标签：#c

Hey HN, A few months ago, I tried to automate some of my work with the popular AI agent OpenClaw, and then I quickly realized how difficult it is to get it to work with APIs and third-party services securely, which is essential for a lot of work-related tasks. Then I realized OpenClaw is more of a personal assistant and it was not designed to get actual work done as a coworker. So I started to build Valmis, an alternative to OpenClaw that works with more than 100 apps and services, with security

GitHub热门项目 | Agentic Coding for Builders who Ship | Stars: 9,908 | 34 stars today | 语言: Rust

GitHub热门项目 | Turn code into clarity. Generate accurate technical docs and AI-ready context in minutes—perfectly structured for human teams and intelligent agents. | Stars: 1,289 | 41 stars today | 语言: Rust

GitHub热门项目 | Privacy first, AI meeting assistant with 4x faster Parakeet/Whisper live transcription, speaker diarization, and Ollama summarization built on Rust. 100% local processing. no cloud required. Meetily (Meetly Ai - https://meetily.ai) is the #1 Self-hosted, Open-source Ai meeting note taker for macOS & Windows. | Stars: 13,224 | 132 stars today | 语言: Rust

GitHub热门项目 | A WiFi security auditing software mainly based on aircrack-ng tools suite | Stars: 677 | 146 stars today | 语言: Rust

GitHub热门项目 | Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞 | Stars: 381,422 | 240 stars today | 语言: TypeScript

GitHub热门项目 | A modern GUI client based on Tauri, designed to run in Windows, macOS and Linux for tailored proxy experience | Stars: 129,170 | 200 stars today | 语言: TypeScript

GitHub热门项目 | Virtual whiteboard for sketching hand-drawn like diagrams | Stars: 126,627 | 84 stars today | 语言: TypeScript

GitHub热门项目 | Specification and documentation for Agent Skills | Stars: 21,425 | 47 stars today | 语言: Python

GitHub热门项目 | Langflow is a powerful tool for building and deploying AI-powered agents and workflows. | Stars: 150,426 | 74 stars today | 语言: Python

GitHub热门项目 | Tensors and Dynamic neural networks in Python with strong GPU acceleration | Stars: 101,079 | 45 stars today | 语言: Python

Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational…

Even the most advanced enterprise systems are tethered to a costly paradox: manual bottlenecks that introduce critical errors, security risks, and slow innovation. These hidden operational anchors are the friction preventing your organization from realizing its full potential. The Challenge: Manual Bottlenecks in Modern Enterprise Operations In an era defined by cloud-native architectures, microservices, and declarative infrastructure, a persistent and costly paradox remains at the heart of enterprise operations. We have built systems capable of immense scale and resilience, yet they are often tethered to manual, human-driven processes that act as operational anchors. These bottlenecks aren't just minor inefficiencies; they are critical points of failure, introducing latency, human error, and security vulnerabilities into our most important workflows. They represent the friction that slows down innovation, drains resources, and prevents organizations from realizing the full potential of their digital investments. Before we can orchestrate an autonomous workspace, we must first dissect the anatomy of these manual constraints. Identifying the High Cost of Manual Invoice Reconciliation To ground this challenge in reality, consider a ubiquitous and deceptively complex business process: accounts payable invoice reconciliation. On the surface, it seems simple. In practice, it's a classic example of a high-friction, manual workflow that silently bleeds enterprise resources. The typical process is a gauntlet of context-switching and swivel-chair integration: An invoice arrives, often as a PDF attached to an email, with no standardized format. A finance professional must manually open the document and visually identify key data points: invoice number, date, vendor, line items, and total amount. They then pivot to an ERP system like SAP or NetSuite to find the corresponding Purchase Order (PO). Next, they might need to access a separate logistics or warehouse management syste