Learn AI in Bits

Dan W

AI explained in bits. Each episode takes one concept, like tokens, embeddings, hallucinations, or prompt injection, and explains it in about five minutes. No jargon, no filler. Just the idea, why it matters, and what to remember.

If you're curious about AI or already building with it, you'll come away understanding how these systems work.

One concept. Five minutes. That's the whole show.

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32 episodes
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English

Episodes 32

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S1 · E20
August 18 · 14 min
020 - A Real Example of Claude Running a Small Business: What It Actually Costs (Deep Dive))
Can a single tech-savvy person actually build and maintain an AI agent for a small business, or is that just a demo-video fantasy? A full, concrete walkthrough answers that: a fictional flower shop called Rosewood Flowers automates its same-day delivery scheduling using Claude, built from an agent loop, a custom skill, and four real-world connectors, then stress-tested against the costs, maintenance, and failure modes that never make it into a product demo. In plain terms: the difference between a tool (a single action Claude can take), a connector built on the Model Context Protocol, or MCP (how Claude reaches external systems like Shopify, Google Calendar, and a mapping service), a skill (a packaged, progressively-loaded set of business rules Claude only pulls into context when relevant, built using Claude's Agent Skills framework and SKILL.md files), and an agent loop using subagents from Claude's Agent SDK, where a main agent delegates focused tasks, intake, routing, and customer notification, to specialized subagents with their own tools and instructions. Listeners get a full order walked end to end through the system, plus a harder edge case that deliberately escalates to a human instead of guessing. It also gets specific about where this kind of system actually runs: building it requires Claude API access through Anthropic's developer console, a separate product and separate billing from a Claude.ai subscription like Pro or Max, and the skill and orchestration code live as ordinary files, a SKILL.md file and Python or TypeScript scripts, in a code repository like GitHub. Two hosting paths are covered: self-hosting the code on a small server or container, or using Managed Agents, the hosted API Anthropic opened to developers in a 2026 public beta that runs the agent and sandbox directly with no server to maintain. The math on ongoing token costs uses Claude's actual current Sonnet pricing, two dollars per million input tokens and ten dollars per million output tokens, and flags that this is an introductory rate expiring September 1, 2026, a concrete example of how AI infrastructure costs shift over time. It also covers realistic build time (two to four weeks part-time), the skills a builder actually needs, and the maintenance nobody advertises: expiring OAuth tokens, connector APIs changing, model version updates requiring re-testing, and business rule changes that require editing the skill file before the next busy weekend arrives. This is a technical explainer built for listeners who want to understand agents, skills, connectors, and hosting through one fully worked, honest example rather than marketing language. Sources & References Claude Agent Skills overview (SKILL.md, progressive disclosure) — https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview Claude MCP connector documentation — https://platform.claude.com/docs/en/agents-and-tools/mcp-connector Claude Agent SDK subagents documentation — https://code.claude.com/docs/en/agent-sdk/subagents Hosting the Agent SDK (self-hosted deployment options) — https://code.claude.com/docs/en/agent-sdk/hosting Claude.ai subscription vs. API/Console access — https://support.claude.com/en/articles/9876003-i-have-a-paid-claude-subscription-pro-max-team-or-enterprise-plans-why-do-i-have-to-pay-separately-to-use-the-claude-api-and-console Anthropic Managed Agents public beta overview — https://www.roborhythms.com/anthropic-managed-agents-2026/ Claude API pricing overview, 2026 — https://www.cloudzero.com/blog/claude-pricing/ Voice narration is AI-generated.
S1 · E19
August 17 · 6 min
019 - Is Your Job Safe From AI? What the Data Says About Layoffs
Is your job safe from AI, or does the data already point the other way, and can you even trust the layoff headlines that say AI did it? Three separate research efforts point to which specific job titles are most exposed to AI right now, and the layoff headlines blaming AI get checked against whether that explanation actually holds up. Microsoft's research team analyzed two hundred thousand real conversations from Bing Copilot in 2024, mapping them against government job classifications to score how much each occupation's daily tasks overlap with what generative AI already does well. The World Economic Forum's Future of Jobs Report named accountants and auditors, alongside cashiers and administrative assistants, among the fastest-declining roles by 2030, while Microsoft's data pointed to customer service representatives and sales representatives as similarly exposed. On the other end, roofers and water treatment plant operators ranked among the least AI-exposed jobs studied, since physical, hands-on work still resists automation. Outplacement firm Challenger, Gray and Christmas tracked more than ten thousand three hundred U.S. layoffs through July 2025 explicitly attributed to AI, with tech sector cuts up thirty-six percent year over year. But the layoff data cuts both ways. Some companies genuinely are redirecting payroll into AI infrastructure: Meta cut roughly eight thousand roles, about ten percent of its workforce, in May 2026, with Mark Zuckerberg tying the move directly to rising AI costs, part of a broader trend as Alphabet, Amazon, Meta, and Microsoft plan to spend roughly seven hundred fifty billion dollars on AI infrastructure in 2026. Other times, AI functions as a convenient label. Investor Marc Andreessen has argued many large companies are overstaffed from pandemic-era hiring by twenty five to seventy five percent, calling AI the easiest excuse for cuts that were coming regardless. Salesforce CEO Marc Benioff has said layoffs from several distinct causes get lumped together under the same AI headline. A Duke University and Federal Reserve survey of company financial officers found AI's real employment impact closer to zero point four percent of jobs nationwide, a much narrower slice than the headlines imply. The full picture includes which jobs the underlying task data actually points to, plus a more skeptical framework for reading the next AI layoff headline. Sources & References Microsoft study on jobs most and least exposed to generative AI — https://www.geekwire.com/2025/microsoft-study-shows-jobs-most-and-least-impacted-by-generative-ai/ World Economic Forum, Future of Jobs Report 2025 — https://www.weforum.org/stories/2025/01/future-of-jobs-report-2025-the-fastest-growing-and-declining-jobs/ Challenger, Gray & Christmas 2025 layoff data — https://fortune.com/2025/08/07/summer-of-ai-layoffs-july-140-percent-spike-challenger-gray-christmas/ AI-washing of layoffs, Andreessen and Benioff commentary, Duke/Federal Reserve CFO survey — https://sfstandard.com/2026/04/02/ai-washing-layoffs/ Companies cutting jobs to fund AI investment — https://247wallst.com/investing/2026/05/26/companies-are-cutting-jobs-for-ai-but-not-the-billions-paid-to-investors/ Voice narration is AI-generated.
S1 · E18
August 17 · 16 min
018 - AI Coding: Does It Actually Make Developers Faster? (Deep Dive)
Does AI actually make coding faster? This Deep Dive works through three questions: how a model learns to write code in the first place, what a coding agent really does when you hand it a goal, and whether the productivity gains everyone assumes are real. The honest answer involves a randomized trial where developers were confidently wrong about their own experience. This is a longer-format episode, running about twenty minutes instead of the usual five. WHAT'S COVERED Part one - How a model learns to write code. Why coding improved faster than almost any other AI capability, and why the reason has nothing to do with code being easy. Code has a property that essays and advice and analysis do not: it can be checked automatically, with no human judgment involved. We walk through both training stages, from predicting the next token across vast amounts of public code, to the feedback loop that trains a model on whether its code actually ran. Then the benchmark that shows the effect: SWE-bench, built from real bug reports in real open-source projects, where early models solved under five percent of tasks and leading models now score around eighty percent. Plus two honest limits on that number, including why passing every test is not the same as writing good code. Part two - What a coding agent actually does. The four rungs of AI coding, from autocomplete through chat and editor integration to full agents like Claude Code and Codex. A worked example of an agent fixing a real bug: searching the codebase, reading the relevant files, making a change, running the test suite, seeing a failure, and correcting itself without being told. How this shifts a developer's job from writing toward specifying and reviewing, why instruction files inside repositories now matter, what vibe coding means, and the two genuine risks — agents that can run destructive commands, and review becoming the real bottleneck. Part three - Does it actually work? Why the widely quoted statistics on AI-written code are close to meaningless, including the fact that published figures for the same moment range from thirty to seventy-five percent because nobody defines the term the same way. Then the most rigorous evidence available: a randomized controlled trial from METR in which sixteen experienced developers completed real tasks nineteen percent slower with AI, after predicting they would be twenty-four percent faster, and still believing afterward that they had been sped up. We give that study its proper caveats rather than treating it as a verdict, then look at where the gains are genuinely large, where they shrink, and what rising code churn and falling developer trust suggest. IN THIS EPISODE AI coding, coding agents, Claude Code, Codex, GitHub Copilot, Cursor, autocomplete, SWE-bench, reinforcement learning, verifiable rewards, vibe coding, METR study, developer productivity, randomized controlled trial, code churn, technical debt, code review, AI-generated code, software engineering. Part of a series covering tokens, agents, MCP, retrieval-augmented generation, transformers, neural networks, parameters, hallucinations, and vector databases. Voice narration is AI-generated.
S1 · E17
August 17 · 4 min
017 - Tools, Connectors, and Skills
Tools, connectors, and skills sound interchangeable, but they solve three different problems. This episode separates them: what a tool actually is and why the model never runs one itself, how MCP turned custom integrations into a shared standard adopted across the industry, and what a skill adds once a model can already reach your systems. In this episode: tools, tool use, function calling, connectors, skills, MCP, Model Context Protocol, agents, integrations, OpenAI, Anthropic, Linux Foundation, AGENTS.md, open standards. Part of a series covering tokens, agents, MCP, retrieval-augmented generation, transformers, and vector databases.Voice narration is AI-generated.
S1 · E16
August 17 · 4 min
016 - What Is a Vector Database?
What is a vector database, and how does a system find the right document when it shares none of your words? This episode explains how text becomes coordinates, why searching by meaning is really searching by distance, the speed trade that makes it practical at scale, and the trap of a search that always returns something. Voice narration is AI-generated.
S1 · E15
August 17 · 4 min
015 - What Is a Transformer?
What is a transformer, and why is it the T in GPT? This episode explains the 2017 design that reshaped AI: why reading a sentence all at once beat reading it word by word, how attention lets every token weigh which other tokens matter, and why that one idea made models both scalable and genuinely good at context. Voice narration is AI-generated.
S1 · E14
August 17 · 4 min
014 - What Is a Neural Network?
What is a neural network, and why does this one idea sit underneath almost all modern AI? This episode keeps it simple: what a single unit actually does, how layers of them turn a handwritten digit into an answer, and why learning features from examples beat decades of humans writing rules by hand. Ends where transformers begin. Voice narration is AI-generated.
S1 · E13
August 17 · 4 min
013 - What Is a Parameter?
What is a parameter, and what do those billion-parameter headlines actually count? This episode builds the smallest possible model out loud to show what a learned number is, then answers the questions that follow: where parameters live inside a model, how much they can hold, whether more of them makes a model better, and whether they ever change. Voice narration is AI-generated. ?
S1 · E12
August 17 · 4 min
012 - What Is a Hallucination?
What exactly is an AI hallucination, and why do models state false things so confidently? This episode explains how hallucinations arise from next-token prediction, why recent research argues that grading models on accuracy actively rewards guessing over admitting uncertainty, what it has cost in real courtrooms, and why asking a model to check its own work doesn't work. Voice narration is AI-generated.
S1 · E11
August 15 · 4 min
011 - Can AI Pick Stocks?
Can AI successfully pick stocks? This episode weighs what the evidence actually shows: studies where language models read news headlines and predicted the next day's moves, against the humbling record of real AI-run funds that have trailed the S&P 500 for years. Learn why a genuine signal still fails to beat the market, and where AI does help. Voice narration is AI-generated.
S1 · E10
August 14 · 5 min
010 - Will AI Take My Job?
Will AI take your job? The honest answer starts with what the research measures — and most of it measures tasks, not job titles. This episode works through the major studies, separates exposure from displacement, and lands on where the evidence is currently strongest. On exposure: a landmark study from OpenAI researchers estimated that around 80% of US workers could have at least 10% of their tasks affected by large language models, with about 19% seeing at least half their tasks affected. The International Monetary Fund puts roughly 40% of jobs worldwide as exposed, rising to about 60% in advanced economies. Both measure exposure, not predicted job losses. The episode surfaces a striking tension inside Gartner's own research. Gartner forecasts that by 2029, agentic AI could autonomously resolve as much as 80% of common customer-service issues. Then in April 2026, Gartner surveyed service and support leaders and found 85% were expanding what their human agents do rather than shrinking it — the same firm, the same industry, with forecast and current practice pointing opposite ways. Worker-level research points the same direction. In a study of more than 5,000 customer-support agents, generative AI raised productivity about 14% overall and 34% for novice and lower-skilled workers, spreading techniques that had belonged to the most experienced staff. On the macro side, the World Economic Forum projects roughly 170 million jobs created and 92 million displaced by 2030 for a net gain near 78 million — though driven by multiple macrotrends, not AI alone — while 39% of workers' current skills change or go stale. The sharpest current evidence concerns younger workers. Stanford researchers, using payroll data covering millions of US workers through June 2026, found employment for 22-to-25-year-olds in highly AI-exposed occupations sitting about 19% below where it would be had it kept pace with less-exposed peers, with no comparable gap for more experienced workers. The effect operates mainly through reduced hiring rather than layoffs. The episode holds this against the productivity finding to surface an uncomfortable pairing: AI helps junior workers most, and junior workers are the ones being hired less. Sources & References GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models, OpenAI — https://openai.com/index/gpts-are-gpts/ Gen-AI: Artificial Intelligence and the Future of Work, IMF — https://www.imf.org/en/Publications/Staff-Discussion-Notes/Issues/2024/01/14/Gen-AI-Artificial-Intelligence-and-the-Future-of-Work-542379 Gartner Predicts Agentic AI Will Autonomously Resolve 80% of Common Customer Service Issues by 2029 — https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290 Gartner Survey Finds 85% of Service and Support Leaders are Expanding Human Agent Responsibilities — https://www.gartner.com/en/newsroom/press-releases/2026-04-28-gartner-survey-finds-eighty-five-percent-of-service-and-support-leaders-are-expanding-human-agent-responsibilities-despite-expectations-of-mass-ai-layoffs Generative AI at Work, Brynjolfsson, Li & Raymond, NBER — https://www.nber.org/system/files/working_papers/w31161/w31161.pdf The Future of Jobs Report 2025, World Economic Forum — https://www.weforum.org/publications/the-future-of-jobs-report-2025/ Canaries in the Coal Mine? Stanford Digital Economy Lab — https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/ Challenger Report: Layoffs Fall, Hiring Picks Up, AI Leads for Fifth Straight Month — https://www.challengergray.com/blog/challenger-report-layoffs-fall-hiring-picks-up-ai-leads-for-fifth-straight-month/ Voice narration is AI-generated.
S1 · E9
August 14 · 4 min
009 - The Data Center Backlash
Part two of a two-episode look at the money and politics behind AI data centers. After part one covered how projects move through planning under shell companies, code names, and non-disclosure agreements, this episode covers what happens when communities find out and push back — and the numbers show they are pushing back at a scale that surprised the industry. Data Center Watch counted at least 75 projects worth roughly $130 billion blocked or delayed in the first three months of 2026 alone, matching the total for all of 2025. Active opposition groups more than doubled over the previous quarter and now operate in 49 states. More than 300 state data-center bills were filed in the first six weeks of the year, with statewide moratorium proposals introduced in 14 states, and Maine came within a single House vote of banning new data centers outright. In July 2026, New York became the first state in the country to impose a statewide pause on new hyperscale data centers while regulators write tougher standards. A national Reuters/Ipsos poll found a majority of Americans opposed having an AI data center built in their own community. The episode uses Pacific, Missouri as a close-up case: hundreds of residents packed meetings over a proposed $16 billion campus, a February zoning meeting ended within minutes when the developer tabled its request, and months later — after the city passed a one-year moratorium — the developer withdrew the application. On what drives the opposition, electricity costs rank near the top. One analysis of utility filings identified more than $4 billion in transmission projects approved in 2024 solely to connect data centers in parts of the PJM grid, with most of those costs passed to utility customers. Water, noise, diesel backup generators, land use, and tax incentives worth more than $1 billion a year in some states add further layers. The economic case on the other side gets equal treatment. Virginia's legislative research agency, JLARC, estimates the industry supports about 74,000 jobs and $9.1 billion in annual state GDP — but the same report found most of that benefit comes from construction rather than ongoing operations. The contrast is stark: a typical build puts roughly 1,500 workers on site at peak, while a typical 250,000-square-foot facility runs on about 50 full-time workers once open, roughly half of them contractors. Supporters also warn that slowing construction too much could cost the United States ground in a technology race with economic and national-security stakes. The episode closes on how the fight is shifting from yes-or-no toward the terms of the deal, covering federal power-cost pledges, Microsoft's commitments on utility rates and tax incentives, and the special rates, water fees, disclosure requirements, and community benefit agreements governments are now testing. Sources & References Q1 2026 Report, Data Center Watch — https://www.datacenterwatch.org/q1-2026 First Statewide Moratorium on New Hyperscale Data Centers, Office of Governor Kathy Hochul — https://www.governor.ny.gov/news/first-statewide-moratorium-new-hyperscale-data-centers-launched-governor-kathy-hochul Data Centers in Virginia, JLARC — https://jlarc.virginia.gov/landing-2024-data-centers-in-virginia.asp Data center opponents have blocked or delayed projects worth nearly $130 billion in 2026, NBC News — https://www.nbcnews.com/tech/tech-news/data-center-opposition-sharply-rising-2026-study-finds-rcna349728 Voice narration is AI-generated.

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