Risk Management for Claude Code in Big Codebases
Failure modes, blast radius, and containment patterns for running Claude Code safely in a large codebase — least privilege, diff caps, and verification gates.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Failure modes, blast radius, and containment patterns for running Claude Code safely in a large codebase — least privilege, diff caps, and verification gates.
The skills and roles founders must hire for to run an AI-native startup on Claude — spec-writing, evals, and agent orchestration in 2026.
The concrete skills, habits, and hiring shifts engineers need to make Claude Code pay off in a large codebase — decomposition, specs, review, and verification.
How founders take agentic AI from one team to many: shared skills, governed MCP infrastructure, orchestration, and the org design that prevents sprawl.
How to scale Claude Code from one team to many on a large codebase — shared skills, federated guardrails, cost control, and rollout without chaos.
Honest founder trade-offs: where Claude agents clearly win, where they quietly cost more than they save, and the simpler alternatives to try first.
An honest guide to when Claude Code is the right tool for a large codebase and when it isn't — the trade-offs, failure modes, and better alternatives.
Governance, trust, and safety guardrails engineering leaders need before scaling Claude Code: permissions, risk-based review, auditability, and ownership.
The governance, trust, and safety guardrails a founder needs before scaling Claude agents: least-privilege permissions, review gates, and audit trails.