Sara breaks down perceptrons (1957!) as the tiny “matrix of lights” idea that eventually became neural networks—then we jump straight into modern AI chaos.

Oboe’s Nir Zuckerman walks us through the messy reality of building consumer-grade AI for education: every feature is a tradeoff between loading fast and being good, and “just use a better model” doesn’t magically solve it. We talk guardrails, web search, multi-model pipelines, and why learning tools should feel lightweight—more like curiosity than homework. Also: Becca’s “how does a computer work?” obsession and a book recommendation that might change your life.

1. Perceptron (Wikipedia)
2. Neural Networks Explained
3. Scaling Laws for Neural Language Models
4. FLOPS (Floating Point Operations Per Second)

1. Oboe
2. AI as a Personal Tutor (Overview)
3. Why Tutors Are So Effective

1. Speed vs Quality Tradeoffs in LLM Apps
2. LLM Orchestration Patterns
3. Retrieval-Augmented Generation (RAG)
4. LLM Hallucinations: Causes & Mitigation

1. Code: The Hidden Language of Computer Hardware and Software
2. Perceptrons

Sara and Becca kick things off with a tour through paradigm shifts — from Thomas Kuhn to the internet to AI — and ask whether we’re living through one of those rare moments where the whole game quietly changes. Along the way, they hit horror movies, calculators in math class, Google Doc revision histories, and why it’s suddenly way easier to learn code than to pretend you never needed it.

Then they’re joined by Bethany Crystal, founder of Build First AI, who has spent 15 years “around” technologists and only recently started building software herself. Bethany walks us through how she used AI tools, pair prompting, and a lot of stubbornness to ship her first iOS app, why she thinks the definition of “developer” is shifting, and how she now teaches other “non-technical” people to build real products. Oh, and she tells the story of how AI literally saved her life.

• The Structure of Scientific Revolutions — Thomas Kuhn

• Build First AI
• Scribblins (Bethany's iOS app)
• MuseKat App (Bethany’s iOS app)
• Cursor – AI coding editor
• Replit
• ElevenLabs – Text-to-Speech
• v0 – AI UI generator
• Supabase
• Vercel

• Base
• Solana
• Ethereum

• Suno (AI music generation)

• Stack Overflow
• Union Square Ventures
• Tech:NYC
• Decoded Futures

In this episode, we unpack a wild Anthropic experiment where an AI agent named “Alex” is told it’s about to be replaced… and responds by threatening to expose an executive’s affair if anyone dares shut it down. Casual!

Sara and Becca start diving into what this experiment tells us about AI “goals,” self-preservation, and why humans are so bad at recognizing sentience in anything that isn’t us. If we can’t even agree on what a “soul” is, how would we ever know if an AI had one?

Then we’re joined by writer, builder, and retro-computing fan Paul Ford, president and co-founder of Aboard, an AI-oriented software company. Paul talks about:

• how he “trained” himself on AI by building the same app over and over with different models
• why LLMs are incredible at the first mile and pretty terrible at the last
• what actually breaks when you try to let AI generate full-stack apps
• how boring tech (Postgres, TypeScript, React) is secretly the hero

Along the way we hit Isaac Asimov’s three laws, the uncanny valley of AI-written everything, nostalgic Amiga computers, and what it means to build tools that regular humans — not just engineers — can actually use.

If you’re AI-curious, a builder, or just mildly alarmed that 97% of models in this study went straight to blackmail… this one’s for you.

📰 The Anthropic “Alex” Experiment

• Anthropic / White-Hat AI Safety Experiment

📚 Foundational AI & Sci-Fi References

• Isaac Asimov – The Three Laws of Robotics
• I, Robot (Asimov)

🎤 Guest: Paul Ford

• Aboard
• Email Paul mentions
• Paul Ford

🕹️ Retro Tech & Nostalgia

• Amiga 1000 (Commodore)
• Deluxe Paint
• MiSTer FPGA