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#python #agent_security #ai_agents #ai_security #benchmark #claude #claude_code #codex #cursor #llm_security #mcp #model_context_protocol #prompt_injection #threat_detection

ADR is an enterprise security system for AI agents that watches what agents do, tests them for attacks, detects risky behavior, and is being used in production at Uber; its paper was accepted to MLSys 2026. The benefit to you is better protection for AI tools like Cursor, Claude Code, and Codex, plus a way to find unsafe actions before they cause harm.

https://github.com/uber/ADR
#python #agent_control_plane #agent_ops #ai_agents #codex #long_running_agents #loop_engineering #loopx #workflow_automation

LoopX is a local control plane for long-running AI agent work. It keeps goals, tasks, evidence, quota, and handoffs in one place so work stays clear, restartable, and easier to pass between agents and people; the benefit is less lost context, safer progress, and easier review across long projects.

https://github.com/huangruiteng/loopx
#python #agent_memory #ai #ai_governance #ai_infrastructure #artificial_intelligence #context_engineering #context_graphs #data_engineering #decision_intelligence #developer_tools #explainable_ai #generative_ai #graph_rag #knowledge_graph #llm #ontology #provenance #python #reasoning #semantic_search

Semantica is an open-source tool that turns messy data into clear, connected knowledge graphs for AI. It helps you track where facts came from, explain AI decisions, find conflicts, and keep systems auditable and trustworthy. This benefits you by making AI easier to trust, easier to check, and safer to use in important work like finance, healthcare, legal, and government.

https://github.com/semantica-agi/semantica
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#python #ai #ast #claude_code #code_analysis #code_understanding #codebase_search #developer_tools #graph_database #knowledge_graph #llm #mcp #mcp_server #memgraph #monorepo #multi_language #python #rag #retrieval_augmented_generation #semantic_search #tree_sitter

Code-Graph-RAG is an open-source tool that reads a codebase, builds a graph of functions, classes, modules, and links, and lets you ask questions, find code, edit safely, and improve code in plain English. It supports many languages and can run as an MCP server for direct use with AI tools. Benefit: you can understand and change large mixed-language projects faster, with less manual searching and fewer mistakes.

https://github.com/vitali87/code-graph-rag
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#python

Harvey LAB is an open-source benchmark that tests how well AI agents can do real legal work, not just answer legal questions. It gives each agent instructions, case documents, and a required work product, then scores the result with expert rubrics. LAB includes more than 1,200 tasks across 24 legal practice areas. This helps you compare models, check agent quality, and build better legal AI tools with a realistic standard.

https://github.com/harveyai/harvey-labs
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#python #ai_agent #aippt #office #powerpoint #powerpoint_generation #ppt #pptx #presentation #slide #slides

PPT Master is a free, open-source tool that turns PDFs, DOCX files, web pages, or text into a real, editable PowerPoint with native shapes, charts, tables, animations, and speaker notes. You run it locally with an AI tool, so your files stay on your machine and you avoid platform lock-in. The benefit to you is faster slide creation with a deck you can still edit in PowerPoint, instead of a flat image file.

https://github.com/hugohe3/ppt-master
#python #cactus #gemini #gemma #llm #on_device_ai

Needle 2 is a very small tool-calling AI model that can run on low memory, work offline, and return structured results like JSON instead of free text. It helps you build apps that call tools, extract data, and fine-tune models more safely and easily, even on devices with limited RAM.

https://github.com/cactus-compute/needle
#python #cli #consumer_gpu #dpo #fine_tuning #gguf #huggingface #llm #llmops #local_ai #local_llm #lora #low_vram #machine_learning #ollama #peft #python #pytorch #qlora #sft #transformers

Soup is a tool that helps you fine-tune and post-train large language models with one command, using one config file and little setup. It can work on a local GPU, even a 4 GB laptop GPU for an 8B model with layer streaming, so you can train without SSH or cloud hassle. This saves time, reduces setup pain, and makes model training easier to start and manage.

https://github.com/MakazhanAlpamys/Soup
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