Default prefix is Ctrl-b
(shown as C-b
below)
tmux new -s name # New named session
tmux ls # List sessions
tmux a -t name # Attach to named session
zshy
automatically handles non-JavaScript assets and copies them to the output directory during builds. This feature enables including custom files like LICENSE, README.md, Protocol Buffer definitions, Ethereum ABIs, and other artifacts in your TypeScript packages without additional configuration.
Key Concept: zshy detects imported assets and copies them to the output directory, but does not make them available as JavaScript modules. The assets remain as separate files that you access at runtime.
#!/usr/bin/env bun | |
"use strict"; | |
const fs = require("fs"); | |
const { execSync } = require("child_process"); | |
const path = require("path"); | |
// ANSI color constants | |
const c = { | |
cy: '\033[36m', // cyan |
This document contains guidelines for Claude Code to follow when working on this project.
# Written by https://github.com/DeterminateSystems/nix-installer. | |
# The contents below are based on options specified at installation time. | |
download-buffer-size = 524288000 | |
max-jobs = 8 | |
cores = 2 | |
trusted-users = root urname |
services: | |
shovel: | |
image: docker.io/indexsupply/shovel:af07 | |
container_name: shovel | |
restart: unless-stopped | |
ports: | |
- "8383:80" | |
env_file: | |
- .env | |
volumes: |
def sieve_of_eratosthenes(n: int) -> list[int]: | |
""" | |
Generate all prime numbers up to n using the Sieve of Eratosthenes algorithm. | |
Args: | |
n (int): Upper bound for generating prime numbers | |
Returns: | |
list[int]: List of all prime numbers up to n |
/* | |
* Example 1: POSIX Shared Memory | |
* This example shows how to create, write to, and read from a POSIX shared memory segment | |
*/ | |
#include <stdio.h> | |
#include <stdlib.h> | |
#include <string.h> | |
#include <fcntl.h> | |
#include <sys/mman.h> |
#!/bin/bash | |
# Help function to display usage | |
show_help() { | |
echo "Usage: $0 [OPTIONS]" | |
echo | |
echo "Options:" | |
echo " --image IMAGE Docker image to use (required)" | |
echo " --address IP Head node IP address (required)" | |
echo " --mode MODE Either 'head' or 'worker' (required)" |
# train_grpo.py | |
import re | |
import torch | |
from datasets import load_dataset, Dataset | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
from peft import LoraConfig | |
from trl import GRPOConfig, GRPOTrainer | |
# Load and prep dataset |