leocomelli

GIT

Estados

• Modificado (modified);
• Preparado (staged/index)
• Consolidado (comitted);

Ajuda

Git-on-my-level

Mac-to-Mac Remote Shell Stack (Tailscale + SSH + Mosh + tmux) — Low-Lag, High-Reliability Setup

This gist documents a reproducible setup for a “remote shell that feels local” between a laptop and one or more headless Macs (e.g., Mac minis). It is optimized for:

• High latency / packet loss links (SSH typing lag, key repeats, “rubber band” echo)
• Roaming networks (Wi-Fi drops, IP changes)
• Long-lived sessions (detach/reattach, crash tolerance)

Core design decisions:

• Tailscale provides a private, encrypted, stable network plane (no port forwarding/NAT complexity).
• OpenSSH (Remote Login) is used for authentication, key management, and compatibility.

sergio-fry

#!/usr/bin/env ruby
# frozen_string_literal: true

# git-diffall — полный diff рабочего дерева ОДНИМ листингом.
# Поведение повторяет bash-версию; полная справка — константа GitDiffAll::HELP.

require 'open3'

module GitDiffAll
  HELP = <<~TEXT

ivellios

import csv
import json
import sys

# This is simple converter for DriveThruRPG JSON data 
# from library into CSV file, so you can manage your
# library

# How to use
# 1. Go to DriveThruRPG site and login

solatticus

FlashAttention-4 Cannot Run on RTX 5090 (SM120) — A Deep Investigation

TL;DR

FlashAttention-4 will not run on the NVIDIA RTX 5090 (SM120, "desktop Blackwell") and no amount of software patching can fix it. Despite sharing the "Blackwell" brand with data center GPUs like the B200 (SM100), the RTX 5090 uses a fundamentally different tensor core architecture. SM100 has a dedicated tensor memory (TMEM) subsystem with its own instruction family (UTCHMMA, UTMALDG, etc.) that FA4's warp-specialized kernel design requires. SM120 uses the older HMMA instruction family (the same register-to-register MMA approach used since Volta/Ampere) and the TMEM hardware is physically absent from the GB202 die. This is not a software lock, not a fuse bit, and not a toolchain oversight — it is a silicon-level architectural difference. FA2 via Triton remains the best available attention kernel for the RTX 5090.

---

Goal

ysantonyance

Software Requirements Specification

Amazon Clone — Платформа електронної комерції

Версія документа	1.0
Статус	Draft for Review
Дата	13 серпня 2026
Автор	IPAY (Incredible Project Aspnet Yes)

elithrar

# syntax=docker/dockerfile:1

ARG SANDBOX_VERSION=0.12.5
ARG SANDBOX_DIGEST=sha256:315b14485d6982774521f3e2f605fdf39ba6de6942d65a022048a54967ca0062
FROM docker.io/cloudflare/sandbox:${SANDBOX_VERSION}@${SANDBOX_DIGEST}

ARG RUNNER_VERSION=2.336.0
ARG RUNNER_SHA256=04cf0be1aff4c3ec3554466c39124ca250e3effd8873bb7e8d68535aa9505d5d
ARG DOCKER_VERSION=29.7.2
ARG DOCKER_SHA256=803d433f226db4776e1768fd319fc6c6e4935a456acf84fcc0080818b854bc8f

arianvp

Native Secure Enclave backed ssh keys on MacOS

It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive

There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!

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.

bitsnaps

Polars Cheat Sheet

Here's a cheat sheet for the Polars Python package, covering many of its key functions and features:

Installation

pip install polars 

# Install Polars with all optional dependencies:
pip install 'polars[all]'