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06:01

What Is an AI Engineer?

AI 工程师≠提示词工程师：一份面向 Web 开发者的入门路线图

A concise role introduction to AI engineering, drawing on Latent Space's 'The Rise of the AI Engineer.' It draws the line at the API boundary: AI Engineers orchestrate models to build applications, while ML Engineers build the model APIs themselves. The post argues newcomers don't need linear algebra or pretraining experience; instead they need strong software fundamentals, evaluation frameworks, and feedback loops. It also distinguishes AI Engineers from AI-assisted developers who merely use tools like Copilot. Web developers are presented as well suited for the transition, and TypeScript is called a fast-growing fit. The article is accessible but conceptual, with a promotional block for the author's AI Hero skills system.

www.aihero.dev · 4 min · AI Engineering · Career Advice · LLM · Prompt Engineering · Software Engineering
06:01

What Can You Use LLMs For? Four Use Cases and a Rule of Thumb

LLM 能做什么：四个用途与一条确定性原则

This article is a practical guide to where LLMs actually shine. It walks through four common use cases: converting unstructured data into structured tables, labeling and classification, question answering, and agents that take actions in the world. It also warns against shipping naive chatbots, pointing out that guardrails are never perfect and citing the infamous Gemini incident where the model told a user to 'please die'. The core argument is a simple rule of thumb: if a system can be built deterministically, it should be. LLMs are only worth the complexity for tasks that are either too expensive for humans to do at scale or too ill-defined for deterministic code to handle. Examples include 19th-century hawk migration logs, The Prompt Report's suicide-risk classification case, and the DeepResearch pattern.

www.aihero.dev · 8 min · Agents · AI Engineering · Deterministic Systems · LLM
06:01

What Is an LLM? Parameters, Sampling, and Training Costs

LLM 扫盲：参数、采样与 200 万美元的训练成本

A beginner-friendly overview of large language models, framing a model as a compressed archive of 16-bit float parameters. It walks through inference, tokenization, common sampling strategies (greedy, top-k, top-p, temperature), and the two-phase training process, citing rough costs: 10TB of data, 6,000 GPUs for 12 days, around $2M, yielding a ~140GB parameter file. The author intentionally keeps things shallow and points to Karpathy's intro and Anthropic interpretability work. Experienced LLM engineers will find little new here; the piece is aimed at newcomers building their first mental model.

www.aihero.dev · 5 min · LLM · LLM Basics · Sampling Strategies · Tokenization