PostHog vs Railway

Build vs Buy: Cookie Banner for React/Next.js

This technical guide compares building a custom cookie banner versus using a consent management platform (CMP) for React and Next.js applications. It argues teams should build their own banner only when requirements are narrow and the engineering team is prepared to maintain consent infrastructure over time. The article highlights key responsibilities of production consent systems — script gating, preference storage, withdrawal support, regional behaviour, and durable records — and presents c15t as a developer-first CMP that supports hosted, self-hosted, and browser-only deployments. Inth is named as a managed hosting provider for c15t. The guide stresses that script gating and backend persistence are critical when auditability and regulatory accountability (e.g., GDPR/UK rules) matter.

• The article recommends building a custom cookie banner only if requirements are narrow and the team is prepared to maintain the logic over time.
• c15t is described as a developer-first consent management platform that supports hosted, self-hosted, and browser-only deployments.
• Inth provides a managed hosted deployment option for c15t teams that do not want to operate the backend themselves.

Introducing Railway Authentication via identity-aware proxy

Railway Authentication implements an identity aware proxy at the service or environment level to lock down sensitive http endpoints

AI Agent Frameworks Have a Critical Engineering Flaw

The author argues that the current enthusiasm for AI "agents" and hot frameworks distracts from the real engineering challenges of production systems. They define a true agent as a system with an objective that decides next actions, handles failure, and knows when it is done. In production, most agent deployments are narrow, purpose-built pipelines (e.g., support triage, document extraction, code review). Teams that succeed focus on tool design, failure handling, and observability rather than swapping models. The author highlights a persistent retrieval problem in RAG pipelines—incorrect chunking and metadata cause context loss and hallucinations—and recommends architectural patterns (plan-then-execute, separate retrieval from reasoning, explicit handoffs) and better data representations over framework chasing.

• Author defines an 'agent' as a system with an objective that decides what to do next, handles failure, and knows when it is done.
• Most production AI agent deployments are narrow and purpose-built (examples: customer support triage, document extraction, code review).
• Successful teams prioritize tool design, failure handling, and observability over only upgrading model versions.

Workplace AI Needs a 'Chief of Staff' Control Plane

The article argues that businesses deploying multiple AI agents face 'agent sprawl'—fragmented context, uncontrolled side effects, and human operator fatigue—and proposes a split architecture: a central control plane (an "AI Chief of Staff") that handles governance, state, routing, human-in-the-loop approvals, and executive synthesis, while domain-specific workers perform execution. The author open-sourced a reference foundation called OpenClaw Control Plane (a TypeScript monorepo) on GitHub and recommends standardizing interfaces using the Model Context Protocol (MCP) and a workflow-neutral runtime, with deployment examples using Railway. The repo is presented as an M1 foundation with future plans for worker specs, MCP tool bridges, and operator dashboards.

• The article frames 'agent sprawl' as a common problem where siloed AI tools operate without centralized governance.
• It proposes a control plane (an "AI Chief of Staff") responsible for intake triage & routing, state & cross-functional memory, human-in-the-loop governance, and executive synthesis.
• The author open-sourced the 'OpenClaw Control Plane' TypeScript monorepo on GitHub: https://github.com/yuens1002/openclaw-control-plane.