std-microblock

结论

传闻中"系统内置反诈数据库、记录截屏时间与通话、屏幕共享行为"这部分属实；"域名用途"部分与实际不符；"GuardProvider 静默录屏"在部分机型上属实；"关掉开关仍在运行""卸载 GuardProvider 无效"属实；"669 次连续读写""每台手机都在录"不成立。

---

一、功能名称与开关位置

传闻描述的行为属于手机管家中的"电诈防护"，不是独立的后台程序。

Er3nico

alias: Display Studio per Präsenz aufwecken
description: >-
  Weckt das Tessera-Display nur, wenn jemand direkt davor steht
  (Erkennungsdistanz unter 60 cm). Standby nach 2 Minuten ohne Person in diesem
  Bereich.
triggers:
  - above: 0
    below: 60
    entity_id: sensor.prasenz_studio_erkennungsdistanz
    id: nah

charignon

Cherri Language Guide for LLMs

Overview

Cherri is a programming language that compiles to Apple Shortcuts. When writing Cherri code, you're essentially creating iOS/macOS Shortcuts using a more traditional programming syntax.

Key Concepts

1. Basic Syntax

• Comments: Use /* */ for block comments, // for line comments
• Variables: Use @varName = value to declare variables

Er3nico

# LD2410C Präsenzsensor am Guition ESP32-S3-4848S040
# Video: Er3nico auf YouTube
#
# Anschluss:
#   Display 5V  -> Sensor VCC
#   Display GND -> Sensor GND
#   Display TX (GPIO43) -> Sensor OUT
#
# Diese Zeilen in die bestehende Tessera-Config einfügen.
# last_touch_ms und wake_display kommen aus der Tessera-Config.

itanka9

Info : No device selected, using first device.
Info : J-Link ARM V8 compiled Nov 28 2014 13:44:46
Info : Hardware version: 8.00
Info : VTarget = 3.313 V
Info : clock speed 1000 kHz
Info : JTAG tap: stm32f2x.cpu tap/device found: 0x4ba00477 (mfg: 0x23b (ARM Ltd.), part: 0xba00, ver: 0x4)
Info : JTAG tap: stm32f2x.bs tap/device found: 0x06411041 (mfg: 0x020 (STMicroelectronics), part: 0x6411, ver: 0x0)
Info : JTAG tap: auto0.tap tap/device found: 0x3ba00477 (mfg: 0x23b (ARM Ltd.), part: 0xba00, ver: 0x3)
Info : JTAG tap: auto1.tap tap/device found: 0x16410041 (mfg: 0x020 (STMicroelectronics), part: 0x6410, ver: 0x1)
Info : JTAG tap: auto2.tap tap/device found: 0x3ba00477 (mfg: 0x23b (ARM Ltd.), part: 0xba00, ver: 0x3)

jboner

Latency Comparison Numbers (~2012 except when noted)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

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

kanginXD

AGENTS

Think and work in English. Reply in Korean.

Repo text (absolute)

Covers code, identifiers, log/error messages, comments, docs, examples, commit messages.

• Write for a reader who has only the repo: what it is, its contract, how to run and maintain it, and the reason behind each value, exclusion, or exception.
• Say what that reader needs once and briefly; link instead of repeating.

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.