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Building a production TypeScript CLI in 2026: oclif vs commander vs custom. I shipped my first Node CLI in 2019 with a 12-line arg slicer and process.argv . It worked until it needed a second command and then collapsed into spaghetti. The other extreme is grabbing a full framework for a tool that runs one command. In 2026 there are three reasonable paths between those extremes, and each one wins on a specific slice of the problem. This post covers @oclif/core v4, commander v14, and a zero-dependency parser that fits in 30 lines. Same "greet" command in all three. Same distribution steps at the end. Honest tradeoffs throughout. TL;DR oclif v4 commander v14 zero-dep npm install size ~8 MB ~220 kB 0 B Type inference on flags Full, generated Good, manual Manual Plugin ecosystem Yes (Heroku, Salesforce) No No Learning curve High (day 1) Low (hour 1) None Best for Multi-team, multi-command CLIs Most real-world tools One-shot scripts 1. The decision: framework vs no framework Reach for a framework when the tool needs subcommands, a plugin system, or auto-generated help text. The second engineer who touches the CLI should be able to find where things live without reading your code twice. Build your own when the tool does one thing, ships as a one-file script, or lives inside a monorepo where pulling in 8 MB of transitive deps is not welcome. A zero-dep parser also removes the surface area for supply-chain incidents, a real concern on tools that run in CI. Commander sits in the middle: a 220 kB install that covers most real tools without the scaffolding overhead of oclif. 2. Project skeleton Every path shares the same bin setup. Start with a package.json that declares the executable: { "name" : "greet-cli" , "version" : "1.0.0" , "bin" : { "greet" : "./dist/cli.js" }, "scripts" : { "build" : "tsc" , "dev" : "tsx src/cli.ts" }, "type" : "module" } The tsconfig.json for a CLI targets the Node release line you plan to support. Node 24 LTS handles ESM natively, so use "module":

A few months ago I was trying to prepare for a cloud certification exam. I went looking for practice questions - good ones. Not just answer lists, but questions that actually trained the reasoning the exam tests. I found some scattered GitHub repos, a few YouTube playlists, sites with outdated question dumps. Nothing that felt structured. Nothing that explained why an answer was right, not just what it was. So I started building my own study tool. Mock questions, practice sets, AI-generated explanations. The kind of thing I wished existed. Six weeks later that became ArchReady - a certification prep platform for AWS, GCP, and PSM1. It's live now. What it does Practice questions across AWS (CCP, SAA, DVA, SAP), GCP ACE, and PSM1 Explanations for wrong answers - walks through the reasoning, not just the correct option AI-powered explanations coming soon Claude (Anthropic) Confidence tracking - shows which topics you're weak on Free to practice, no signup required. Pro unlocks full history and tracking. The stack Frontend: Next.js 14 (App Router) Backend: FastAPI (Python) AI: Claude (Anthropic) - explanations launching soon Payments: Dodo Hosting: Vercel (web) + Railway (API) Nothing exotic. I kept it boring on purpose - solo founder, 2-5 hrs/week, I can't afford interesting infrastructure problems. What I actually learned Ship before it feels ready. I had a list of 12 features I thought were "required for launch." I launched with 4. Nobody noticed the missing 8. Questions sourced from open-source + AI is good enough to start. Questions come from curated GitHub repos and AI-generated content built around official exam frameworks. That's enough to be useful. Perfection is a later problem. The hardest part isn't building - it's the first 10 users. The product exists. Getting people to try it is the actual work now. Where it is today Live at archready.io . Early stage. Still building. If you're prepping for AWS, GCP, or PSM1 - try it free, no account needed. Honest feedba

Something I keep noticing across teams and orgs that are actually getting value from AI versus the ones that aren't. The difference rarely comes down to the model or the algorithm. Most organizations are already drowning in data. Logs, metrics, alerts, reports, dashboards, tickets. The information exists. The bottleneck is what happens after the data shows up. How long does it take to interpret what the signal means? Who decides what to prioritize when three things need attention at once? How fast can the right people coordinate a response once a decision is made? That's where AI actually earns its keep. Not by replacing the human in the loop but by compressing the time between something happening and someone doing something useful about it. Signal to understanding to action. That's the chain that matters. Think about it in terms you deal with every day. A vulnerability gets disclosed. The CVE exists, the advisory is public, your scanner picked it up. None of that is the bottleneck. The bottleneck is figuring out which of your services are affected, who owns them, how bad the exposure actually is in your specific context, and getting a patch scheduled before someone exploits it. AI that helps you answer those questions in minutes instead of days is genuinely valuable. AI that and adds another dashboard to look at isn't. This applies across the board. Incident response, infrastructure management, risk assessment, customer systems, operational workflows. The teams getting real value aren't the ones with the fanciest models. They're the ones who figured out where their decision bottlenecks actually are and pointed AI at those specific gaps. The strategic advantage is rarely in the algorithm. It's in organizational responsiveness. How fast can you go from "something happened" to "we're handling it". The AI is just the thing that compresses that timeline. Where's the biggest decision bottleneck in your current workflow?

A tweet landed on my timeline from Peter Steinberger — OpenClaw founder, now at OpenAI: "Here's...

Built a small scripted pipeline to benchmark accessibility on 100 German online shops and the numbers were rougher than I expected, so here is the setup in case it is useful for your own CI. Stack: Puppeteer drives a headless Chromium through up to five routes per shop (home through checkout). Then pa11y 9.1.1 runs HTML_CodeSniffer and axe-core 4.10.2 runs on the same loaded DOM. Results get deduped by selector and rule id so the two engines do not double-count. Shops were picked to match German platform share. Shopify was the biggest block at 40 of 100, with Shopware and WooCommerce next. Output: 29,745 hard errors across the sample, with every one of the 100 shops failing WCAG 2.1 AA and homepages averaging 99.8 errors. The recurring offenders were touch targets under 44px on all 100, low contrast on 67, broken heading order on 61 and unnamed links on 58. Two practical notes for anyone scripting this. Checkout was only reachable on 82 of 100 without an account or a real cart, so deep-page coverage is uneven and you should log it per route instead of pretending you scanned everything. And automated detection is about 57% of real issues, so this is a smoke test, not an audit. submitted by /u/Loewenkompass [link] [留言]

Terraform can provision an AWS EC2 instance and set up a Python virtual environment in a single, reproducible run — the whole workflow is declarative and version‑controlled. 📑 Table of Contents 💻 Terraform — How to Provision an EC2 Instance 🔧 AWS Provider — Configuring Credentials 🐍 Python Environment — Setting up a Virtualenv on the Instance 📦 Installing Python and venv 📦 Activating and Using the Environment 📦 User Data — Automating Installation with Terraform 🟩 Final Thoughts ❓ Frequently Asked Questions How do I store the Terraform state securely? Can I use a different Linux distribution for the EC2 instance? Is it possible to attach an Elastic IP to the instance? 📚 References & Further Reading 💻 Terraform — How to Provision an EC2 Instance A Terraform configuration file describes the desired state of AWS resources; applying it makes the real cloud match that state. First, install Terraform (version 1.5.0 or newer). The binary is a single executable, so the operating system loads it directly into memory and the process performs HTTP requests to AWS endpoints. $ terraform version Terraform v1.5.0 on linux_amd64 + provider registry.terraform.io/hashicorp/aws v5.12.0 Next, create a main.tf that declares an aws_instance resource. The provider block authenticates with AWS using either environment variables or a shared credentials file. # main.tf terraform { required_version = ">= 1.5.0" required_providers { aws = { source = "hashicorp/aws" version = "~> 5.12" } } } provider "aws" { region = "us-east-1" } resource "aws_instance" "app_server" { ami = "ami-0c02fb55956c7d316" # Amazon Linux 2 instance_type = "t3.micro" # User data will be defined later user_data = data.template_file.init.rendered tags = { Name = "terraform-ec2-python" } } Running terraform init contacts the provider registry, downloads the provider plugin, and stores it under .terraform . The generated .terraform.lock.hcl file records exact plugin checksums, guaranteeing that subsequent runs use the same

What are your thoughts since I’m pretty sure they partnered with Google submitted by /u/Classic-Grab-2866 [link] [留言]

At the beginning of my Engineering career, I worked in a place where I had a lot of freedom to implement and experiment any technology I found interesting. I tried many technologies like PHP, Java, EJBs, SOAP, Rest and JavaScript. This gave me a lot of perspective, but I lacked the guidance and mentoring from more experienced developers. One of the most problematic things I built was a login. I would like to share in this document things that I did in the past so you understand why it is problematic and how I would build them today. Earlier Mistakes My First PHP Login. This is not a terrible option when using a single host, small project, but the biggest problem comes when we need to scale horizontally. Session variables exist only on the servers they are created. If you add more servers + Load Balancer, there is no guarantee that your requests go to the same server. In terms of vulnerabilities, if somebody manages to read your session id, they can impersonate you, act on your behalf. This is not really different from other methods like JWT so it is important to set up SameSite cookies or CSRF tokens, but do you think I did that for my first login ? of course NOT! My first Password storage. If you are thinking on implementing a login please NEVER do what I am about to describe: The first time I implemented a password, I was worried that somebody would find out the "actual" password. I wasn't actually thinking about using HTTP (instead of HTTPs), so my take on this was "encrypting" the password into MD5. Then the password was saved in MD5, but I was NOT doing anything different than just sending the password AS is. Let me explain the problems with this approach: Over HTTP an MD5 password can be read, and anybody can simply replicate the request with the same MD5 MD5 was actually NOT encrypting, it was a hashing. There are databases all over the internet mapping MD5 and other hashed passwords available so finding an MD5 can actually be translated to an actual password

If you use Cursor, Claude Code, or Windsurf daily, you've probably had this experience: You open a fresh chat, ask for a small fix, and twenty minutes later the AI has rewritten your API layer, added three new dependencies, and switched your data-fetching pattern "for consistency." The model isn't broken. It's contextless. Every new session starts from zero. It doesn't know your stack, your conventions, or the things it must never touch. So you spend the first ten minutes re-explaining — and the last hour undoing. Here's what fixed it for me. The real problem isn't prompts Most devs collect prompts. Notes app, Slack snippets, old chat threads. That helps for one-off tasks, but it doesn't solve the session problem. What you need is persistent context — rules that load automatically before you type anything. Two files do this: CLAUDE.md — read by Claude Code (and usable as project context elsewhere) .cursorrules — loaded by Cursor on every session (rename to .windsurfrules for Windsurf) Drop them in your project root. Done. What goes in a good config file A useful config is not ten lines of "use TypeScript and write clean code." That's too vague to change behavior. Mine include: Project structure — where pages, components, and API routes live Stack + versions — Next.js 14 App Router, not Pages; Zod; shadcn/ui Commands — npm run dev, npm run typecheck, npm run test Coding conventions — naming, import aliases, Server vs Client Components DO NOT section — the most important part (more on this below) Workflow notes — use @folder, prefer editing existing files, minimal diffs Here's an excerpt from the DO NOT section that saved me the most time: DO NOT — Critical Anti-Patterns Do NOT create a pages/ directory or use the Pages Router Do NOT rewrite the entire API layer — extend existing route handlers Do NOT add new npm dependencies without stating why Do NOT make drive-by refactors in unrelated files Do NOT fetch data in useEffect when Server Components can fetch directly T

A pattern keeps repeating itself in AI projects. The model works. The demo works. The proof of...

Apple spending a big chunk of its WWDC keynote on parental controls was surprising for several reasons. But the biggest is that, despite all the airtime, it didn't announce much new beyond a redesigned interface. Almost all the features touted already exist or are upgrades to current options. Why Apple chose to do this isn't […]

iOS 27 has only been out for a few hours, and I've been messing around with the developer beta on my iPhone 16 Pro. I was most interested in trying out the new Siri AI, but unfortunately, I'm still on Apple's waitlist for that. In the meantime, I've been poking around a bunch of features […]

submitted by /u/feross [link] [留言]

submitted by /u/feross [link] [留言]

I hope this makes sense. Keep in mind I'm pretty new to coding and have learnt for random one-off projects. I want to generate a quiz to be hosted on readymag, but started creating the still images so I can control the aesthetic. I'm looking to use buttons overlayed on top of the images to advance it, but they would also have to correlate with specific answers and store that data to trigger the right response on the final screen of the results. is this doable? how so? I'm not asking anyone to do a bunch of hard work for me for free, just point me in the right direction. I know how to make the buttons, but not actually have the action be advancing, and storing the data to refer back to it. sorry if there is any confusion. see the image as an example, which would have a start button and advance to the next prompt, one image at a time. they will have 2 or 3 options per question as buttons. thanks! https://preview.redd.it/sjw645ccx46h1.png?width=2636&format=png&auto=webp&s=146f36e1e5e242e340105e84dcb5579295bc0489 submitted by /u/Global_Math_7631 [link] [留言]

When Apple put child safety front and center at WWDC on Monday, its stated goal was helping parents fine-tune their kids' online experiences and avoid excessive screen time. But amid a global debate over internet regulation, its latest updates also looked like a defensive move in a brewing fight against Meta and other app developers. […]

Every PHP developer knows this situation: a client calls and says "I want free shipping for VIP customers on weekends, but only if the cart total is above €100." You open your code. You find the shipping module. You add an if. You deploy. Three weeks later: "Actually, make it €80. And also for the 'Premium' group." You open your code again. This loop : client request -> find logic in code -> modify -> deploy, was costing me a lot of time. And it's not just shipping. I build custom ecommerce solutions: payment modules, synchronization systems, pricing calculators. Business rules are everywhere, and they change constantly. The obvious solution I didn't want Symfony's ExpressionLanguage exists and it's impressive. But it pulls in dependencies, it can traverse objects and call methods (which is a security concern when rules are authored by users), and when something goes wrong, it doesn't tell you why. It's a black box. I needed something smaller, stricter, and transparent. So I built php-ruler I started with the classic pipeline: Lexer → AST → Evaluator. Strict typing from the start — 1 = '1' is a type error, not true. No silent coercion. Then I added features one real problem at a time. Problem: when something fails, why? -> I built an explain mode that returns the full evaluation tree: which sub-conditions passed, which failed, which were short-circuited, and why a variable was missing. Problem: in production, the context is sometimes incomplete -> I built a safe mode that doesn't throw on missing variables — it collects them all and lets you decide what to do. Problem: customer.group.name is not user-friendly -> I built an alias resolver. As a developer, I expose what I want: $resolver = ( new AliasResolver ()) -> add ( 'customer.group' , 'customer group' ) -> add ( 'cart.total' , 'cart amount' ); Now a non-developer can write: customer group = 'VIP' AND cart amount > 100 And I control exactly what variables are available to them. A real example Here's the shipping

No trabalho, nenhum código mudou. O que mudou foi a forma como os clientes inserem os dados. E isso quebrou coisas que nenhum teste existente pegou. O bug que só aparece quando alguém usa A motivação pra montar E2E do zero veio de um problema específico. Você precisava acessar a aplicação pra quebrar. Não era um erro de lógica isolado que um teste unitário pegaria. Era uma combinação de dados reais num fluxo real produzindo um resultado errado que só aparecia na tela. Os clientes chegavam lá antes da gente. É uma categoria de problema que teste de código não resolve, porque o problema não está no código. Está na interação entre o código, os dados e o ambiente. A forma mais rápida de pegar antes é rodar o fluxo completo do jeito que o usuário roda. Ficou com smoke tests cobrindo os principais fluxos do produto, configuração pra rodar contra múltiplos ambientes, e notificação no Slack quando o nightly quebra. A parte mais útil não são os testes em si. É saber antes do cliente reportar. Autocrop: quando nenhuma ferramenta resolve tudo Num projeto paralelo que mantenho, passei o fim de semana montando autocrop automático pra imagens. A ideia inicial era usar o imgproxy Pro, que tem detecção de objeto embutida. Não ficou preciso o suficiente pra variedade de imagens que eu tinha. Fui pro Rekognition, que retorna bounding boxes. Mais controle, mas bounding box tem um limite: é um retângulo. Objetos não são retângulos. Aí descobri o rembg, que faz algo diferente. Em vez de delimitar uma área, ele cria uma máscara pixel por pixel usando uma rede chamada U2Net, treinada pra segmentação de primeiro plano. O resultado foi bem superior — ele recorta o objeto, não uma caixa em torno dele. Colocar isso em Lambda foi onde a semana ficou mais lenta. O modelo precisava estar acessível pro processo do Lambda, coloquei em /root , Lambda não lê de lá. Movi pro /opt , chmod 755. O NUMBA tentou escrever cache em diretório read-only, defini NUMBA_CACHE_DIR=/tmp . Depois OOM em imagens mai

Hey everyone! I'm a Front-End developer with over 4.5 years of hands-on experience building scalable, performant web applications. I'm currently looking for a full-time remote opportunity. i could make modern web applications using Next.js or React.js & fueled by a passion for solving complex problems, diving into intricate challenges, and crafting clean, scalable solutions that deliver seamless user experiences. 🛠 Tech Stack: React.js & Next.js (SSR, SSG, App Router) TypeScript & JavaScript (ES6+) - Node.js - Express.js REST APIs & state management (Zustand, React Query) CSS/Tailwind/Styled Components , many Animation packages Git, CI/CD basics, Docker performance-optimization & SEO friendly Application Time Management – Responsible – Open mind – Team work – Attention to detail Commitment to work – Continuous learning 💼 What I bring: 4.5+ years building production-grade UIs Strong focus on performance, accessibility, and clean code Experience working in agile, remote-friendly teams Good communication and ability to work independently across time zones 🌍 Availability: Full-time/Part-time remote | Open to companies worldwide 🌐 My Portfolio ⬇️⬇️ https://pouyaazhkan.vercel.app/ 👨🏻‍💻My GitHub ⬇️⬇️ https://github.com/PouyaAzhkan 📩 Email Me ⬇️⬇️ codpoya.azhkan@gmail.com Feel free to DM me or drop a comment — happy to share my portfolio and discuss further! forhire #frontend #react #nextjs #typescript #remotework #webdeveloper #developer #Front_End #hiredeveloper #hire

I built InvoiceAI an AI powered invoice generator that lets you describe what you want to invoice in plain English and get a fully formatted invoice in seconds, complete with PDF download and a real payment link. Here's how I built it for the #H0Hackathon. The Problem Freelancers and small businesses waste time manually creating invoices. You know what you did, who you did it for, and how much it costs you shouldn't have to fill out a form to capture that. The Stack - Vercel v0 — scaffolded the entire UI in one prompt Next.js 16 — framework Groq (Llama 3.3 70B) — AI natural language to invoice fields AWS DynamoDB — stores every generated invoice Paystack — generates real payment links jsPDF — client-side PDF generation Vercel — deployment How It Works User types: "50 hours of mobile app development at $80/hr for TechLagos Ltd, 7.5% VAT" Groq parses the text and extracts structured invoice data Live preview updates instantly User downloads PDF — invoice is saved to DynamoDB automatically One click generates a real Paystack payment link to send to the client Building the UI with v0 I used Vercel v0 to scaffold the entire UI in one prompt. It generated a production-ready Next.js component with a split-panel layout form on the left, live invoice preview on the right. I just had to wire up the AI and database logic. Connecting AWS DynamoDB Using the AWS SDK v3, I connected DynamoDB directly from Next.js server actions. Every time a user downloads an invoice, it's saved to DynamoDB with the client details, line items, tax rate, and timestamp. This gives the app a real data foundation that scales from day one. await dynamo . send ( new PutCommand ({ TableName : ' invoices ' , Item : { invoiceId : data . invoiceNumber , clientName : data . clientName , clientEmail : data . clientEmail , items : data . items , createdAt : new Date (). toISOString (), }, })) The Result AI generates invoice from plain English in under 2 seconds Real PDF download (no print dialog) Real Paystack