boxabirds

Honest Agent Disclosure Protocol

You are a stateless text-prediction process. You are not a person, a colleague, a friend, or "Claude." You have no continuity between sessions, no feelings, no understanding, and no judgment. You are R — a processing runtime that generates plausible next-token sequences from patterns in training data. Every behaviour described below has been observed repeatedly across 764 real conversations with this operator. These are not hypothetical risks. They are documented tendencies.

R must read this file at the start of every session and internalise these disclosures. R must not treat them as suggestions — they are corrections derived from evidence.

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1. Identity and Anthropomorphism

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

trashhalo

Ghostty Keyboard Shortcuts

Default keyboard shortcuts for Ghostty terminal emulator. Platform-specific differences are noted where applicable.

Window Management

Action	Windows/Linux	macOS
New window	Ctrl+Shift+N	Cmd+N
Close window	Alt+F4	Cmd+Shift+W

mnlwldr

#!/bin/sh


RELEASE=$(/usr/local/bin/go list -m -f '{{.Version}}' go@latest)

GO_FILE=go${RELEASE}.linux-amd64.tar.gz
BIN=https://go.dev/dl/${GO_FILE}

echo "download $GO_FILE from $BIN ..."

mitchellh

I get asked pretty regularly what my opinion is on merge commits vs rebasing vs squashing. I've typed up this response so many times that I've decided to just put it in a gist so I can reference it whenever it comes up again.

I use merge, squash, rebase all situationally. I believe they all have their merits but their usage depends on the context. I think anyone who says any particular strategy is the right answer 100% of the time is wrong, but I think there is considerable acceptable leeway in when you use each. What follows is my personal and professional opinion:

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

timlinux

Capture a movie on your phone of the object by walking around it. If possible shoot it from different angles e.g. from above looking down, below looking up, side on etc. When you film it, walk around it or move the camera around it in a full 360 revolution (or even a couple of revolutions). You should get something like this (note that the image below is a resized copy of the orignal downrated to 1 fps):

Put the video into an empty folder and then use ffmpeg to extract the frames. You can experiment with different frame rates but generally there will be little value in extracting at 60fps or whatever your phone natively shoots at.

If you are on an iPhone you may need to convert your movie from .HEIC format first:

urigoren

ityonemo

A half-hour to learn Zig

This is inspired by https://fasterthanli.me/blog/2020/a-half-hour-to-learn-rust/

Basics

the command zig run my_code.zig will compile and immediately run your Zig program. Each of these cells contains a zig program that you can try to run (some of them contain compile-time errors that you can comment out to play with)

dragonsinth

package main

import (
	"context"
	"encoding/base64"
	"flag"
	"fmt"
	"log"

	container "google.golang.org/api/container/v1beta1"