sarath-menon

Code Bash command prefix detection

This defines risk levels for actions that the ${K4} agent may take. This classification system is part of a broader safety framework and is used to determine when additional user confirmation or oversight may be needed.

Command prefix extraction examples

Examples:

• cat foo.txt => cat
• cd src => cd

willccbb

Approaches to Solving the ARC-AGI Challenge: A Comprehensive Analysis

Table of Contents

1. Introduction
2. Understanding the ARC-AGI Challenge
3. Historical Progress on ARC-AGI
4. Current Approaches
   • Program Synthesis Approaches
   • Large Language Model Approaches

• Hybrid Approaches

jwbee

Make Ubuntu packages 90% faster by rebuilding them

TL;DR

You can take the same source code package that Ubuntu uses to build jq, compile it again, and realize 90% better performance.

Setting

I use jq for processing GeoJSON files and other open data offered in JSON format. Today I am working with a 500MB GeoJSON file that contains the Alameda County Assessor's parcel map. I want to run a query that prints the city for every parcel worth more than a threshold amount. The program is

philschmid

import os
from google import genai
from google.genai import types

client = genai.Client(api_key=os.getenv("GEMINI_API_KEY","xxx"))

# Repalce with the youtube url you want to analyze
youtube_url = "https://www.youtube.com/watch?v=RDOMKIw1aF4" 

# Prompt to analyze and summarize the Youtube Video

yanndebray

glennmatlin

Claude Code Hooks for working with uv

by Glenn Matlin / glennmatlin on all socials

What This Does

Prevents Claude Code from using pip, python, pytest, etc. directly in projects that use uv for Python environment management. Instead, Claude is guided to use uv run, uv add, etc.

Smart detection:

• Only activates in directories with pyproject.toml

rekram1-node

{
  "agent_servers": {
    "OpenCode": {
      "command": "opencode",
      "args": ["acp"]
    }
  }
}

lodestone-rock

import os
import wandb
from tqdm import tqdm
import argparse

import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader

neudinger

#include <ranges>

#include <print>

#include <cctype>
#include <string>
#include <string_view>

using namespace std::literals;
using namespace std::string_view_literals;

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