#typescript #agent #agentic_rag #ai_coding #claude_code #code_generation #code_search #cursor #embedding #gemini_cli #mcp #merkle_tree #nodejs #openai #rag #semantic_search #typescript #vector_database #vibe_coding #voyage_ai #vscode_extension

Claude Context is a plugin that adds semantic code search to Claude Code and other AI tools, using your full codebase as context via a vector database like Zilliz Cloud. It finds relevant code instantly with natural language queries, indexes efficiently (only changed files), and cuts token use by ~40% for the same quality. You save costs on large projects, get precise results without loading whole files, and code faster with deep, relevant context across millions of lines. Setup needs free Zilliz/OpenAI keys and Node.js 20+; works with VS Code, Cursor, and more.

https://github.com/zilliztech/claude-context

#python #agent #ai #aigc #gemini #llm #quant #quantitative_trading #rag #stock

This AI-powered stock analysis system uses large models to automatically analyze your chosen A-shares, HK, or US stocks daily, delivering a decision dashboard with core conclusions, scores, buy/sell points, risk alerts, and checklists via WeChat, Telegram, email, or more. Deploy it free in 5 minutes via GitHub Actions—no server needed—or run locally/Docker. You save hours of manual research, get multi-dimensional insights (technicals, news, funds), backtesting, and interactive Agent queries for smarter, automated trading decisions.

https://github.com/ZhuLinsen/daily_stock_analysis

#jupyter_notebook #agentmemory #agents #ai #ai_developer #artificial_intelligence #generative_ai #kubernetes #kustomize #oracle_database #oracleaidatabase #oraclejet #rag

Oracle AI Developer Hub offers apps, notebooks, guides, workshops, and agent memory tools to build AI apps, agents, and systems using Oracle AI Database and OCI services. Explore ready examples like FitTracker fitness app, RAG agents, and memory-augmented workflows with LangChain or OpenAI. This saves you time by providing complete code, tutorials, and best practices to quickly create production-grade AI solutions without starting from scratch.

https://github.com/oracle-devrel/oracle-ai-developer-hub

#python #agents #llm #rag #skills #video_analytics #video_search #vlm

NVIDIA's Video Search and Summarization blueprint lets you build AI agents for fast video analysis, including real-time intelligence, alerts, natural language search, Q&A, and long-video summaries using vision models and NIM microservices. Deploy easily via Docker or cloud notebooks on supported NVIDIA hardware. This saves you time analyzing huge video volumes for smart monitoring, warehouses, or operations, boosting decisions and efficiency with ready workflows and custom options.

https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization

#python #ai_agents #amd #comfyui #docker #llama_cpp #llm #local_ai #n8n #nvidia #open_webui #rag #self_hosted #speech_to_text #strix_halo #text_to_speech #workflow_automation

Dream Server lets you run AI on your own machine instead of renting it from a cloud service. It works on Linux, Windows, and macOS, and it can set up chat, voice, agents, search, image tools, and privacy tools with one command. The main benefit is more control: your data stays with you, costs can be lower, and you can keep using AI even without a cloud account.

https://github.com/Light-Heart-Labs/DreamServer

#python #agent #ai #anthropic #claude_code #compression #context_engineering #context_window #cursor #fastapi #langchain #llm #mcp #openai #prompt_engineering #proxy #python #rag #token_optimization #tokens #typescript

Headroom is a local tool for AI agents that shrinks prompts, logs, files, and chat history before sending them to an LLM, often cutting tokens by 60–95% while keeping the same answer quality. It can work as a library, proxy, MCP server, or agent wrapper, so you can save tokens, speed up workflows, and still recover the original content when needed.

https://github.com/chopratejas/headroom

#python #ann #avx512 #embedding #embeddings #faiss #nearest_neighbor #neon #python #quant #quantization #rag #rust #simd #turboquant #vector_search

turbovec is a fast, local vector search tool that stores large embedding sets in much less memory, can search faster than FAISS in many cases, and works with Python and Rust. It helps you build private RAG systems because data stays on your machine, while online adding, filtering, and stable IDs make it easier to grow, control, and update your index without retraining or rebuilding.

https://github.com/RyanCodrai/turbovec

#typescript #ai #ai_agents #ai_memory #anthropic #artificial_intelligence #claude #claude_agent_sdk #claude_agents #claude_code_plugin #claude_skills #codex #embeddings #long_term_memory #memory_engine #openclaw #openclaw_skills #postgres #rag

Hivemind is a shared memory system for coding agents that saves prompts, tool calls, and replies, then turns repeated patterns into reusable skills for the whole team. It can make agents faster and cheaper, improve recall, and help you search past work, reuse solutions, and keep team rules and goals in one place.

https://github.com/activeloopai/hivemind

#python #ai #ai_agents #cli #hypergraph #information_extraction #knowledge #knowledge_graph #llm #python #rag

Hyper-Extract is a tool that uses AI to turn messy documents into clean, structured knowledge like lists, graphs, and linked data with one command. It gives you fast search, easy visual reports, and ready-made templates, so you can save time, understand information faster, and keep adding new documents without rebuilding everything.

https://github.com/yifanfeng97/Hyper-Extract

#python #antigravity #claude_code #codex #gemini #graphrag #knowledge_graph #leiden #openclaw #rag #skills #tree_sitter

Graphify is an AI tool that maps your entire project—code, docs, PDFs, images, and videos—into a searchable knowledge graph instead of forcing you to grep through files. You simply type `/graphify .` in your AI coding assistant (like Cursor, Claude, or Copilot) to get three outputs: an interactive HTML map, a report with key concepts and surprising connections, and a JSON file for instant queries. The main benefit to you is that you can instantly ask complex questions like "what connects auth to the database?" and get precise answers from the graph, saving hours of manual file searching and helping you understand your project's architecture faster.

https://github.com/safishamsi/graphify