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06:01

Context Windows Are Not About Size: Tokens, Limits, and Lost in the Middle

别只看窗口大小：token 上限与 lost in the middle 才是关键

The context window is the combined total of input tokens (system prompt, user prompt) and output tokens (the assistant's reply). Every model has a hard-coded token limit, so long conversations eventually produce API errors—sometimes mid-generation. Models can't work around their own limits, and a larger window actually worsens the well-known "lost in the middle" effect: the beginning and end of the conversation get more attention while the middle fades, making the model less able to retrieve its own context. Even with a huge window, you'll get better results from using fewer tokens. This is a concise explainer for engineers getting started with LLMs or designing agent context.

www.aihero.dev · 3 min · AI Engineering · Context Engineering · LLM
06:00

What Are Tools? A Precise Walkthrough of LLM Tool Calling

LLM 工具调用底层循环：工具定义、tool call 与结果回传

This article walks through LLM tool calling using a simple write-file example. Tools are not magic: each tool is defined by a name, a description, and JSON Schema parameters, then injected into the system prompt. When the model decides to act, it returns only a tool-call message with an id and parameters; nothing actually happens until the developer intercepts that message, executes a matching function, and sends the result back under the same id. Errors are sent back too, so the LLM can adapt its next step. The post is aimed at engineers starting to build agents who want a precise mental model of the loop.

www.aihero.dev · 4 min · Agent Engineering · Agents · LLM · Tool Calling
06:00

What Is an Agent? Agents vs Workflows

Agent 还是 Workflow：停止权在代码还是模型？

This short post clarifies Anthropic's now-canonical distinction between agents and workflows. Both orchestrate multiple LLM calls, but workflows hard-code the sequence and termination condition in developer-written code, while agents hand tools to the LLM and let it decide which to call and when to stop. The post notes that a single LLM call is neither, and argues that workflows often outperform agents on clearly specified, repetitive tasks. Includes simple diagrams contrasting the two. A useful conceptual primer for engineers new to agent architecture.

www.aihero.dev · 3 min · Agents · LLM · Orchestration · Workflow