Turbopuffer

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.'

Benchmarking semantic code retrieval on Claude Code — Kuba Rogut, Turbopuffer

Jun 3, 2026 · 16:08

Kuba Rogut from Turbopuffer benchmarks semantic code retrieval on Claude Code, finding that raw Claude Code wastes one in every three file reads (65% precision), windowed grep drops that to one in five, and adding semantic search cuts it to one in eight (87% precision). Semantic search wins on behavior-adjacent tasks where files share no keywords; grep wins on import tracing. Cursor's production numbers show a 24% relative improvement in answer accuracy from semantic retrieval plus a 2.6% increase in code retention in large codebases. Rogut explains Cursor's model knows when and why to call semantic search, while Claude Code treats it as just another tool, limiting gains.

Continuous Profiling for GPUs — Matthias Loibl, Polar Signals

Jul 22, 2025 · 11:31

Matthias Loibl of Polar Signals explains how continuous profiling for GPUs maximizes GPU efficiency using low-overhead, always-on sampling via eBPF. He contrasts tracing (high cost) with sampled profiling (e.g., 100 Hz, <1% overhead) and details GPU metrics collected from NVIDIA NVMe, including utilization, memory, clock speed, power, temperature, and PCIe throughput. The platform correlates these with CPU stack traces to identify bottlenecks, such as Python and CUDA functions underutilizing the GPU. A new GPU time profiling feature records the duration of CUDA kernel executions, showing actual time spent by functions on the GPU. Deployment runs on Linux with a binary, Docker, or Kubernetes DaemonSet; early adopters like TurboPuffer use it to optimize their vector engine.