Poolside

The Messy Reality of Scale: Synthetic Data and Pre-Training — Marah Abdin & Robert McHardy, poolside

Jul 26, 2026 · 17:31

Marah Abdin and Robert McHardy from poolside detail their synthetic data pipeline and pre-training tribulations at scale, culminating in a new 118-billion-parameter model for agentic coding that outperforms competitors. Marah describes using synthetic data to rephrase content and fill gaps, with a configurable pipeline (Hive) involving agents, orchestrators, and supervisors, covering rephrasing, multistage workflows, cross-domain porting, and multi-turn chats. Robert recounts failures like broken GPUs causing data corruption, a BF16 accumulation bug that stalled training, and a race condition in FP8 kernels silently corrupting 0.5% of gradients, all caught by model replica hashing. Their Laguna S model (118B total, 8B active) trained on 30 trillion tokens across 4,000 GPUs beats GLM 4.5 Air and other models on coding benchmarks like BigCodeBench and SpeedBench agentless, while remaining competitive on general knowledge.

Everything Is a Rollout — Alex Shaw + Ryan Marten, Terminal-Bench, Harbor, Laude Institute

Jul 24, 2026 · 21:11

Alex Shaw presents Harbor, an open-source framework for evaluating and optimizing AI agents through sandboxed environments, arguing that agent development is a form of machine learning requiring empirical evaluation. He contrasts this with traditional software engineering, showing how agents' behavior is best treated as a black box artifact. Harbor provides a common format for specifying agentic tasks, enabling parallel rollouts across any model, sandbox, and task. Shaw outlines four evaluation use cases: assessing agents building internal products, using external APIs, powering product features, and automating processes. He highlights adoptions by companies like Cognition, Scale, and Poolside, and notes Harbor's role in benchmarks like Frontier Suite and Rune Bench for Runescape. The framework also supports training via SFT and reinforcement learning, with integration partners like Tinker and LangChain.

Your agent is blindfolded — Johan Lajili, Poolside AI

Jul 8, 2026 · 9:58

Johan Lajili of Poolside AI argues that the key difference between successful and disappointing AI coding agent experiences is whether the agent has a tight feedback loop to verify its work, essentially giving it "eyes" to test its own outputs. He demonstrates this with his CLI tool Spoolside for VS Code extensions that extracts logs and screenshots to reproduce bugs. Lajili predicts engineers will shift from product engineering to "AIX engineering" — focusing on making AI work on the product rather than on the product itself. He advises building ephemeral testing primitives and investing in agent self-serve tools, analogous to putting on your own mask first on an airplane.

AGI: The Path Forward – Jason Warner & Eiso Kant, Poolside

Dec 27, 2025 · 15:56

Jason Warner and Eiso Kant, co-founders of Poolside, present their vision and roadmap towards AGI-level capabilities for knowledge work, demonstrating their second-generation model Malibu Agent converting ADA code to Rust live on stage. They argue that next-token prediction paired with reinforcement learning is the key breakthrough, a contrarian bet they made two and a half years ago. The episode centers on their work in high-consequence code environments for defense and government, where agents must operate with tight permissions. They announce a large compute cluster of over 40,000 GB300s coming online and a public API release early next year via AWS Bedrock. Warner recounts meeting Kant through a failed GitHub acquisition, and Kant invites the audience to build with their models, emphasizing that future agents will handle tasks over days as intelligence scales.