brev

Unix Cookbook

System

Find system naming:

uname  # os
uuname -l  # short
uname -a # detailed

threepointone

(slop, but accurate)

Most AI chat examples look simple.

Take a list of messages. Send them to a model. Stream tokens back to the browser. Persist the result somewhere. Repeat.

That is a useful starting point, but it hides the actual problem. A production AI application is not just a model call. It is a small distributed system: clients disconnect, users open multiple tabs, tools run on both sides of the

chayanforyou

DS18B20 Proteus Simulation Fix

Overview

This project provides a solution to fix the DS18B20 temperature sensor simulation issue in Proteus, where incorrect readings (e.g., -127.00°C) were observed despite the code and circuit working on real hardware.

Problem

The DS18B20 sensor in Proteus simulation returned alternating readings of -127.00°C (indicating a communication failure) and 27.00°C (likely the default simulated temperature). This issue is simulation-specific and does not occur on real hardware due to timing mismatches in the OneWire protocol.

Solution

The fix involves adjusting the DS18B20 component properties in Proteus to align with the OneWire timing requirements. Follow these steps:

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.

hexsprite

#!/usr/bin/env bash
# pr-restack — rebuild a working branch by cherry-picking my open PRs
# on top of an upstream base. Relies on git rerere to replay conflict
# resolutions across runs.
#
# Usage:
#   pr-restack                       # from inside any git repo with gh access
#   pr-restack -b my-branch          # custom branch name
#   pr-restack -r upstream/main      # custom base ref
#   pr-restack -o origin -u upstream # remote names

boringmarketer

The Direct Response Copy Skill

Write copy that converts. Landing pages, emails, sales copy, headlines, CTAs, social posts — anything persuasive.

This is an AI skill file. It turns any AI into a direct response copywriter trained on the frameworks of Schwartz, Hopkins, Ogilvy, Halbert, Caples, Sugarman, and Collier. Instead of getting generic AI copy, you get internet-native writing that sounds like a smart friend explaining something — while quietly deploying every persuasion principle in the book.

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How to use this

davidalves1

Resumo Git

Inicializando
Visualizar modificações
Adicionando e fazendo commit
Trabalhando com remoto
Desfazendo coisas
Alterando configurações

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hmnd

List of Anonymous Google Profile Images

Image	Name	Link
	Anonymous Alligator	https://ssl.gstatic.com/docs/common/profile/alligator_lg.png
	Anonymous Ameisenbär	https://ssl.gstatic.com/docs/common/profile/anteater_lg.png
	Anonymous Axolotl	https://ssl.gstatic.com/docs/common/profile/axolotl_lg.png
![Anonymous Dachs ](htt

bertsky

#!/usr/bin/env bash

fname=$1

# if the log file does not exist yet, record it (stop by pressing ctrl+c)
if ! test -f $fname; then
    nvidia-smi -f $fname -l 1 --format=csv --query-gpu=timestamp,memory.total,memory.used,utilization.gpu,utilization.memory &

    bg=$!