Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.
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Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
Get your documents ready for gen AI
TranslatedLLM API management & key redistribution system, unifying multiple providers under a single API. Single binary, Docker-ready, with an English UI. Supports mainstream models such as OpenAI, Azure, Anthropic Claude, Google Gemini, DeepSeek, 字节豆包 (Doubao, "bean bun", from ByteDance), ChatGLM, 文心一言 (Wenxin Yiyan, "literary heart, one utterance"), 讯飞星火 (Xunfei Xinghuo, "iFlytek Spark"), 通义千问 (Tongyi Qianwen, "Tongyi thousand questions"), 360 智脑 (360 Zhinao, "360 smart brain") and 腾讯混元 (Tencent Hunyuan, "primordial chaos"), with unified API adaptation, usable for key management and secondary distribution. One-click deployment, ready to use out of the box.
PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
- semantic-search
- information-retrieval
- ai
- python
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
LLM Wiki is a cross-platform desktop application that turns your documents into an organized, interlinked knowledge base — automatically. Instead of traditional RAG (retrieve-and-answer from scratch every time), the LLM incrementally builds and maintains a persistent wiki from your sources。
A 100% free modern JS SaaS boilerplate (React, NodeJS, Prisma). Full-featured: Auth (email, google, github, slack, MS), Email sending, Background jobs, Landing page, Payments (Stripe, Polar.sh), Shadcn UI, S3 file upload. AI-ready with tailored AGENTS.md, skills, and Claude Code plugin. One cmd deploy. Powered by Wasp full-stack framework.
- full-stack
- open-source
- saas
- nodejs
Go ahead and axolotl questions
TranslatedA toolbox of film styles made in code for Claude Code: 66 ready to use, and your own. Each style is a style prompt plus a demo film made entirely in code by Claude Opus 5.5, with no video model. By Lemomo.
Convert any URL to an LLM-friendly input with a simple prefix https://r.jina.ai/
Run 35B MoE models in ~2.5 GB of RAM. Weights stream from SSD; a trained router predicts loads a step ahead. Mac, iPhone, Android, Windows supported
README file generator, powered by AI.
- readme-generator
- readme
- readme-md-generator
- ai
🎬 Evidence-led Seedance 2.5 / 2.0 prompt library: 840+ cases traced to original posts, 490+ cross-model retest runs, 27 copy-ready templates, 79 installable AI-video Skills. Synced daily from goodcase.ai. EN / 中文 / 日本語.
- ai-video
- awesome
- bytedance
- prompt-engineering
A curated, battle-tested AI-coding toolkit: Claude Code plugins, subagent orchestration, quality gates, and ready-to-copy prompts, extracted from real production use.
WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.
The fastest PDF library for Python and Rust. Text extraction, image extraction, markdown conversion, PDF creation & editing. 0.8ms mean, 5× faster than industry leaders, 100% pass rate on 3,830 PDFs. MIT/Apache-2.0.
- document-processing
- pdf-editor
- pdf-generation
TranslatedCopy-ready prompts & 54 cases of Opus 5.5 code-rendered videos. Claude Opus 5.5 generates videos with code: cases from across the web and prompts you can copy directly.
- claude
- claude-code
- motion-graphics
- opus
Rewrites AI drafts so they read like a person wrote them — runs locally, keeps the facts
This repo offers a simple interface that helps you to read&summerize research papers in pdf format. You can ask some questions after reading. This interface is developed based on openai API and using GPT-3.5-turbo model.
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
- deep-learning
- protein-sequences
- protein-structure
- semi-supervised-learning
37 tok/s from Qwen3-Next-80B (48 GB) on a 32 GB laptop, 24 without drafts vs 2.8 for llama.cpp; reading 62 vs 16 tok/s. Faster than llama.cpp on all 13 models compared in Q4_K_M. LLMs bigger than your RAM, pure C, CPU or GPU.
- avx2
- c
- cpu-inference
- gguf
AI jailbreaks: 35 paste-ready AI jailbreak prompts for 25+ frontier and open-weight models - system prompts, AGENTS.md specs, and Ollama Modelfiles. Built for AI security research and red-team testing.
- ai
- ai-safety
- jailbreak
- jailbreaking