@Tonyjoixt · X

Instant SEO Meta Descriptions

@jrzscodes · X

Have you seen

@BobbySBX · X

We just dropped Assay v0.2.6, the open source evaluation engine powering Most registries judge an AI tool by its README. We built Assay to actually run skills, MCP servers, and plugins inside isolated sandboxes with live models to observe what they do before you install them. What is new in v0.2.6: - NVIDIA API Catalog provider: Native support is here, allowing you to use NVIDIA driver and judge models to run behavioral benchmarks and multi-tier audits. - Zero runtime dependencies: Still a self-contained ~240 KB tarball that speaks directly to @nvidia , @AnthropicAI , @OpenAI , @OpenRouter , and local HTTP endpoints without SDK bloat. - Adversarial and static checks: Out of the box verification against prompt injection, tool scope creep, destructive shell commands, and supply chain drift. Every tool on is tested through Assay. That means 4,200+ skills, MCP servers, and plugins are commit-pinned, graded on actual runtime behavior, and rea

@neimansview · X

vatsalaggarwal · HN

Production duplex speech model for revenue calls

zxhywork · V2EX

Muse Image AI Meta Superintelligence Labs 7 月 7 号发布了他们第一个图像模型 Muse Image ，内部代号 Mango 。玩了两天，这个模型有几个点确实有意思： - **Agentic 生成**：出图前先规划布局，需要事实的场景会联网搜，画完自查一轮再交稿，长 prompt 一次过的概率明显高 - **图内文字能看了**：海报标题、图表、甚至可扫的二维码都能直接画进图里，这是大部分生图模型至今糊掉的地方 - **多参考图合成**：从几张照片里分别取人脸、服装、产品、背景，融成一张不违和的图 官方入口在 Meta AI App / meta.ai / Instagram Stories ，国内用着别扭，还得装 App 。我就照着上一个站的路子做了个纯网页版：[museimages.io]( https://museimages.io/)。 产品上砍得很狠，只做图像，三个入口： - [Meta Muse Image 在线生成器]( https://museimages.io/ai-image-generator)

@damosdjoro · X

@ninakolari · X

Google just dropped Stitch and it changes the entire vibe coding workflow. You describe what you want your app to look like. It builds full high-fidelity screens in seconds. No Figma. No designer. No credit card. Here is how to use it: Go to Sign in with your Google account. That’s it. You’re in. Instead of typing “make me an app” type this: “A lesson planning app for teachers. Clean and calm. Inspired by Notion. Minimal. White space.” The more specific you are the better your screens. Then refine with follow up prompts. Change the nav. Try dark mode. Get 3 menu variations. Or just speak it. Your screens become a clickable prototype in seconds. Click a button and Stitch generates the next screen automatically. Your entire app flow. Before you write one line of code. Then take it further. Export to Lovable or Cursor and build the logic on top. Use the MCP server to keep your design and code in sync. Pull any website’s design system in seconds. Save everything as

chenglong-hn · HN

Data visualizations are the bridge between user and data. But building AI agents that can generate visualizations reliably can be very tricky: - simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple

@describegames · X

We are seeing so many games on the timeline built with Claude Fable But what happens on July 7 when Fable becomes too expensive for most people, and everyone still wants to build cool AI games? is your answer.

@SpalaAi · X

@EcoSEOone · X