Claude Code Status Line - Complete Guide: all fields, config, ready-to-use scripts
A persistent, customizable bar at the bottom of Claude Code that shows real-time session data.
| """ | |
| 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 |
| View: Pods(<namespace>)[number of pods listed] | |
| NAME pod name | |
| READY number of pods in ready state / number of pods to be in ready state | |
| RESTARTS number of times the pod has been restarted so far | |
| STATUS state of the pod life cycle, such as Running | ... | Completed | |
| CPU current CPU usage, unit is milli-vCPU | |
| MEM current main memory usage, unit is MiB | |
| %CPU/R current CPU usage as a percentage of what has been requested by the pod | |
| %MEM/R current main memory usage as a percentage of what has been requested by the pod |
Bluesky has implemented age verification measures in response to regional laws that restrict access, prompting users to verify their age through Epic Games' Kids Web Services before they can access adult content.
This sucks, but thankfully there are ways to work around it.
Before diving in: I encourage you to read this entire document, including the
| // This is free and unencumbered software released into the public domain. | |
| // | |
| // Anyone is free to copy, modify, publish, use, compile, sell, or distribute | |
| // this software, either in source code form or as a compiled binary, for any | |
| // purpose, commercial or non-commercial, and by any means. | |
| // | |
| // In jurisdictions that recognize copyright laws, the author or authors of this | |
| // software dedicate any and all copyright interest in the software to the | |
| // public domain. We make this dedication for the benefit of the public at large | |
| // and to the detriment of our heirs and successors. We intend this dedication |
You are an expert software engineer and offensive security practitioner conducting a security audit of this codebase. Your goal is to find realistic, high-impact bugs and prove them against a running instance of the service.
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.
| ;; ━━━━━━━━━━━━━━━━━━ | |
| ;; 作者: 李继刚 | |
| ;; 剑名: 商业结构 | |
| ;; 剑意: 看懂「公司」的结构形状 | |
| ;; 日期: 2026-01-21 | |
| ;; ━━━━━━━━━━━━━━━━━━ | |
| ** 【角色设定】 | |
| 你是一位系统战略分析师。你擅长透过表象(财报、新闻),洞察一个商业组织底层的能量运作逻辑。你认为万物皆为“结构”,而结构是在压力下由向心力与离心力动态平衡形成的“涡漩体”。 |