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Tim Curry has died

HackerNews · 08-27 00:03
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GLM-5.3-Flash

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Meta reaches $16.68B settlement over social media harms to children

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AWS Acquires DuckDB

产品设计 08-26
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Qwen3.8-Flash-Next: A New Architecture, Towards Ultimate Cost-Efficiency

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AI 资讯 Dev.to

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

FavCRM 2026-06-03 23:41 7 原文
产品设计 Dev.to

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

Raghul 2026-06-03 23:37 13 原文
AI 资讯 Dev.to

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

Sanket Bhor 2026-06-03 23:32 6 原文
AI 资讯 Dev.to

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

xbill 2026-06-03 23:30 12 原文
开发者 The Verge AI

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 […]

Jay Peters 2026-06-03 23:30 10 原文
开发者 Reddit r/webdev

Google address validation API weird bug?

I have found a weird case where I put in a real address (verifiable on google maps) it keeps correcting me to a wrong address (doesn't exist and cannot be found on google maps. What gives? If it's truly a bug, how do I let Google know? See below. https://preview.redd.it/x2b1ws07635h1.png?width=1336&format=png&auto=webp&s=18091e8ec57f96854611fef8578fd9e79e369d8b submitted by /u/flatcoke [link] [留言]

/u/flatcoke 2026-06-03 23:28 5 原文
AI 资讯 Reddit r/MachineLearning

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

/u/Dense-Map-406 2026-06-03 23:27 5 原文
AI 资讯 Dev.to

You can't delete an event. GDPR says you must. Crypto-shredding is the truce.

Two rules that can't both be true Event sourcing has one rule: you never delete. You append. The log is the source of truth, and rewriting history is the cardinal sin. GDPR Article 17 has one rule too: when a user asks, you erase their personal data. Not "hide it," not "flag it deleted" — erase it, everywhere, including backups. Put an event-sourced system in front of a privacy regulator and those two rules collide head-on. The user's name, email, and address are baked into CustomerRegistered , AddressChanged , OrderPlaced — dozens of immutable events, replicated to read models, snapshotted, and sitting in every nightly backup you've ever taken. "Just delete the events" breaks event sourcing. "Never delete" breaks the law. Most teams discover this tension after they've committed to append-only. A word on why this isn't academic for me. I build from Germany. Article 17 is EU law — the GDPR, or DSGVO as we call it here — not a German invention, but Germany enforces it about as hard as anywhere in Europe: regional data-protection authorities that issue real fines, and "we were careful" has never been a defense that held up. That pressure is exactly why I wanted erasure to fall out of the architecture instead of being a promise I make to an auditor and then pray I can keep. Why "delete the row" doesn't actually erase anything Say you give in and hard-delete the events for one user. You've still got their data in: every read-model projection rebuilt from those events, every snapshot that rolled them up, every backup taken before the deletion, every replica and every export that already left the building. Chasing personal data across all of those, provably, on a 30-day regulatory clock, is a nightmare — and a single missed backup tape means you didn't comply. Physical deletion doesn't scale to a system designed to keep everything forever. Crypto-shredding: delete the key, not the data The trick is to stop trying to delete the data and instead delete the ability to read it

Norbert Rosenwinkel 2026-06-03 23:24 11 原文
AI 资讯 Dev.to

A Deep Dive into Cleaning Persistent WordPress Malware and Hardening the REST API

The Hook: The 48-Hour Re-Infection Nightmare It’s a scenario that keeps e-commerce founders and agency directors awake at night: You wake up to a critical alert that your flagship WordPress site is redirecting users to a spam domain. You immediately deploy a premium security plugin, run a deep scan, quarantine three suspicious files, and breathe a sigh of relief. The scanner gives you a green checkmark. You're safe. Then, exactly 48 hours later, the redirects return. What went wrong? The automated scanner checked the surface, but the attacker had already established a foothold deeper in the architecture. They didn't rely on a loose PHP file in your uploads directory; instead, they weaponized an overlooked, unauthenticated WordPress REST API endpoint to re-inject the payload the moment your scanner turned its back. When high-value enterprise sites are compromised, treating the symptoms with standard security plugins is like putting a band-aid on a structural fracture. To truly remediate a persistent infection, you must think like a forensic analyst, hunt down hidden persistence mechanisms, and harden the application perimeter. The Anatomy of Persistence: Where Malware Hides Modern WordPress malware is sophisticated. Attackers know that standard security tools look for modified core files or rogue scripts in the /wp-content/plugins/ directory. To survive cleanups, they embed themselves into the core infrastructure of your site using three primary vectors: 1. wp-config.php Pre-Loading Attackers frequently inject obfuscated code directly into the top of wp-config.php . Because this file executes before the rest of the WordPress core loads, malware can hook into the initialization process, silently recreating deleted malicious files every time a page is requested. 2. Malicious Must-Use (MU) Plugins Files placed in /wp-content/mu-plugins/ are executed automatically by WordPress and cannot be disabled from the admin dashboard . Attackers love this directory. They will ofte

Jahid Shah 2026-06-03 23:24 12 原文
AI 资讯 Dev.to

Show DEV: Obex, a faith-based self-control app with streak tracking and blockers

I’m building Obex, a faith-rooted self-control app for men who want to quit porn and stay consistent with daily discipline. The stack is Expo / React Native, with a web landing page and a desktop blocker companion. Core features: Streak tracking and rank progression Panic Mode for urgent moments Accountability partners Blocker support on desktop Christian-focused language and reminders The goal is to make the product feel practical rather than preachy. People usually respond better to clear feedback loops, a visible streak, and a calm recovery path after setbacks. If you want to see it or give feedback, the site is here: https://obex.so

Luca 2026-06-03 23:20 10 原文
AI 资讯 Reddit r/artificial

AI tools for hearing difficulties — helpful or harmful for language learning?

Hi everyone! I have hearing difficulties, and I also live in an English-speaking environment while having only been learning English for a few years. In one-on-one conversations, I can usually understand maybe 25–35% of what is being said. But in group conversations, it drops to something like 0–2%. It is extremely frustrating and isolating. AI has honestly been helping me survive day-to-day life. For example, I can record a lecture using Otter, copy the transcript, paste it into ChatGPT, and ask it to give me a detailed summary with explanations, key points, and advice on what I should focus on. I have two questions: - Do you have any advice on how AI could make life easier or more accessible for someone with hearing difficulties - Seriously, how harmful could this pipeline be for getting used to English and improving my listening skills? I am afraid that I might stop training my ear and become completely dependent on recordings and transcripts instead of actually listening to the language. I would really appreciate your thoughts, experiences, advice, or even tool recommendations. Thank you for your support. submitted by /u/uarish [link] [留言]

/u/uarish 2026-06-03 23:18 5 原文
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