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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 :

AI has made it easier to produce working software. That part is real. It can write code, draft documents, research a topic, scaffold a prototype, and debug a problem faster than most teams can finish writing a decent ticket. But faster building doesn't automatically mean better product decisions. That's the part I keep coming back to. For decades, software teams optimized around delivery. Requirements, design, development, QA, release. Waterfall softened into Agile. Agile grew into DevOps. The practices changed, but the assumption underneath stayed pretty stable: building software is expensive, so plan carefully before you start. That made sense because, for a long time, it was true. Now that assumption is breaking. AI is doing to software what calculators did to accounting. It isn't eliminating the job. It's moving the job up a level. The syntax, boilerplate, first draft, and some of the debugging are getting offloaded. The work doesn't disappear. The bottleneck moves. Learning is still expensive Here's what didn't get cheaper: understanding what people actually need getting stakeholders aligned deciding what evidence would change your mind putting something real in front of users reading the signal without fooling yourself The old question was: Can we build it fast enough? The new question is: Do we understand the problem well enough? That sounds like a small shift, but it changes the work. It changes what strong engineers spend time on. It changes what product people need from engineering. It changes how teams should define "done." If the code ships but nobody learns anything, did the team actually move forward? Sometimes yes. Often no. Users don't know until they can touch it People are not great at specifying requirements up front. Not because they're difficult. Because they're human. Most of us don't know how we feel about something until we can react to a version of it. A mockup. A prototype. A rough slice. A real workflow with sharp edges. So the fastest pat

Você já fez um commit no repositório do trabalho e percebeu que estava com o seu e-mail pessoal? Ou o contrário? Esse é um dos erros mais comuns de quem usa Git com múltiplas contas no mesmo computador. Neste tutorial você vai aprender a configurar tudo corretamente, de uma vez, usando chaves SSH separadas e .gitconfig condicional — sem gambiarras. O problema Por padrão o Git usa uma configuração global: git config --global user.name "Seu Nome" git config --global user.email "seu@email.com" Isso significa que todos os repositórios no seu computador usam o mesmo usuário. Quando você tem contas separadas (ex: joao@empresa.com no GitLab da empresa e joao@gmail.com no GitLab pessoal), os commits vão sair com o e-mail errado. A solução profissional envolve duas partes: Chaves SSH separadas para cada conta .gitconfig condicional que aplica o usuário certo — e a chave SSH certa — por pasta Passo 1 — Gerar as chaves SSH Abra o terminal e gere uma chave para cada conta. Use nomes diferentes para não sobrescrever: # Chave para a conta pessoal ssh-keygen -t ed25519 -C "joao@gmail.com" -f ~/.ssh/id_ed25519_pessoal # Chave para a conta do trabalho ssh-keygen -t ed25519 -C "joao@empresa.com" -f ~/.ssh/id_ed25519_trabalho 💡 Por que ed25519 ? É o algoritmo mais moderno, mais seguro e recomendado pelo GitHub, GitLab e Bitbucket. Evite RSA a menos que seu servidor seja muito antigo. Ao final você terá quatro arquivos em ~/.ssh/ : id_ed25519_pessoal ← chave privada (nunca compartilhe) id_ed25519_pessoal.pub ← chave pública (você registra no GitLab) id_ed25519_trabalho id_ed25519_trabalho.pub Passo 2 — Registrar as chaves no GitLab Para cada conta: Copie o conteúdo da chave pública: # Pessoal cat ~/.ssh/id_ed25519_pessoal.pub # Trabalho cat ~/.ssh/id_ed25519_trabalho.pub Acesse Settings → SSH and GPG keys → New SSH key na conta correspondente e cole o conteúdo. Faça isso nas duas contas , cada uma com a sua respectiva chave pública. Passo 3 — Configurar o Git por pasta (o pulo do gato)

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PDF work shows up in dev life more often than we'd like to admit — exporting docs, compressing build artifacts, merging client deliverables, or converting a spec sheet someone sent as a scanned PDF into something you can actually search. Paid suites like Adobe Acrobat are overkill for most of these one-off tasks. Here are 10 free, no-signup tools that get the job done, ranked roughly by how often you'll reach for them. 1. ToolTiny — PDF to Word/Excel/PowerPoint ToolTiny converts PDFs into editable DOCX, XLSX, or PPTX files directly in the browser, alongside the usual merge/split/compress/watermark/password toolkit. No account, no watermark on output. What's actually useful for dev workflows: it handles presentation-style PDFs (think exported slide decks or design-heavy one-pagers) reasonably well — most converters flatten these into a single unreadable text blob, but ToolTiny keeps the layout intact while still giving you editable text. Good for the "client sent a PDF, I need it as a Word doc by EOD" scenario. 2. Smallpdf The OG in this space. Smallpdf's PDF-to-Word conversion is excellent at preserving layout — it renders the page as a background image and overlays editable text boxes at the correct coordinates, which is why it handles complex layouts better than most. Free tier caps you at 2 tasks/day though. 3. iLovePDF Similar feature set to Smallpdf, slightly more generous free tier. Their "Organize PDF" drag-and-drop page reordering is one of the smoother UX implementations out there if you need to quickly reshuffle a multi-doc PDF before sending it out. 4. PDF24 A German tool that's been around forever and quietly does everything — OCR, forms, signing, comparison. Less polished UI than the others but the OCR accuracy on scanned technical docs is genuinely strong. 5. Stirling-PDF If you want something self-hosted, Stirling-PDF is the open-source answer. It's a Docker container you spin up yourself, giving you a full PDF toolkit (split, merge, compress, OCR, wa

Here's a thinking process: Analyze User Input: Role: Ghostwriter for Roberto Luna Osorio Editorial Profile: Developer Storytelling (primary), Build in Public, Técnico, Reflexivo. Philosophy: Share the process/learnings, not just the perfect result. Mandatory narrative questions: Problem, First attempt, What went wrong, How solved, What learned, What's next. Tone: First person, direct, honest, technical but accessible, curious. Avoid corporate jargon, clickbait, marketing hype, false modesty, empty summaries. Language: Spanish (natural/colloquial tech MX/LatAm) OR English (conversational, dev-friendly). Wait, the prompt says "English only (Dev.to is primarily English)" for this specific article. I'll stick to English as instructed for the Dev.to angle. Angle: TECHNICAL DEEP-DIVE ("HOW I BUILT IT"). Focus on code, architecture, specific files. Different from Medium's narrative style. Style: Technical, precise, code-forward. Real file/function names, error messages. First person but engineer-voice. English only. Mandatory Structure: TL;DR, The Problem, What I Tried First, The Implementation, Key Takeaway, What's Next. Length: 800-1200 words. Tags: #vibecoding #buildinpublic + relevant tech tags. Input Data: Repo: VS, Date: 2026-06-23. Commits show v1.6.0 sprint 1, booking reminders, /confirm/:token, notifications, bulk actions, pipeline v2. Files modified: booking-reminders.service.ts , booking.controller.ts , email.cron.ts , notifications.controller.ts , NotificationBell.tsx , confirm/[token]/page.tsx , ventas/pipeline/page.tsx , etc. Diffs show version bumps, cron integration, notification controller refactor, new components. Constraint: Do NOT repeat angles from this week: "El control de acceso y la caja negra de las publicaciones". Choose a completely different angle. Final Instruction: Write complete article. Start directly with TL;DR. Include actual code snippets. Do NOT write a story — write a technical breakdown. Map to Editorial Profile & Constraints: Profile

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You don't need a 5-person engineering team to run like one. Here's how small businesses are cutting manual work — without the overhead. The Problem Nobody Talks About Openly You're running a business. You have leads coming in from your website, follow-up emails to send, invoices to track, onboarding tasks to assign — and somehow, you're still doing most of it manually. You've probably heard the advice: "Just hire a developer." But a full-time developer costs $60,000–$120,000/year in the US. A dev team? Multiply that by four. For a growing small business, that's not an option yet. Here's what nobody tells you: you don't need a full dev team to automate 80% of your operations. You need the right tools — and someone who knows how to connect them. What "Workflow Automation" Actually Means (No Jargon) Workflow automation is just this: if X happens, do Y automatically — without you touching it. Some real examples: A new lead fills out your form → they get an automated welcome email + a task is created for your sales rep An invoice is marked paid → a receipt is sent + your spreadsheet updates + a Slack message goes to your finance channel A support ticket comes in → it's categorized, assigned, and a reply is sent based on the topic You already know these need to happen. Automation just removes you as the middleman. The Modern Stack: 4 Tools That Do 80% of the Work 1. Zapier / Make (formerly Integromat) These are no-code automation platforms. Think of them as the "if this, then that" engine for your apps. Connect 5,000+ apps (Gmail, Shopify, Notion, Slack, HubSpot, etc.) Build multi-step automations visually — no coding needed Best for: Email triggers, form responses, basic data sync Cost: Free tier available; paid from ~$20/month 2. Salesforce (with OmniStudio / Flow) If your business is scaling or you're in B2B sales, Salesforce isn't just a CRM — it's an automation engine. Flow Builder lets you automate record updates, approvals, emails, and task assignments visually Omn