Guest on AI Engineer.

MCPs are Boring (or: Why we are losing the Sparkle of LLMs) - Manuel Odendahl
Jun 10, 2025 · 28:32
Manuel Odendahl argues that MCPs (Model Context Protocols) are boring because they constrain LLMs to rigid, predefined tool calls, while LLMs truly shine as code generators that can create dynamic tools on the fly. He critiques the inefficiency of tool calling—repeating context, wasting tokens, and struggling with many tools—and advocates for an 'Eval' approach where the LLM writes and executes code (e.g., SQL, JavaScript) to solve tasks directly. Demonstrating with a JavaScript sandbox, he shows how a single Eval tool can introspect databases, write queries, create REST APIs, and even build a full CRM interface in one call, saving time and tokens. Odendahl urges engineers to think recursively: ask the LLM to write code that writes more code, unlocking infinite loops of creation and restoring the sparkle of LLMs beyond boring function calls.

10x Development: LLMs For the working Programmer - Manuel Odendahl
Aug 21, 2024 · 1:14:46
Manuel Odendahl presents a workshop on using LLMs as a translation engine to achieve 10x development productivity, arguing that treating models as cultural rather than purely technological artifacts enables novel techniques like world simulation for code reviews and domain-specific language creation. He demonstrates concrete methods: regenerating responses, editing model outputs, clearing context frequently, and summarizing transcripts into actionable formats. Odendahl introduces his tool Prompto for managing code fragments and emphasizes that fundamental programming knowledge remains essential while API-specific details become less critical. The episode focuses on decomposing problems into language translation steps, using examples such as transforming meeting transcripts into GitHub issues and building applications by instructing the LLM to simulate itself as the app. Odendahl advocates for divergent thinking and human-centered output, noting that all generated artifacts should ultimately serve human understanding.
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