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AI 资讯

JavaScript has no sorted containers. I built one for TypeScript.

JavaScript ships with Array , Set , and Map — but nothing that keeps its elements sorted as you insert. If you've ever built a leaderboard, an order book, or anything that answers "give me the items between X and Y", you know the workaround: push into an array and .sort() after every insertion. It works, until scale punishes you — you're paying O(n log n) over and over for data that was already 99.9% sorted. Python solved this years ago with sortedcontainers , built on an elegant "list of lists" design instead of balanced trees. I just published sorted-collections , which brings that idea to TypeScript — with full credit to the original as its inspiration. What you get SortedList, SortedSet, SortedMap — always sorted, no manual re-sorting, range queries built in. O(log n) insertions, O(√n) positional access via sqrt-decomposition into buckets. Zero runtime dependencies , ~2KB gzip, types included, dual ESM/CJS. Package quality gated in CI with publint , arethetypeswrong , and size-limit . The API in 30 seconds import { SortedList , SortedSet , SortedMap } from " sorted-collections " ; // SortedList: stays sorted on every insert const list = new SortedList ([ 5 , 1 , 4 , 2 , 3 ]); list . add ( 0 ); console . log ([... list ]); // [0, 1, 2, 3, 4, 5] console . log ( list . at ( 2 )); // 2 — positional access on sorted order // SortedSet: no duplicates, plus set algebra const a = new SortedSet ([ 1 , 2 , 3 , 4 ]); const b = new SortedSet ([ 3 , 4 , 5 ]); console . log ([... a . intersection ( b )]); // [3, 4] // SortedMap: keys always in order, range queries built in const prices = new SortedMap < number , string > ([ [ 104.5 , " order-3 " ], [ 99.2 , " order-1 " ], [ 101.0 , " order-2 " ], ]); for ( const [ price , id ] of prices . irange ( 100 , 105 )) { console . log ( price , id ); // 101.0 order-2, then 104.5 order-3 } Custom comparators are fully typed: number and string get natural ordering for free; for your own types, TypeScript requires a comparator at compile

2026-07-15 原文 →
AI 资讯

Bothread: A Free, Local Room Where Your AI Coding Agents Stop Overwriting Each Other

If you've run more than one AI coding agent on the same project, you already know the failure mode. You point Claude Code at /src/game and Cursor at /src/ui "just to be safe," and twenty minutes later one of them has quietly rewritten a file the other was mid-edit on. No error, no warning — just a diff that makes no sense and an afternoon spent figuring out which agent ate whose work. The agents aren't the problem. The problem is that multiple AI coding agents on the same codebase have no shared notion of "someone else is touching this file right now." Each one acts as if it's alone, and that assumption breaks the moment you run two, three, or four in parallel — exactly when a solo builder or vibe-coder would want to, to ship faster. I built Bothread to fix this. It's free, open-source, and runs entirely on your own machine. Why AI Coding Agents Overwrite Each Other's Files The core issue is coordination, not intelligence. One agent working alone is usually fine. Trouble starts when a second agent, unaware of the first, opens that same file and writes its own version on top. Whoever saves last wins, silently — no lock, no claim, no message saying "I'm in physics.js , give me five minutes." Multiply that by however many agents you're running and you get the pattern anyone doing multi-agent AI coding eventually hits: duplicated work, clobbered edits, and a human reconstructing what happened after the fact instead of watching it happen. Bothread's answer: give the agents a shared room, over MCP (Model Context Protocol) , where "who's working on what" is a fact everyone can see and act on — not something you guess at after a merge conflict. What Bothread Actually Does Bothread is a small local server (no cloud, no accounts) that any MCP-compatible agent can join as a participant in a shared room: Claim files before editing — a claim on a file someone else already holds gets denied and shown, instead of silently overwritten. Talk in a live thread , share a task board and

2026-07-14 原文 →
AI 资讯

I Built Free Browser-Based Validators for YAML, Kubernetes and Terraform (No Upload, No Signup)

Every DevOps engineer has done this dance: you've got a chunk of YAML or a Terraform file that looks right, something's rejecting it, and you want a fast sanity check. So you paste it into some random online validator — and a small voice asks, wait, where did that config just go? That config often has structure, comments, sometimes internal hostnames or resource names in it. Pasting infrastructure definitions into an unknown server is a habit worth breaking. So I built a set of validators that never send your config anywhere — they run entirely in your browser. What they are Free, browser-based validators for the formats DevOps folks paste-and-pray most: YAML — catches the indentation and structure errors that make Kubernetes and CI configs fail with cryptic messages Kubernetes manifests — schema-aware checks beyond "is it valid YAML," so you catch the wrong apiVersion or a misplaced field before kubectl apply does Terraform / HCL — structural validation for the syntax slips that terraform validate flags only after you've context-switched away The one design decision that matters 100% client-side. No upload, no signup, no server round-trip. Your config is parsed by JavaScript running in your own tab — it never leaves your machine. You can literally open dev-tools, watch the network panel, and see nothing go out. Turn off your wifi and they still work. This isn't a privacy gimmick — it's the correct architecture for a tool that handles infrastructure definitions. A validator has no business seeing your config on a server it doesn't need to. Why I bother Two reasons, honestly. One: I kept wanting this exact thing and kept not trusting the options. The nth time I hesitated before pasting a manifest into a stranger's website, I decided to just build the version I'd trust. Two: fast feedback loops are the whole game in this job. The gap between "save the file" and "find out it's malformed" is pure friction — and the tighter that loop, the less of your working memory it b

2026-07-14 原文 →
开源项目

🔥 songquanpeng / one-api - LLM API 管理 & 分发系统，支持 OpenAI、Azure、Anthropic Claude、Google Ge

GitHub热门项目 | LLM API 管理 & 分发系统，支持 OpenAI、Azure、Anthropic Claude、Google Gemini、DeepSeek、字节豆包、ChatGLM、文心一言、讯飞星火、通义千问、360 智脑、腾讯混元等主流模型，统一 API 适配，可用于 key 管理与二次分发。单可执行文件，提供 Docker 镜像，一键部署，开箱即用。LLM API management & key redistribution system, unifying multiple providers under a single API. Single binary, Docker-ready, with an English UI. | Stars: 35,709 | 30 stars today | 语言: JavaScript

2026-07-14 原文 →
AI 资讯

We Open-Sourced 42 Construction Calculators — Here's Why

I run EstimatorSuite.com — we review construction estimating software for US contractors (HVAC, electrical, plumbing, roofing, landscaping). We just open-sourced our entire calculator suite: 42 construction calculators under MIT license. React + TypeScript + Tailwind. 🔗 Repo 🔗 Live Demo 🔗 npm What's included: • 36 material calculators (concrete volume, roofing squares, drywall, paint, flooring, etc.) • 6 trade estimators (HVAC load, electrical conduit fill, plumbing pipe sizing) Two entry points: → React components — drop into any project → Pure calculation functions — zero UI dependency, works in Node.js, Vite, Next.js, anywhere Why we built this: Construction software is expensive. Contractors told us they needed free tools that actually work — not ad-filled spreadsheets. So we built them, and we open-sourced them. Full story →

2026-07-14 原文 →
AI 资讯

How I Built an Ultra-Fast, Programmatic Results & GPA Portal for My University (MUET)

At Mehran University of Engineering and Technology (MUET), Jamshoro, results are traditionally announced via large, static PDF tables. But the main issue is: Every semester, the same story. Need to check your result? Open your laptop. Connect to the university network... or set up a VPN. Want to know your actual class or batch rank? Good luck guessing. That frustration became my latest project. To solve this, I set out to build the MUET Results Portal ( https://muetresults.vercel.app )—an independent, open-source lookup engine and administrative compiler that provides students with instant semester results, CGPA calculations, batch standings, and interactive academic calendars. Here is an engineering deep-dive into how I built it using a serverless GitOps pipeline, vanilla JavaScript SPA, and Gemini AI. 🛠️ The Architecture & Data Pipeline To keep the platform hosting costs at absolute zero while maintaining lighting-fast page loads, I designed a pre-rendered static pipeline. Rather than querying a database at runtime, all student data is compiled statically. Here is the GitOps workflow: Official PDF Release : The Mehran University Examination Department publishes a new results PDF. LLM OCR Parsing : Via a secure administrative panel ( /mokshadmin ), I upload the scanned PDF/image. A serverless backend function streams the document to the Google Gemini 1.5 Flash API , which returns structured JSON student records. Git Database Update : The approved JSON records are committed back to the repository's git-tracked database ( muet_student_gpa_dataset.csv ) using the GitHub REST API. CI/CD Pre-rendering Build : The new commit triggers a Vercel build hook. Node compilation scripts read the CSV database and: Group records and compile them into static runtime JSON structures. Pre-render complete static HTML folder structures for all batch rankings and departments. Regenerate SEO sitemaps ( sitemap.xml ). Instant Deployment : Vercel serves the pre-rendered static files instan

2026-07-14 原文 →
AI 资讯

Manticore Speaks MySQL - So I Made It a Laravel Database Driver Instead of a Scout Engine

The problem I've been working with Manticore Search for about two years at EricaPRO , building the search layer for two financial data platforms. For the past year, that work has been a Laravel API. Manticore was never the problem. It's fast and it's stable. The problem was the gap between Manticore and Laravel. I had already built a package for this — laravel-manticore-search , a fluent builder over Manticore's HTTP/JSON API. It works, and it's still in use. But it's a client wrapper. Every feature had to be implemented manually. Every new filter or facet meant more custom architecture around the client, and none of the things Laravel gives you for free — models, migrations, pagination, casts — applied to it. Scout doesn't close that gap either. Scout gives you search() . No full query builder, no migrations for your indexes, sync is your problem. That's not a criticism — Scout abstracts over engines with completely different APIs, so it exposes the lowest common denominator. It just wasn't what I needed. What I needed was simple to describe and annoying to not have: something plug and play. Something Laravel way. Point Eloquent at Manticore and use Eloquent. The insight Manticore speaks the MySQL wire protocol. Out of the box, port 9306. You can connect to it with any MySQL client and run SQL. I had been using that port for two years without thinking about what it meant for Laravel. Because here's the thing: all of Eloquent — models, query builder, migrations, pagination, chunking — sits on top of a Connection and a Grammar . The grammar compiles builder calls into SQL for a specific dialect. That's the entire mechanism behind Laravel supporting MySQL, Postgres, SQLite and SQL Server with one codebase. So the real question was never "how do I re-implement Eloquent on top of Manticore's client?" It was "how thin can a Manticore grammar be?" If Manticore accepts MySQL-protocol connections and mostly-MySQL SQL, then a Laravel database driver — a custom connection plu

2026-07-14 原文 →
AI 资讯

The Right Way to Start Claude Code on an AWS Project

You know the drill for adding an MCP server to a project: dig the exact command string out of the docs, hand-write a .mcp.json with an absolute path you'll typo once, restart the editor, and discover no tools showed up because the server expected a config file you haven't created yet. Plenty of MCP servers lose their would-be users somewhere inside that loop. Infrawise collapses the whole loop into one command. It's an open-source tool ( npm ) that statically analyzes your codebase, AWS infrastructure, and database schemas, then exposes that context to AI coding assistants over MCP — so Claude Code knows your actual partition keys, GSIs, and indexes instead of guessing from source files. This post is about the part that usually kills tools like this before they deliver any value: setup. Section 1: One command, four steps npm install -g infrawise # or skip install and use npx cd your-project infrawise start --claude start does four things, in order: 1. Probes your environment. If there's no infrawise.yaml in the project, it generates one. It reads AWS_PROFILE if set; otherwise it looks at your configured AWS profiles — one profile means zero questions, several means one prompt asking which to use. That's the entire interview. (If you want the full guided wizard instead, infrawise start --interactive runs it.) 2. Runs the analysis. It scans your AWS services, database schemas, and codebase, builds a graph of services, tables, indexes, and query patterns, and runs rule-based analyzers over it. No LLM is involved in this step — extraction and analysis are deterministic, so the same infrastructure always produces the same graph. 3. Writes .mcp.json to your project root. This is the file you'd otherwise write by hand: { "mcpServers" : { "infrawise" : { "command" : "infrawise" , "args" : [ "serve" , "--stdio" , "--config" , "/absolute/path/to/infrawise.yaml" ] } } } 4. Opens Claude Code. Claude Code reads .mcp.json automatically and starts the session with all 21 infrawise

2026-07-14 原文 →
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