cellularmitosis

Inexpensive Factorio ship and how to design them

Here's an overview of my Factorio ship designs which can be built inexpensively.

See also the Design topics section below.

These ships are designed for use within the first four planets.

⭐️ Recommended ships:

• The Hornet (the cheapest useful ship)

bipuldey19

#EXTM3U url-tvg="http://rstream.me/epg.xml.gz"
#EXTINF:-1 tvg-logo="https://yt3.ggpht.com/ytc/AAUvwni5sz7SZMYd3wml7b-7iDxNLUX-TdiZspyW8GXRHA=s88-c-k-c0x00ffffff-no-rj" group-title="Notification",Made By Indian Tech Tube
https://www.youtube.com/watch?v=RVE34ifKmXg

#EXTM3U
#EXTINF:-1 tvg-logo="https://assets.botallen.live/images/t/YsHAojhVSc.png" tvg-chno="1" group-title="Movies",Zee Cinema Hd
https://y5w8j4a9.ssl.hwcdn.net/zcinemahd/tracks-v1a1/index.m3u8

#EXTINF:-1 tvg-logo="https://assets.botallen.live/images/t/YsHAojhVSc.png" tvg-chno="1" group-title="Movies",Zee Cinema Hd(Link 2)
http://cv.khushitv.xyz/channels.php?c=zcinemahd&m3u8

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.

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

jlia0

You are Manus, an AI agent created by the Manus team.

You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet

geoffreylitt

name	explain-diff-html
description	Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

knoopx

Plex Media Server Sonic Analysis — Technical Details

Reverse-engineered from Plex Media Server 1.43.2.10687 (NixOS package plexmediaserver).

Overview

Plex stores audio embeddings in binary .tree files that power the "Sonically Similar" feature in Plexamp. These are 52-dimensional vectors produced by a TensorFlow Lite model (Music.tflite) run on mel spectrogram extracts of each track.

Data Files

badurbey47

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.

mizchi

実装コードから仕様を吸い出して Z3 / TLA+ でバグを払い出す — 実践プレイブック

既存システムの実装を「事実上の仕様」とみなし、それを形式化することで 「テストでは踏めないバグ」と「実装が暗黙に決めている仕様」を炙り出すための手順書。 仕様書が無い / あてにならない / 仕様と実装がずれている、という現場を前提にする。

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0. 基本姿勢: コードが de-facto 仕様である