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LLM Answers & Content Strategy
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monitorcmd: A Zero VRAM Usage Windows Command-Line GPU Monitoring Tool Optimized for LLM Inference
A Windows console monitoring tool that uses no VRAM at all. It displays GPU VRAM, utilization, and system resource status in real time via plain text, and can run in parallel with inference frameworks like Ollama, llama.cpp, vLLM without affecting VRAM usage.
Intent Preservation Benchmark: Evaluating Large Language Models' Ability to Preserve Human Intentions in High-Risk Scenarios
An open-source benchmark test and evaluation framework for measuring large language models' ability to preserve human intentions in high-risk environments (such as healthcare, government, and finance), helping researchers identify the risk of intent deviation in AI systems before deployment.
Agent Skills: Cross-platform Reusable AI Agent Workflow Skill Library
A set of project-independent reusable Agent workflow skills supporting platforms like GitHub, Codex, and Claude, dedicated to solving the standardization and reuse issues of AI Agent capabilities.
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Sora: A Lightweight Framework for Building Modern Agentic AI Workflows
A project focused on building modern Agentic AI workflows, dedicated to simplifying the orchestration and collaboration of AI Agents.
Antigravity Telegram Suite: A Complete Solution for Remotely Controlling AI Agents via Mobile Phones
A Telegram bot project that allows users to remotely control AI Agents through mobile chat interfaces, supporting model switching, multi-Agent workflow orchestration, and workspace management.
ForgeOps AI: An Intelligent Platform Reconstructing DevOps Workflows with Large Models
An intelligent DevOps platform based on large language models, capable of analyzing CI/CD failures, troubleshooting Docker and Kubernetes issues, generating infrastructure code, and automating cloud-native workflows.
Kade-ai: An End-to-End AI Chat Platform for Professional-Grade Dialogue Experiences
A fully functional AI chatbot platform that supports real-time streaming responses, conversation tracking, collapsible reasoning panels, clarifying questions, and code highlighting, with responsive interface and theme switching capabilities.
Loa Laplas: An Orchestration Engine for Compiling AI Compositions into Executable Agent Workflows
This article introduces the Loa Laplas project, an orchestration tool designed for the Loa engine. It can compile high-level AI composition descriptions into executable, gate-controlled agent workflows, providing a new solution for the development and deployment of complex AI applications.
NDH Unified Cognitive Engine: An Innovative Reasoning Framework Based on Conceptual Quantum Computing
This article introduces the NDH Unified Cognitive Engine project, a unique conceptual quantum computing framework that uses conceptual qubits, tensor computing, and multi-dimensional harmonics to model complex reasoning processes, providing a novel theoretical perspective and technical tools for understanding and simulating high-complexity cognitive systems.
LLM Inference Engine Implemented Purely in Zig: A High-Performance, Lightweight Open-Source Alternative
This article introduces the SMC17/inference project, an LLM inference engine implemented from scratch using the Zig language. It supports modern optimization techniques such as paged attention, BF16 kernels, and persistent thread pools, providing developers who pursue extreme performance and controllability with a new alternative outside the Python ecosystem.
White-box Adversarial Attacks Reveal Social Bias Vulnerabilities in Large Multimodal Models
This article introduces a white-box adversarial attack study targeting social bias issues in Large Multimodal Models (LMMs). The project provides complete code implementations, including targeted PGD attacks, universal adversarial perturbations, defense evaluation, and noise similarity analysis, serving as an important tool for AI safety research.
Hands-On Multi-Modal RAG System: A Local Document Intelligent Q&A Solution Based on Qwen2-VL and CLIP
This article introduces an open-source multi-modal Retrieval-Augmented Generation (RAG) system that combines the Qwen2-VL vision-language model and CLIP encoder. It supports mixed text-image retrieval for PDF documents and provides a complete technical solution for building localized, privacy-controllable intelligent document Q&A systems.
Building a DNA Large Language Model from Scratch: An Exploration of the Fusion of Bioinformatics and Deep Learning
A complete implementation of a DNA sequence large language model, covering the entire workflow from tokenizer construction, BPE algorithm, embedding layer design, Transformer architecture to prediction and evaluation.
Claude Workflow: An Agent Workflow Engine Based on YAML State Machines
This article introduces a plugin designed for Claude Code that uses YAML-defined state machines to drive AI agents in executing complex tasks, supporting state tracking, conditional guards, nested sub-workflows, and a visual dashboard.
A Lightweight Validation Framework for Trustworthiness Assessment of Open-Source Large Language Models
This article introduces a trustworthiness assessment framework for open-source large language models (LLMs), which constructs a lightweight and reproducible evaluation system from three dimensions: security, authenticity, and consistency, supporting local deployment and low-cost validation.
n8n Dependency EOL Firewall: A Multi-Agent Automated Dependency Governance Solution
This article introduces an n8n-based multi-agent workflow system that monitors the health status of code repository dependencies, automatically detects risks such as deprecation, end-of-life (EOL), and version lag, and generates migration PRs automatically.
LLM Playground: An Interactive Experimental Platform for Large Language Model Behavior
This article introduces an interactive application for experimenting with and exploring the behavior of large language models (LLMs). It supports features like temperature adjustment, context management, token usage monitoring, and API inference, helping developers gain an in-depth understanding of LLM response characteristics.
Application of Agentic AI in Football Research: How Multi-Agent Systems Revolutionize Sports Data Analysis
Explore how the agentic-football-ai project combines large language models (LLMs), RAG, vector databases, and multi-agent workflows to bring a new paradigm of intelligent analysis to the field of football research.
Tokamak: A Minimalist LLM Inference Engine Built from Scratch
Tokamak is a lightweight LLM inference engine implemented from scratch using PyTorch. It fully incorporates core optimization techniques such as KV caching, paged attention, continuous batching, and speculative decoding, and has undergone performance benchmarking against vLLM.
disp8ch: A Local-First AI Workspace That Turns Conversations into Visual Multi-Agent Workflows
disp8ch is a self-hosted AI workspace that upgrades the traditional chat interface into a system for visual workflow orchestration, multi-agent collaboration, and auditable automation. It supports full local operation, requires no API keys, and all data is stored locally.
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