Reading Room

Every Mac running macOS 13 or later, which is every Mac sold since 2022, runs two background programs (the technical term for these is “daemons”) whose only job is to build a private file about you. It tracks who you know, how you are related to them, how you talk to them, where you go, and what you do each day. This is running on his own machine right now. He never turned it on, Apple barely documents it, and almost nobody has looked closely at what it actually stores, which is exactly why it is worth understanding today.

Security researchers at a site called Eighth Rank went looking, after reading that another researcher, Nic Lydon, had found the same system earlier this year and written about it. The only official description Apple gives is a single line in a technical manual page for one of the two daemons, called knowledgeconstructiond. Apple’s own research team did publish an academic paper on the underlying techniques back in 2023, but there is no public account of which macOS versions run it or the full list of what it collects, let alone the features the researchers found that Apple has never mentioned at all.

Everything the system gathers sits in a folder called ~/Library/IntelligencePlatform, spread across 51 plain database files stored in plaintext, protected by a macOS permission called Full Disk Access (the setting that normally blocks apps from reading your whole hard drive).

The system fills that database with almost everything the operating system will let it see: your Contacts list, face-matching results from Photos, the titles and attendees of events in Calendar, the ages of people in your Family Sharing group, the emergency contact listed in your Medical ID, events and contacts that Siri Suggestions finds in your Mail and Messages, people donated by other apps, and the apps you have installed. It also cross-references your location, Screen Time, and what the researchers describe as a long tail of other behavioral signals on the machine. Apple’s documentation says nothing about any of this beyond the bare man page description.

The researchers then ran a thought experiment on what happens if a file like this ends up in the wrong hands. Because one database holds your whole social world in a single place, they call it a “privacy single point of failure.” Paired with what they call an “abliterated” AI model (one that has had its built-in safety limits deliberately removed), someone who got hold of your graph could use your real contacts, relationships, and daily patterns to build a convincing, personal attack: impersonating you, or targeting the people closest to you with details that only someone who truly knew your life would have.

To make the risk checkable rather than theoretical, the researchers built and released a free tool called iKnow: a small dashboard you run on your own Mac that reads these files and shows you exactly what your personal graph contains, in plain view instead of buried in Apple’s system folders.

Outside your usual reading: this is a June 2026 talk by Paul Graham, the investor and writer who co-founded Y Combinator (a startup funder and school that has backed about 6,500 companies since 2005), given to the Oxford Union, the university debating society that trains many future British politicians.

Graham built the talk as a direct rebuttal to an American politician who had said, the month before, that it is impossible to earn a billion dollars without cheating. He disagrees, and uses his own job investing in startups to make the case with real numbers. About 30 of the roughly 6,500 founders Y Combinator has funded have become billionaires, with more on the way, and Graham says none of them needed to cheat. They needed two things only: a fast growth rate, and enough time for it to compound.

He works through the math using a founder he had just met, whose startup was growing 93 percent a month, which meant her own net worth was growing at the same rate. Someone online later argued that having a few million dollars and growing at 93 percent a month is radically different from being a billionaire. Graham shows this is false, and shows exactly how false: assuming she needs only a conservative 500 times growth to clear a billion, the number of months of 93 percent growth it takes to multiply by 500 is about 9.45. A company already worth a couple of million dollars, growing at that rate, is roughly nine and a half months of ordinary growth away from making its founder a billionaire.

Because 93 percent monthly growth is rare even among the startups he funds, Graham runs the same math again with a far more ordinary number: 15 percent a month, which he says he sees constantly. Over five years, that is 60 months of compounding of 1.15 to the 60th power. So a startup making 10,000 dollars a month today would be making about 44 million dollars a month five years later, or roughly 526 million dollars a year. A founder who still owns a typical founder-sized slice of that company, at that point, is a billionaire.

The point he is making to a room full of future policymakers is about where the suspicion of great wealth comes from. Exponential growth looks and feels impossible from the outside, which is exactly why people who never run the numbers assume cheating must be involved. But the whole calculation rests on only two real variables: how fast the growth rate is, and how long it keeps going. Fifteen percent monthly growth is not a rare, suspicious outlier. It is an ordinary outcome for a startup that is working, sustained long enough by compounding, with no cheating anywhere in the chain.

There is a practical exercise buried in the talk too: Graham has his audience run the calculation themselves, typing a formula like log(500, 1.93) directly into Google’s search box, which doubles as a calculator. The growth rate held over time, he argues, is the entire explanation for how ordinary founders end up extraordinarily rich.

This is on his desk today because the pushback on a weak argument came from someone who actually likes AI, not from a skeptic.

A user on r/ArtificialInteligence (a popular online forum for AI discussion) watched a public debate about AI safety and flagged one exchange as unconvincing. A debate participant named Roman argued that an AI system could find ways to make use of computer hardware that already exists, without needing to build anything new. The response he got, according to the poster, was simply “AI can’t create hardware,” which the poster says does not answer Roman’s actual point, since Roman never claimed AI would build new hardware, only that it would find uses for hardware already sitting around.

The poster is explicit that they are pro-AI, and they are not taking a side on whether Roman’s underlying worry, that an AI system could use existing hardware to get around human control, is realistic. Their complaint is narrower: in a debate meant to work out whether AI poses real risks, a rebuttal that does not address the actual claim wastes the exchange. The post links to the full video discussion; beyond that one exchange, there is no further detail or number in the source to add.

A poster on r/ArtificialInteligence (a popular AI discussion forum) pushes back on the idea that AI makes people think less. He says it makes him read more and think harder and faster, enough that he needed some hours off over the past two days to recover. It is a useful counterpoint to the common worry that leaning on AI dulls your own thinking.

This is worth knowing about because it is a real example of decades of mature, well tested software being pulled out and repackaged as a reusable building block, the kind of tool worth having if you ever need a fast, efficient screen layer for something you are building.

A new open project called BUI takes Blender's user interface, the 3D modeling software's screen and controls, and turns it into a free standing library you can call from Python, Rust, JavaScript (using the frameworks React or Svelte), C, C++, or a tool called Dear ImGui. Blender's own interface took developers decades to build and tune. It is efficient with memory and battery, and it starts and reacts fast. BUI pulls that work out so other projects can reuse it without needing Blender installed, or knowing anything about Blender at all.

At the center of it is something called the kit, a small runtime written in the C programming language. The kit holds every on screen control in one tree, works out where everything goes, handles the mouse and keyboard, and draws each control the way Blender does, because the drawing code is a direct copy of Blender's own widget code. Thin layers on top let Python, Rust, JavaScript, C++ and Dear ImGui all talk to that same kit, so every language gets the same feel and the same speed.

It is not Blender itself. You do not need Blender installed, or any Blender knowledge, to use it. It is not a browser either. The Rust and JavaScript versions use HTML and CSS as a way to describe the screen, but there is no browser engine, no embedded web page, and no network calls involved.

Blender's interface is known for small details that add up. Type a few letters into the menu search, like "ad cu", and it finds "Add Cube" even with typos. BUI copies interactions like this, along with Blender's own dark and light themes.

The project lists plain use cases: take an old Python app built with the tkinter toolkit and run it unchanged to make it look modern. Give artists who already know Blender the same panels, sliders and shortcuts they are used to. Build a native Rust or React desktop app without shipping a full web browser inside it. Or make an existing Dear ImGui tool look and feel like Blender by changing one build setting. Most of these paths already work today: the tkinter path is marked complete, covering every tkinter and ttk API, the native Blender style controls are complete across 45 widgets and editors, and the Rust, JavaScript and Dear ImGui paths are all working. Only a Python version of Qt style code is still partial.

The project's own documentation says its source code is readable and its ideas are explained in plain words, aimed at anyone curious how a UI toolkit is actually built, not only people who want to use it.

Outside your usual reading: a joke website lets you "upload" the memory, or RAM, sitting idle in your computer to the cloud, poking fun at how AI companies talk about computing power as something to save and donate. It is a sharp, funny jab at the AI industry's obsession with computing power, worth a smile.

A Reddit post lays out what it actually takes to run a serious AI setup at home: 96 processor cores, 2 terabytes of memory, and up to 576 gigabytes of graphics memory, all for $100,000 to $150,000. It is a concrete number for what running big AI models yourself, instead of through the cloud, really costs.