NotebookLM

Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI

Jun 26, 2026 · 39:32

Paul Iusztin and Louis-François Bouchard introduce an AI Research OS that transforms a personal Second Brain (e.g., an Obsidian vault of 5,000+ notes growing by 250 files/month) from a passive archive into a live, queryable memory for AI agents. They argue that bigger context windows aren’t enough; proper memory and context management are needed. The system evolved through three iterations: first a simple deep research loop generating static research Markdown files (used for 35 course lessons), then targeting both the public web and the user’s Second Brain, and finally adding a persistent wiki layer. The architecture uses plain files (Markdown, YAML index) instead of vector databases, with a hierarchy of raw sources, source summaries, and wiki derivatives (concepts, comparisons, entities). The wiki evolves with every question, not just during ingestion. Demos show ingesting Obsidian notes, GitHub repos, and arbitrary links, then querying the wiki to update it dynamically.

Prompt to Pipeline: Building with Google's Gen Media Stack — Paige & Guillaume, Google DeepMind

May 23, 2026 · 1:54:35

Paige Bailey, Guillaume Vernade, and Ian Valentine from Google DeepMind demonstrate the company's full generative media stack—from Gemini 3.1 Flash Light's video analysis at $0.25 per million tokens to Genie 3's playable world models and Gemma 4's on-device agentic coding—showing developers how to build multimodal apps without cloud APIs. Paige shows AI Studio's Build feature creating a bookshelf scanning app with Firestore and OAuth. Guillaume walks through a workshop using Nano Banana 2 for character portraits, VO 3.1 Lite for video generation at $0.05 per image, LIA 3 for chapter scores, and text-to-speech with distinct voices. Ian runs Gemma 4's 26B mixture-of-experts model on a MacBook to generate 10 SVGs in parallel and build a game from a spec, all locally. The episode argues that Google's models absorb common agent patterns, making custom fine-tunes less necessary.

Any-to-Any: Building Native Multimodal Agents - Patrick Löber, Google DeepMind

May 20, 2026 · 16:21

Patrick Löber, a member of the technical staff at Google DeepMind, explains how to build native multimodal agents using the Gemini API ecosystem, covering multimodal understanding, native image and speech generation, and real-time interaction via the Live API. He demonstrates constructing a NotebookLM clone as an agentic system where a reasoning Gemini model decides whether to generate an infographic or podcast-style audio using function calls to specialized models like Nano Banana for images and a text-to-speech model for speech. The episode details practical implementation: uploading PDFs, video, and audio files; using context caching to reduce costs by 90%; and generating infographics or multi-speaker audio directly from prompts. Löber highlights that native generation models understand world context—like drawing arrows on a map to produce the Golden Gate Bridge—and that the Live API enables audio-to-audio interactions with a single architecture, supporting multiple languages and accents.

Let's go Bananas with GenMedia — Guillaume Vernade, Google DeepMind

May 18, 2026 · 1:17:14

Guillaume Vernade from Google DeepMind demonstrates the full GenMedia stack—Nano Banana for images, Veo for video, Lyria for music, and TTS—by illustrating an open-source book live. The core insight is that Gemini acts as the prompt engineer for every other model, and this works partly because the gen media models were trained on prompts written by Gemini. He shows how to generate character portraits, chapter scenes, animated video clips using images as first frames, and distinct music per chapter, with Lyria Realtime allowing continuous music that responds to new prompts like a DJ. A new interactions API caches context server-side to make chained multi-turn calls cheaper. For TTS, he uses a trick: assigning different speaking styles (e.g., long poetic pauses vs. breathless stutter) to the same two voices to create four distinct-sounding characters.

Layering every technique in RAG, one query at a time - David Karam, Pi Labs (fmr. Google Search)

Jul 29, 2025 · 20:22

David Karam of Pi Labs (formerly Google Search) walks through layering every technique in RAG, from in-memory retrieval to planet-scale search with 70+ corpus mix of token, embeddings, and knowledge graphs, jointly retrieved and re-ranked at 160,000 queries per second in under 200msec. He advocates a Quality Engineering Loop: baseline with simplest methods, analyze losses, then apply incremental techniques based on complexity-adjusted impact. Karam explains why queries like "falafel" are notoriously hard due to ambiguous intent, highlights failures of chunking documents, and shows when BM25 suffices, when relevance embeddings are needed, and when custom embeddings or domain-specific signals (price, popularity, user clicks) become essential. He covers query orchestration via fan-out, supplementary retrieval across back ends, and distillation for cost optimization, concluding that at sufficient complexity, problems must be punted to LLM or UX for graceful degradation.

Everything is ugly, so go build something that isn't — Raiza Martin, Huxe (ex NotebookLM)

Jul 28, 2025 · 25:15

Raiza Martin, former lead of Google's NotebookLM and founder of Huxe, argues that the current chaotic phase of AI product design is a once-in-a-career opportunity to rebuild from first principles, calling everything we use 'the ugliest that it will ever be.' Drawing from her experience forcing NotebookLM into existence against skepticism, she emphasizes that personal clarity of vision fuels product building, and purpose must be relentlessly focused on a single outcome—for NotebookLM, enabling users to upload 50 files and interact with them. She stresses earning trust by nailing deterministic behavior first, noting that 90% of first queries were summarization and failures drove users away forever, then layering on delightful probabilistic features like podcast generation. Finally, she warns against the 'kitchen sink' approach, citing her own Huxe app that did everything but users only used one feature, advocating restraint as an innovation multiplier and focus on one excellent outcome to avoid building ugly products.

12-Factor Agents: Patterns of reliable LLM applications — Dex Horthy, HumanLayer

Jul 3, 2025 · 17:06

Dex Horthy, founder of HumanLayer, presents the 12-Factor Agents framework for building reliable LLM-powered applications, arguing that production-grade agents are primarily deterministic software with targeted LLM steps rather than fully autonomous loops. He distills patterns: own prompts and context windows, treat tools as JSON and code, use small focused agents with three to ten steps, contact humans via tool calls. Horthy emphasizes context engineering—LLMs are pure functions—and shows how to compact errors, unify state, and add pause/resume via APIs. He shares a DevOps agent that became a bash script, and advocates for outer-loop agents. The framework, which gained 4,000 GitHub stars in two months, treats agents as stateless reducers that meet users on any channel, with engineers controlling the inner loop of token and control flow.