LLM 模型排行

:memo: 记录个人博客，见证成长之路 https://wangtunan.github.io/blog/

rUv's Claude-Flow, translated to the new Gemini CLI; transforming it into an autonomous AI development team.

Efficient translation tool based on ChatGPT or any OpenAI compatible LLM chat completion API

Framework to bring LLM applications to production

Cut LLM costs by up to 80% and unlock sub-millisecond responses with intelligent semantic caching.A drop-in, provider-agnostic LLM proxy written in Go with sub-millisecond response

Free & open-source AI research-paper writer: 19 agents draft 20k-word academic papers in ~10 min with citations verified against CrossRef/OpenAlex/arXiv. Export PDF/Word/LaTeX. Hosted at openpaper.dev

LLM Benchmark for Throughput via Ollama (Local LLMs)

Unified CloudOps platform with AI-SRE, AI-FinOps, AI-K8sOps, and the Agentic Automation Builder without fragmented tools, context switching, or model lock-in.

React components for visualizing traces from AI agents

Parse files (e.g. code repos) and websites to clipboard or a file for ingestions by AI / LLMs

Conversations with your files! Manage and run your AI presets!

This is The most comprehensive prompt hacking course available, which record our progress on a prompt engineering and prompt hacking course.

A Java library to use the OpenAI Api in the simplest possible way.

Open-WebUI-Functions is a collection of custom pipelines, filters, and integrations designed to enhance Open WebUI. These functions enable seamless interactions with Azure AI, N8N, Google Gemini and other AI models, providing dynamic request handling, preprocessing, and automation.

Curated Claude Fable 5 use cases, tutorials, integrations, demos, and benchmark evidence with source links. Access Claude Fable 5 exclusively via MuAPI.

Make your OpenClaw AI agent faster, smarter, and cheaper. Speed optimization, memory architecture, context management, model selection, and one-shot development guide.

distill any book down to its spine

Awesome_Multimodel is a curated GitHub repository that provides a comprehensive collection of resources for Multimodal Large Language Models (MLLM). It covers datasets, tuning techniques, in-context learning, visual reasoning, foundational models, and more. Stay updated with the latest advancement.

Jupyter Notebooks for Mastering LLM with Advanced RAG Course

Code for "LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding", ACL 2024