Claude Code's auto mode delegates tool-call approval to a model-based safety classifier. Before every Bash command (and other tool calls), Claude Code fires a separate HTTP request to a small model — by default a hard-coded name like claude-haiku-4-5-20251001 — that sees only the user messages and the proposed tool call, then returns an allow/deny judgement. When you route Claude Code through a proxy instead of Anthropic's API, that classifier traffic is easy to lose: it either fails silently (the proxy has no model alias for the classifier name) or gets remapped at the client into the same model as the main session

The snippets below demonstrate how to split MC events originally intended for the 2024 data-taking period into orthogonal parts that can be used to simulate the 2024, 2025 and 2026 data-taking periods. The splitting is done event-by-event and only depends on the event ID. The underlying algorithm is deterministic and splits events into three parts with frequencies that reflect the relative recorded luminosities in 2024, 2025 and 2026.

The unbiased behavior is strictly necessary to avoid that algorithms based on event IDs that are used downstream in analyses see a bias towards specific event IDs in any of the three years. For instance, event splitting for ML training / validation purposes, usually done via (e.g.) event % 2 == 0 or alike, should remain statistically unaffected. If there was a bias in the event ID distribution, this would lead to skewed training and validation datasets, which is not desira

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.

1. Create a Secure Secrets File

Store your API keys securely in a separate file so they aren't exposed directly in your .bashrc or .zshrc

nano ~/.secrets

Better tutorial (a little bit more complicated): https://gist.github.com/karolba/a3f1c5f8d50c67f5a19e6c8f38e53e12

1. Download the "virtual" type aarch64 ISO file from https://www.alpinelinux.org/downloads/ with wget
2. Execute sudo dd if=alpine.iso of=/dev/sda
3. On the Oracle Cloud panel, setup a console connection and connect to the serial console.
4. Execute sudo reboot
5. When Alpine is launched and you are logged in as root, execute these commands in the serial console:

by Flamerix AI · MIT License

Tested on Linux, Claude Code 2.1.257–2.1.312; macOS reported working by others (see Tested versions). The binary is not modified (BUN_OPTIONS=--preload). An update can silently undo it, so see Verify.

Every ~30 seconds, Claude Code re-sends each background agent's full transcript to the model just to fill that agent's status line in the agents panel. no-agent-summary.js is a Bun --preload