16 posts
The first article in Programmer Craft in the AI Era revisits program = algorithms + data structures, and explains why requirements must first become state, rules, complexity, and acceptance examples before a developer can know what to ask AI to build.
The second article in Programmer Craft in the AI Era looks at queues, hash tables, trees, indexes, and logs as they appear in real systems, and explains why they determine system boundaries, performance, and failure modes.
The third article in Programmer Craft in the AI Era covers processes, threads, coroutines, event loops, and blocking calls, and explains why concurrency is not just wrapping code in async or adding more workers.
The fourth article in Programmer Craft in the AI Era discusses function calls, HTTP/RPC, message queues, event streams, and shared databases. The point is not choosing a fashionable technology, but clarifying coupling, transactions, failure, and observability boundaries.
The fifth article in Programmer Craft in the AI Era covers request and response schemas, error codes, idempotency, pagination, permissions, version compatibility, and contract tests. Before asking AI to write an API, write down the promise first.
The sixth article in Programmer Craft in the AI Era covers table design, indexes, transaction boundaries, locks, slow queries, and migrations. A database is not a universal JSON box; it is the hardest business-constraint layer in a system.
The seventh article in Programmer Craft in the AI Era covers caches, queues, and asynchronous jobs. A cache is not a magic accelerator, a queue is not a trash bin, and async jobs still need idempotency, retries, dead letters, batching, and backpressure.
The eighth article in Programmer Craft in the AI Era covers connection pools, batching, rate limiting, degradation, hot spots, cascading failures, and capacity estimation. Concurrency is not a single coding trick; it is pressure design across the whole call chain.
The ninth article in Programmer Craft in the AI Era explains how unit tests, integration tests, fixtures, contract tests, regression tests, and failure cases can constrain AI-generated code so the result is reproducible, reviewable, and maintainable.
The tenth article in Programmer Craft in the AI Era explains how logs, metrics, traces, audit records, and product events form an observability boundary, so AI-generated services do not become black boxes.
The eleventh article in Programmer Craft in the AI Era explains how to review, isolate, test, draw boundaries, delete code, and refactor in small steps so an AI prototype can become maintainable software.
The twelfth and closing article in Programmer Craft in the AI Era returns to computer fundamentals, systems thinking, product judgment, communication, and collaboration, and discusses which abilities AI will amplify.
A practical guide to service-side event tracking: why it matters, what to collect at each traffic stage, how frontend, backend, gateway, and queue events differ, and how to choose between PostgreSQL, MongoDB, and ClickHouse.
A/B testing is not only for frontend pages. API services also need experiment discipline for ranking, throttling, provider selection, fallback logic, and cost control: stable bucketing, real exposure, cross-request attribution, guardrail metrics, and cleanup after the test ends.
MCP makes tool integration more uniform, but once tools can read files, query databases, and send messages, security becomes a problem of permissions, identity, audit trails, data flow, and supply chain.
A practical note on using Cursor, GitHub CLI, Lark CLI, tests, MCP browser checks, stats scripts, and small skills to automate everyday development workflow steps.