Essays, implementation notes, and research from building project systems that keep planning, delivery, and review work coordinated.
16 posts
Claude Code Tasks shipped January 22, 2026 — a real, well-designed feature that persists agent work across sessions on one machine. Three months later, developers were still publishing workarounds. This post maps the four boundaries Tasks was designed to have, and tells you exactly when native Tasks is sufficient and when it is not.
The April 2026 Bitwarden CLI attack showed why static `.env` files are too broad for AI agents. The safer path is a staged move from scoped allowlists to runtime injection to workload identity.
Seven MCP-native project, task, and memory tools appeared on Show HN across roughly a year. They point to the same missing layer: durable project context for external AI assistants.
Context-window decay is a memory problem, not an intelligence problem. Learn why long prompts lose the plan and why explicit acceptance criteria survive where prose plans fail.
GitHub Actions was never built for the fast closed loop an agent needs — going back, redoing a step, fixing its own work. Local agent fan-out solved the loop on one laptop and broke on two. The Agiflow CLI is the convenience wrapper we use internally to drive workflow locks, work units, and artifacts through the Agiflow API — so agents on different machines can pull the same backlog without stepping on each other.
Architect multi-agent code systems that stay coherent. Learn role separation patterns, handoff contracts, and verification gates to prevent coordination failures.
Learn how to architect frontend applications that scale with AI assistance. Discover how atomic design methodology, component libraries, and design systems dramatically reduce token consumption while ensuring consistent, maintainable codebases.
Optimized tool integration can reduce token consumption by 81% compared to baseline approaches. A controlled study reveals how architectural decisions—not protocol choices—determine scalability and cost in production AI systems.
AI coding assistants excel at single tasks but struggle with multi-task feature coordination. Learn how work units + Project MCP enable structured AI agent workflows, reducing feature delivery time by 40% and maintaining context across 5-8 related tasks.
Deep dive into Claude Code internals by instrumenting network traffic to understand how CLAUDE.md, output styles, slash commands, skills, hooks, and sub-agents actually work under the hood.
AI coding assistants are shipping features 3x faster—but silently breaking architectural patterns. Learn how to reduce violations by 92% with MCP-based validation and feedback loops.
From "can AI write code?" to "can AI write code that scales?" Discover how intelligent scaffolding systems solve the consistency crisis in AI-assisted development.
Autonomous Agents show immense potential in solving complex problems, but they can also be brittle, prone to hallucinations, expensive, and slow to run.
Learn best practices for mocking LLM responses during development to improve testing efficiency and reduce API costs while maintaining code quality.
Explore the unique challenges and opportunities that generative AI brings to product analytics, and how to adapt traditional analytics approaches for AI-powered products.
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