21m ago · 14 min read · The standard agentic loop — give the agent a task, get code back — has no checkpoint between your intent and the agent's execution. You find out what the agent decided to do by reading what it already did. The CORE workflow closes that gap by splitti...
Join discussion11h ago · 4 min read · What you'll learn: How to architect AI agents for production environments with reliability at the core Best practices for containerization and orchestration of AI agent workloads Setting up monitoring and observability systems to track agent perform...
Join discussion1d ago · 13 min read · An April 2026 CVSS 9.4 vulnerability demonstrated that crafted PR titles can prompt-inject Claude Code agents running in GitHub Actions and cause them to exfiltrate ANTHROPIC_API_KEY values to attacker-controlled endpoints. The fix is a five-control ...
Join discussion2d ago · 14 min read · A developer who walked away from iOS development in 2014 — before Swift even existed — returned in 2026 and shipped a production app called BaselineBody without a team. Not a prototype. An App Store release. The workflow that made it possible wasn't ...
Join discussion2d ago · 4 min read · While learning Spring Boot, I came across an essential tool mentioned everywhere - Maven. Initially, it felt like just another configuration file (pom.xml). But as I explored more, I realized Maven is
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6d ago · 11 min read · AI coding agents — Cursor, Claude Code, Codex — produce mobile UIs that break in consistent, predictable ways: viewport-snapping breakpoints, modals that trap background scroll, touch targets that are visually present but physically untappable, and f...
Join discussion6d ago · 13 min read · AI coding agents like Claude Code, Codex, and Open Code generate code faster than any developer can review line by line — and that speed gap is where real risk lives. The practical solution isn't to review less; it's to review at the right moments. A...
Join discussion6d ago · 14 min read · Building an AI coding agent is not primarily about choosing the right model. It's about building the infrastructure around the model that keeps it safe, bounded, and trustworthy. A production agent harness contains only about 1–2% actual AI logic — t...
Join discussion6d ago · 13 min read · Running two or three AI coding agents in parallel on the same codebase is a legitimate productivity multiplier — until they silently collide. Without isolation and explicit ownership boundaries, agents overwrite each other's changes, launch conflicti...
Join discussion6d ago · 15 min read · When you hand off a multi-hour task to an AI coding agent and come back to the results, the right question isn't "did it finish?" — it's "did it stay within scope?" Agents running Claude Code, Codex,
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