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AI 资讯

I Pointed a Skill Linter at a 52k-Star Repo. Here Is What 84/100 Looks Like.

Every AI agent skill you write burns context on every turn. Not just when the skill is running. On every turn. The agent keeps each skill's name and description loaded permanently so it knows when to invoke them. A vague description is not just a documentation problem. It is a tax you pay per message, forever. That is the problem I built skillscore to catch. When addyosmani/agent-skills hit 52,000 stars and went to #1 trending on GitHub, I had my benchmark. 24 production-grade skills written by people who clearly know what they are doing. If a static linter has anything useful to say at this level, this is where to find out. So I ran it. One command. 24 skills. Two seconds. This is what skillscore 0.2.0 can do now: skillscore /path/to/agent-skills/ One command scores everything in the tree. Here is the output: Three skills from addyosmani/agent-skills scored in one command, then a drill-down into the lowest scorer. The full results Skill Score Grade spec-driven-development 91 A browser-testing-with-devtools 91 A deprecation-and-migration 91 A frontend-ui-engineering 91 A test-driven-development 88 B code-review-and-quality 88 B interview-me 86 B ci-cd-and-automation 85 B code-simplification 85 B context-engineering 85 B documentation-and-adrs 85 B incremental-implementation 85 B security-and-hardening 85 B shipping-and-launch 85 B source-driven-development 85 B using-agent-skills 85 B doubt-driven-development 80 B observability-and-instrumentation 80 B planning-and-task-breakdown 80 B api-and-interface-design 78 C debugging-and-error-recovery 77 C git-workflow-and-versioning 77 C idea-refine 77 C performance-optimization 77 C Average: 84/100 (B) To be clear: 84 across 24 production skills is excellent. No failures. No D grades. Most skill libraries I have tested do not get close to this. The instruction content inside these skills is genuinely good. What the linter found is at the edges, not in the core. Two gaps. Five skills. Every single C. I drilled into all five

2026-06-13 原文 →
AI 资讯

Building CompanioxVPS — I'd Love to Hear What Developers Actually Want From a VPS Platform

I've been working on CompanioxVPS, a VPS and cloud infrastructure platform that aims to make deploying and scaling applications much simpler for developers. Right now, I'm building core features such as: VPS provisioning Load balancing Autoscaling Custom domains Developer APIs Simple billing and infrastructure management Before going too far down the road, I'd love to hear from developers who actively ship products: What do you dislike most about current VPS or cloud providers? What features do you wish existed? What would make you switch to a new platform? What's one thing that would make your deployment workflow significantly easier? I'm also open to connecting with developers, DevOps engineers, and infrastructure enthusiasts who find this space interesting and would like to contribute ideas, provide feedback, or potentially collaborate as the project grows. Still early, still building, and still learning. Every piece of feedback helps shape the direction of CompanioxVPS. Looking forward to hearing your thoughts. 🚀

2026-06-13 原文 →
AI 资讯

How to Convert Word to PDF in the Browser with Vue 3, mammoth, and html2pdf.js

Converting Word documents to PDFs on the server is the classic approach: upload the file, run LibreOffice or a cloud API, send the result back. But that means your users’ resumes, contracts, and reports touch your infrastructure. I wanted something simpler for en.sotool.top : pick a .docx file in the browser, preview the parsed content, and download a PDF. No server involved. Here is how I built it with Vue 3, mammoth , and html2pdf.js . Why Client-Side? The main reason is privacy. Resumes, contracts, tax documents — users do not want them on a stranger’s server. Client-side conversion also means: No upload bandwidth limits No file size caps from your server No storage to clean up Works offline after the page loads The trade-off is that very complex documents are limited by the browser’s rendering capabilities. For typical office documents, that is fine. The Stack Vue 3 — UI, file handling, and reactive state mammoth — Parse .docx files into clean HTML html2pdf.js — Render the HTML into a PDF using html2canvas + jsPDF Native File API — File selection npm install mammoth html2pdf.js Loading the Word Document The first step is reading the uploaded .docx file into an ArrayBuffer , then converting it to HTML with mammoth . import mammoth from ' mammoth ' ; async function convertDocxToHtml ( file ) { const arrayBuffer = await file . arrayBuffer (); const result = await mammoth . convertToHtml ({ arrayBuffer }); return result . value ; } mammoth intentionally produces simple, clean HTML. It ignores complex formatting like text boxes and embedded fonts, which makes the output predictable. I keep the HTML in a reactive ref and render it in a preview panel: < template > <div ref= "previewRef" class= "word-preview" v-html= "htmlContent" ></div> </ template > < script setup > import { ref } from ' vue ' ; const htmlContent = ref ( '' ); const previewRef = ref ( null ); </ script > Generating the PDF Once the user is happy with the preview, html2pdf.js turns the preview element

2026-06-13 原文 →
AI 资讯

From Scaling Data to Transcribing Voices: Building Resilience Under Pressure

As my backend engineering internship wraps up, I’ve been reflecting on the tasks that pushed me the hardest. Building minimum viable products is one thing, but making them resilient, scalable, and fault-tolerant is an entirely different beast. Here are two of the most memorable tasks from my time here—one solo dive into system scaling, and one team effort tackling asynchronous voice processing. Task 1: Scaling the Insighta Labs+ Query Engine (Individual) What it was Insighta Labs+ is a demographic intelligence platform where analysts and engineers run structured queries on user profiles via a CLI and a Web Portal (backed by GitHub OAuth and RBAC). My task was to take a functional MVP and evolve it into a robust query engine capable of handling tens of millions of records and hundreds of concurrent queries per minute. The problem it was solving The initial architecture worked flawlessly for a few thousand records, but under scale, it started showing cracks. Latency : Without indexing, every filter query triggered a full-table scan. Redundancy : Identical queries from different users wasted CPU and DB cycles. Write-Pressure : Users needed to bulk-upload CSVs containing up to 500,000 rows. Processing these synchronously locked the database, bringing read operations to a halt. How I approached it Instead of blindly throwing more server power at the problem, I focused on doing less work. Targeted Indexing : I added indexes only to frequently filtered columns. Caching & Normalization : I introduced Redis for TTL-based caching. To maximize cache hits, I built a query normalization layer. Whether a user queried "young males" or "men under 30" , the parser normalized the filter object into a canonical form before hashing the cache key. Connection Pooling : I set up PgBouncer to manage database connections and prevent exhaustion under high concurrency. Chunked Ingestion : For the massive CSV uploads, I implemented chunked streaming. Rows were validated individually; valid row

2026-06-13 原文 →
AI 资讯

⚠️ The Kotlin Multiplatform division-by-zero trap

If you write Kotlin Multiplatform code that involves integer division, you may have already hit this: the exact same expression behaves completely differently depending on which platform compiles it. 🐛 The problem Take this innocuous expression: val quotient = 12 / 0 val remainder = 12 % 0 On JVM and Native , both lines throw an ArithmeticException . That is the behavior most Kotlin developers expect and design around. On JavaScript , both lines execute without any exception and silently return 0 . Here is a concrete illustration drawn directly from the Kotlin test suites for each platform: // Kotlin/JS check ( 12 / 0 == 0 ) // passes — no exception check ( 12 % 0 == 0 ) // passes — no exception // Kotlin/JVM and Kotlin/Native val quotient : Result < Int > = runCatching { 12 / 0 } val remainder : Result < Int > = runCatching { 12 % 0 } check ( quotient . exceptionOrNull () is ArithmeticException ) // passes check ( remainder . exceptionOrNull () is ArithmeticException ) // passes Summary table: Expression JVM / Native JavaScript 12 / 0 ArithmeticException 0 12 % 0 ArithmeticException 0 🤔 Why it happens On Kotlin/JS, Int values are represented as JavaScript numbers, and 12 / 0 evaluates to Infinity while 12 % 0 evaluates to NaN . Kotlin/JS truncates Int arithmetic to 32 bits using JavaScript's | 0 operator, and per the ECMAScript ToInt32 conversion, both Infinity | 0 and NaN | 0 evaluate to 0 — so the division-by-zero result silently becomes 0 , with no exception thrown. JVM and Native follow Java's long-standing contract: integer division by zero is always an ArithmeticException . The practical consequence is that any guard you write and test on JVM — a try/catch(ArithmeticException) or a pre-condition check that relies on an exception — is silently bypassed when the same code runs on JS. No compile error, no warning, just a wrong result. ✅ The fix: Integer from Kotools Types 5.1.1 The Integer type in Kotools Types explicitly checks for a zero divisor before delegat

2026-06-13 原文 →
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