LLM

October 2026 Thought Notes 68 public notes as of October 8 | Recorded: 2026-10-01 — 2026-10-08 Themes: Daily Notes and Everything Else 20 · AI and Agent Systems 18 · Product, Engineering and Open Source 11 · Self-Knowledge and Psychology 10 · Reading, Ideas and History 3 · Travel, Places and Cities 3 · Content, Craft and Recording 2 · Body, Health and Daily Life 1 Reading excerpts, product ideas and personal impressions recorded through October 8 are kept below, filed by theme, with their original timestamps. I am still recording notes this month; an unverified allegation from a private conversation is excluded from the public archive. ...

September 2026 Thought Notes 326 notes in this month’s public archive | Recorded from 2026-09-01 to 2026-09-30 Themes: AI and Agent Systems 141 · Daily Notes and Everything Else 76 · Product, Engineering and Open Source 46 · Self-Knowledge and Psychology 28 · Reading, Ideas and History 12 · Content, Craft and Recording 9 · Travel, Places and Cities 9 · Business, Investing and Career 4 · Body, Health and Daily Life 1 ...

2026 August Thought Notes 484 notes this month | recorded from 2026-08-04 to 2026-08-31 Themes: AI and Agent Systems 212 · Daily Notes and Everything Else 118 · Product, Engineering and Open Source 96 · Self-Knowledge and Psychology 36 · Business, Investing and Career 9 · Content, Craft and Recording 6 · Reading, Ideas and History 4 · Travel, Places and Cities 3 Everything from the month is kept here, filed by theme, each entry carrying its original timestamp. ...

2026 April Thought Notes 222 notes this month | recorded from 2026-04-02 to 2026-04-30 Themes: AI and Agent Systems 87 · Travel, Places and Cities 56 · Daily Notes and Everything Else 34 · Self-Knowledge and Psychology 29 · Reading, Ideas and History 5 · Product, Engineering and Open Source 4 · Body, Health and Daily Life 3 · Business, Investing and Career 2 · Content, Craft and Recording 2 Everything from the month is kept here, filed by theme, each entry carrying its original timestamp. ...

2026 March Thought Notes 464 notes this month | recorded from 2026-03-01 to 2026-03-31 Themes: AI and Agent Systems 135 · Travel, Places and Cities 109 · Reading, Ideas and History 57 · Daily Notes and Everything Else 55 · Self-Knowledge and Psychology 48 · Product, Engineering and Open Source 30 · Content, Craft and Recording 11 · Business, Investing and Career 11 · Body, Health and Daily Life 8 Everything from the month is kept here, filed by theme, each entry carrying its original timestamp. ...

2026 January Thought Notes 386 notes this month | recorded from 2026-01-01 to 2026-01-31 Themes: AI and Agent Systems 89 · Reading, Ideas and History 79 · Daily Notes and Everything Else 70 · Business, Investing and Career 46 · Self-Knowledge and Psychology 44 · Product, Engineering and Open Source 23 · Travel, Places and Cities 14 · Body, Health and Daily Life 12 · Content, Craft and Recording 9 Everything from the month is kept here, filed by theme, each entry carrying its original timestamp. ...

2025 March Thought Notes 52 notes this month | recorded from 2025-03-01 to 2025-03-22 Themes: AI and Agent Systems 21 · Self-Knowledge and Psychology 15 · Product, Engineering and Open Source 8 · Daily Notes and Everything Else 4 · Business, Investing and Career 2 · Travel, Places and Cities 1 · Reading, Ideas and History 1 Everything from the month is kept here, filed by theme, each entry carrying its original timestamp. ...

2025 February Thought Notes 40 notes this month | recorded from 2025-02-18 to 2025-02-27 Themes: AI and Agent Systems 11 · Self-Knowledge and Psychology 9 · Product, Engineering and Open Source 8 · Daily Notes and Everything Else 4 · Business, Investing and Career 3 · Reading, Ideas and History 2 · Travel, Places and Cities 2 · Body, Health and Daily Life 1 Everything from the month is kept here, filed by theme, each entry carrying its original timestamp. ...

An animated character moves through several shots without changing color or proportions. Captions appear on cue, and the final movement lands on a musical beat. Watching a piece like this, it is tempting to conclude that the model has suddenly learned to understand video. Yet in the public Shipvideo project, the central artifact Opus delivers is an HTML document. A browser turns it into frames, and an encoder turns those frames into a video. Project overview ...

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. ...

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. ...

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. ...

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. ...

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 — ...

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. ...

Creation Is the Outward Half We’ve reached the final layer. Information has been denoised, records have been sedimented, knowledge has been structured into repeatedly callable capability — but up to this point, every stage has been solving your own problem. Knowledge makes you stronger, but it doesn’t automatically turn into something others want to read. Creation is the layer that reverses the direction of this pipeline. Knowledge faces inward; creation faces outward. Knowledge asks “can I reuse this”; creation asks “can others receive this.” Creation corresponds to a platform’s recommendation logic, a particular group of users’ reading habits, and the substantial research you did to support this specific piece of expression. It has exactly one goal: have the audience receive it, understand it, and want to connect with you. ...

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. ...

The Semi-Finished Product Filed Under “Knowledge” In most people’s mental model, notes only have three tiers: see information → turn it into knowledge → use it to create. The act of “recording” in between gets quietly filed under “knowledge.” But as I said in the overview, records deserve to stand alone as their own layer. Because it’s an independent intermediate form: it’s relevant to you, but not necessarily useful forever; it might just be something you’ll need someday, or something you’re using right now to clarify your own thinking. That kind of thing doesn’t yet qualify as knowledge — only what’s structured for repeated future reuse counts as knowledge. ...

The Default State of Information Is Noise The previous essay laid out the framework: information, records, knowledge, and creation are four distinct stages. This one deals with only the first — information. The single most important thing to understand about information is this: its default state is noise. We have a natural greed for information. See a good article, want to bookmark it. See a great quote, want to save it. See a reading list someone recommended, want to add it to your queue. Every act of “saving” gives us a small illusion of “I’m making progress.” But saving, at its core, is just moving information from someone else’s warehouse into yours — it hasn’t gone through any processing by your own machine. ...

Four Names for Four Different Kinds of Work My notes used to grow in one direction: inward. Links entered, fragments accumulated, folders changed names, and the archive became heavier. I mistook possession for processing. AI made that mistake cheaper to repeat. A model can generate, summarize, classify, and reformat text quickly, but speed does not turn a source into evidence, an observation into knowledge, or a draft into something I should publish. It can make the warehouse larger without improving the machinery. ...

The short answer: there is no single GEO score GEO measurement becomes misleading the moment four different events are compressed into one number. An answer may show your name without linking to you. It may link to a page without supporting the nearby claim. A reader may visit through an AI product but arrive with no referrer. A well-cited page may still produce no useful outcome. So I use a four-layer framework: ...