17-year internet software developer specializing in AI applications, networking, architecture, and open source. Led the delivery of network services handling hundreds of millions of concurrent devices and tens of millions of QPS, and has three years of experience designing and building an agent platform. Follow to stay updated.
The article traces the decline of interruptive ad formats—from banner blindness to AI‑driven search summaries—showing how AI eliminates the need for disruptive ads and instead enables ultra‑precise, intent‑based matching that serves only the 5% of users who truly want the product.
The article explains why fixed‑interval retries cause thousands of producers to synchronize their retries and overload brokers, and how exponential backoff, full jitter, retry budgeting, and differentiated policies per topic can safely handle send failures even at tens of millions of QPS.
A midnight bandwidth alarm triggers a deep dive into message compression, revealing how algorithm choice, compression placement, batch handling, and selective policies evolve from simple no‑compression setups to multi‑layered strategies that balance CPU, bandwidth, disk costs, and latency in million‑QPS systems.
The article analyses how synchronous Kafka producer calls block business threads on ACK, limiting throughput, and shows that moving to asynchronous sending with the Accumulator decouples submission from confirmation, unlocking batch, compression, and back‑pressure mechanisms that enable stable operation at tens of millions of QPS.
The article explains how using instant‑messaging for direct requests creates costly synchronous communication, contrasts it with asynchronous task links, cites media‑richness theory and remote‑work research, and offers three practical actions—task‑system entry, board‑first view, and long‑form docs—to make collaboration more efficient.
The article shows that asking team members to scroll through long chat histories (“go check the floor”) forces them to replay log‑style data, which is costly and error‑prone, and argues that using state‑oriented tools such as task boards and documentation—akin to materialized views—eliminates this inefficiency.
While DAU and other growth metrics are useful for external reporting, the true differentiator for AI agent teams is evolution speed—defined as the product of problem exposure, diagnosis, and resolution rates—and a finely tuned observability system can shrink diagnosis time from a day to a minute.
At ten‑million QPS scale, a Kafka topic becomes a cross‑team contract rather than a simple name, requiring standardized naming, granularity, partitioning, schema registry, lifecycle management, and governance platforms to transform ad‑hoc topics into machine‑verifiable infrastructure assets.
The article explains why partition count is not a simple integer but the solution of a multi‑constraint capacity‑planning equation, walks through three design stages—from naïve defaults, through CPU‑core‑based heuristics, to a precise engineering model that balances throughput, ordering, scaling, fault domains and storage costs for million‑plus QPS workloads.
In systems handling ten‑million QPS, generic message queues like Kafka hit scalability and latency limits, so the article breaks down a three‑question framework—loss tolerance, latency sensitivity, and ordering needs—to match five specialized queue types and guide a transition from a single‑queue approach to a queue matrix.
