Mongo

From Blind Spots to Merged PRs: Continuous Agentic Performance Optimization - May Walter, Hud

Jul 19, 2026 · 22:46

May Walter, CTO of Hud, presents a case study on using coding agents with runtime intelligence to automate continuous performance optimization in production. The system analyzes real production traces, queries, and latencies to surface high-ROI fixes like N+1 queries and missing indexes, scoring them by complexity and impact. It runs weekly via GitHub Actions and Claude Code, generating reports with verified fixes and evidence of impact, such as P90 latency improvements after deployment. Key challenges included 'plausible unverified' fixes, complex ClickHouse queries, and lazy fixes that only mask exceptions rather than root causes. Walter emphasizes the need for prod-to-code context mapping, scoring guardrails, and human review to achieve reliable automation. The talk concludes that context over cleverness works, and agents can automate the investigation phase that teams rarely do proactively, turning dormant performance debt into actionable merged PRs.

Jack Morris: Stuffing Context is not Memory, Updating Weights is

Dec 29, 2025 · 1:02:44

Jack Morris argues that large language models fail at niche, long-tail knowledge tasks, such as optimizing AMD GPU kernels or answering private company queries, because they rely on context windows and RAG, which suffer from quadratic self-attention costs and context rot. He advocates for a third paradigm—training knowledge directly into model weights—using synthetic data generation (e.g., synthetic continued pretraining from Stanford) to expand small datasets and parameter-efficient methods like LoRA or memory layers to avoid catastrophic forgetting. Morris demonstrates that full fine-tuning on a 3M 10-K report causes the model to only regurgitate exact sentences, whereas generating diverse synthetic question-answer pairs enables better generalization. He notes that RL-based fine-tuning (e.g., GRPO) can achieve improvements with as few as 14 parameters, while memory layers offer the best trade-off between learning and forgetting. The episode also explores temporal information handling, federated learning resurgence, and the practical decision boundary between RAG and weight-based injection based on data freshness and volume.