S3

The State of Model Routing — NVIDIA, Cognition, OpenRouter

Aug 6, 2026 · 48:17

Cognition's Walden Yan, OpenRouter's Alex Atallah, NVIDIA's Tanay Varshney and Carter Abdallah argue routing should orchestrate frontier and cheaper models, not per-task benchmark picks; Devin Fusion cuts Fable-level intelligence cost by 40%. Yan: task-type routing is fragile because a session shifts from codebase question to feature request to live debugging; Devin keeps a frontier model planning while a cheap sidekick executes. Atallah: OpenRouter's auto router sat unused for two years until OpenClaw heartbeats every ten minutes created an app with two intelligence needs; out-of-distribution, small models thrash: Opus scores three times better at a tenth of Haiku's cost on terminal bench. Varshney cites jagged capabilities for up to 10% higher accuracy; Abdallah adds local/cloud routing.

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.

How Forward Deployed Engineering is done at Kepler — Vinoo Ganesh

Jul 28, 2026 · 22:20

Vinoo Ganesh, a former Palantir engineer who built the Frontline rotation program, argues that Forward Deployed Engineering is a product strategy, not a go-to-market motion, and shows how Kepler applies this philosophy. He illustrates with stories from Palantir: solving a shipping customer's 47-page requirements with a four-hour Slack alert, building a Parquet viewer after watching a data quality engineer manually spot-check CSVs, and the Groovy script that became a product supporting 100,000 people. Ganesh emphasizes detecting real problems by observing users' actions—any repeated task hints at a missing feature, and pulling out a phone mid-workflow is a bug report you'll never find in documentation. He also explains defining ontology: when different teams call the same entity 'clients,' 'billing,' or 'accounts,' the FDE must canonicalize terms to become the linguistic foundation. The hardest skill is discarding—ship everything as if it will run 18 months, because every hack goes into production. Ganesh concludes that FDEs drive product leverage by solving small problems on-site, then generalizing solutions into the core product.

Thinner Agents on a Smarter Substrate: The Ontology-based Semantic Layer — Emil Eifrem, Neo4j

Jul 22, 2026 · 11:06

Emil Eifrem of Neo4j argues that scaling AI agents at large enterprises requires thin agents on a smarter shared substrate built from three pillars: a business ontology naming real concepts (customers, accounts) in plain language, a technical ontology cataloging all data sources and schemas with a mapping between them, and execution traces recording each agent's attempts and outcomes. These three layers solve four problems: data discovery (teams no longer hunt from scratch), trust (top-down curation and bottom-up success signals), DRY (a single mapping propagates changes across all agents), and cross-agent learning (agents improve over time via traces). The approach is based on work with a Fortune 20 bank, a Bay Area tech platform, and a leading fintech. Eifrem says this enables many more agents without re-engineering each time.

When Agents Meet Physical Data: The Other Physics of Agent Harnesses - Dmitry Petrov, DataChain

Jul 20, 2026 · 27:33

Dmitry Petrov of DataChain argues that AI agents fail on unstructured physical data because their intuitions assume cheap recompute, while large-scale video, sensor, and robot data requires a different harness. He cites Anthropic's finding that agents achieve only 21% accuracy on data projects without specific data harnesses, and OpenAI's need for six layers of context even on structured data. Petrov demonstrates DataChain, an open-source Python framework that uses Pydantic schemas to turn messy binary files into queryable databases, an execution engine for distributed processing, incremental checkpoints to avoid recomputing on failure, and a knowledge base of datasets and source code so agents can answer follow-up questions in seconds instead of reprocessing terabytes. In a live demo with Claude Code analyzing 90 dashcam videos, the harness took 24 minutes to extract 100,000 object records, then instantly answered 'how many clips have people?' without rerunning inference. Petrov emphasizes that the key is organizing metadata into star schemas and sharing data lineage across teammates so no one pays the compute cost twice.

Everything we knew about software has changed — Theo Browne, @t3dotgg ​

Jul 8, 2026 · 16:02

Theo Browne argues that AI model evolution—from Sonnet 3.5’s tool-calling to Opus 4.5's long-running tasks and Mythos's orchestration—requires engineers to think bigger and wider. He compares current developer habits to skeuomorphism in iOS 7, urging rejection of legacy constraints like Git's inability to commit environment files and terminal-centric workflows. Browne introduces a shifted tier system: what was a startup is now a side project; a Markdown file running on a cron job can replace a company's product. His own PR triage service became a Markdown file updated daily via cron. He advocates building breadth over depth—architecting products so users can extend features, enabling small teams to compete with AWS or Salesforce. 'If your idea doesn't feel stupid, it's because your idea is not big enough,' he concludes.

How Building with AI Can Double the Throughput of Your Engineering Team — Brian Scanlan, Intercom

May 15, 2026 · 21:49

Brian Scanlan of Intercom explains how treating Claude Code like a new hire—onboarding it to a 15-year-old Rails monolith, writing skills for every recurring task, and connecting it to production systems—doubled engineering PR throughput in under a year. By going all in on one platform instead of letting everyone choose their own tool, Intercom achieved a 17.6% automatic PR approval rate with SOC 2 sign-off, and its CI infrastructure collapsed under the volume. The key principle: give agents problems, not tasks. Scanlan recounts a security incident where Claude automatically pulled files, ran analysis, and handed back next steps in two minutes using a skill he didn't know existed. The talk emphasizes that all engineering work—debugging, testing, planning—should be agent-first, and that companies must invest in platform-level adoption and continuous skill improvement.

Spec-Driven Development: Agentic Coding at FAANG Scale and Quality — Al Harris, Amazon Kiro

Jan 9, 2026 · 1:03:50

Al Harris, principal engineer at Amazon, presents Spec-Driven Development (SDD) with Kiro, an agentic IDE that transforms prompts into structured requirements (EARS format), designs, and property-based tests to ensure code correctness. He argues that upfront specification—augmented by MCP servers for external context—improves reproducibility and quality over pure vibe coding. The talk includes a live demo: building an S3-backed checkpointer for a LangGraph dad joke generator, then discovering Agent Core’s native memory is more idiomatic. Harris contrasts SDD with Cursor’s planning mode, explains how specs evolve as living documentation, and discusses handling session length, context pruning, and brownfield codebases. The result is a claim that spending 5–10 minutes on specs yields higher accuracy and reliable, testable outputs.

Using OSS models to build AI apps with millions of users — Hassan El Mghari

Jul 15, 2025 · 18:47

Hassan El Mghari, a software engineer at Together.ai, explains how he builds open source AI apps that attract millions of users, including roomGPT.io (2.9 million users), restorePhotos.io (1.1 million), Blinkshot.io (1 million visitors), and LlamaCoder.io (1.4 million visitors). He details his simple four-step tech stack: user input, a single API call to an open source model on Together.ai, storing results in a database, and displaying output. His process emphasizes keeping ideas simple enough to describe in five words, spending 80% of time on UI, launching early, and incorporating the latest models like Fluxionel for virality. He advises naming apps with short, memorable names, making them free and open source to encourage sharing, and iterating rapidly—most apps fail, but the key is to keep building. He funds compute through sponsorships from Together.ai and partners like Neon and Clerk, which provide free services for his open source projects.

Graph Intelligence: Enhance Reasoning and Retrieval Using Graph Analytics - Alison & Andreas, Neo4j

Jun 27, 2025 · 1:41:16

Alison Cossette and Andreas Kollegger from Neo4j demonstrate how graph data science algorithms enhance GraphRAG systems by connecting, clustering, and curating unstructured data. They walk through running KNN similarity (K=25) on document embeddings, then applying Louvain community detection to identify clusters of near-identical chunks—one community had 49 documents with 0.98 average similarity—which can be collapsed via APOC to improve retrieval efficiency while preserving lineage. Using page rank and betweenness centrality, they show how to surface influential documents and track conversation paths across communities. The session uses a preloaded Neo4j AuraDB database with 17K nodes and 774K relationships from the Agent Neo project, and includes practical cipher queries for projecting graphs and running algorithms. They emphasize using graph analytics to diversify retrieval, manage content hygiene, and build accountable agent systems.

[Full Workshop] How to add secure code interpreting in your AI app: Vasek Mlejnsky

Feb 6, 2025 · 1:48:16

Vasek Mlejnsky, CEO of E2B, demonstrates how to add secure AI code execution to any app using E2B’s Code Interpreter SDK alongside Anthropic's Sonnet 3.5 and Vercel’s AI SDK. The workshop builds an open-source version of Claude’s artifacts UI, running Python code in isolated Firecracker VMs that start in ~900ms and support custom environments via Dockerfiles. Vasek explains how E2B’s Jupyter-server-based sandbox returns stdout, stderr, runtime errors, and rich outputs like PNG charts, which are streamed to the front end using Vercel’s stream data. He covers security architecture—full root access in a VM that self-destructs on escape—and plans for AWS/GCP self-hosting and snapshot-based agent branching. The episode also addresses customizing sandboxes with private packages, mounting cloud storage (S3, GCS), and interpreting table previews as HTML or images.

The Hierarchy of Needs for Training Dataset Development: Chang She and Noah Shpak

Oct 15, 2024 · 16:32

Chang She (CEO of LanceDB) and Noah Shpak (AI data platform lead at Character AI) argue that data infrastructure is the critical bottleneck for LLM training, and LanceDB's columnar format solves the 'new cap theorem' for AI: needing fast scans, random access, and handling large multimodal blobs simultaneously. Noah explains how Character AI structures pre-training around wide domain coverage and post-training around granular analytics like token counts and difficulty scores, using synthetic data, quality scoring, and dataset selection to improve models. Chang details how Lance format provides zero-copy schema evolution, time travel, and indexing extensions for vector, scalar, and full-text search, enabling a single table to serve SQL analytics, PyTorch training, and production vector search. The episode emphasizes that speed and iterative dataset management are key to accelerating AI research, with LanceDB facilitating cheap random access and low-infra billion-scale vector search.