Vector Databases

Building Turbopuffer: Gergely Orosz (@pragmaticengineer ) × Simon Eskildsen (CEO)

Aug 3, 2026 · 56:30

Gergely Orosz talks with Turbopuffer CEO Simon Eskildsen about how napkin-math engineering took him from high-school Shopify hire to building the S3-based vector database that cut Cursor's bill by 95%. Eskildsen recounts scaling Shopify's infra to 1M RPS, building failure-injection proxy ToxyProxy, and launching Turbopuffer at $1 per million vectors on S3 with an Nginx cache. Cursor became the first customer after he helped debug Postgres/autovacuum issues; he also tells how Jensen Huang ribbed him for choosing CPUs over GPUs. He argues RL workloads are gobbling CPU capacity, making CPU SKUs scarce, and lists six reasons to raise capital, noting his first raise funded R&D and the second let employees cash out. He closes on Turbopuffer's remote culture of campfires and turbo credits for business-class flights.

RAG is dead, right?? — Kuba Rogut, Turbopuffer

Jun 9, 2026 · 11:13

Kuba Rogut (Turbopuffer) argues that RAG isn't dead—it's evolving into agentic retrieval, contrasting Cursor's upfront indexing (which boosted answer accuracy 24% in their composer model) with Claude Code's per-session grep approach. He frames embeddings as cached compute, citing Jeff Dean: 'You don't need a trillion at once, you need the right million.'

We accidentally made an AI platform: Jamie Turner

Oct 8, 2024 · 5:36

Jamie Turner, co-founder of Convex, explains how his company's backend platform accidentally became an AI platform because its reactive data flow paradigm—extending React's state reactivity to the server with subscribable queries and mutations—perfectly suits generative AI workflows. He describes Convex's architecture as seamlessly syncing state between backend steps and the application, enabling concurrent chains of tasks like automatic speech recognition, summarization, embedding generation, and finding related notes. Turner notes that post-ChatGPT, over 90% of Convex projects are generative AI. To support this shift, Convex added native vector indexes for schema fields and launched a startups program with discounts and exclusive forums. Upcoming high-level components will encapsulate state machines for sophisticated workflows. Turner presents Convex as a way for generative AI engineers to ship quickly and confidently.

Git push get an AI API: Ryan Fox-Tyler

Aug 23, 2024 · 45:01

Ryan Fox-Tyler and Matt Johnson Pint from Hypermode present a hands-on workshop demonstrating how to build and iteratively improve AI features using their platform, focusing on a GitHub issue triage app. They first illustrate the process with a multiplayer game (Hypercategories) that uses AI for classification and scoring. Then they build a trend summary function using OpenAI GPT-4 to summarize repository issues, and a classify issue function with a Hugging Face DistilBERT model for labeling issues as bug, feature, or question. Finally, they add natural language search for similar issues by creating an embeddings model (MiniLM) and a Hypermode collection, enabling vector search without external databases. The workshop emphasizes incremental iteration, mixing AI models with traditional code, and using Hypermode's automatic GraphQL generation and observability to speed development.