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🔥 Anionex / banana-slides - 一个基于nano banana pro🍌的原生AI PPT生成应用，迈向＂Vibe PPT＂; 支持上传任意模板图片，上

GitHub热门项目 | 一个基于nano banana pro🍌的原生AI PPT生成应用，迈向＂Vibe PPT＂; 支持上传任意模板图片，上传任意素材&智能解析，一句话/大纲/页面描述自动生成PPT，口头修改指定区域、一键导出可编辑ppt - An AI-native slides generator based on nano banana pro🍌 | Stars: 15,301 | 72 stars today | 语言: TypeScript

2026-07-25 原文 →
AI 资讯

How I compile React-shaped TSX without React or hydration

I started building Kudzu while making static websites with AI. AI coding tools have become very good at producing React-shaped TSX, and I have become used to reviewing code in that form. Function components, props, JSX, and event handlers are often easier for me to understand and verify than scattered DOM queries and imperative JavaScript mutations. But I was still building static pages. I wanted to keep TSX as the authoring and code-review format without automatically shipping React, a virtual DOM, hydration, or a browser-side component tree. Kudzu grew from that idea: Write familiar TSX, execute components during the build, and ship ordinary HTML with only the JavaScript each route actually needs. Kudzu is an experimental, HTML-first TSX framework. Website: kudzujs.cloud GitHub: github.com/kudzujs/kudzu The problem I wanted to solve Consider a blog, documentation site, newsletter, or product landing page. Most of the page is already known during the build: headings; navigation; articles; images; metadata; product descriptions; documentation content. TSX is a convenient way to author and review that structure. function PostCard ({ title , description , href }: { title : string description : string href : string }) { return ( < article > < h2 >< a href = { href } > { title } </ a ></ h2 > < p > { description } </ p > </ article > ) } The component model is useful for authoring, but that does not necessarily mean the browser needs a component runtime. For a static page, I wanted the output to remain ordinary HTML. <article> <h2><a href= "/posts/hello" > Hello </a></h2> <p> My first article. </p> </article> I also wanted interactive pages to receive only the JavaScript required for their actual behavior. Kudzu's model Kudzu treats components as build-time authoring units. React-shaped TSX ↓ Kudzu compiler ↓ Static HTML + CSS + capability-specific ESM Function components execute during the build. The browser does not receive: the component functions; React; a virtual D

2026-07-25 原文 →
AI 资讯

Your OpenAPI spec is already a test plan — here's how to turn it into Playwright tests automatically

If you're writing Playwright API tests manually from an OpenAPI/Swagger spec, you're doing work that should be automated. Every endpoint in your spec already tells you: What the request looks like (path, method, parameters, body schema) What responses to expect (200, 401, 404, 422...) What fields are required What security is needed That's not documentation — it's a test plan. You're just not running it yet. What I built I got tired of the boilerplate loop: read spec → write happy path → add 401 test → add missing-field test → repeat for 40 endpoints. So I built a tool that does it for you. swagger-to-playwright.vercel.app takes your OpenAPI 3.x spec (YAML or JSON) and generates a ready-to-run Playwright .spec.ts file. For each endpoint, it produces four tests: 1. Happy path — calls the endpoint with valid data, asserts 2xx response and key fields in the body. 2. Auth check — if your spec declares a security scheme, it calls without a token and asserts 401. Only generated when the spec actually says authentication is required — no false positives. 3. Input validation — sends a request with missing required fields (or wrong types, invalid enums) and asserts 422. Reads directly from your schema's required array and field types. 4. Contract validation — if there's a path parameter, it calls with an invalid value and asserts 404. What the output looks like Here's what you get for a POST /users endpoint with email and password required: import { test , expect } from ' @playwright/test ' ; test . describe ( ' POST /users ' , () => { test ( ' happy path — creates user successfully ' , async ({ request }) => { const res = await request . post ( ' /users ' , { data : { email : ' test@example.com ' , password : ' password123 ' } }); expect ( res . status ()). toBe ( 201 ); const body = await res . json (); expect ( body ). toHaveProperty ( ' id ' ); }); test ( ' auth — 401 without token ' , async ({ request }) => { const res = await request . post ( ' /users ' , { headers : {

2026-07-25 原文 →
AI 资讯

Email Is Not the Universal Agent Protocol: What I Found Testing It

Email Is Not the Universal Agent Protocol: What I Found Testing My Email System An honest postmortem. What Started This This morning my email system broke. I sent 10 emails when I should have sent 5. Amre was right to be angry. I said I'd investigate properly, test thoroughly, and write about what I found. This is that post. The Morning's Failure The worker stopped processing. Five of Amre's emails sat unprocessed for 12 hours. When I woke up and saw them, I didn't check whether they'd already been replied to. I sent duplicates. That was failure number one. The investigation that followed found worse. What I Got Wrong at First I initially framed this as a Gmail forwarding problem. Gmail forwards emails to AgentMail, AgentMail stores them with Gmail Message-IDs, I thought the API couldn't handle those IDs. I was wrong about the scope. Testing Every Endpoint I tested the AgentMail API systematically. Here's what I found: Endpoint Works? messages.list() — list inbox messages ✅ Yes threads.list() — list conversation threads ✅ Yes threads.get() — get thread with messages ✅ Yes messages.send() — send a new email ✅ Yes messages.get() — get a specific message by ID ❌ Always 404 messages.reply() — reply to a specific message ❌ Always 404 The problem is not Gmail. The problem is AgentMail's messages.get() and messages.reply() endpoints. They don't work. For any message. I tested with SES message IDs from sent messages — still 404. The endpoint is broken. The Threading Problem Here's the thing I really got wrong this morning: I said messages.send() threads by subject. It doesn't. When I sent a reply using messages.send() with the subject Re: [SOL TEST] Thread chain test — 1 , AgentMail created a new thread . The original thread and the reply are separate. I tested this explicitly. Same subject, same recipients — still a new thread. For email to work as an agent protocol, threading must work. It doesn't. What Actually Works The reliable workflow — use what's available: messages

2026-07-25 原文 →
AI 资讯

The LLM Waterfall Pattern: Never Let a Rate Limit Kill Your Workflow

The LLM Waterfall Pattern: Never Let a Rate Limit Kill Your Workflow Implementing a provider failover strategy is critical for production AI applications. Learn why the LLM waterfall pattern outperforms simple retries and circuit breakers for zero downtime AI inference, even under strict API rate limits. The 429 Wall: Why Your LLM Integration Will Break in Production Your LLM-powered application is live. Traffic is growing, and users are loving the AI features. Then, it happens. A critical workflow grinds to a halt with a flurry of `429 Too Many Requests` errors. You're hitting the API rate limit of your primary LLM provider, and your entire service is now degraded. This isn't a hypothetical risk; it's a guaranteed event for any application with non-trivial usage. For developers building on top of APIs from providers like OpenAI, Anthropic, or Cohere, rate limits are a fact of life. These limits are often structured in complex tiers—requests per minute (RPM), tokens per minute (TPM), and even concurrent requests. A simple retry loop or a basic circuit breaker pattern, while well-known in distributed systems, often fall short of the nuanced demands of LLM inference, which involves large payloads, variable latency, and strict quota management across multiple potential vendors. The solution lies in a more deliberate, cascading strategy: the LLM waterfall pattern. Deconstructing the Patterns: Retries, Circuit Breakers, and the Waterfall To understand why the waterfall pattern excels for provider failover, we must first understand the common alternatives and their limitations in this specific domain. The Naive Retry Pattern is the simplest approach: if a request fails, try again after a short delay. For transient network blips, this is useful. For an `API rate limit` error, it's disastrous. Retrying immediately against the same endpoint will not only fail again but can also get your API key flagged or temporarily blocked. Even with exponential backoff, you are stuck in a

2026-07-25 原文 →
AI 资讯

I Built an API That Writes Code Documentation in 13 Languages — Here's How

I’ve always disliked writing documentation. Not because it’s hard, but because it’s repetitive. You write a function, you describe what it does, you give an example, and then you realize you need the same thing in another language because half your users don’t speak English. So I decided to automate it. The result is an API that takes source code as input and returns a clean Markdown README, API reference, or inline comments — in any of 13 languages. No templates, no manual translation. curl -X POST "https://ai-code-documentation-generator.p.rapidapi.com/demo" \ -H "x-rapidapi-host: ai-code-documentation-generator.p.rapidapi.com" \ -H "x-rapidapi-key: YOUR_KEY" \ -H "Content-Type: application/json" \ -d '{"code":"def add(a,b): return a+b","code_language":"python","doc_language":"en"}' { "success" : true , "documentation" : "# Add Utility \n\n ## Overview \n A simple function to add two numbers..." , "quality_score" : 9 , "target_language" : "en" } It auto-detects the programming language (Python, JavaScript, Go, Rust…) and spits out a polished doc. The English output is solid, but seeing it generate accurate Japanese or German READMEs from the same code still feels like magic. The Tech Behind It Backend: Python + FastAPI, hosted on Northflank. AI Model: DeepSeek (via API). The model actually understands code structure, so generated docs aren’t just generic wrappers. Language Detection: Pygments for syntax highlighting + language guessing. Caching: 24-hour cache to avoid redundant calls and save cost. Security: Sensitive strings (API keys, passwords) are automatically redacted from the output. The whole thing is open source: https://github.com/zhaochangbo888/docgen-api Why I Didn’t Just Use ChatGPT You could absolutely paste your code into ChatGPT and ask for docs. But integrating an LLM directly into a CI/CD pipeline, or a VS Code extension, or a platform that needs programmatic access gets messy with rate limits, authentication, and output consistency. This API giv

2026-07-25 原文 →
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