nazeshinjite

#!/usr/bin/env python3
# Repack a split GGUF so one tensor (default: the qwen4exp engram table
# per_layer_token_embd.weight) sits ALONE in the final shard, with no other tensors
# sharing that file. Motivation: on Metal, llama.cpp wires the mmap'd regions that
# back GPU tensors; a tensor interleaved with them in the same file gets wired along
# for the ride (measured +24 GiB on Qwen3.8-Flash-Next). A file containing only
# CPU-side tensors keeps its own mapping and stays pageable.
#
# Tensor bytes are copied verbatim (no requantization). Output layout for an N-file
# input: shard 1 = byte-for-byte copy of input shard 1 (metadata-only; split.count and

sammcj

/**
 * Local Models Extension
 *
 * Manage self-hosted LLM endpoints entirely from the TUI.
 * Commands:
 *   /local-models  - Open the local models manager
 *
 * Config is persisted across sessions. Models show up in /model selector
 * automatically when their endpoint is reachable.
 *

geoffreylitt

name	explain-diff-html
description	Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

ahumanft

LLM Wiki V3: Segmentation

Karpathy gave us the foundation. Rohitg00 warned us what breaks. V3 is how you structure it to scale.

This is a concept document in the same spirit as V1 and V2.

V1 was intentionally vague. Build on it. V2 was intentionally open. Solve it.

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

liskl

The Complete Guide to Building Skills for Claude

Contents

• Introduction
• Chapter 1: Fundamentals
• Chapter 2: Planning and Design
• Chapter 3: Testing and Iteration
• Chapter 4: Distribution and Sharing
• Chapter 5: Patterns and Troubleshooting

mikeckennedy

# Standard zshrc stuff like path, etc.
# ...

# Enable using the two files above:

# Functions
source venv-auto-activate.sh

# Venv security whitelist/blocklist
alias venv-security='uv run -q --no-project ~/scripts/venv-security.py'

i2cjak

import requests
import json
import argparse
from rich.console import Console
from rich.table import Table
from rich.live import Live
from rich.spinner import Spinner

def search_for_part(description, show_all, no_sort):
    """

ubergarm

The Great Quant Wars of 2025

"All things leave behind them the Obscurity... and go forward to embrace the Brightness..." — Dao De Jing #42

tl;dr;

• Q: Who provides the best GGUFs now?
• A: They're all pretty good.

Skip down if you just want graphs and numbers comparing various Qwen3-30B-A3B GGUF quants.

sammcj

import asyncio
import aiohttp
import os
from pathlib import Path
import logging
from bs4 import BeautifulSoup
from typing import List, Dict
from dataclasses import dataclass
from datetime import datetime
import time