AI Product Manager with 8 years of internet experience, the last 4 focused on building AI products.
This blog has two threads: AI research — notes on LLM reasoning, agent architectures, and context engineering; and PM thinking — product decisions and lessons from building AI in a regulated industry.
I believe understanding the technology makes you a better product manager.
从换手机这件小事说起 一、我开始用 Excel 决定换不换手机 前几天我盯着 iPhone 17 Pro 的以旧换新页面，认真打开 Excel 算了一笔账。 15 Pro 的折抵价、12 期免息分摊、明年 18 Pro 发布后 15 Pro 的残值预估、存储芯片涨价会不会传导到 18 Pro 的定价……我用一个产品经理做 ROI 分析的全部工具，去回答一个本来应该很简单的问题:要不要换。 ...
——读 Jaclyn Konzelmann《Looking Ahead into 2026》笔记 她今年放弃了惯例的"AI 预测清单"——因为变化太快，预测毫无意义。改为定下几条原则： ...
在过去的一年里，AI Agent 的演进出现了两个非常重要的趋势： 智能体正在变得更通用（Generalist）：可以承担越来越多类型的任务； 智能体的任务时长变得更长（Long-horizon）：能够连续执行几十甚至上百个步骤的复杂任务。 根据 METR 的基准测试，AI 能自动完成的人类任务等效时长大约 每 7 个月翻倍。这意味着智能体从“短对话助手”，发展为“能够连续运行数百甚至上千步的自主系统”。 ...
大家可能都已经对 LLM 很熟悉了。大概在两三年前，ChatGPT、Claude、Llama、DeepSeek 等模型相继出现，可以说是彻底改变了世界。但在使用这些强大工具的同时,一个核心问题值得探讨：这些模型到底是如何训练的？ ...
By 2025, existing models have already become remarkably intelligent. However, even the smartest system cannot perform effectively without understanding what it is being asked to do. Prompr engineering refers to the practice of phrasing tasks in an optimal way for large language model-based chatbots. Context engineering, on the other hand, represents the next stage - aiming to automate this process within dynamic systems. What is Context Engineering? Tobi, from Shopify, shared an interesting post in which he expressed his appreciation for the term “Context Engineering.” Later, Karpathy followed up with a brilliant definition: ...
The following insights are drawn from the Reasoning with o1 video course by DeepLearning.ai. This article explores how to effectively prompt and utilize the new generation of reasoning models. Models released over the past year have demonstrated remarkable progress in reasoning and planning tasks. OpenAI has deeply optimized Chain of Thought (CoT) processing, using reinforcement learning to fine-tune models so they automatically integrate step-by-step reasoning into their response process. ...
In today’s rapidly evolving AI landscape, the remarkable progress we’ve witnessed is largely attributed to open scientific research and fully open models. However, as time progresses, more and more research and development work is becoming increasingly closed off. We still need to delve deeper into how language models work, improve their capabilities, and make them safer, more efficient, and more reliable. Simultaneously, we need to extend language models’ abilities beyond text into domains like healthcare, science, and even complex decision-making processes. Most importantly, we must bring these models into real-world applications, ensuring they are deployable, interpretable, and effectively mitigate biases and risks. ...
Language Agents have emerged as one of the most exciting research directions in AI over the past two years. This article explores three core components: long-term memory via HippoRAG, reasoning capabilities with Grokked Transformers, and world modeling through WebDreamer. Why Agents Again? Russell & Norvig in “Artificial Intelligence: A Modern Approach” define an agent as “anything that can perceive its environment through sensors and act upon that environment through actions.”（@ArtificialIntelligenceModern） ...
Introduction To understand LLM agents, we need to break the term into two foundational components: Large Language Models (LLMs) and Agents. While LLMs have gained widespread recognition, the concept of “agent” in this context requires deeper exploration. What is an Agent? In artificial intelligence, an agent is an “intelligent” system that perceives and interacts with an “environment” to achieve specific goals. The classification of agents varies based on their operational environment: ...
本文内容来自 Jason Weston (Meta) 在 UC Berkeley Advanced Large Language Model Agents 课程中的分享，探讨了大语言模型的推理能力提升 。以下为讲座内容： AI 能力正在快速发展，如 O1、R1 等模型在推理基准测试中取得的突破性进展。本文将聚焦于模型的自我提升能力(self-improvement)。 ...