GraphQL

The Infinite Software Crisis – Jake Nations, Netflix

Dec 20, 2025 · 18:57

Jake Nations, engineering lead at Netflix, argues that AI-generated code accelerates the software crisis by conflating easy with simple—producing tangled, incomprehensible systems. He traces the crisis from 1968 to today's infinite code generation, citing Fred Brooks' 'No Silver Bullet' and Rich Hickey's definition of simple as 'one fold, no entanglement.' Nations presents a three-phase methodology—research (compressing 5 million tokens of code into a 2,000-word spec), planning (paint-by-numbers implementation steps), and implementation (using a manual migration seed)—to maintain human understanding. He warns that without this approach, engineers lose the ability to recognize dangerous complexity, and challenges listeners: will we still understand our own systems when AI writes most of the code?

Agents vs Workflows: Why Not Both? — Sam Bhagwat, Mastra.ai

Aug 1, 2025 · 15:37

Sam Bhagwat, co-founder of Mastra and author of 'Principles of AI Agents', argues that the debate between agents and workflows is misguided—developers should combine both. He criticizes OpenAI's anti-workflow stance as 'being that guy' and LangChain's graph/node/edge APIs as harmful, advocating for fluent syntax over graph theory. Bhagwat defines agents as turn-based games and workflows as rules-engine dependency chains, noting that nondeterminism makes workflow tracing 10x more important in AI engineering. He demonstrates composition patterns: agents can be steps, workflows can be tools, and nested workflows handle dynamic tool injection. He recommends starting with agent power, then adding workflow control for reliability—e.g., breaking one LLM call into 12 for medical PDFs. Bhagwat concludes that practice trumps theory in this young field.

Tool Calling Is Not Just Plumbing for AI Agents — Roy Derks

Feb 22, 2025 · 25:18

Roy Derks argues that tool calling is the most critical yet overlooked component of AI agents, far more than mere 'plumbing.' He contrasts traditional tool calling—where developers manually manage callbacks, retries, and errors within the agent loop—with embedded tool calling, a black-box approach used by frameworks like LangChain’s createReactAgent. Derks advocates for separation of concerns via the Model Context Protocol (MCP) from Anthropic, which splits tool logic into MCP servers communicating with clients, and via standalone tool platforms such as IBM’s wxflows, Composio, and Toolhouse that let teams build tools once and reuse them across LangChain, CrewAI, or AutoGen. He also introduces dynamic tools, where an agent generates queries on the fly—e.g., using GraphQL or SQL schemas—instead of defining hundreds of static tools, noting that LLMs like Claude handle GraphQL well but may hallucinate on deeply nested schemas. The episode emphasizes that 'an agent is only as good as its tools' and provides practical guidance on designing tool descriptions (which act like system prompts) and output schemas to enable type-safe, chainable tool calls.

Git push get an AI API: Ryan Fox-Tyler

Aug 23, 2024 · 45:01

Ryan Fox-Tyler and Matt Johnson Pint from Hypermode present a hands-on workshop demonstrating how to build and iteratively improve AI features using their platform, focusing on a GitHub issue triage app. They first illustrate the process with a multiplayer game (Hypercategories) that uses AI for classification and scoring. Then they build a trend summary function using OpenAI GPT-4 to summarize repository issues, and a classify issue function with a Hugging Face DistilBERT model for labeling issues as bug, feature, or question. Finally, they add natural language search for similar issues by creating an embeddings model (MiniLM) and a Hypermode collection, enabling vector search without external databases. The workshop emphasizes incremental iteration, mixing AI models with traditional code, and using Hypermode's automatic GraphQL generation and observability to speed development.