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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
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
The company now expects to ship a few thousand more vehicles by the end of 2026 than it previously expected, after launching its R2 SUV last month.
Originally published on productize.life . Quick answer: pm-skills is a marketplace of around 68 Claude skills for product management across 9 plugins, from strategy and discovery to market research and AI shipping. It is built by Pawel Huryn, author of the Product Compass newsletter. Each skill is not a loose prompt but a named, sourced framework, and one of them audits the gap between documentation and code, a PM lens built for the era of AI-written code. Last week I was reading through a run of repos that pack product work into skills. Some pick one topic and go deep. This one does the opposite: it is the broadest of the bunch. It is called pm-skills, by Pawel Huryn, the author of the Product Compass newsletter. He packs almost the entire product management craft into around 68 skills across 9 plugins, from setting strategy, running discovery, and researching the market, to analyzing data, executing, and shipping software that AI wrote. Usually something this broad ends up shallow. But when I actually opened it, it was not, and one skill in particular made me stop and look for a while , because it covers an angle that only recently became necessary in the era where AI writes code for us. I will tell it in three parts, starting with what it is , then why it is not just a prompt box , and closing with lessons for anyone building products . Terms, gathered once, right here skill a ready-made set of instructions an AI agent (such as Claude Code) can invoke, like a shortcut that wraps one way of doing a task. framework a ready-made way of thinking from the PM world, such as SWOT, JTBD, or RICE, that you once had to read a book to use well. plugin (category) a group of skills that belong to the same topic, such as the discovery category or the go-to-market category. PRD a product spec document that says what will be built, for whom, and how success is measured. Part 1: What pm-skills is It is a marketplace of around 68 Claude skills for PM, organized into 9 plugins, eac
Originally published on productize.life . Quick answer: gstack is an open-source (MIT) skill set that Garry Tan, president of Y Combinator, builds with every day. It turns Claude Code into a team of 23 specialists, CEO, engineers, designers, QA, and a release engineer, forcing every change through a multi-lens review before shipping. The point is not speed; it is taste written into software. Last week I was going through a repo that collects skills for coding, several of them. Most share one theme: helping AI write code in a systematic way, and faster. But one made me stop longer than the rest, called gstack, for two reasons. One: its owner, Garry Tan, president and CEO of Y Combinator, took the stack he actually builds with every day and opened it for free. Two: it does not sell "code faster," it sells "review before you ship." Once I actually opened it, it was not just a toolbox but one of the clearest examples of an idea I have been interested in for a while. On the day AI can write code very fast, the bottleneck of the work is no longer speed. I will tell it in three parts, starting with what it is , then what gstack believes , and closing with lessons for people who build products, not just people who write code . Terms, gathered here in one place agentic coding letting an AI agent run the coding work in its own steps, from planning to writing to review to shipping, not just autocompleting a line at a time. skill a packaged set of instructions an AI agent (like Claude Code) can call, like a shortcut that wraps one way of doing one thing. review lens reviewing one piece of work from several roles, for example as a CEO, an engineer, a designer. taste the sense and judgment of what is good and what is bad, what to build and what not to ship. The part that is still human. Part 1: What gstack is Garry Tan describes gstack in the README plainly, as the way he works. "It turns Claude Code into a virtual engineering team: a CEO who rethinks the product, an eng manager
I am working in a research group in Germany, not that well known but in general good output. I have one colleague who does nothing in his PhD. He does not want to work, or he is not able to do any good research, his level is super bad. Plus He doesn’t even care about that. To wrap it up, he is just here for the money. Since he doesn’t want to work or he can’t really do anything good, instead what he does is “paper fishing”, he searches for people in the group doing some good research, and asks that they put his name on the paper. In this case he has something to cover up for him when the professor asks him about his progress. As long as his name is on the paper, progress is checked and funding is renewed. But he actually does nothing. I know this is very unprofessional and unethical. But people tell me it’s normal in academia. Professors all the time put names of their friends and this is how it works in academia. What are your thoughts of this behaviour? submitted by /u/impressivestatus21 [link] [留言]