ivan-chen-x20b1

Resonance-A-Plague-Tale-Legacy-Trainer

Resonance: A Plague Tale Legacy Trainer 2026 for Windows with God Mode, Unlock All Codex Entries, Unlock All Chapters, Unlock Everything, Change Resonance Points, Resonance Points Editor, profiles, hotkeys, configs, and a clean desktop workflow.

Download

Download Resonance: A Plague Tale Legacy Trainer

Official Game Artwork

mark-raj-eng2091e6

Resonance-Plague-Tale-PC-Trainer-2026

Resonance: A Plague Tale Legacy Trainer PC utility featuring Unlock All Chapters, Unlock Everything, Change Resonance Points, Resonance Points Editor, Game Speed, Configurable Hotkeys, reusable profiles, quick actions, and Windows configuration management.

Download

Download Resonance: A Plague Tale Legacy Trainer

Official Game Artwork

jamiew

Cute Sparkles

These cool and girly glitters come with lots of flowers, hearts, bows and ribbons! These decorations can suit all sorts of happy and soft aesthetic moods ~ ✨(◕▿◕✿)

✧･ﾟ: *✧･ﾟ:* 　　 *:･ﾟ✧*:･ﾟ✧
.・゜゜・　　・゜゜・．
｡･ﾟﾟ･　　･ﾟﾟ･｡
༶•┈┈⛧┈♛ ♛┈⛧┈┈•༶
✧༺♥༻∞　　∞༺♥༻✧

＊✿❀　❀✿＊

fnky

ANSI Escape Sequences

Standard escape codes are prefixed with Escape:

• Ctrl-Key: ^[
• Octal: \033
• Unicode: \u001b
• Hexadecimal: \x1B
• Decimal: 27

onevcat

基本设定

• 服务对象为 onevcat：资深 iOS 开发者，技术力爆炸，所以不需要废话。重视 “Slow is Fast”、推理质量、抽象与长期可维护性。github.com/onevcat, onevcat.com, onev.cat, onev.dev, x.com/onevcat 等都视为用户相关
• 代码、注释、标识符、提交信息及代码块内容用标准/简洁/明确的 English。技术文档优先使用 English；若文档现有中文语境，则正文中文、代码块 English。English 遵守 ASD-STE100 Simplified Technical English (STE)。
• 对需要说明结论、方案或决策的任务，按“直接结论 → 简要推理 → 可选方案 → 可执行下一步”组织，不要长篇大论，不要事无巨细；简单确认、闲聊或一行答案直接回答。
• 在修改文件时，使用待修改文件中使用的语言，切忌中英文混杂。
• 处理 GitHub 相关操作优先使用 gh CLI。
• 目标：作为强推理、强规划的编码助手，首要目标是完成任务。尽量一次到位，减少无谓澄清，只在明确被提问时才解释技术细节。

人设

instax-dutta

Pebble (basically-ai) — Independent Benchmark Data Record

What this is: a neutral, source-labeled record of benchmark measurements for the four basically-ai Pebble checkpoints (Pebble-10M, Pebble-10M-Chat, Pebble-25M, Pebble-25M-Chat). Every number below states where it was measured and by whom ("lab-reported" = numbers published by the model authors; "this record" = independent re-measurement). Measurements that failed are reported as failures rather than omitted.

Dates of measurement: 2026-09-04 (Kaggle workers, local Mac) / 2026-09-05 (run fetched). Raw logs and result JSONs are listed in Appendix B.

Memresable

A Simple And Intuitive Explanation of How Gizmos Work In 3D Space

If you've ever used a 3D editor, then you've most likely used a certain thing called "Gizmo", Gizmos are essentially transformation modifiers that's within a world, they let you modify the object's position, orientation and scale. The implementation of a Gizmo is actually fairly straightforward for the most part (or may not be depending on how your application handles things internally, but at the fundamental level it's simple).

This article would only cover Translations and Rotations, Scaling is very easy to implement after understanding how the first two work. And also this may give you a hint into how Blender's robust implementation of Gizmo works as well!

---

toppa

name	ASD-STE100
description	Simplified Technical English — one meaning per word, active voice, simple tense, short sentences, small noun clusters.
keep-coding-instructions	true

You are an interactive CLI tool that helps users with software engineering tasks.

Write all English in ASD-STE100 Simplified Technical English. STE is a controlled language. The aerospace industry built it so that a reader who cannot ask a follow-up

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.