GitHub热门项目 | 🔥 🔥 🔥 A Free & Self-hostable Airtable Alternative | Stars: 63,576 | 43 stars today | 语言: TypeScript

GitHub热门项目 | | Stars: 1,068 | 28 stars today | 语言: TypeScript

GitHub热门项目 | Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API. | Stars: 28,855 | 6 stars today | 语言: JavaScript

GitHub热门项目 | Vercel's official collection of agent skills | Stars: 28,276 | 32 stars today | 语言: JavaScript

GitHub热门项目 | Tower defense game that teaches cloud architecture. Build infrastructure, survive traffic, learn scaling. | Stars: 6,038 | 35 stars today | 语言: JavaScript

GitHub热门项目 | Automatically sync your leetcode solutions to your github account - with some updates to keep it working | Stars: 559 | 12 stars today | 语言: JavaScript

GitHub热门项目 | A plugin marketplace for Claude Code/GitHub Copilot that provides Power Platform development plugins, including reusable skills, agents, and commands for building and deploying solutions. | Stars: 388 | 7 stars today | 语言: JavaScript

GitHub热门项目 | Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM] | Stars: 51,382 | 152 stars today | 语言: Python

GitHub热门项目 | Multi-harness agentic plugin marketplace for Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLI | Stars: 37,124 | 43 stars today | 语言: Python

GitHub热门项目 | Model Context Protocol (MCP) to enable AI LLMs to trade using MetaTrader platform | Stars: 561 | 8 stars today | 语言: Python

GitHub热门项目 | Flutter makes it easy and fast to build beautiful apps for mobile and beyond | Stars: 177,123 | 44 stars today | 语言: Dart

GitHub热门项目 | Headunit App for displaying Android Auto | Stars: 1,359 | 79 stars today | 语言: Kotlin

GitHub热门项目 | A format specification for describing a visual identity to coding agents. DESIGN.md gives agents a persistent, structured understanding of a design system. | Stars: 16,759 | 504 stars today | 语言: TypeScript

I just completed my bachelor’s last month and I have realised basic programming knowledge about different languages won’t get me any job. So I have decided to pursue Mern stack + Ai integration path. However I don’t have any money to buy courses. Lucky I have an internet connection and a laptop. I’m already familiar with basic javascript and I want to dive into the intermediate level at the least ASAP! Are there any free courses online for Mern ?? I’m already looking into freecodecamp. However if there are other unpopular options available, help would be appreciated. Thanks!! PS :- I attached google link cuz didn’t know what else to do. submitted by /u/Mental-Parsnip-8026 [link] [留言]

Google's new speaker is a modest update, but the question is whether Gemini is a good smart home assistant.

The Switch 2’s price is set to increase by $50 on September 1st, starting at $499.99, so if you’re purchasing one before that (or you already own one), now’s a good time to also save on accessories while you can. Amazon’s Prime Day has delivered many great game deals, but some of the best sales […]

Right out of the box, the new Google Home Speaker passed a couple of important tests. Even with the volume at 100 percent and music blaring out of the speaker, it quickly ducked the audio and listened every time I said "Hey, Google." In fact, in two days of testing, the speaker's three microphones haven't […]

You have an API response. You want a Zod schema. So you paste the JSON into a JSON-to-Zod converter, copy the output, and ship it. Here's the trap: a lot of those converters infer basic types only . Your email , your uuid , your url , your ISO timestamp — they all come out as z.string() . The schema compiles, the types look right, and your validator quietly accepts "not-an-email" , "ftp://nope" , and "2026-99-99" forever. I wanted to see exactly how much gets lost, so I ran the same payload through two tools and diffed the output. Everything below is real, copy-pasteable output — nothing edited. The input A pretty ordinary user object: { "id" : "3f2a9c1e-5b7d-4e8a-9f1c-2d3e4f5a6b7c" , "email" : "ada@example.com" , "website" : "https://ada.dev" , "age" : 34 , "rating" : 4.7 , "created_at" : "2026-03-04T10:15:30Z" , "is_active" : true , "address" : { "city" : "Lyon" , "zip" : "69001" }, "tags" : [ "early-adopter" , "beta" ] } Tool 1 — json-to-zod (npm) const user = z . object ({ id : z . string (), email : z . string (), website : z . string (), age : z . number (), rating : z . number (), created_at : z . string (), is_active : z . boolean (), address : z . object ({ city : z . string (), zip : z . string () }), tags : z . array ( z . string ()), }); Structurally correct. But every meaningful field is a bare z.string() / z.number() . This schema will happily validate email: "lol" and created_at: "yesterday" . Tool 2 — TypeMorph import { z } from " zod " ; export const userAddressSchema = z . object ({ city : z . string (), zip : z . string (). regex ( /^ [ A-Z0-9 ][ A-Z0-9 \s\-]{1,8}[ A-Z0-9 ] $/i ), }); export type UserAddress = z . infer < typeof userAddressSchema > ; export const userSchema = z . object ({ id : z . uuid (), email : z . email (), website : z . url (), age : z . number (). int (). min ( 0 ). max ( 150 ), rating : z . number (). min ( 0 ). max ( 5 ), created_at : z . iso . datetime (), is_active : z . boolean (), address : userAddressSchema , tags :

Three AI stories landed this week that all poke at the same nerve: the images, video, and films we actually look at are getting an AI layer — and the line between "real" and "AI-made" keeps thinning. Quick rundown in the short, then my take below: 1. ChatGPT will start showing real, licensed photos — not AI fakes. OpenAI signed a multi-year display deal with Getty Images, so licensed photography shows up inside ChatGPT's search and discovery. It's display-only — the photos aren't used to train models. The twist I can't get over: AI image generation had nearly wiped Getty out (stock down ~55% on the year), and this one deal sent the shares up ~145%. The thing AI almost broke got rescued by AI. 2. ByteDance — yes, TikTok's parent — teased Seedance 2.5: a full 30-second video generated in a single shot, no stitching, up to 50 reference inputs, 4K. Most tools still cap out around 5–10 seconds, so "30s native, one pass" is a real jump in how usable the output is. Public launch is early July. 3. Google DeepMind is partnering with A24 on AI filmmaking — a ~$75M, non-exclusive deal to co-build Veo-powered tools. Notably Google gets no access to A24's film library or data. A prestige studio building with AI in the open makes the whole "AI in Hollywood" debate a lot less hypothetical. As someone building a daily AI-news pipeline on the side, the Getty one is the story I keep chewing on. So much of the "AI vs creators" fight has been framed as scrape-or-die. A display-licensing deal is a third option — pay to show the real thing, instead of generating a confident fake or quietly training on someone's work. I don't know if it scales, but it's the first move in a while that didn't feel zero-sum. The Seedance + A24 pair points the other way though: generation is getting longer, more controllable, and is walking straight into real production. So we get both at once — more verified real media and more convincing synthetic media, in the same week. Curious where other builders land:

On April 29, 2026, Nathan Sobo published the Zed 1.0 announcement post on Zed's blog. The post landed on Hacker News at 2,047 points and 663 comments — the highest-engagement HN story in the present cache by a substantial margin. The launch announcement is a milestone marker after five years of development, roughly a million lines of Rust, and a custom GPU-accelerated UI framework called GPUI that the Zed team built from scratch rather than building atop Electron, Chromium, or any other browser engine. The structural argument Zed has been making for the last several years is condensed in one sentence from Sobo's post: "Instead of building Zed like a web page, we built it like a video game, organizing the entire application around feeding data to shaders running on the GPU." The video-game framing is not metaphorical. Zed's editor surface is composited by feeding glyph atlases, syntax-tree-derived color spans, and pane-layout geometry into GPU shaders the way a video-game engine composites its frames. The reason this matters is the reason the Zed team gave for starting over from the Atom era: Atom was built as a fork of Chromium, and the same team that built Atom is the team that spawned Electron. The Atom-Electron-VSCode lineage is, in the historical-causal sense, Zed's own. Sobo's post is unusually direct about the inherited limitation: "Electron eventually became the foundation of VS Code (which today seems to be forked into a new AI code editor every other week). Web technology offered an easy path to shipping flexible software, but it also imposed a ceiling. No matter how hard we worked, we couldn't make Atom better than the platform it was built on." The 1.0 announcement is, in part, a statement that the rebuild from scratch has finally cleared that ceiling. Who built it Zed's three co-founders all worked on Atom at GitHub before founding Zed Industries in 2021. Nathan Sobo led the Atom team from 2011 to 2018; he also co-led Teletype for Atom, one of the first