Granola

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.

Feedback Loops are All You Need — Mehedi Hassan, Granola

May 10, 2026 · 10:11

Mehedi Hassan, a product engineer at Granola, argues that shipping AI features into production requires building feedback loops rather than one-shotting better prompts. Granola's chat feature for meeting notes revealed problems with web search — token costs ballooning to 10p per chat, overnight provider updates silently degrading results — and with prompt personalization, as a single prompt cannot serve salespeople, engineers, and HR managers equally. To close the gap, Granola built custom internal tracing exposing tool calls, search trails, reasoning, and cost in a UI accessible to non-engineers, not just CloudWatch queries. They also refactored their Electron app's renderer to run as a web app, enabling preview links on every PR and allowing Cursor to automatically test changes and upload screenshots. The result is faster iteration and confidence that shipped features actually work for customers.

Shipping something to someone always wins — Kenneth Auchenberg (ex. Stripe, VSCode)

Jul 28, 2025 · 16:17

Kenneth Auchenberg, former VSCode and Stripe developer platform lead, argues that great AI products come from rapid iterative loops — shipping continuously viable 'skateboards' to real users rather than aiming for a perfect final product. He advocates for writing launch blog posts before building, working intimately with a few real customers (even texting them), and ignoring constraints like legal initially to design the best product. APIs are harder to change than UI, so early user feedback is critical. In AI, the fundamentals haven't changed: customer knowledge and iteration velocity matter more than ever, even as AI tools accelerate building. The goal should be to run a product feedback loop in under a day.

Survive the AI Knife Fight: Building Products That Win — Brian Balfour, Reforge

Jul 14, 2025 · 14:10

Brian Balfour, CEO of Reforge, argues that winning in today's AI knife-fight requires answering 'What do I build and why will it win?' by focusing on proprietary data, unique functionality, and unmet customer needs rather than building custom AI. He illustrates with Granola, which entered a crowded AI note-taker market by understanding users wanted help taking better notes, not full automation, and assembled off-the-shelf AI (DeepGram, Anthropic, OpenAI) with unique data (user notes plus transcription) and functionality (Mac app, calendar integration) to create a competitive edge. Balfour warns competitive advantages now last only 2-3 weeks, so teams must sequence smaller moats continuously, each buying time to execute faster. The talk emphasizes treating AI as Lego blocks—assembling pre-trained models, data, and product superpowers into a system that spins a data flywheel.