deploy(hf): sync szl-holdings/a11oy@main derived COPY set

Reusable Dockerfile-COPY-derived deploy from szl-holdings/a11oy main.
Files: 942 Pruned: 0
Derived from Dockerfile COPY sources (NO hand-maintained allowlist).

Signed-off-by: SZL Holdings <noreply@szlholdings.ai>
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

brain/harvest/energy_defense.jsonl

@@ -31,10 +31,10 @@
 {"type": "node", "id": "repo:Repello-AI/Agent-Wiz", "kind": "repo", "label": "Repello-AI/Agent-Wiz", "url": "https://github.com/Repello-AI/Agent-Wiz", "source": "github", "axis": "agents", "layer": 0, "stars": 381, "lang": "Python", "desc": "A CLI tool for threat modeling and visualizing AI agents built using popular frameworks like LangGraph, AutoGen, CrewAI, and more."}
 {"type": "node", "id": "repo:adamwlarson/ai-book-writer", "kind": "repo", "label": "adamwlarson/ai-book-writer", "url": "https://github.com/adamwlarson/ai-book-writer", "source": "github", "axis": "agents", "layer": 0, "stars": 392, "lang": "Python", "desc": "Experimenting with AutoGen to see if an entire book can be written with AI agents"}
 {"type": "node", "id": "repo:JayZeeDesign/vision-agent-with-llava", "kind": "repo", "label": "JayZeeDesign/vision-agent-with-llava", "url": "https://github.com/JayZeeDesign/vision-agent-with-llava", "source": "github", "axis": "agents", "layer": 0, "stars": 75, "lang": "Python", "desc": "Agent with vision ability via llava & autogen"}
-{"type": "node", "id": "repo:SageMindAI/autogen-agi", "kind": "repo", "label": "SageMindAI/autogen-agi", "url": "https://github.com/SageMindAI/autogen-agi", "source": "github", "axis": "agents", "layer": 0, "stars": 268, "lang": "Python", "desc": "AutoGen AGI: Advancing AI agents using AutoGen towards AGI capabilities. Explore cutting-edge enhancements in group chat dynamics, decision-making, and complex task proficiency. Join our journey in shaping AI's future!"}
+{"type": "node", "id": "repo:SageMindAI/autogen-agi", "kind": "repo", "label": "SageMindAI/autogen-agi", "url": "https://github.com/SageMindAI/autogen-agi", "source": "github", "axis": "agents", "layer": 0, "stars": 268, "lang": "Python", "desc": "AutoGen AGI: Advancing AI agents using AutoGen towards AGI capabilities. Explore advanced enhancements in group chat dynamics, decision-making, and complex task proficiency. Join our journey in shaping AI's future!"}
 {"type": "node", "id": "repo:Azure-Samples/dream-team", "kind": "repo", "label": "Azure-Samples/dream-team", "url": "https://github.com/Azure-Samples/dream-team", "source": "github", "axis": "agents", "layer": 0, "stars": 237, "lang": "TypeScript", "desc": "This repo helps you to build a team of AI agents with Autogen"}
 {"type": "node", "id": "repo:ag2ai/fastagency", "kind": "repo", "label": "ag2ai/fastagency", "url": "https://github.com/ag2ai/fastagency", "source": "github", "axis": "agents", "layer": 0, "stars": 544, "lang": "Python", "desc": "The fastest way to bring multi-agent workflows to production."}
-{"type": "node", "id": "repo:crewAIInc/crewAI", "kind": "repo", "label": "crewAIInc/crewAI", "url": "https://github.com/crewAIInc/crewAI", "source": "github", "axis": "agents", "layer": 0, "stars": 55067, "lang": "Python", "desc": "Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."}
+{"type": "node", "id": "repo:crewAIInc/crewAI", "kind": "repo", "label": "crewAIInc/crewAI", "url": "https://github.com/crewAIInc/crewAI", "source": "github", "axis": "agents", "layer": 0, "stars": 55067, "lang": "Python", "desc": "Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together smoothly, tackling complex tasks."}
 {"type": "node", "id": "repo:crewAIInc/crewAI-examples", "kind": "repo", "label": "crewAIInc/crewAI-examples", "url": "https://github.com/crewAIInc/crewAI-examples", "source": "github", "axis": "agents", "layer": 0, "stars": 6076, "lang": "Jupyter Notebook", "desc": "A collection of examples that show how to use CrewAI framework to automate workflows."}
 {"type": "node", "id": "repo:crewAIInc/crewAI-tools", "kind": "repo", "label": "crewAIInc/crewAI-tools", "url": "https://github.com/crewAIInc/crewAI-tools", "source": "github", "axis": "agents", "layer": 0, "stars": 1455, "lang": "Python", "desc": "Extend the capabilities of your CrewAI agents with Tools"}
 {"type": "node", "id": "repo:strnad/CrewAI-Studio", "kind": "repo", "label": "strnad/CrewAI-Studio", "url": "https://github.com/strnad/CrewAI-Studio", "source": "github", "axis": "agents", "layer": 0, "stars": 1309, "lang": "Python", "desc": "A user-friendly, multi-platform GUI for managing and running CrewAI agents and tasks. Supports Conda and virtual environments, no coding needed. "}
@@ -96,7 +96,7 @@
 {"type": "node", "id": "repo:X-D-Lab/LangChain-ChatGLM-Webui", "kind": "repo", "label": "X-D-Lab/LangChain-ChatGLM-Webui", "url": "https://github.com/X-D-Lab/LangChain-ChatGLM-Webui", "source": "github", "axis": "agents", "layer": 0, "stars": 3315, "lang": "Python", "desc": "\u57fa\u4e8eLangChain\u548cChatGLM-6B\u7b49\u7cfb\u5217LLM\u7684\u9488\u5bf9\u672c\u5730\u77e5\u8bc6\u5e93\u7684\u81ea\u52a8\u95ee\u7b54"}
 {"type": "node", "id": "repo:langchain-ai/langgraph", "kind": "repo", "label": "langchain-ai/langgraph", "url": "https://github.com/langchain-ai/langgraph", "source": "github", "axis": "agents", "layer": 0, "stars": 36686, "lang": "Python", "desc": "Build resilient agents."}
 {"type": "node", "id": "repo:google-gemini/gemini-fullstack-langgraph-quickstart", "kind": "repo", "label": "google-gemini/gemini-fullstack-langgraph-quickstart", "url": "https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart", "source": "github", "axis": "agents", "layer": 0, "stars": 18245, "lang": "Jupyter Notebook", "desc": "Get started with building Fullstack Agents using Gemini 2.5 and LangGraph"}
-{"type": "node", "id": "repo:langgraph4j/langgraph4j", "kind": "repo", "label": "langgraph4j/langgraph4j", "url": "https://github.com/langgraph4j/langgraph4j", "source": "github", "axis": "agents", "layer": 0, "stars": 1799, "lang": "Java", "desc": "\ud83d\ude80 LangGraph for Java. A library for develop AI Agentic Architectures in the Java ecosystem. Designed to work seamlessly with both Langchain4j and Spring AI."}
+{"type": "node", "id": "repo:langgraph4j/langgraph4j", "kind": "repo", "label": "langgraph4j/langgraph4j", "url": "https://github.com/langgraph4j/langgraph4j", "source": "github", "axis": "agents", "layer": 0, "stars": 1799, "lang": "Java", "desc": "\ud83d\ude80 LangGraph for Java. A library for develop AI Agentic Architectures in the Java ecosystem. Designed to work smoothly with both Langchain4j and Spring AI."}
 {"type": "node", "id": "repo:langchain-ai/langgraphjs", "kind": "repo", "label": "langchain-ai/langgraphjs", "url": "https://github.com/langchain-ai/langgraphjs", "source": "github", "axis": "agents", "layer": 0, "stars": 3093, "lang": "TypeScript", "desc": "Framework to build resilient language agents as graphs."}
 {"type": "node", "id": "repo:mayooear/ai-pdf-chatbot-langchain", "kind": "repo", "label": "mayooear/ai-pdf-chatbot-langchain", "url": "https://github.com/mayooear/ai-pdf-chatbot-langchain", "source": "github", "axis": "agents", "layer": 0, "stars": 16565, "lang": "TypeScript", "desc": "AI PDF chatbot agent built with LangChain & LangGraph "}
 {"type": "node", "id": "repo:langchain-ai/langgraph-example", "kind": "repo", "label": "langchain-ai/langgraph-example", "url": "https://github.com/langchain-ai/langgraph-example", "source": "github", "axis": "agents", "layer": 0, "stars": 512, "lang": "Python", "desc": ""}
@@ -146,7 +146,7 @@
 {"type": "node", "id": "repo:cacheplane/angular-agent-framework", "kind": "repo", "label": "cacheplane/angular-agent-framework", "url": "https://github.com/cacheplane/angular-agent-framework", "source": "github", "axis": "agents", "layer": 0, "stars": 100, "lang": "TypeScript", "desc": "Angular SDK for Building Agentic Apps + Generative UI"}
 {"type": "node", "id": "repo:NapthaAI/naptha-sdk", "kind": "repo", "label": "NapthaAI/naptha-sdk", "url": "https://github.com/NapthaAI/naptha-sdk", "source": "github", "axis": "agents", "layer": 0, "stars": 179, "lang": "Python", "desc": "Naptha is a framework and infrastructure for developing and running multi-agent systems at scale with heterogeneous models, architectures and data"}
 {"type": "node", "id": "repo:symbolica-ai/agentica-typescript-sdk", "kind": "repo", "label": "symbolica-ai/agentica-typescript-sdk", "url": "https://github.com/symbolica-ai/agentica-typescript-sdk", "source": "github", "axis": "agents", "layer": 0, "stars": 65, "lang": "TypeScript", "desc": "The official TypeScript SDK for the Agentica agent framework from Symbolica"}
-{"type": "node", "id": "repo:sslava/ai-sdk-agents", "kind": "repo", "label": "sslava/ai-sdk-agents", "url": "https://github.com/sslava/ai-sdk-agents", "source": "github", "axis": "agents", "layer": 0, "stars": 209, "lang": "TypeScript", "desc": "A flexible toolkit for building, running, and managing AI-powered agents - built on top of the Vercel AI SDK. Integrates seamlessly with your favorite frameworks."}
+{"type": "node", "id": "repo:sslava/ai-sdk-agents", "kind": "repo", "label": "sslava/ai-sdk-agents", "url": "https://github.com/sslava/ai-sdk-agents", "source": "github", "axis": "agents", "layer": 0, "stars": 209, "lang": "TypeScript", "desc": "A flexible toolkit for building, running, and managing AI-powered agents - built on top of the Vercel AI SDK. Integrates smoothly with your favorite frameworks."}
 {"type": "node", "id": "repo:sentient-engineering/sentient", "kind": "repo", "label": "sentient-engineering/sentient", "url": "https://github.com/sentient-engineering/sentient", "source": "github", "axis": "agents", "layer": 0, "stars": 569, "lang": "Python", "desc": "the framework/ sdk that lets you build browser controlling agents in 3 lines of code. join chat @ https://discord.gg/umgnyQU2K8"}
 {"type": "node", "id": "repo:atomicstrata/atomicmemory", "kind": "repo", "label": "atomicstrata/atomicmemory", "url": "https://github.com/atomicstrata/atomicmemory", "source": "github", "axis": "agents", "layer": 0, "stars": 434, "lang": "TypeScript", "desc": "Portable semantic memory for AI agents: core engine, TypeScript SDK, framework adapters, MCP server, CLI, and host plugins."}
 {"type": "node", "id": "repo:hybroai/a2a-adapter", "kind": "repo", "label": "hybroai/a2a-adapter", "url": "https://github.com/hybroai/a2a-adapter", "source": "github", "axis": "agents", "layer": 0, "stars": 89, "lang": "Python", "desc": "Open Source A2A Protocol Adapter SDK for Different Agent Frameworks"}
@@ -194,7 +194,7 @@
 {"type": "node", "id": "repo:larics/synthetic-UAV", "kind": "repo", "label": "larics/synthetic-UAV", "url": "https://github.com/larics/synthetic-UAV", "source": "github", "axis": "defense", "layer": 0, "stars": 21, "lang": "", "desc": "Sim2air: synthetic texture-invariant dataset for object detection of UAVs"}
 {"type": "node", "id": "repo:takzen/yolo-military-drone-detection", "kind": "repo", "label": "takzen/yolo-military-drone-detection", "url": "https://github.com/takzen/yolo-military-drone-detection", "source": "github", "axis": "defense", "layer": 0, "stars": 23, "lang": "Python", "desc": "Real-time military drone detection system using YOLO11. Detects Shahed-136, Lancet, Orlan-10, and other UAVs with 94.8% mAP50 accuracy. Features Streamlit interface with overlay statistics, video processing, and webcam monitoring. Trained o"}
 {"type": "node", "id": "repo:anasbadawy/YOLOv3-Object-Detection", "kind": "repo", "label": "anasbadawy/YOLOv3-Object-Detection", "url": "https://github.com/anasbadawy/YOLOv3-Object-Detection", "source": "github", "axis": "defense", "layer": 0, "stars": 13, "lang": "Python", "desc": "This project implements a real-time image and video UAVs(unmanned aerial vehicle) detection classifier using a new trained yolov3 model."}
-{"type": "node", "id": "repo:jashswayam/Automating-Tree-Counting-through-UAVs-and-YOLOv5-Object-Detection", "kind": "repo", "label": "jashswayam/Automating-Tree-Counting-through-UAVs-and-YOLOv5-Object-Detection", "url": "https://github.com/jashswayam/Automating-Tree-Counting-through-UAVs-and-YOLOv5-Object-Detection", "source": "github", "axis": "defense", "layer": 0, "stars": 4, "lang": "Jupyter Notebook", "desc": "This repository presents an innovative approach to automate tree counting using YOLOv5, a state-of-the-art object detection model, coupled with high-resolution UAV imagery. By leveraging the power of deep learning, our algorithm intelligent"}
+{"type": "node", "id": "repo:jashswayam/Automating-Tree-Counting-through-UAVs-and-YOLOv5-Object-Detection", "kind": "repo", "label": "jashswayam/Automating-Tree-Counting-through-UAVs-and-YOLOv5-Object-Detection", "url": "https://github.com/jashswayam/Automating-Tree-Counting-through-UAVs-and-YOLOv5-Object-Detection", "source": "github", "axis": "defense", "layer": 0, "stars": 4, "lang": "Jupyter Notebook", "desc": "This repository presents an innovative approach to automate tree counting using YOLOv5, a high-performing object detection model, coupled with high-resolution UAV imagery. By leveraging the power of deep learning, our algorithm intelligent"}
 {"type": "node", "id": "repo:dmdobrea/shieldUAV", "kind": "repo", "label": "dmdobrea/shieldUAV", "url": "https://github.com/dmdobrea/shieldUAV", "source": "github", "axis": "defense", "layer": 0, "stars": 7, "lang": "Python", "desc": "This repository was developed in the context of the Pervasive AI Developer Contest with AMD "}
 {"type": "node", "id": "repo:anhuipl2010/SOD-YOLO", "kind": "repo", "label": "anhuipl2010/SOD-YOLO", "url": "https://github.com/anhuipl2010/SOD-YOLO", "source": "github", "axis": "defense", "layer": 0, "stars": 37, "lang": "", "desc": "SOD-YOLO (Small Object Detection YOLO) builds upon the foundational YOLOv8 model to address the unique challenges of detecting small objects in complex backgrounds typical of UAV imagery. This repository contains the code for the model, inc"}
 {"type": "node", "id": "repo:Xjh-UCAS/YoloOW", "kind": "repo", "label": "Xjh-UCAS/YoloOW", "url": "https://github.com/Xjh-UCAS/YoloOW", "source": "github", "axis": "defense", "layer": 0, "stars": 13, "lang": "Python", "desc": "YoloOW: A Spatial Scale Adaptive Real-time Object Detection Neural Network for Open Water Search and Rescue from UAV Aerial Imagery"}
@@ -216,21 +216,21 @@
 {"type": "node", "id": "repo:amyworrall/QuickRadar", "kind": "repo", "label": "amyworrall/QuickRadar", "url": "https://github.com/amyworrall/QuickRadar", "source": "github", "axis": "defense", "layer": 0, "stars": 531, "lang": "Objective-C", "desc": "Mac app to simplify posting bug reports to Apple's Radar bug tracking system."}
 {"type": "node", "id": "repo:BLE-Research-Group/MetaRadar", "kind": "repo", "label": "BLE-Research-Group/MetaRadar", "url": "https://github.com/BLE-Research-Group/MetaRadar", "source": "github", "axis": "defense", "layer": 0, "stars": 1456, "lang": "Kotlin", "desc": "A tool for BLE environment monitoring. Find and track Bluetooth devices around, and get notified when the target device is detected."}
 {"type": "node", "id": "repo:Research-and-Project/mmWave_radar_tracking", "kind": "repo", "label": "Research-and-Project/mmWave_radar_tracking", "url": "https://github.com/Research-and-Project/mmWave_radar_tracking", "source": "github", "axis": "defense", "layer": 0, "stars": 161, "lang": "MATLAB", "desc": "object tracking based on millimeter wave radar"}
-{"type": "node", "id": "repo:radarlabs/react-native-radar", "kind": "repo", "label": "radarlabs/react-native-radar", "url": "https://github.com/radarlabs/react-native-radar", "source": "github", "axis": "defense", "layer": 0, "stars": 197, "lang": "Objective-C", "desc": "React Native module for Radar, the leading geofencing and location tracking platform"}
+{"type": "node", "id": "repo:radarlabs/react-native-radar", "kind": "repo", "label": "radarlabs/react-native-radar", "url": "https://github.com/radarlabs/react-native-radar", "source": "github", "axis": "defense", "layer": 0, "stars": 197, "lang": "Objective-C", "desc": "React Native module for Radar, the widely-used geofencing and location tracking platform"}
 {"type": "node", "id": "repo:ThasianX/SpotifyRadar", "kind": "repo", "label": "ThasianX/SpotifyRadar", "url": "https://github.com/ThasianX/SpotifyRadar", "source": "github", "axis": "defense", "layer": 0, "stars": 644, "lang": "Swift", "desc": "Allows users to pull in new song releases from their favorite artists and provides users with important metrics like their top tracks, top artists, and recently played tracks, queryable by time range."}
 {"type": "node", "id": "repo:JunshengFu/tracking-with-Unscented-Kalman-Filter", "kind": "repo", "label": "JunshengFu/tracking-with-Unscented-Kalman-Filter", "url": "https://github.com/JunshengFu/tracking-with-Unscented-Kalman-Filter", "source": "github", "axis": "defense", "layer": 0, "stars": 171, "lang": "C++", "desc": "Object (e.g Pedestrian, biker, vehicles) tracking by Unscented Kalman Filter (UKF), with  fused data from both lidar and radar sensors."}
-{"type": "node", "id": "repo:radarlabs/radar-sdk-ios", "kind": "repo", "label": "radarlabs/radar-sdk-ios", "url": "https://github.com/radarlabs/radar-sdk-ios", "source": "github", "axis": "defense", "layer": 0, "stars": 78, "lang": "Objective-C", "desc": "iOS SDK for Radar, the leading geofencing and location tracking platform"}
-{"type": "node", "id": "repo:radarlabs/radar-sdk-android", "kind": "repo", "label": "radarlabs/radar-sdk-android", "url": "https://github.com/radarlabs/radar-sdk-android", "source": "github", "axis": "defense", "layer": 0, "stars": 96, "lang": "Kotlin", "desc": "Android SDK for Radar, the leading geofencing and location tracking platform"}
+{"type": "node", "id": "repo:radarlabs/radar-sdk-ios", "kind": "repo", "label": "radarlabs/radar-sdk-ios", "url": "https://github.com/radarlabs/radar-sdk-ios", "source": "github", "axis": "defense", "layer": 0, "stars": 78, "lang": "Objective-C", "desc": "iOS SDK for Radar, the widely-used geofencing and location tracking platform"}
+{"type": "node", "id": "repo:radarlabs/radar-sdk-android", "kind": "repo", "label": "radarlabs/radar-sdk-android", "url": "https://github.com/radarlabs/radar-sdk-android", "source": "github", "axis": "defense", "layer": 0, "stars": 96, "lang": "Kotlin", "desc": "Android SDK for Radar, the widely-used geofencing and location tracking platform"}
 {"type": "node", "id": "repo:XiangyuDing/Radar-Detecting-and-Tracking", "kind": "repo", "label": "XiangyuDing/Radar-Detecting-and-Tracking", "url": "https://github.com/XiangyuDing/Radar-Detecting-and-Tracking", "source": "github", "axis": "defense", "layer": 0, "stars": 61, "lang": "Matlab", "desc": "ECE 2595 Radar Signal Processing Projects"}
 {"type": "node", "id": "repo:duckduckgo/tracker-radar-detector", "kind": "repo", "label": "duckduckgo/tracker-radar-detector", "url": "https://github.com/duckduckgo/tracker-radar-detector", "source": "github", "axis": "defense", "layer": 0, "stars": 210, "lang": "JavaScript", "desc": "Code used to build a Tracker Radar data set from raw crawl data."}
 {"type": "node", "id": "repo:duckduckgo/tracker-radar-wiki", "kind": "repo", "label": "duckduckgo/tracker-radar-wiki", "url": "https://github.com/duckduckgo/tracker-radar-wiki", "source": "github", "axis": "defense", "layer": 0, "stars": 81, "lang": "JavaScript", "desc": "Generation scripts and source for Tracker Radar Wiki"}
 {"type": "node", "id": "repo:elsbrock/hetzner-radar", "kind": "repo", "label": "elsbrock/hetzner-radar", "url": "https://github.com/elsbrock/hetzner-radar", "source": "github", "axis": "defense", "layer": 0, "stars": 345, "lang": "TypeScript", "desc": "\ud83d\udd75\ufe0fTrack prices of the Hetzner dedicated server auction"}
 {"type": "node", "id": "repo:LJacksonPan/RaTrack", "kind": "repo", "label": "LJacksonPan/RaTrack", "url": "https://github.com/LJacksonPan/RaTrack", "source": "github", "axis": "defense", "layer": 0, "stars": 218, "lang": "Python", "desc": "[ICRA2024] RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud"}
-{"type": "node", "id": "repo:radarlabs/radar-sdk-js", "kind": "repo", "label": "radarlabs/radar-sdk-js", "url": "https://github.com/radarlabs/radar-sdk-js", "source": "github", "axis": "defense", "layer": 0, "stars": 52, "lang": "TypeScript", "desc": "Web JavaScript SDK for Radar, the leading geofencing and location tracking platform"}
+{"type": "node", "id": "repo:radarlabs/radar-sdk-js", "kind": "repo", "label": "radarlabs/radar-sdk-js", "url": "https://github.com/radarlabs/radar-sdk-js", "source": "github", "axis": "defense", "layer": 0, "stars": 52, "lang": "TypeScript", "desc": "Web JavaScript SDK for Radar, the widely-used geofencing and location tracking platform"}
 {"type": "node", "id": "repo:dhananjaymenon/SpeedRadar-OpenCV-", "kind": "repo", "label": "dhananjaymenon/SpeedRadar-OpenCV-", "url": "https://github.com/dhananjaymenon/SpeedRadar-OpenCV-", "source": "github", "axis": "defense", "layer": 0, "stars": 55, "lang": "Python", "desc": "This code focusses on Object Tracking and Speed Estimation of vehicles passing by a road."}
 {"type": "node", "id": "repo:thkruz/keeptrack.space", "kind": "repo", "label": "thkruz/keeptrack.space", "url": "https://github.com/thkruz/keeptrack.space", "source": "github", "axis": "defense", "layer": 0, "stars": 1521, "lang": "TypeScript", "desc": "\ud83c\udf0e\ud83d\udce1 TypeScript Astrodynamics Software for Non-Engineers. 3D Visualization of satellite data and the sensors that track them."}
 {"type": "node", "id": "repo:drpwchen/paper-radar", "kind": "repo", "label": "drpwchen/paper-radar", "url": "https://github.com/drpwchen/paper-radar", "source": "github", "axis": "defense", "layer": 0, "stars": 68, "lang": "Python", "desc": "\u8ad6\u6587\u5b78\u7fd2\u96f7\u9054 \u2014 a personal literature-tracking & learning radar: dozens of journal/PubMed feeds \u2192 interest-scored \u2192 private Cloudflare-Access web triage \u2192 flows into your notes"}
-{"type": "node", "id": "repo:radarlabs/flutter-radar", "kind": "repo", "label": "radarlabs/flutter-radar", "url": "https://github.com/radarlabs/flutter-radar", "source": "github", "axis": "defense", "layer": 0, "stars": 29, "lang": "Java", "desc": "Flutter package for Radar, the leading geofencing and location tracking platform"}
+{"type": "node", "id": "repo:radarlabs/flutter-radar", "kind": "repo", "label": "radarlabs/flutter-radar", "url": "https://github.com/radarlabs/flutter-radar", "source": "github", "axis": "defense", "layer": 0, "stars": 29, "lang": "Java", "desc": "Flutter package for Radar, the widely-used geofencing and location tracking platform"}
 {"type": "node", "id": "repo:micropython-IMU/micropython-fusion", "kind": "repo", "label": "micropython-IMU/micropython-fusion", "url": "https://github.com/micropython-IMU/micropython-fusion", "source": "github", "axis": "defense", "layer": 0, "stars": 345, "lang": "Python", "desc": "Sensor fusion calculating yaw, pitch and roll from the outputs of motion tracking devices"}
 {"type": "node", "id": "repo:Fusion-Goettingen/ExtendedObjectTracking", "kind": "repo", "label": "Fusion-Goettingen/ExtendedObjectTracking", "url": "https://github.com/Fusion-Goettingen/ExtendedObjectTracking", "source": "github", "axis": "defense", "layer": 0, "stars": 93, "lang": "MATLAB", "desc": "Sensor Data Fusion Group"}
 {"type": "node", "id": "repo:bernwang/latte", "kind": "repo", "label": "bernwang/latte", "url": "https://github.com/bernwang/latte", "source": "github", "axis": "defense", "layer": 0, "stars": 450, "lang": "Python", "desc": "LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking"}
@@ -301,7 +301,7 @@
 {"type": "node", "id": "repo:soham2002/GreenRoots", "kind": "repo", "label": "soham2002/GreenRoots", "url": "https://github.com/soham2002/GreenRoots", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy", "stars": 3, "lang": "Jupyter Notebook", "desc": "GreenRoots: An AI/ML project dedicated to promoting mangrove sustainability and fostering community engagement in mangrove conservation efforts."}
 {"type": "node", "id": "repo:Rishabhmannu/amazon-greencart-hackathon", "kind": "repo", "label": "Rishabhmannu/amazon-greencart-hackathon", "url": "https://github.com/Rishabhmannu/amazon-greencart-hackathon", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy", "stars": 58, "lang": "TypeScript", "desc": "AI-powered sustainable e-commerce platform with EarthScore ratings, group buying via DBSCAN clustering, and multi-agent chatbot. Gamifies eco-friendly shopping with real-time carbon tracking, achievements, and community bulk purchasing. Bui"}
 {"type": "node", "id": "repo:NaiveNinjas/greenforce", "kind": "repo", "label": "NaiveNinjas/greenforce", "url": "https://github.com/NaiveNinjas/greenforce", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy", "stars": 0, "lang": "TypeScript", "desc": "GreenForce is an AI-powered sustainability assistant that helps organizations automate and coordinate environmental workflows aligned with the UN Sustainable Development Goals (SDGs 7, 12 and 13)."}
-{"type": "node", "id": "repo:mwakidenis/Green-AI", "kind": "repo", "label": "mwakidenis/Green-AI", "url": "https://github.com/mwakidenis/Green-AI", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy", "stars": 12, "lang": "TypeScript", "desc": "\ud83c\udf31 Green- AI-Powered Waste-to-Energy Platform Transform waste into sustainable energy using cutting-edge AI technology. GreenTech combines real-time monitoring, predictive analytics, and community engagement to optimize waste processing and "}
+{"type": "node", "id": "repo:mwakidenis/Green-AI", "kind": "repo", "label": "mwakidenis/Green-AI", "url": "https://github.com/mwakidenis/Green-AI", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy", "stars": 12, "lang": "TypeScript", "desc": "\ud83c\udf31 Green- AI-Powered Waste-to-Energy Platform Transform waste into sustainable energy using advanced AI technology. GreenTech combines real-time monitoring, predictive analytics, and community engagement to optimize waste processing and "}
 {"type": "node", "id": "repo:Pampaj7/GreenAI", "kind": "repo", "label": "Pampaj7/GreenAI", "url": "https://github.com/Pampaj7/GreenAI", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy", "stars": 6, "lang": "HTML", "desc": "This repository is a companion page for the revision at the 9th International Workshop on Green and Sustainable Software (GREENS\u201925)"}
 {"type": "node", "id": "repo:Deepsayan-Das/Green-Lens", "kind": "repo", "label": "Deepsayan-Das/Green-Lens", "url": "https://github.com/Deepsayan-Das/Green-Lens", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy", "stars": 2, "lang": "JavaScript", "desc": "An AI & Blockchain sustainability platform. Track eco-actions, get ML-verified rewards (Green Tokens), and support real-world NGOs. [React, Node.js, Python, Solidity, Document AI]"}
 {"type": "node", "id": "repo:ErnestMunyoki/Green-Path", "kind": "repo", "label": "ErnestMunyoki/Green-Path", "url": "https://github.com/ErnestMunyoki/Green-Path", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy", "stars": 0, "lang": "Python", "desc": "AI-Powered sustainability tracker "}
@@ -682,10 +682,10 @@
 {"type": "node", "id": "repo:subhash307/SpringBoot-BitsAndBytes", "kind": "repo", "label": "subhash307/SpringBoot-BitsAndBytes", "url": "https://github.com/subhash307/SpringBoot-BitsAndBytes", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 8, "lang": "Java", "desc": ""}
 {"type": "node", "id": "repo:bitsandbytes-foundation/bitsandbytes-intel", "kind": "repo", "label": "bitsandbytes-foundation/bitsandbytes-intel", "url": "https://github.com/bitsandbytes-foundation/bitsandbytes-intel", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 3, "lang": "Python", "desc": "An extension to enable performance acceleration for bitsandbytes on Intel platforms."}
 {"type": "node", "id": "repo:vllm-project/vllm", "kind": "repo", "label": "vllm-project/vllm", "url": "https://github.com/vllm-project/vllm", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 85586, "lang": "Python", "desc": "A high-throughput and memory-efficient inference and serving engine for LLMs"}
-{"type": "node", "id": "repo:NVIDIA/TensorRT-LLM", "kind": "repo", "label": "NVIDIA/TensorRT-LLM", "url": "https://github.com/NVIDIA/TensorRT-LLM", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 14061, "lang": "Python", "desc": "TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create P"}
+{"type": "node", "id": "repo:NVIDIA/TensorRT-LLM", "kind": "repo", "label": "NVIDIA/TensorRT-LLM", "url": "https://github.com/NVIDIA/TensorRT-LLM", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 14061, "lang": "Python", "desc": "TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports high-performing optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create P"}
 {"type": "node", "id": "repo:openlake-project/openlake", "kind": "repo", "label": "openlake-project/openlake", "url": "https://github.com/openlake-project/openlake", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 1767, "lang": "Rust", "desc": "OpenLake is a high performance storage engine for efficient LLM inference and GPU Training"}
 {"type": "node", "id": "repo:NVIDIA/Star-Attention", "kind": "repo", "label": "NVIDIA/Star-Attention", "url": "https://github.com/NVIDIA/Star-Attention", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 393, "lang": "Python", "desc": "Efficient LLM Inference over Long Sequences"}
-{"type": "node", "id": "repo:quic/efficient-transformers", "kind": "repo", "label": "quic/efficient-transformers", "url": "https://github.com/quic/efficient-transformers", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 90, "lang": "Python", "desc": "This library empowers users to seamlessly port pretrained models and checkpoints on the HuggingFace (HF) hub (developed using HF transformers library) into inference-ready formats that run efficiently on Qualcomm Cloud AI 100 accelerators."}
+{"type": "node", "id": "repo:quic/efficient-transformers", "kind": "repo", "label": "quic/efficient-transformers", "url": "https://github.com/quic/efficient-transformers", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 90, "lang": "Python", "desc": "This library empowers users to smoothly port pretrained models and checkpoints on the HuggingFace (HF) hub (developed using HF transformers library) into inference-ready formats that run efficiently on Qualcomm Cloud AI 100 accelerators."}
 {"type": "node", "id": "repo:VectorInstitute/vector-inference", "kind": "repo", "label": "VectorInstitute/vector-inference", "url": "https://github.com/VectorInstitute/vector-inference", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 105, "lang": "Python", "desc": "Efficient LLM inference on Slurm clusters. "}
 {"type": "node", "id": "repo:mit-han-lab/Quest", "kind": "repo", "label": "mit-han-lab/Quest", "url": "https://github.com/mit-han-lab/Quest", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 396, "lang": "Cuda", "desc": "[ICML 2024] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference"}
 {"type": "node", "id": "repo:intel/neural-speed", "kind": "repo", "label": "intel/neural-speed", "url": "https://github.com/intel/neural-speed", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 353, "lang": "C++", "desc": "An innovative library for efficient LLM inference via low-bit quantization"}
@@ -706,7 +706,7 @@
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 {"type": "node", "id": "repo:ModelCloud/GPTQModel", "kind": "repo", "label": "ModelCloud/GPTQModel", "url": "https://github.com/ModelCloud/GPTQModel", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 1201, "lang": "Python", "desc": "LLM model quantization (compression) toolkit with HW acceleration support for Nvidia, AMD, Intel GPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang."}
 {"type": "node", "id": "repo:Lightning-AI/lit-llama", "kind": "repo", "label": "Lightning-AI/lit-llama", "url": "https://github.com/Lightning-AI/lit-llama", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 6082, "lang": "Python", "desc": "Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed."}
-{"type": "node", "id": "repo:intel/neural-compressor", "kind": "repo", "label": "intel/neural-compressor", "url": "https://github.com/intel/neural-compressor", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 2676, "lang": "Python", "desc": "SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime"}
+{"type": "node", "id": "repo:intel/neural-compressor", "kind": "repo", "label": "intel/neural-compressor", "url": "https://github.com/intel/neural-compressor", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 2676, "lang": "Python", "desc": "SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; widely-used model compression techniques on PyTorch, TensorFlow, and ONNX Runtime"}
 {"type": "node", "id": "repo:IST-DASLab/gptq-gguf-toolkit", "kind": "repo", "label": "IST-DASLab/gptq-gguf-toolkit", "url": "https://github.com/IST-DASLab/gptq-gguf-toolkit", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 64, "lang": "Python", "desc": "Efficient non-uniform quantization with GPTQ for GGUF"}
 {"type": "node", "id": "repo:wejoncy/QLLM", "kind": "repo", "label": "wejoncy/QLLM", "url": "https://github.com/wejoncy/QLLM", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 190, "lang": "Python", "desc": "A general 2-8 bits quantization toolbox with GPTQ/AWQ/HQQ/VPTQ, and export to onnx/onnx-runtime easily."}
 {"type": "node", "id": "repo:taishan1994/LLM-Quantization", "kind": "repo", "label": "taishan1994/LLM-Quantization", "url": "https://github.com/taishan1994/LLM-Quantization", "source": "github", "axis": "quant", "layer": 0, "maps_to_surface": "energy", "stars": 77, "lang": "Python", "desc": "\u8bb0\u5f55\u91cf\u5316LLM\u4e2d\u7684\u603b\u7ed3\u3002"}

brain/harvest/pass2_deepening.jsonl

@@ -340,7 +340,7 @@
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 {"type": "node", "id": "p2:repo:jeomon--meta-agent-with-more-agents", "kind": "repo", "label": "Jeomon/Meta-Agent-with-More-Agents (47\u2605) \u2014 The purpose of the \"Meta Agent with More Agents\" project is to dynamically solve complex queries by breaking them down into smaller tasks and assigning each to specialized AI agents. The Meta Agent co", "url": "https://github.com/Jeomon/Meta-Agent-with-More-Agents", "source": "github", "axis": "agents", "layer": 0}
 {"type": "node", "id": "p2:repo:anionex--llm-react-agent", "kind": "repo", "label": "Anionex/LLM-ReACT-Agent (7\u2605) \u2014 \u624b\u6413\u4e00\u4e2a\u57fa\u4e8eReact\u63a8\u7406\u6846\u67b6\u7684agent\uff0c\u652f\u6301\u6dfb\u52a0\u5de5\u5177 | Build a LLM agent using ReAct reasoning framework from scratch, supporting adding custom tools", "url": "https://github.com/Anionex/LLM-ReACT-Agent", "source": "github", "axis": "agents", "layer": 0}
-{"type": "node", "id": "p2:repo:cerno-ai--cerno-agentic-local-deep-research", "kind": "repo", "label": "Cerno-AI/Cerno-Agentic-Local-Deep-Research (68\u2605) \u2014 Cerno is a local-first research platform that leverages agentic AI to break down complex queries into verifiable, multi-step workflows. Switch seamlessly between cloud LLMs and self-hosted models, tra", "url": "https://github.com/Cerno-AI/Cerno-Agentic-Local-Deep-Research", "source": "github", "axis": "agents", "layer": 0}
+{"type": "node", "id": "p2:repo:cerno-ai--cerno-agentic-local-deep-research", "kind": "repo", "label": "Cerno-AI/Cerno-Agentic-Local-Deep-Research (68\u2605) \u2014 Cerno is a local-first research platform that leverages agentic AI to break down complex queries into verifiable, multi-step workflows. Switch smoothly between cloud LLMs and self-hosted models, tra", "url": "https://github.com/Cerno-AI/Cerno-Agentic-Local-Deep-Research", "source": "github", "axis": "agents", "layer": 0}
 {"type": "node", "id": "p2:repo:thepradip--nexussql", "kind": "repo", "label": "thepradip/NexusSQL (8\u2605) \u2014 The SQL agent that thinks before it queries ReAct reasoning, semantic caching, multi-LLM, 100+ tables, zero config", "url": "https://github.com/thepradip/NexusSQL", "source": "github", "axis": "agents", "layer": 0}
 {"type": "node", "id": "p2:repo:om-ai-lab--open-agent-leaderboard", "kind": "repo", "label": "om-ai-lab/open-agent-leaderboard (36\u2605) \u2014 Reproducible Language Agent Research", "url": "https://github.com/om-ai-lab/open-agent-leaderboard", "source": "github", "axis": "agents", "layer": 0}
 {"type": "node", "id": "p2:repo:balaji-r-05--visa-hackathon", "kind": "repo", "label": "Balaji-R-05/visa-hackathon (11\u2605) \u2014 An Agentic AI framework that combines deterministic rules with LLM reasoning for automated data quality auditing and compliance mapping.", "url": "https://github.com/Balaji-R-05/visa-hackathon", "source": "github", "axis": "agents", "layer": 0}
@@ -491,7 +491,7 @@
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 {"type": "node", "id": "p2:repo:gokulprasanth-m--vimbaformer-a-hybrid-vision-mamba-unet-architecture-for-uav-semantic-segmentation", "kind": "repo", "label": "GOKULPRASANTH-M/ViMbaFormer-A-Hybrid-Vision-Mamba-Unet-Architecture-for-UAV-Semantic-Segmentation (0\u2605) \u2014 ViMbaFormer is a hybrid semantic segmentation framework designed for high-resolution UAV imagery. It integrates Vision Transformers, Mamba State Space Models, and a U-Net\u2013style encoder\u2013decoder to", "url": "https://github.com/GOKULPRASANTH-M/ViMbaFormer-A-Hybrid-Vision-Mamba-Unet-Architecture-for-UAV-Semantic-Segmentation", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:harsh543--nemotron-vllm-l4-infra", "kind": "repo", "label": "harsh543/nemotron-vllm-l4-infra (0\u2605) \u2014  Nemotron-Hybrid vLLM Orchestrator This infrastructure is designed to serve the NVIDIA Nemotron-Nano-9B-v2, a hybrid model combining Transformer and Mamba-2 architectures. Running this on a single-nod", "url": "https://github.com/harsh543/nemotron-vllm-l4-infra", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
-{"type": "node", "id": "p2:repo:anany25--advanced-colorization-gan", "kind": "repo", "label": "Anany25/Advanced-Colorization-GAN (0\u2605) \u2014 A state-of-the-art image colorization model in PyTorch, featuring a hybrid Vision Transformer (ViT) and U-Net generator. This GAN operates in the LAB color space and is stabilized with modern techniqu", "url": "https://github.com/Anany25/Advanced-Colorization-GAN", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
+{"type": "node", "id": "p2:repo:anany25--advanced-colorization-gan", "kind": "repo", "label": "Anany25/Advanced-Colorization-GAN (0\u2605) \u2014 A high-performing image colorization model in PyTorch, featuring a hybrid Vision Transformer (ViT) and U-Net generator. This GAN operates in the LAB color space and is stabilized with modern techniqu", "url": "https://github.com/Anany25/Advanced-Colorization-GAN", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:arks0001--hymba-1.5b", "kind": "repo", "label": "ArkS0001/Hymba-1.5B (0\u2605) \u2014 The model has hybrid architecture with Mamba and Attention heads running in parallel", "url": "https://github.com/ArkS0001/Hymba-1.5B", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:fabian-bautista--mlx-hymba", "kind": "repo", "label": "fabian-bautista/mlx-hymba (0\u2605) \u2014 Run NVIDIA Hymba on Apple Silicon via MLX. Hybrid attention + Mamba SSM heads, native to M1/M2/M3/M4, no CUDA required.", "url": "https://github.com/fabian-bautista/mlx-hymba", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:dogacel--auto-gpu-kernel", "kind": "repo", "label": "Dogacel/auto-gpu-kernel (147\u2605) \u2014 Winner \ud83c\udfc6 (Agent-only) MLSys 2026 - FlashInfer AI Kernel Generation Contest for the DeepSeek Sparse Attention (DSA) track with an average speedup of 34.93x", "url": "https://github.com/Dogacel/auto-gpu-kernel", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
@@ -557,7 +557,7 @@
 {"type": "node", "id": "p2:repo:yixing-li--transmamba", "kind": "repo", "label": "Yixing-Li/TransMamba (6\u2605) \u2014 Official code for paper: TransMamba: A Sequence-Level Hybrid Transformer-Mamba Language Model (AAAI 2026 Poster)", "url": "https://github.com/Yixing-Li/TransMamba", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:lihongzhao99--rrgmambaformer", "kind": "repo", "label": "lihongzhao99/RRGMambaFormer (4\u2605) \u2014 RRGMambaFormer: A Hybrid Transformer-Mamba Architecture for Radiology Report Generation", "url": "https://github.com/lihongzhao99/RRGMambaFormer", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:vukrosic--hybrid-llm-mamba-transformer-research-3", "kind": "repo", "label": "vukrosic/hybrid-llm-mamba-transformer-research-3 (2\u2605) \u2014 hybrid-llm-mamba-transformer-research-3", "url": "https://github.com/vukrosic/hybrid-llm-mamba-transformer-research-3", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
-{"type": "node", "id": "p2:repo:varun3ware--paged-attention", "kind": "repo", "label": "VARUN3WARE/Paged-Attention (8\u2605) \u2014 Implementation of PagedAttention from vLLM paper - a breakthrough attention algorithm that treats KV cache like virtual memory. Eliminates memory fragmentation, increases batch sizes, and dramatically", "url": "https://github.com/VARUN3WARE/Paged-Attention", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
+{"type": "node", "id": "p2:repo:varun3ware--paged-attention", "kind": "repo", "label": "VARUN3WARE/Paged-Attention (8\u2605) \u2014 Implementation of PagedAttention from vLLM paper - a notable attention algorithm that treats KV cache like virtual memory. Eliminates memory fragmentation, increases batch sizes, and dramatically", "url": "https://github.com/VARUN3WARE/Paged-Attention", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:icecreammilkytea--pagedkvcomm", "kind": "repo", "label": "IceCreamMilkyTea/PagedKVCOMM (0\u2605) \u2014 Accelerating multi-agent system with paged attention and kv cache reuse .", "url": "https://github.com/IceCreamMilkyTea/PagedKVCOMM", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:mukhal--micro-vllm", "kind": "repo", "label": "mukhal/micro-vllm (2\u2605) \u2014 A single-file implementation of KV cache paged attention ", "url": "https://github.com/mukhal/micro-vllm", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
 {"type": "node", "id": "p2:repo:jnros--cuda-attn-ref", "kind": "repo", "label": "jnros/cuda-attn-ref (2\u2605) \u2014 Paged Attention: virtual memory for KV Cache in CUDA", "url": "https://github.com/jnros/cuda-attn-ref", "source": "github", "axis": "attention", "layer": 0, "maps_to_surface": "ringattn"}
@@ -673,7 +673,7 @@
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 {"type": "node", "id": "p2:repo:fijishi-enterprises--eco2ai-power-comsumption", "kind": "repo", "label": "Fijishi-Enterprises/ECO2AI-Power-Comsumption (1\u2605)", "url": "https://github.com/Fijishi-Enterprises/ECO2AI-Power-Comsumption", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy"}
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+{"type": "node", "id": "p2:repo:danieleschmidt--hf-eco2ai-plugin", "kind": "repo", "label": "danieleschmidt/hf-eco2ai-plugin (1\u2605) \u2014 A Hugging Face Trainer callback that logs CO\u2082, kWh, and regional grid intensity for every epoch. Built on Eco2AI's strong energy tracking.", "url": "https://github.com/danieleschmidt/hf-eco2ai-plugin", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy"}
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@@ -722,7 +722,7 @@
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+{"type": "node", "id": "p2:repo:raghavsingraur--energy-_efficient-_systems", "kind": "repo", "label": "raghavsingraur/Energy-_Efficient-_Systems (0\u2605) \u2014 The development and features of a Flask-based web application designed for testing and training purposes, integrating terminal functionality smoothly. Additionally, the application incorporates PyJo", "url": "https://github.com/raghavsingraur/Energy-_Efficient-_Systems", "source": "github", "axis": "energy", "layer": 0, "maps_to_surface": "energy"}
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@@ -1066,7 +1066,7 @@
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+{"type": "node", "id": "p2:repo:crewaiinc--crewai", "kind": "repo", "label": "crewAIInc/crewAI (55069\u2605) \u2014 Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together smoothly, tackling complex tasks.", "url": "https://github.com/crewAIInc/crewAI", "source": "github", "axis": "agents", "layer": 0}
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@@ -1074,7 +1074,7 @@
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+{"type": "node", "id": "p2:repo:run-llama--llama_index", "kind": "repo", "label": "run-llama/llama_index (50705\u2605) \u2014 LlamaIndex is the widely-used document agent and OCR platform", "url": "https://github.com/run-llama/llama_index", "source": "github", "axis": "agents", "layer": 0}
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@@ -2443,7 +2443,7 @@
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+{"type": "node", "id": "p2:paper:2506.12286", "kind": "paper", "label": "The SWE-Bench Illusion: When High-performing LLMs Remember Instead of Reason", "url": "https://arxiv.org/abs/2506.12286", "source": "arxiv", "axis": "agents", "layer": 0}
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