Latest Articles

Connecting an open-source model to an API is only the first step. SGLang focuses on high-performance serving for LLMs and multimodal models, offering an OpenAI-compatible interface, native generation APIs, and multiple deployment paths. This article examines the problems it addresses, how to get started, the boundaries of performance engineering, and which teams should consider trying it first.

Paperclip is an open source control plane that places AI agents from different runtimes inside one organization, task, and governance model. This article examines its goals, org charts, heartbeats, budgets, approvals, and audit logs, and explains how it moves multi agent automation from scattered sessions toward an observable, recoverable work system with clear boundaries.

AutoGen separates multi-agent systems into Core, AgentChat, and Extensions, with AgentTool, MCP Workbench, and AutoGen Studio supporting orchestration from prototype to tool integration. This article examines the architecture, permissions, observability, maintenance-mode trade-offs, and adoption and migration strategies for existing and new projects.

googleworkspace/cli builds gws in Rust and uses Google Discovery Service to generate a dynamic command surface. With structured JSON, schema discovery, pagination, OAuth, Agent Skills, and helper commands, it gives humans, shells, and AI Agents one composable interface for Workspace automation. This article explains its architecture, benefits, security boundaries, and best use cases.

MCP is not only about letting a model call tools. Its larger value is connecting tools, data, and reusable interaction interfaces through one protocol. FastMCP uses Python decorators, type annotations, and ready-made server and client APIs so teams can start with a small verifiable MCP server, then add transport, deployment, authorization, and governance as the real requirements become clear.

Most RAG systems split documents into fixed chunks and use vector similarity to retrieve context. PageIndex uses a hierarchical document tree and LLM reasoning instead. This article explains its architecture, quickstart, costs, and limits, and shows why it is not a universal replacement for vector databases.