the model emits a structured call, your code executes it and returns the observation. The model never runs anything itself, which is the security model. Writing effective tools for agents gives the practical rules: few high-impact tools, clear namespaces, meaningful identifiers, token-efficient responses, descriptions treated as prompt engineering. Eff…
MLA 027 The AI Media Pipeline: Voice, Music, ComfyUI, APIs, and Finishing

14 juli 2025 · 35 min
Afspelen

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 provider queues, finish with ffmpeg, and stay inside licensing at every layer. Lees meer

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 of the media pipeline. For weekly, hands-on coverage of the video half, from a first usable clip to scenes that cut together, listen to AI Video Generation on Gnothi.

Try a walking desk - stay healthy & sharp while you learn & code

Pipeline, not prompt

Once you need thirty clips with the same character, a narrator who sounds identical every episode, a matched music bed, word-accurate captions and a platform-safe export, the prompt is one node in a graph and the graph is the product. The engineering lives in the edges: how one model's output becomes the next model's input, how failures retry, what each job cost, and whether the run is reproducible next week. Model choice at the nodes is covered in the two sibling episodes; this one covers everything else: voice with ElevenLabs and Qwen3-TTS, licensed music, ComfyUI on your own card, the fal and Replicate APIs, ffmpeg assembly, and licensing.

Voice: cloning, open TTS, and consent

ElevenLabs remains the reference point (current flagship Eleven v3, character-based pricing). Professional voice cloning is locked to the requester's own voice behind a live voice check, and the terms require consent attestation for any uploaded voice. On the open side, Breeze TTS 2 topped the open-weights column of the Artificial Analysis speech arena in August 2026, ahead of Fish Audio's S2 Pro; the code is Apache 2.0 but the weights are research/non-commercial, so it is not a commercial self-hosting option. The working set for programmers: Qwen3-TTS (Apache 2.0, 0.6B/1.7B, cloning from seconds of reference audio; the preset-speaker variant does…
MLA 026 AI Video Generation 2026: Veo, Gemini, Kling, Runway, MiniMax, Sora

12 juli 2025 · 32 min
Afspelen

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, which models do native audio, how reference consistency works, and why the unit of work is the shot. Lees meer

Links

Notes and resources at ocdevel.com/mlg/mla-26

More OCDevel shows - this one has siblings, each on its own subject and produced the same way.

Companion show: for weekly, hands-on coverage of the AI 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 learn & code

Second of three episodes on AI media generation, covering Veo, Kling, Runway and MiniMax H3. Four questions: what a usable clip costs, which models generate sound and dialogue natively, how character and shot consistency work now, and where open-weight video fits for a programmer. Ends with the shot-to-scene mental model.

What changed since 2025

Native audio is now the baseline at the frontier: Veo 3.1, Kling 3.0, MiniMax H3 and LTX-2.5 sample audio and frames from one model, so lip movement and sound effects land on the right frame. Clips grew from four or five seconds to eight to fifteen, with a few models advertising thirty. Every serious product ships a reference-conditioning feature (Google "ingredients", Kling "elements", Runway references) that holds a character or object across clips. Image-to-video, not text-to-video, is the professional path: lock the first frame with an image model (see AI Image Generation and Editing), then ask the video model to move it. The old "storyteller vs animator" split resolved in favor of the animators.

Google: Veo 3.1 and Gemini Omni

Google now runs two video models in two places. Veo 3.1 is the developer baseline on the Gemini API and Vertex, in Quality, Fast and Lite tiers, all with native audio; clips are 4, 6 or 8 seconds, and 1080p/4K are upscales of the 8-second clip. It accepts u…
MLA 025 AI Image Generation 2026: GPT Image, Nano Banana, Midjourney, Flux

9 juli 2025 · 31 min
Afspelen

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. Lees meer

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 learn & code

First of three episodes on AI media generation (this one is images and editing; then video, then the pipeline). A decision guide rather than a leaderboard: where GPT Image, Nano Banana, Midjourney and Flux each fit in late 2026, why instruction-driven editing replaced generation as the core task, what open weights buy you, and how licensing and provenance work.

What changed: editing, instruction-following, references, text

The 2025 "artist vs collaborator" split is over and the collaborators won. Every frontier image model now sits behind a language model that reads the prompt with world knowledge and accepts images as input, so the unit of work became "here is an image, change this one thing and keep everything else." Text rendering, precise instruction following and identity-preserving reference images all landed at once for one reason: the image model became, or was paired with, a multimodal language model. Generation from scratch is now the special case where the input image is empty.

OpenAI: GPT Image 2.5

The lineage runs gpt-image-1 (2025), gpt-image-2, then ChatGPT Images 2.5 in September 2026, with a precision variant (Sunburst) and a fast default (Flare); the same models power ChatGPT and the Images API. As of this recording it holds the top slots on both the LMArena text-to-image and image-edit boards and on Artificial Analysis. For editing it doe…
MLG 036 Autoencoders

30 mei 2025 · 1 uur 6 min
Afspelen

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. Lees meer

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 intrep.io for recording this episode!

Fundamentals of Autoencoders

Autoencoders are neural networks designed to reconstruct their input data by passing data through a compressed intermediate representation called a "code."

The architecture typically follows an hourglass shape: a wide input and output separated by a narrower bottleneck layer that enforces information compression.

The encoder compresses input data into the code, while the decoder reconstructs the original input from this code.

Comparison with Supervised Learning

Unlike traditional supervised learning, where the output differs from the input (e.g., image classification), autoencoders use the same vector for both input and output.

Use Cases: Dimensionality Reduction and Representation

Autoencoders perform dimensionality reduction by learning compressed forms of high-dimensional data, making it easier to visualize and process data with many features.

The compressed code can be used for clustering, visualization in 2D or 3D graphs, and input into subsequent machine learning models, saving computational resources and improving scalability.

Feature Learning and Embeddings

Autoencoders enable feature learning by extracting abstract representations from the input data, similar in concept to learned embeddings in large language models (LLMs).

While effective for many data types, autoencoder-based encodings are le…
MLG 035 Large Language Models 2

8 mei 2025 · 45 min
Afspelen

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 chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction. Lees meer

Links

Notes and resources at ocdevel.com/mlg/mlg35

Build the future of multi-agent software with AGNTCY

Try a walking desk stay healthy & sharp while you learn & code

In-Context Learning (ICL)

Definition: LLMs can perform tasks by learning from examples provided directly in the prompt without updating their parameters.

Types:

Zero-shot: Direct query, no examples provided.

One-shot: Single example provided.

Few-shot: Multiple examples, balancing quantity with context window limitations.

Mechanism: ICL works through analogy and Bayesian inference, using examples as semantic priors to activate relevant internal representations.

Emergent Properties: ICL is an "inference-time training" approach, leveraging the model's pre-trained knowledge without gradient updates; its effectiveness can be enhanced with diverse, non-redundant examples.

Retrieval Augmented Generation (RAG) and Grounding

Grounding: Connecting LLMs with external knowledge bases to supplement or update static training data.

Motivation: LLMs' training data becomes outdated or lacks proprietary/specialized knowledge.

Benefit: Reduces hallucinations and improves factual accuracy by incorporating current or domain-specific information.

RAG Workflow:

Embedding: Documents are converted into vec…
MLG 034 Large Language Models 1

7 mei 2025 · 51 min
Afspelen

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 task performance. Lees meer

Links

Notes and resources at ocdevel.com/mlg/mlg34

Build the future of multi-agent software with AGNTCY

Try a walking desk stay healthy & sharp while you learn & code

Transformer Foundations and Scaling Laws

Transformers: Introduced by the 2017 "Attention is All You Need" paper, transformers allow for parallel training and inference of sequences using self-attention, in contrast to the sequential nature of RNNs.

Scaling Laws:

Empirical research revealed that LLM performance improves predictably as model size (parameters), data size (training tokens), and compute are increased together, with diminishing returns if only one variable is scaled disproportionately.

The "Chinchilla scaling law" (DeepMind, 2022) established the optimal model/data/compute ratio for efficient model performance: earlier large models like GPT-3 were undertrained relative to their size, whereas right-sized models with more training data (e.g., Chinchilla, LLaMA series) proved more compute and inference efficient.

Emergent Abilities in LLMs

Emergence: When trained beyond a certain scale, LLMs display abilities not present in smaller models, including:

In-Context Learning (ICL): Performing new tasks based solely on prompt examples at inference time.

Instruction Following: Executing natural language tasks not seen during training.

Multi-Step Reasoning & Chain of Thought (CoT): So…
MLA 024 Agentic Software Engineering: Specs, Verification, and the Review Loop

13 april 2025 · 33 min
Afspelen

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. Lees meer

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 software with Claude Code, Codex, and Antigravity without shipping garbage. Specs, verification loops, agent review, parallel worktrees, headless CI, cost discipline, and the failure modes.

From vibe coding to agentic engineering

Andrej Karpathy coined vibe coding in early 2025 and, a year later, proposed "agentic engineering" for professional work. His older idea of jagged intelligence (models clear hard problems and trip on trivial ones, unpredictably) is why the job is judgment rather than button-pressing. The frame for the episode: when implementation is cheap, the value moves to the two ends of the pipeline, specification (saying exactly what should be true) and verification (proving it). Vibe coding stays fine for throwaways; the rest applies to codebases with users.

Specs: issues as prompts, plan mode in Claude Code, Codex, and Antigravity

The prompt is a spec whether you meant it or not. The unit of work is a tracker issue with four parts: what's wrong, where to look, acceptance criteria a machine or a five-second human check can verify, and an explicit out-of-scope fence. Every serious agent has a read-only planning phase: Claude Code's plan mode (Shift+Tab or /plan), Codex's plan mode on the same keystroke with its own plan-mode reasoning effort, and Antigravity's implementation plan artifact. Judge a plan on three things: the files it names, the verification step it …