Tech notes
The article explains how to turn a well‑written spec into reliable AI‑driven code by introducing Harness Engineering’s five‑layer architecture, five precise human‑intervention moments, three‑tier third‑party documentation, and a detailed 24‑week rollout plan that boosts success rates and reduces release failures.
Boris Cherny outlines 13 concrete engineering practices—spanning standardization, automated verification, workflow acceleration, and feedback loops—that transform Claude Code from a clever code generator into a reliable, cost‑effective productivity tool for software teams, complete with real‑world metrics and step‑by‑step guidance.
The article explains how AI programming agents expand automation from simple tools to the entire software engineering workflow, converting tacit developer knowledge into documented rules and files like CLAUDE.md, thereby turning team experience into reusable, machine‑understandable infrastructure that continuously boosts productivity.
The article deep‑dives into Agent Harness, comparing it to Spring’s IoC container, explains its five‑layer design, lifecycle management, skill registration, memory handling, security sandboxing, checkpointing, multi‑model routing, and multi‑agent collaboration, and even provides a minimal 20‑line implementation for Java developers.
The article outlines a systematic approach to selecting Superpowers skills across development stages, risk levels, task coupling, and problem types, provides concrete scenario examples and a decision table, and highlights common pitfalls to avoid over‑use or mis‑application of these skills.
This guide explains how to use JLink together with jdeps and a multi‑stage Docker build to create a minimal custom Java runtime, reducing Spring Boot Docker image size from over 200 MB to under 50 MB while improving startup speed and security.
This guide walks through enabling CRaC in a Spring Boot application, building a CRaC‑compatible Docker image, creating a checkpoint job in Kubernetes, restoring the snapshot at pod start, and comparing the startup speed with GraalVM native compilation.
The article identifies eight easy-to-overlook SQL habits—such as using SELECT *, applying functions on indexed columns, careless LIKE patterns, improper IN/OR logic, deep offset pagination, unconditional UPDATE/DELETE, excessive indexing, and ignoring execution plans—that can cause slow queries, lock tables, index failures, and even data loss, and provides concrete examples and safer alternatives.
This article introduces Linux’s three essential text‑processing tools—grep for searching, sed for stream editing, and awk for column‑based reporting—explaining their key options, demonstrating practical command‑line examples, and showing how each excels in specific data‑handling scenarios.
Most slow SQL statements stem from poor query writing—such as index‑breaking functions, implicit type casts, leading wildcards, unnecessary column selection, improper joins, sorting, grouping, and pagination—rather than merely the size of the data, and the article shows how to diagnose and fix each issue.
