Calvin Qi

Automating Large Scale Refactors with Parallel Agents - Robert Brennan, OpenHands

Jan 8, 2026 · 1:16:21

Robert Brennan, CEO of OpenHands, and colleague Calvin explain how parallel agents can automate large-scale code refactors, demonstrating that breaking tasks into agent-sized batches with human oversight achieves 90% automation. They trace the evolution from context-unaware snippets to autonomous coding agents and now agent orchestration, where multiple agents work in parallel on tasks like CVE remediation — one client saw a 30x improvement in time-to-resolution. Calvin shows a pipeline using OpenHands' SDK to batch code files, run a verifier to detect code smells, and spin up fixer agents that generate focused pull requests. Brennan details strategies: task decomposition into single-PR-sized chunks, dependency ordering, and context sharing via agent.md files. The episode closes with a live-coding walkthrough building a parallel agent script that scans for vulnerabilities and opens individual PRs.

Scaling Enterprise-Grade RAG: Lessons from Legal Frontier - Calvin Qi (Harvey), Chang She (Lance)

Jul 29, 2025 · 16:40

Calvin Qi of Harvey and Chang She of LanceDB detail how Harvey builds enterprise-grade RAG for legal use cases, tackling massive scale (tens of millions of documents per corpus), spikey workloads, and non-negotiable privacy. They explain why domain-specific reranking is essential for ambiguous, expert queries like 'What is the applicable regime to covered bonds issued before 9 July 2022 under Directive EU 2019/2062?' and stress that evaluation-driven development—from expert reviews to automated metrics—is key to accuracy. Chang shows how LanceDB's AI-native multimodal lakehouse, built on the open-source Lance format, enables low-latency, high-throughput retrieval across billions of vectors (indexing 3–4 billion in under 3 hours) while supporting offline analytics, GPU indexing, and compute-storage separation. The talk emphasizes that creative data understanding, iteration speed via good evals, and flexible infrastructure are critical for scaling RAG in domain-specific, privacy-sensitive environments.