Agent

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. ...

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. ...

“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. ...

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. ...

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. ...

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 — ...

The quiet dashboard can be the dangerous one A morning briefing with no new items may mean that nothing relevant happened. It may also mean an RSS route broke, an API began returning empty pages, or a credential expired during the night. From the reader’s side, those very different states look identical: silence. That is the first lesson of a personal intelligence system. Gathering more information is easy to demonstrate; knowing whether the machine is healthy, whether its summaries are faithful, and whether a signal deserves action is the real work. The system I want is not an account that publishes automatically. It is a pipeline that can show me what changed, why it may matter, what evidence supports the claim, and which decision—if any—should follow. ...

How many agents can one person afford to keep running? The wrong way to answer is to count agents. An “agent” might classify one paragraph, search for twenty minutes, or refactor a repository through eighty tool calls. The useful unit is not the agent. It is the successful task, with its input tokens, output tokens, tool charges, retries, and human cleanup attached. That distinction changed how I think about two related but different choices: low-cost hosted model APIs and open-weight models that can be self-hosted. Falling API prices matter, but “90% cheaper” is meaningful only when three things are visible: ...

A counterintuitive signal: it starts prompting you Start with a question. Suppose one day you open your workspace and the agent isn’t sitting there quietly waiting for your next command. It speaks first: “I noticed section three of yesterday’s proposal is still unfinished. I drafted something in the voice you used last week — want to take a look now?” Is that thoughtful, or is it presumptuous? Over the past six months I’ve noticed a counterintuitive signal while following agent launches, primary sources, papers, and benchmarks: more products are experimenting with agents that do not wait for the next instruction. They use available context, estimate what is worth doing, and bring a suggestion forward. ...

Suppose you ask an AI system to track one field — papers, releases, benchmarks, first-party changelogs, and the conversations around them. How far can it get on your behalf? This essay is not a census of every product. It is a field note from the systems I tested between January and June 2026, across AI research and developer-tool sources. In that sample, three patterns kept recurring: subscription aggregation, change monitoring, and agentic search. They were capable, but they shared a boundary: automation can haul information remarkably well; without your goals, context, and feedback, it cannot reliably make the final judgment for you. ...

A Year Later, Why Does AI Still Feel Like Extra Work? AI now writes copy, translates documents, summarizes meetings, and inspects spreadsheets. Yet one honest question cuts through the excitement: has it taken over a business step, or do you still explain the background, judge the answer, and decide what happens next every time? The model may be smarter. The person carrying the context and the responsibility often has not changed. ...

After Three Hours, What Is Left on the Table? I have had evenings that began with a modest intention: settle the angle of an essay. Soon I was discussing titles with AI, then business models, then the meaning of work. The conversation flowed beautifully. When I closed the window, the page was still blank. That does not make the conversation worthless. It reveals that exploration and execution are different kinds of work. ...

“It Runs” Is Not a Reliability Standard Most personal AI workflows begin with one acceptance test: did it produce something? A draft appears, a patch compiles, ten pages become one, and the task feels finished. After enough repetitions, however, the costly failures are rarely dramatic. They arrive quietly: a polished paragraph built on a stale source, a tool call that never completed, a plausible plan that solved the wrong problem. ...

A Second Brain Is a Working System, Not a Larger Notebook Most note systems are built for a future reader called you. You save a link, polish a heading, add two tags, and trust that one day you will return. Usually, you do not. An AI-native second brain begins with a different question: What useful work should this note make possible? The answer might be modest: turn an inbox note into a project brief, retrieve your earlier judgment before a meeting, or draft an article from claims you have already verified. The point is not to make AI read everything. The point is to give it a narrow, legible field in which it can help without quietly rearranging your life. ...

Growing Bigger, Getting Less Useful We’ve reached layer three. Information has been captured and denoised; records have been written down and polished into semi-finished products — now the question is: how do you turn these semi-finished products into actual knowledge? Let’s start with a definition. Knowledge is structured, repeatedly reusable material relevant to you: a mental model, a handful of skills, a methodology, along with your judgment, positioning, and values. Its keyword is reusability, and it solves your own problems. ...

open-lovable begins with a compact promise: give it a website, get back a React application you can keep editing. The generated page is the visible trick. The more useful subject is the plumbing underneath—how scraping, model output, and untrusted code execution are joined without surrendering the whole process to the model. First, pin the clock. This article audits firecrawl/open-lovable commit 69bd93bae7a9c97ef989eb70aabe6797fb3dac89 , dated November 19, 2025. “In the project” below means that exact revision. “The platform today” refers to current vendor documentation. A fixed commit is a small discipline, but it prevents later product features from being smuggled into an older implementation. ...

“Software is eating the world.” — Marc Andreessen, 2011 “Now AI is eating software—and the question for the rest of us is: what’s left for one human, alone, in front of a screen?” — me, asking myself one night in 2026. Prologue: How Big Does One Person Need to Be? In February 2026, I ran my first complete overnight agent. I set a prompt, dropped it into Claude Code in a loop, and went to sleep. At 7 a.m. the next morning, what I saw on the screen was: 6 commits, 4 PRs, 3 auto-rolled-back failures, and a research brief I hadn’t even read myself. ...

An architecture diagram is a promise. A code audit asks which parts of that promise have acquired weight. My first version of this article treated Relay as a public open-source proposal whose Agent layer had not yet been built. Both claims are now wrong. As of July 31, 2026, the public GitHub URL previously cited by this article returns 404, while I can inspect a private local checkout. This piece is therefore a private/local implementation audit, fixed to local commit 22586e17ccd43cfaff0512511e71a100c5341608. Readers should not assume that repository or commit is publicly downloadable. ...

Context engineering is the set of strategies for curating, ordering, and evicting the tokens that enter an LLM’s context window on each inference call—system instructions, retrieved documents, conversation history, tool definitions, and memory. The short version is this: prompt engineering optimizes the wording of an instruction; context engineering optimizes the wiring of the whole window. Anthropic has articulated this distinction in its engineering guidance, and Karpathy helped popularize the term. The rest of this article takes the emerging discipline apart. ...

A small model loop becomes a system only after we decide what it may remember, touch, spend, and survive. An agent demo can be a loop around an API call. A production agent is the surrounding discipline: state, tools, permissions, recovery, evaluation, and cost. This article offers a map of that discipline rather than a framework tutorial. Each pillar is described through the gap it fills, a minimal implementation, and the boundary where it stops helping. ...