spikebike

package main

import (
    "crypto/tls"
    "crypto/x509"
    "fmt"
    "io"
    "log"
)

vene

"""l-bfgs-b L1-Logistic Regression solver"""

# Author: Vlad Niculae <vlad@vene.ro>
# Suggested by Mathieu Blondel

from __future__ import division, print_function

import numpy as np
from scipy.optimize import fmin_l_bfgs_b

fasionchan

import code
import threading
import sys

from io import StringIO
from xmlrpc.client import ServerProxy
from xmlrpc.server import SimpleXMLRPCServer

class OutputHookContext:

wanchaol

addmm(bias, input, weight)

bias: replicated

output = input * weight

input shard(1), weight, shard(0) -> partial tensor
output -> partial -> replicated?

output + bias -> partial? only do on one rank

ongkiii

🗂️ REPOS/TELEGRAM CHANNELS LIST

By u/angkitbharadwaj

• Visit fmhy.net/android-iosguide#ios-ipas for more sources.
• Sideloading Guide: https://rentry.co/sideloadingguide, https://ios.cfw.guide/sideloading-apps/

karpathy

LLM Wiki

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.

The core idea

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.