StarKnightt

You are given a task to integrate an existing React component in the codebase.

The codebase should support:

• shadcn project structure
• Tailwind CSS
• TypeScript

If it doesn't, provide instructions on how to setup the project via shadcn CLI, install Tailwind or TypeScript.

ariannamethod

//go:build ignore

/*
 * molequla.c
 * A dependency-free, single-file, continually-learning GPT organism in pure C.
 *
 * Compile: gcc -O2 -o molequla molequla.c -lsqlite3 -lpthread -lm
 * With BLAS: gcc -O2 -DUSE_BLAS -o molequla molequla.c -lsqlite3 -lpthread -lm -lopenblas
 * macOS:     gcc -O2 -DUSE_BLAS -o molequla molequla.c -lsqlite3 -lpthread -lm -framework Accelerate
 *

chr15m

#!/bin/sh

# Scrapes documentation from a URL and converts it to Markdown suitable for aider convention files
# to provide context to the LLM.

if [ $# -eq 0 ]; then
    echo "Usage: $(basename "$0") <URL> [URL...]"
    echo
    echo "Generate aider 'convention' Markdown context from documentation URLs."
    echo "suitable for providing LLM context about a project's conventions and style."

ab2005

Run:

1. brew install libuvc
2. gcc uvccam.c -luvc -o exx
3. To get device info run: ./exx 1>/dev/null
4. To save YUV422 to file: ./exx 2>/dev/null 1>video.yuv

Some ffmpeg experiments:

Record raw YUV422 from camera:

nafiesl

How to get Telegram Bot Chat ID

Content :

1. Create a Telegram Bot and get a Bot Token
2. Get Chat ID for a Private Chat
3. Get Chat ID for a Channel
4. Get Chat ID for a Group Chat
5. Get Chat ID for a Topic in a Group Chat

Create a Telegram Bot and get a Bot Token

championswimmer

How Personal AI Agents and Agent Orchestrators like OpenClaw or GasTown are Made

Over the last few months, projects like Gas Town by Steve Yegge and OpenClaw by Peter Steinberger have made “AI agent orchestrators” feel suddenly mainstream. It is tempting to treat them as a new kind of intelligence, but under the hood they are still a small set of primitives wired together with discipline: an LLM API call, a state loop, tools, memory, and orchestration.

This raises a practical question: what is actually inside an “agent,” and how is it different from ChatGPT (a chat UI over a model) or coding tools like Claude Code (an agentic coding surface)? Gas Town’s README frames it as a “multi‑agent orchest

velvet-shark

OpenClaw after 50 days: all prompts

Companion prompts for the video: OpenClaw after 50 days: 20 real workflows (honest review)

These are the actual prompts I use for each use case shown in the video. Copy-paste them into your agent and adjust for your setup. Most will work as-is or the agent will ask you clarifying questions.

Each prompt describes the intent clearly enough that the agent can figure out the implementation details. You don't need to hand-hold it through every step.

My setup: OpenClaw running on a VPS, Discord as primary interface (separate channels per workflow), Obsidian for notes (markdown-first), Coolify for self-hosted services.

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

ko1nksm

(original: http://furukawablog.spaces.live.com/Blog/cns!1pmWgsL289nm7Shn7cS0jHzA!2225.entry)
(archived: https://web.archive.org/web/20061105073147/http://furukawablog.spaces.live.com/Blog/cns!1pmWgsL289nm7Shn7cS0jHzA!2225.entry)

November 04
私のマイコン遍歴、日本のパソコン30年史、その１


私がその昔、秋葉原少年だった頃（今のアキバ系とちょっと違うとは思うのだけど、まぁ普通の人から見ると同類項だったのかな？）秋葉原にはアスターインターナショナル、コンピュータLab、若松通商、ビットイン、本田通商、そして新宿のムーンベース、タンディ・ラジオシャック、御苑前のアスターインターナショナル本店などに当時のマイコン少年は毎日たむろしていたのでした。
当時はTK-80、KIM-1、SCAMP、HitachiやL-Kit16などの10万円ほどする、いわゆるワンボードマイコンが全盛期でありました。Altair、IMSAI、SOL-20、Horizon North Star、クロメムコといったS-100バスのコンピュータはテレタイプに紙テープで操作するもので50万から80万円、8インチのフロッピー・ディスクを搭載したものが100万から150万円もする時代でありました。（ご参考までに、大卒初任給は10万円ぐらいの時代のことです。）