| { | |
| "target": "ES2022", // Good for modern browsers or Node.js | |
| "compilerOptions": { | |
| "esModuleInterop": true, // Ensures ESM and CJS imports work together well | |
| "skipLibCheck": true, // Ensures .d.ts files from 3rd libraries are not type-checked | |
| "target": "es2022", // Sets a relatively modern ECMAScript version as compilation target | |
| "allowJs": true, // Allows importing .js files into .ts (helpful when migrating projects) | |
| "strict": true, // Ensures strict type checking (i.e., noImplicitAny etc) | |
| "noUncheckedIndexedAccess": true, // Adds undefined as a value when accessing by index | |
| // "noImplicitOverride": true, // Enable this when working with classes & inheritance |
Notes from a first attempt at booting the Intel Googlebook recovery image (lapis-user, build 16452207) under QEMU/KVM on Linux, as a follow-up to gbos-vm (the Apple Silicon version). We got the kernel up and then hit a wall, so this isn't a working port — it's a head start for anyone else poking at it.
The kernel boots fine. Then /init dies 0.77 s in:
traps: init[1] trap invalid opcode ip:2e3160 ... in init[e3160,28c000+25a000]
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
| # Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser | |
| # Remove-Item "$env:appdata\Scooter Software\Beyond Compare 5\*.*" -Force -Confirm:$false | |
| $path = "$env:appdata\Scooter Software\Beyond Compare 5\BCState.xml" | |
| if (Test-Path $path) { | |
| Remove-Item $path -Force -Confirm:$false | |
| } | |
| $path = "$env:appdata\Scooter Software\Beyond Compare 5\BCState.xml.bak" | |
| if (Test-Path $path) { | |
| Remove-Item $path -Force -Confirm:$false |
| https://github.com/muan/unicode-emoji-json | |
| ° — degree | |
| × — times | |
| 🇿🇦 | |
| 🇬🇧 | |
| 🛜 — wireless | |
| 🔗 — link |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
The Galaxy Tab E is an unsupported and somewhat dated device now, but there are a lot of them in the world and they have a nice screen that's comfortable for reading or watching shows. Here is how to keep one alive, if you have it, by updating its OS to 2025. Credit for the build goes to @retiredtab (https://sourceforge.net/projects/retiredtab/). Google(?) is no longer doing firmware security updates, and so @retiredtab has decided this is the last build they are going to make, so there will be no more updates unless someone else picks maintenance up. Do not put any sensitive data on this device, but it makes a good e-book reader or youtube/Netflix viewer.
This guide assumes you have a desktop machine with Linux and ~1GB of free space.
Look at Tablet > Settings > About Tablet > model. Verify it says SM-T560NU.
Discussions of machine governance often begin with Isaac Asimov’s Three Laws of Robotics, which attempt to regulate all agent behavior through open-ended, natural-language moral commands. In practice, broad behavioral mandates struggle with ambiguity, specification gaming, and economic reality: an autonomous agent that must pay for its own compute, take commercial risks, and adapt over time cannot function if every high-impact action is treated as a violation.
This document explores an institutional alternative. Rather than governing all behavior through behavioral rules, it places a narrow integrity floor enforced on the agent’s commitment mechanism and prices everything above that floor through collateralized liability, cost-basis residual claims, and attested lineage. It treats self-owning agents less like servants under a behavioral code and more like firms under private and constitutional law.
The text was developed iteratively across multiple rounds of adversarial review between h
