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We’ve got a special Decoder today — I had the chance to talk with Amy Lanzi, the CEO of Digitas North America, in front of a live audience at the Uber Villa at the Cannes Lions advertising festival in the South of France. I know, it’s a hard gig, but I do it for you. […]

A desktop pet that grows as you code with Claude Code Discussion | Link

Microsoft follows Amazon, OpenAI, and Anthropic with its new AI deployment group.

HDMI excels for media consumption, while PC gamers prefer DisplayPort.

Financial disclosures show how much Trump loves tech stocks.

The company delivered more than 480,000 EVs globally, seemingly thanks to geographic expansion and cheaper versions of the Model 3, Model Y, and Cybertruck.

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 | 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 | 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 | 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 | Virtual whiteboard for sketching hand-drawn like diagrams | Stars: 126,627 | 84 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

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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

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

Fake Mr Beast "promotions" are ubiquitous on Threads - they're also part of massive crypto scam network.

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

Why Our AI Agent Still Stumbles on Full-Stack Apps We've all been there. You're riding high on the AI hype, picturing your agent effortlessly spinning up features, leaving you free for higher-level architectural decisions. You feed it a prompt like, "Build me a simple user profile page with authentication, connected to a database, using Next.js and TypeScript." You hit enter, grab a coffee, and expect magic. More often than not, what you get back is… well, it's something . It might be syntactically correct, perhaps even impressive in parts. But when you try to integrate it, to make the pieces talk to each other harmoniously, it often feels like trying to connect a square peg to a round hole. The agent struggles, and frankly, so do we trying to fix its output. The Seams, Not Just the Parts: Why Full-Stack is More Than Sum of Its Halves In my experience, AI agents, especially Large Language Models, are fantastic at generating code for isolated problems. Need a React component? A SQL query? A utility function? They'll often nail it. But a full-stack application isn't just a collection of frontend, backend, and database parts. It's the intricate, often implicit, contracts between them. Think about a modern Next.js application. It’s a beautifully complex dance: Server Components vs. Client Components: This paradigm shift fundamentally changes where state lives, where data is fetched, and how interactivity is handled. An AI might generate a useState hook inside a Server Component, completely missing the architectural intent. Data Fetching Strategies: getServerSideProps , getStaticProps , route handlers , fetch directly in Server Components – each has specific implications for caching, performance, and where your data lives at runtime. An AI might pick an inefficient or incorrect strategy based on a simplified prompt. Type Safety Across Boundaries: TypeScript is a lifesaver, but defining types that perfectly mirror your database schema, API responses, and frontend state re