AI

Prologue: A slack surface, and high density underneath In June I came back from Laos to Shenzhen. If you only look at the state, this was a slack month. What it looked like in practice — I would only come alive after dark, stay up to two or three a.m. every night, lulled to sleep by one short video clip after another. Waking up in the day to Shenzhen’s gray-white sky, I could not even gather the strength to leave the apartment. I told myself it was the weather, but inside I knew it was not just that. Every day I bargained with myself: tomorrow I will wake early, I will be disciplined, I will start working — and the next day I lost again to that version of me curled up in bed. The body felt empty, not tired-empty but hollowed-out-and-idling-empty. There were many bothers, but I could not point at any specific one. ...

Prologue: A May Without flomo For the entire month of May, I did not write a single flomo note. I barely posted to my feed either. This was not laziness. It was a conscious decision. I had already decided on April 30 that I would stop. The reason is simple: I no longer felt I needed it. Or more accurately — could it have a better form? I want to use my own product. It is not perfect yet, but I want to fully think through and ship the thing I have in my head. The remaining steps are few. It is close. ...

2026年4月思考笔记 AI 知识时效性：从 RAG 到元认知的解决思路 2026 年 04 月 30 日 - 14:46:26 有些工具现在用的是时效性的方法 比如说是通过一些 API 或者 RAG 系统挂在最新的文档 或者是 web search 最新的数据源 元认知层，让模型知道自己不知道，这是更难的问题 显式标注知识截止日期 训练模型在时效敏感问题上主动表达不确定性 系统提示里注入当前日期，让模型对比自身知识边界 时间戳溯源，每一条知识附带获取时间，超时自动触发重新验证 越旧的信息置信度越低，触发 fallback 到搜索 对高频变动领域（金融、法律、医疗）设置强制校验节点 一个模型生成答案，另一个专门质疑时效性 #格物/ai 利他性记录：有意义才有持续驱动 2026 年 04 月 30 日 - 14:07:21 利他性的前提，是这件事情对他很有意义 这个平台很 有记录的价值 可以展现，或者发挥自己的特长 这样可以激励用户去持续的记录和创作 旅途影响的涟漪效应 2026 年 04 月 30 日 - 13:33:53 身边受到自己影响的旅游的影响的人好像也有一些哈哈哈 日本人的规则感：老挝砍价与文化观察 2026 年 04 月 30 日 - 13:24:11 ...

March 2026 Thought Notes 468 entries this month | Recorded: March 1 — March 31, 2026 Monthly Theme: Angkor Wat · AI Agent · Self-Narrative Core Topics: Gewu/AI (~53), Gewu/Cambodia (~38), Gewu/Angkor Wat (~23), Gewu/Hinduism (~15), Guanwo (~14), Gewu/Vietnam (~11) Daily Notes Archive March 1, 2026 Sunday (29 entries) Northeast China’s Direct Culture: How Labor Independence Shapes Non-Draining Relationship Patterns 2026-03-01 10:35:04 Women’s status in Northeast China is slightly dominant. ...

Preface: This article was written on a night in Lhasa. At an altitude of over 3,000 meters, the air is thin and the stars are very close. One evening I stood in the courtyard, alone, and watched the moon in the sky for a long time. That moon doesn’t lose a bit of its light because of AI’s existence. The moonlight is still truly beautiful — What do humans still possess in the AI era? ...

January 2026 Thought Notes | 2026 年 1 月思考笔记 Total: 388 notes this month | January 1-31, 2026 Monthly Themes: AI Deterministic Value, Finance Patterns, Self-Exploration Core Topics: AI Products & Agent Design (55 notes), Finance & Investment (19 notes), Self-Discovery (11 notes), Biology (10 notes), Social Observation (7 notes), Buddhism (6 notes) Selected Notes of the Month A-Share Industry Rotation: “Three-Times” Rule 2026-01-01 16:05:09 The general rule of A-shares (RMB ordinary stocks) is “shi bu guo san” (事不过三)—an industry rarely rises for three consecutive years. ...

During a 48-hour engineering task, I wrote down one sentence: “At this point, I’ve spent half the time designing, researching, understanding the requirements, judging the boundaries, and developing my taste and intuition. I haven’t started writing code.” The task began as a limited feature. After I kept discussing it with AI, it quickly grew a product model, system boundaries, and a complete design. Every step had a rationale. Every piece connected to the next. I could feel myself thinking faster and farther, holding more layers in mind at once. ...

Calling Claude Tag a Slack bot misses its most consequential product decision. It turns a channel into a place where an agent can be authorized, remember, and keep working: a team shares one executor; each task runs asynchronously in a thread-level sandbox hosted by Anthropic; Agent Proxy injects external credentials at the network boundary; and the result returns to the public thread with a traceable record. The Claude Tag documentation describes these mechanisms in unusually concrete terms. ...

At the beginning of 2026, my GitHub contribution graph stayed nearly blank for several weeks. A few months later, the same graph showed hundreds of contributions in a single week. In July, this blog published 55 articles in Chinese , while GitHub’s contribution calendar recorded 733 contributions. Those numbers prove neither quality nor user value, much less a business result. GitHub also makes clear that contributions may include qualifying commits, pull requests, issues, reviews, and other activity; the number cannot simply be relabeled as a commit count. GitHub’s profile contributions reference ...

“Is common sense the same as the mechanism underneath?” That was where I got stuck while testing ideas across several industries. I had also reviewed the published work of 22 frontier AI teams and more than 70 people whose work is public. A pattern kept returning: pursue one long-running question, define what good work looks like, connect papers, code, and products, then turn production failures into the next round of tests. But the pattern still did not tell me where industry analysis should begin. ...

I had only asked my second brain to organize the thoughts of a friend. It placed scattered notes in order by time, theme, and cause, then tried to explain how she thought about work, money, and freedom. A few thousand words later, I looked at the resulting portrait and realized that the person it had revealed most clearly was not her. It had shown me why I never stop building systems. ...

This piece is a thought experiment in simulated interviews. The interviewees are synthesized from field anthropology and documentary practice perspectives and do not represent any real person’s actual words. I work with AI, yet I am consistently moved by life outside of AI. The blackness of caves in Laos, the sting of a wound after a motorcycle fall, the wind along the Mekong, a stranger who read a story in my eyes, and the stretched-out time of walking instead of riding on the Four Thousand Islands—none of these experiences has model-like efficiency or can be reproduced with one click. ...

This piece is a thought experiment conducted through simulated interviews from the perspectives of cognitive neuroscience and AI memory engineering—representing no actual experts. In March 2026, I wrote in a note: In the AI era, perhaps forgetting is more important than remembering. The image that came to mind was simple: a water layer with hot, warm, and cold strata. Recently used knowledge resides in the hot layer, while content untouched for a while gradually sinks downward—only being retrieved under exceptional circumstances. If a knowledge system absorbs information without releasing it, it will eventually degenerate from wisdom into a mere repository. ...

This piece is a thought experiment-style simulated interview. The respondents are composed of cognitive science, archival studies, and product design perspectives and do not represent any actual expert’s original words. In May 2026, I stopped using flomo. That month had no daily reminders, no faint sense of debt from “today’s notes still pending,” and no rapid conversion of thoughts into cards. Surprisingly, life didn’t thin out. Instead, Laotian cities, caves, injuries, strangers, and bars coalesced in late May into a more complete self-reading than fragmented pieces. ...

This is the sixth essay in Reckoning with Reality. The previous essay separated fast feedback from slow results. This one turns to a question that technological progress makes easy to neglect: when an old bottleneck disappears, where does the new one appear? Whenever a new tool arrives, people first imagine what it will eliminate. AI makes writing faster, code cheaper, translation nearly instantaneous, and video production possible without a full team. Work that once took days can now be completed in minutes. ...

Open Design is easy to misread. The name also belongs to the older open-design movement around shared product blueprints, but this article is about nexu-io/open-design : an open-source workspace that turns coding agents into a design production system. It is not a model, and it is not simply an image generator. It is a harness. The harness gives an agent a controlled vocabulary, reusable workflows, visual constraints, an artifact loop, and a place to inspect the result. That distinction matters because the quality ceiling still comes from the model and the operator; Open Design improves the path between intention and output. ...

Put the Tip List Down for a Moment I began this article as a collection of Boris Cherny’s Claude Code habits. That version had a problem: social posts age with models and product releases, while fan-maintained collections tend to mix personal advice, previews, and impressive-looking statistics. A sharp opinion can become a historical footnote before the article reaches its readers. So I took the slower route. Product claims in this revision come from Anthropic or the official Claude Code documentation. Untraceable numbers, second-hand quotations, and claims of universal superiority are gone. Boris’s way of working remains an inspiration, but it is not treated as a specification. ...

Why Does Getting Better at This Make Me More Tired? Let me start with a strange thing I banged my head against for a long time before I understood it. When I first started using Claude Code, the productivity gain was visible to the naked eye: an afternoon’s work covered what used to take two days. Once I got fluent, the gains kept coming — but so did the exhaustion at the end of each day. Because I was doing the same thing all day long: watch it finish, judge whether it’s right, think about what to say next, hit enter again. ...

Last winter I ran a very ugly set of numbers. I’d spent an entire afternoon in a café, writing code and shooting footage in between — about forty minutes of material across phone and screen recording. I started editing that evening and exported at 1:30 in the morning. The finished cut was fifty-eight seconds. Forty minutes of footage. Six hours of editing. Fifty-eight seconds of output. And the numbers on it were mediocre. ...

Same product, different name, different score There’s something I’ve been watching for a long time without finding the right place to write it down. In the same product community, you’ll regularly see two small tools with nearly identical features. One launches and sinks without a trace; the other gets shared repeatedly and draws serious questions. Look at both product pages: similar craftsmanship, similar pricing, landing pages built from the same template, even. The real difference sits in the small line under the maker’s name — “maintainer of such-and-such project,” “has written about xx for three years.” ...

I wasted years on this one sentence “If the product is good enough, people will come.” I believed that line for years. It fits the engineer’s worldview: code is verifiable, performance is measurable, and there’s an objective difference between good and bad. If the difference is objective, then the good thing winning is just a matter of time — the market is efficient, it just needs a while. That was my logic through my open-source years. Write the project well, keep test coverage high, take the docs seriously, and then wait. Wait for stars, wait for issues, wait for someone to mention it in a blog post somewhere. Sometimes the wait paid off. More often it didn’t. And every time it didn’t, my first reaction was the same one: the thing isn’t good enough yet, go back and keep improving it. ...

One morning, and three things that shouldn’t exist One morning I got up, made coffee, opened my laptop, and found the agents I’d left running overnight all parked at “done”: one had added full multilingual support to a small tool of mine — Chinese, English, Japanese copy, an i18n layer extracted; one had built reading-progress analytics for my blog; one had refactored a CLI I wrote six months ago into a plugin architecture. The code was clean. The tests were green. ...

This is part two of “The Super Individual’s Gear Stack.” If you haven’t read the overview , start there — every judgment in this piece rests on the yardstick that essay proposed: does an advance in a layer of gear help only you, or does it help all of your competitors at the same time? ...

The fifteenth tool list changed the question Over six months, I read roughly twenty or thirty “AI-era super individual” tool lists. Around the fifteenth, the pattern became hard to ignore: the lists differed far less than the competitive advantage each one promised. I use most of the tools they recommend. The problem was not that the recommendations were bad. It was that ten thousand builders could install the same stack in the same week. Their absolute capacity might rise, while the distance between them barely moved. ...

I am building a developer-machine checkup skill called devbox-doctor. The idea sounds simple: inventory a Mac, identify tools that may no longer earn their keep, find actual toolchain conflicts, and trace data left behind by uninstalled apps. The dangerous word in that sentence is identify. A scanner can prove that two tools are installed. It cannot prove that one is useless. A directory can resemble an app’s name. That does not make the directory safe to delete. Spotlight can return no last-used date. That does not mean the app was never opened. ...

What makes an Agent Skill valuable is not a clever prompt, but a clean division of responsibility: deterministic work goes to code, judgment goes to the model, and execution confirmation returns to the human. Structured contracts hold those parts together. I reached that conclusion by dissecting a storage-cleanup Skill that can delete local files from a web page. Deletion is one of the most consequential powers an agent-adjacent tool can expose. The design did not make me fearless; it gave me specific controls I could inspect before deciding whether to click. ...

After the Framework Stood Up, I Realized I Had No “Shared Workshop” The five-part Info-to-Creation series got the framework standing: information gets collected and denoised, records settle into half-finished goods, knowledge gets structured into capability, creation reorganizes it for an audience. But after writing that line in Layer 3: Knowledge — “your knowledge base is the workshop you share with your AI” — something kept nagging at me. ...

What actually happened here How large a team does it take to rebuild a four-year-old blog with 120+ posts — content architecture, operations, all of it? My answer, delivered over the first half of this year: one person and a crew of agents with clearly divided jobs. “Crew” is a working metaphor, not a fixed headcount of digital employees. As of July 2026, the repository contains seven Claude skills and four workflows whose names begin with seo-; Claude Code, Codex, the Claude jobs inside GitHub Actions, and temporary review sessions are started only when a task calls for them. The human owns judgment, boundaries, and the final signature. Agents own scoped work that can be tested. The result is the site you’re reading — cubxxw.com . It isn’t merely a redesign. It turned “a blog” from a static site into a system that generates a daily report, prepares fix proposals, and exposes its own search interface. ...

Suppose you had to place a bet today on one direction in AI agents. What is the first question you should ask? Most people ask whether the thing can be built. By mid-2026, that is often the least interesting part. The colder question is: why has nobody made this opportunity routine yet? The answer is a useful filter. If nobody has solved it because everyone just noticed it, competition is probably coming. If nobody has solved it because the work is dirty, slow, and carries consequence, there may be something defensible—or there may be no market. The rest of the article is how I try to separate the two. ...

Suppose you actually have one now — an agent that takes a job end to end. Pulls the data, writes the code, runs the tests, opens the PR, updates the docs. It doesn’t need you feeding it prompts line by line. You hand it the task at night and go to sleep. The real question isn’t whether it finishes. In coding, research, and content workflows, model capability is often already sufficient to produce a plausible result. That does not mean capability has stopped mattering everywhere: in unfamiliar domains and genuinely novel tasks, it can still be the limiting factor. But once an agent is capable enough to act, a different bottleneck appears — ...