AI 资讯
Tool count is a vanity metric. Annotation coverage is what makes an AI agent safe.
Syndicated from the FavCRM blog . The number that predicts whether an agent is safe to let loose isn't the tool count. When people compare agentic CRMs, they count tools. The number that actually predicts whether an agent is safe to let loose is a different one: annotation coverage . An MCP tool annotation tells the agent what a tool does to the world — whether it reads or mutates, whether it's safe to retry, whether it reaches an external service. Without annotations, the agent is guessing. This is what they are, and why a catalog's annotation coverage matters more than its tool count. What an MCP annotation is Every MCP tool can carry hints alongside its input and output schemas: readOnlyHint — the tool only reads; it changes nothing. Safe to call freely. destructiveHint — the tool mutates or deletes. The agent should confirm before calling. idempotentHint — calling it twice with the same input has the same effect as once. Safe to retry on a timeout. openWorldHint — the tool reaches an external service (sends an email, charges a card), so its effects leave the system. These are not documentation for humans. They are machine-readable signals the agent reasons over before it acts. Why they prevent the worst failures The dangerous class of agent failure is not "the agent couldn't do something." It's "the agent did the wrong destructive thing because it misread an ambiguous instruction." Delete the customer instead of the tag. Refund the wrong invoice. Cancel every booking instead of one. Annotations let the agent self-gate. A well-annotated catalog means the agent calls list_members without ceremony but pauses to confirm before cancel_booking , because one is marked read-only and the other destructive. Pre-MCP function-calling had no equivalent — every tool looked the same to the model, so safety lived entirely in the prompt. Why coverage matters more than count A 190+ tool catalog with 100% annotation coverage is safer than a 30-tool catalog with none. A tool that l
产品设计
HTML TAGS & CSS PROPERTIES
What are HTML Tags? HTML documents consist of a series of elements, and these elements are defined using HTML tags. HTML tags are essential building blocks that define the structure and content of a webpage. HTML tags are composed of an opening tag, content, and a closing tag. The opening tag marks the beginning of an element, and the closing tag marks the end. The content is the information or structure that falls between the opening and closing tags. For Example: <h1>Hello</h1> HTML Elements HTML elements are the essential components of a webpage and provide structure, organization, and meaning to content. Elements are defined by HTML tags which define how different types of content will appear in a browser window. For Example: <p> This is an element. </p> Block-Level Elements A page’s entire width is occupied by a block-level element. The document always begins with a new line. An HTML page generally has three tags i.e., <html> , <head> , and <body> tag. Example: The following is an unordered list, an example of block-level elements. List item 1 List item 2 Inline Elements A block-level element’s inner content can be formatted with an inline element by adding links and stressed strings. These elements help you to format text without disrupting the content’s flow. Example: The following code creates a hyperlink to a URL. It is an inline element because it is used within paragraphs, headings, or other text content to create hyperlinks. It does not disrupt the flow of the document by forcing new lines before or after its content. <a href="https://www.example.com"> Visit Example </a> CSS Properties CSS properties are used to decorate your web page and assign a unique behavior to your HTML element. CSS properties are the foundation of web design, used to style and control the behaviour of HTML elements. They define how elements look and interact on a webpage. Used to control layout, colors, fonts, spacing, and animations on web pages. It is essential for making web pa
开发者
Generating OG images in Elixir
submitted by /u/joladev [link] [留言]
AI 资讯
You're Not Paying for Code Generation. You're Paying for Context
The hidden cost of AI isn't generating code. It's understanding your codebase. For a long time, I assumed AI coding tools became expensive because they generated a lot of code. These tools can produce components, tests, SQL queries, documentation, and sometimes entire features on demand. If costs were climbing, the output volume must be the reason. The more I used these tools, the more I realized I was measuring the wrong thing. The expensive part isn't writing code. The expensive part is understanding what code should be written — and that work is mostly invisible. That realization changed how I think about AI-assisted development entirely. Two Prompts, Two Very Different Problems Consider these two requests: "Create a utility function that formats dates" and "Review this feature and suggest improvements." At first glance, both look ordinary. Both might even produce short answers. But they require completely different levels of understanding. The first is narrow and well-defined. The AI needs very little information before it can produce a useful answer. The second is open-ended. Before suggesting a single improvement, the AI may need to read multiple files, understand dependencies, follow existing patterns, compare implementations, and build a mental model of why the feature exists at all. The output might still be small. The work required to reach it is not. Why Agent Workflows Feel Different From Autocomplete This became much clearer when I started using AI agents. Traditional autocomplete is predictive — you type, the AI guesses what comes next. It's fast, cheap, and deliberately context-light. Agents behave differently. When you ask one to improve a feature or review a workflow, it doesn't immediately start generating code. It starts reading. It follows imports, finds related files, and tries to understand the system before touching it. That is exactly what makes agent workflows feel slower and more resource-intensive than autocomplete: they are spending effor
AI 资讯
Still facing copyright lawsuits, AI music generator Suno raises another $400M
The prominent AI music generation startup is now valued at over $5.4 billion -- about seven months ago, it raised at a $2.45 billion valuation.
AI 资讯
Cross Cloud A2A Agent Benchmarking
Building a Benchmarking Agent with A2A and MCP This tutorial aims to build and test benchmarking Agents using the A2A protocol across several mainstream Cloud providers. A Master Orchestrator Agent is exposed via MCP to allow Antigravity CLI to be used as a MCP client to co-ordinate the benchmarks. Deja Vu — What is Old is New! This paper is a re-visiting of the original benchmark series with Gemini CLI over Node, GO, and Python: Cross Language A2A Agent Benchmarking with Gemini 3 and Gemini CLI In this updated version, the Antigravity CLI is used to push Rust Agents cross-cloud and co-ordinate Mersenne Prime Calculations. Why would I need Multi-Cloud Support? And Rust? Can’t I just use Python? Most mature Agent development tools and libraries are Python based. Python allows for rapid prototyping and evaluation of approaches. Python is also an interpreted language- which has trade-offs in memory safety, and performance. Other languages like GO and Rust offer high performance and memory safe operations. With a language neutral communication protocol — the actual Agent implementation of each Agent can be coded in the most appropriate language. What is this Approach actually Benchmarking? The high level goal was to measure the actual time spent running an algorithm in the native language code inside the A2A agent. Each language had a slightly different implementation due to the language syntax. After running the algorithm- each Agent was instructed to calculate and return the elapsed time for cross cloud comparison. What is the A2A protocol? The Agent2Agent (A2A) protocol, an open communication standard for AI agents, was initially introduced by Google in April 2025. It is specifically engineered to facilitate seamless interoperability within multi-agent systems, enabling AI agents developed by diverse providers or built upon disparate AI agent frameworks to communicate and collaborate effectively. A good overview of the A2A protocol can be found here: A2A Protocol Lan
开发者
PlayStation is getting back to what it’s good at
PlayStation used its most recent State of Play showcase to make it clear where its focus is. After a series of costly live-service stumbles, it's getting back to focusing on premium, narrative-driven, single-player games. That statement was made clear with how it started and ended the hourlong show. The showcase began with an extended look […]
AI 资讯
A semantic tokenization scheme where token geometry reflects semantic relationships [R]
I have been thinking about an alternative tokenization and representation scheme for language models and would be interested in hearing whether similar ideas have been explored before, as well as potential advantages or flaws. The core observation is that modern tokenizers (BPE, SentencePiece, etc.) primarily capture statistical structure in text. While this is highly effective, the resulting token assignments are not explicitly organized according to semantic relationships. Concepts that are semantically related may end up with completely unrelated token identifiers, and semantic structure is learned later through embeddings and training. The idea is to construct a tokenization scheme in which the symbolic representation itself carries semantic information. For example, instead of assigning arbitrary identifiers to concepts, we could learn a mapping from concepts to short character strings such that semantically similar concepts receive similar codes. A concept like “dog” might receive a code close to those assigned to “wolf” and “fox”, while more distant concepts such as “car” would receive codes that are farther away in the code space. One possible implementation would be: 1) Build a semantic graph using resources such as WordNet, embedding similarity, or a combination of both. 2) Learn a compact symbolic encoding for concepts. 3) Optimize the encoding so that distances between codes correlate with semantic distances in the graph. 4) Train language models directly on these codes. An extension of the idea is to treat a standard keyboard layout as a fixed geometric space. The keyboard itself is not semantically meaningful, but it provides a globally agreed-upon metric structure. The learned encoding could exploit distances between characters and positions when constructing semantic codes. For example, if two concepts are semantically close, their symbolic representations would differ only slightly. Ambiguous concepts could potentially occupy positions that reflect
科技前沿
Autonomous vehicles were supposed to cut traffic—what if they don't?
Data shows Waymo's robotaxis are empty for almost half of the miles they drive.
开发者
offset-path
The offset-path property in CSS defines a movement path for an element to follow during animation. This property began life as motion-path . This, and all other related motion-* properties, are being renamed offset-* in the spec . We’re changing … offset-path originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.
科技前沿
Greg Bovino Was the Star at a European Remigration Conference
The man who headed Trump’s invasions of US cities joined the US and European far right in Portugal to preach “remigration”—a plan to expel all minorities and immigrants.
AI 资讯
Publishers will be able to opt out of AI Search, thanks to new regulation
U.K. regulators are requiring Google offer a tool allowing website publishers to opt-out of generative AI search features. The option will be tested in the U.K. then rolled out globally.
开发者
Every byte matters
submitted by /u/lelanthran [link] [留言]
AI 资讯
Inside Google’s System for Coordinated A/B Testing Across Its Global Service Fleet
Google has shared details of its fleet wide large scale A/B experimentation system designed to standardize experiment assignment, exposure logging, and configuration propagation across distributed services. The approach enables consistent measurement across products, reduces experiment conflicts, and improves reliability of data driven decision making at scale. By Leela Kumili
AI 资讯
Dreame’s L20 Ultra robovac is an unbeatable deal for $280
The Dreame L20 Ultra isn’t the company’s newest model, but it’s still a great robovac / mop hybrid that offers strong performance while requiring very little day-to-day maintenance thanks to its included trash bin and AI obstacle avoidance. Verge readers can get for its best-ever price right now. Originally $1,400 when it launched in 2023, […]
开发者
[Sebastian Lague] - I Tried Optimizing my Rubik's Cube Solver
submitted by /u/Pink401k [link] [留言]
开发者
How I took my Rust GUI from 135 MB to 30 MB by ditching the GPU
submitted by /u/TryallAllombria [link] [留言]
开源项目
Encodec.cpp, a portable C++ implementation of Meta's EnCodec using Eigen [P]
I built a C++ implementation of Meta’s EnCodec using Eigen . Github: https://github.com/pfeatherstone/encodec.cpp Motivation: - A lightweight implementation of EnCodec with no runtime dependencies, in C++ - No ML runtime - Easy integration in CMake project - Maximum performance on single-thread What it supports: - State-of-the-art audio codec - Audio tokenizer - Performance comparable to or exceeding onnxruntime (in my tests) - Dynamic sizes (no batches though) - Weights are compiled into the binary. No need to worry about weights files I'm looking for some feedback. Thank you very much. submitted by /u/Competitive_Act5981 [link] [留言]
AI 资讯
Microsoft and OpenAI broke up — now they’re ready to fight
At Microsoft's annual Build conference on Tuesday, the company announced a slew of new or expanded AI initiatives, including a super app, in-house reasoning models, a cybersecurity tool, and OpenClaw-esque AI agents. All this news added up to a clear message: Microsoft is positioned to be one of the biggest players in AI, and it's […]
开源项目
Meet Wander, a StumbleUpon-inspired tool for discovering the ‘small web’
This open-source community project lets you create a StumbleUpon-like experience for recommending your favorite sites.