AI Observability & Evaluation
- llmops
- ai-monitoring
- ai-observability
- llm-eval
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AI Observability & Evaluation
TranslatedChatGPT Chinese guide🔥, ChatGPT Chinese tuning guide, prompt guide, application development guide, curated resource list, to use ChatGPT better and make your productivity go up up up! 🚀
TranslatedA Codex jailbreak prompt and test pack for gpt.
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
Streamlined interface for generating images with AI in Krita. Inpaint and outpaint with optional text prompt, no tweaking required.
Library for building powerful interactive command line applications in Python
"Context engineering is the delicate art and science of filling the context window with just the right information for the next step." — Andrej Karpathy. A frontier, first-principles handbook inspired by Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization.
Curated prompts, frameworks and papers for LLMs and agents, with an engineering bias. Prompt engineering, context engineering, agent harnesses, red teaming.
An interactive command-line HTTP and API testing client built on top of HTTPie featuring autocomplete, syntax highlighting, and more. https://twitter.com/httpie
The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.
TranslatedA trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples.
EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
TranslatedA summary and organization of the various kinds of knowledge that a natural language processing (NLP) engineer needs to accumulate, including interview questions, all kinds of foundational knowledge, engineering skills, and so on, to improve core competitiveness
Reverse-engineer any video, shot by shot: filmed, AI-generated or coded, and how to remake it. Breakdowns in the open VIDEO.md format. 逆向一切视频,逐个镜头看是拍的、AI 生成的还是代码做的,再告诉你怎么复刻;拆解结果按 VIDEO.md 格式输出。
A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.
LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows
The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai
[EMNLP'23, ACL'24] To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
The most comprehensive Claude Code guide: agentic workflows, hooks, skills, MCP servers, quizzes, and production-ready templates. 430K+ lines.
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
A beautiful and useful prompt for your shell
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models