The article analyzes how AI agents can generate code at massive scale, creating "dark" software factories that accumulate comprehension debt, and argues that verification, back‑pressure limits, and a human‑owned outer loop are essential to keep the system reliable.
Rate limiting safeguards distributed systems by controlling request rates through algorithms such as leaky bucket, token bucket, fixed and sliding windows, and back pressure, while client‑side tactics like exponential backoff, jitter, and careful retries, and requires atomic distributed storage solutions (e.g., Redis+Lua) to avoid race conditions.
This article provides an in‑depth technical overview of Apache Storm, covering its core architecture, key components such as Nimbus, Supervisor, Worker, Executor, and Task, the role of ZooKeeper, high‑availability setup, API interfaces, code examples, grouping strategies, metrics, back‑pressure handling, and essential configuration parameters for building low‑latency stream processing topologies.
