19 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.
The useful part of vibe coding is not quickly throwing together a pretty page. It is using real data to find a product shape fast, then leaving behind contracts, fixtures, tests, and handoff notes that engineering can actually pick up.
The real heat in tech is no longer just another model release. AI coding agents are entering real development workflows: taking tasks, editing repositories, running tests, opening pull requests, and forcing old questions about context, safety, and review back onto the table.
Starting from Andrew Ng's comments on small teams and high-context engineers, this article explains why AI pushes senior developers toward a broader product full-stack: problem framing, prototype, code, copy, compliance, launch, operations, and iteration.
Cursor's TypeScript SDK exposes the agent capability behind IDE, CLI, and Cloud Agents as a programmable interface. This article explains runtimes, hooks, MCP, examples, use cases, and a CI failure repair workflow.
A personal look at the reported SpaceX and Anysphere Cursor agreement, how Cursor differs from Claude Code and Codex, and what model ownership, capital, billing, and product direction may mean for developers.
A practical note for computer science students and junior developers who use AI for assignments or daily work but worry that they cannot handle real projects without deeper coding fundamentals.
An early hands-on note on Cursor as a VS Code-based AI editor, using a small C++ Gomoku program to test code understanding, alpha-beta AI logic, object-oriented refactoring, and project-level assistance.