标签：#EV

Hey pessoal! 👋 Queria compartilhar um projeto que venho desenvolvendo para resolver um problema comum: encontrar uma biblioteca de ícones limpa, consistente e fácil de integrar, sem peso desnecessário no projeto. Apresento o coreIcons — uma coleção organizada de 352 ícones de desenvolvimento feita para workflows modernos. 📦 Por que o coreIcons? Leve e Rápido: Impacto mínimo no carregamento das suas aplicações. Organizado: Desenvolvido com uma grade (grid) e estilo totalmente consistentes. Focado no Dev: Feito para se encaixar perfeitamente nos seus projetos de frontend ou full-stack. 🚀 Como Usar É super simples! Basta acessar a nossa demonstração ao vivo, navegar pela coleção e clicar em qualquer ícone. O sistema vai fornecer instantaneamente a URL correta ou o snippet do ícone escolhido para você copiar e usar na hora. 🌟 Apoie o Projeto & Conecte-se! Se você achar essa biblioteca útil, por favor, considere deixar uma estrela ⭐️ no nosso repositório do GitHub ! Seu apoio ajuda o projeto a crescer e a alcançar mais desenvolvedores na comunidade. Também quero deixar um agradecimento enorme a todos que apoiam projetos open-source, contribuem com feedbacks e ajudam a construir um ecossistema melhor para todos nós. Vamos construir juntos! 🔗 Acesse o projeto Repositório no GitHub: https://github.com/mauriciospark/coreIcons Demonstração / Site: https://mauriciospark.github.io/coreIcons/ O que você achou? Deixe suas ideias, feedbacks ou sugestões nos comentários abaixo! 👇 Hey everyone! 👋 I wanted to share a project I've been working on to solve a common problem: finding a clean, consistent, and easy-to-integrate icon library without overhead. Meet coreIcons — an organized collection of 352 development icons built for modern workflows. 📦 Why coreIcons? Lightweight & Fast: Minimal footprint for your applications. Organized: Designed with a consistent grid and style. Developer-Centric: Built to fit smoothly into your frontend or full-stack projects. 🚀 How to Use It's extremely

This is a submission for the June Solstice Game Jam What I Built Beat the Oracle is a daily FIFA World Cup 2026 prediction game where you go head-to-head against an AI — Google's Gemini 1.5 Flash — to call match scores before kickoff. Out-predict the machine and you win the day's Turing Test. Lose, and the Oracle has outsmarted you... until tomorrow. Every day you're served the same 5 matches as everyone else: some already played (scored instantly), some upcoming (lock in your call and come back). The Oracle reads each team's recent World Cup form and makes its own prediction with written reasoning — which you only see after you've locked in yours. No peeking, no cheating. Just you versus the machine. Scoring: Result Points Exact scoreline 3 🎯 "Enigma Cracked!" Correct result (W/D/L) 1 ✅ Miss 0 ❌ Why this fits the June Solstice Game Jam This jam asked for a game inspired by the solstice or any June celebration — and Beat the Oracle is stitched to June on two threads the challenge itself calls out: The World Cup is June's global celebration. The prompt names it directly: "the electric teamwork and high stakes of the World Cup, bringing the entire planet together in the spirit of play." That's the playground this game lives in — and as a bonus, I built it from 🇨🇦 Canada, a 2026 host nation . June is Alan Turing's month. Born June 23rd, Turing is the reason this jam has a "father of computing" prize at all. So I didn't bolt a Turing reference onto a football game — I built a playable Turing Test and gave it a World Cup costume. Turing's 1950 question, "Can machines think?" , becomes a question you answer with your gut every single day: can a machine predict football better than you? And the "daily" loop — new matches each day, your score reset, the machines winning "for today" — leans into the solstice's own theme of cycles and the passage of time. Every day is a fresh test. Every match is a new cipher. 🔗 Live demo: hema-nambi.github.io/BeatTheOracle Code Hema-Nambi /

WP Maintenance Manager ships from a single Python codebase to both Windows and macOS. "Python is cross-platform — write once, run anywhere," the saying goes. The reality is that the distribution pipeline is completely separate per OS , each with its own pitfalls. PyInstaller / Inno Setup / Apple Notarization / eSigner — the release cycle is a combination of OS-specific toolchains. Here's the full picture, plus what to watch out for at each step. (The choice of internal architecture, Flask + browser UI, is covered separately in why we built a desktop app on local Flask + browser UI ; this post is about distributing that architecture across two operating systems.) The per-OS pipeline at a glance Step Mac Windows Build PyInstaller ( --target-arch x86_64 ) PyInstaller Distribution format .app bundle → .dmg folder → .exe installer Installer creation hdiutil / create_dmg.sh Inno Setup ( .iss script) Code signing codesign + Developer ID certificate eSigner CSC (cloud signing) Pre-distribution validation Apple Notarization SmartScreen reputation buildup Final artifact WP_Maintenance_Pro_X.X.X.dmg WP_Maintenance_Pro_Setup_X.X.X.exe Both OSes share PyInstaller, but the path diverges from there. Mac sits inside Apple's review process; Windows runs through Microsoft's reputation system. They're fundamentally different ecosystems. Mac — PyInstaller → sign → Notarization → DMG The Intel / Apple Silicon trap The first trap in Mac PyInstaller builds is architecture . Running pip install + python build_app.py on an Apple Silicon Mac without thinking produces native binaries (like cffi ) for arm64 only — which then don't run on Intel Macs at all. The fix is to run the entire build through arch -x86_64 : arch -x86_64 pip3 install -r requirements.txt arch -x86_64 python3 build_app.py That produces an .app containing only x86_64 binaries, which runs natively on Intel Macs and through Rosetta 2 on Apple Silicon — a unified distribution. Sign inside-out The .app PyInstaller produces conta

We’ve all been there: it’s 2 AM, you’re deep in a "Refactoring Rabbit Hole," your coffee is cold, and your heart is racing. You feel productive, but is your body paying the price? As developers, we often ignore the physical signals of burnout until it's too late. In this tutorial, we are going to quantify the "Dev Grind." We'll build a Programmer Stress Warning Dashboard using the Oura Ring API to track Heart Rate Variability (HRV) and correlate it with your GitHub commit frequency . By the end of this guide, you'll have a real-time visualization of how that complex Kubernetes migration is actually affecting your nervous system. We will be utilizing HRV monitoring , biometric data visualization , and the Oura Ring API to create a predictive stress model for high-performance engineers. The Architecture 🏗️ The logic is simple: we fetch your physiological "readiness" and stress markers from Oura and overlay them with your activity from GitHub. If your commits are spiking while your HRV is tanking, it's time to step away from the keyboard. 🥑 graph TD A[Oura Cloud API] -->|HRV & Stress Levels| B(Next.js Backend) C[GitHub API] -->|Commit Frequency| B B -->|Data Aggregation| D{Correlation Engine} D -->|JSON Stream| E[D3.js Visualization] E -->|Alerts| F[Developer Dashboard] style F fill:#f96,stroke:#333,stroke-width:2px Prerequisites 🛠️ To follow this tutorial, you'll need: Oura Ring & a Personal Access Token (from the Oura Developer Portal ). Next.js (App Router) for our frontend and API routes. D3.js for crisp, reactive data visualizations. Vercel for instant deployment. Step 1: Fetching HRV Data from Oura API Heart Rate Variability (HRV) is the gold standard for measuring autonomic nervous system stress. A high HRV usually means you're recovered; a low HRV means you're under pressure. Here is a clean implementation of a Next.js API route to grab your daily stress metrics: // app/api/oura/route.ts import { NextResponse } from ' next/server ' ; export async function GET (

When I started coding, I made so many silly mistakes 😅 Today I’m sharing 3 small mistakes that every beginner developer makes: 1. Trying to write "Perfect Code" on Day 1 Bro, your code will be messy at the start. Just make it work first. Perfect comes later. 2. Watching tutorials but not coding yourself Watching videos is easy. But you only learn when you type the code on your own laptop. 3. Getting scared of errors Red error ≠ Failure. Error = Teacher. Copy it to Google, you’ll find the fix. What mistake did YOU make when you started? Tell me in the comments 👇

A few days ago I published a post about the three-layer auth model and the invoice incident that made...

Introduction Dans le cadre de mon apprentissage des pratiques DevOps modernes, j’ai conçu et implémenté un pipeline CI/CD (Continuous Integration / Continuous Deployment) capable de déployer automatiquement une application web frontend sur deux environnements de production distincts : Vercel et GitHub Pages . Cette mission constitue une application concrète des concepts fondamentaux du DevOps, notamment l’automatisation des processus, la réduction des interventions manuelles et la mise en place d’une chaîne de livraison logicielle fiable et reproductible. Tableau comparatif des plateformes Dans ce projet, le déploiement sur les deux plateformes n’est pas un doublon mais une démarche pédagogique et technique délibérée permettant de tester la flexibilité de l'orchestrateur. Voici comment elles se comparent : Critère GitHub Pages Vercel Hébergement Statique uniquement Statique + SSR + Serverless Domaine gratuit username.github.io projet.vercel.app CI/CD intégré Via GitHub Actions Natif + GitHub Actions Performance Bonne Excellente (Edge Network) Previews PR Non Oui (automatique) Gratuit Oui (illimité) Oui (avec limites) Cas d’usage Portfolios, docs Apps React/Next.js, SaaS ⚠️ Le problème du double déploiement : Laisser Vercel en mode automatique génère un conflit critique avec GitHub Actions. Pour éviter que deux builds s'exécutent en parallèle, j'ai désactivé le déploiement natif de Vercel en ajoutant un fichier vercel.json à la racine contenant "git": { "deploymentEnabled": false } . Le pipeline complet — deploy.yml Voici le code source du fichier de configuration de l'orchestrateur GitHub Actions ( .github/workflows/deploy.yml ). Ce script gère séquentiellement l'installation, le build et la publication vers nos deux cibles : name : CI/CD -- Deploy to Vercel & GitHub Pages # Declenchement : uniquement sur push vers main on : push : branches : - main jobs : build-and-deploy : runs-on : ubuntu-latest permissions : contents : write steps : # Etape 1 : Recuperer le code

Transferring genes across species doesn't just happen in microbes.

There is a comparison that the artificial intelligence industry had kept out of the public conversation until now. Satya Nadella brought it up this Sunday in a post on X that garnered over a thousand responses in just a few hours. The metaphor he used is "industrial offshoring" . Just as the first wave of globalization hollowed out industrial economies, wiping out factory jobs and decades of competitive advantage with consequences we still feel today, artificial intelligence threatens to do the same to corporate knowledge. The Silent Drain of Expertise The mechanism Nadella describes is concrete. If an organization hands over its workflows, its domain knowledge, and the accumulated judgment of its teams to external AI models, those models absorb it. What was once a unique advantage, now could become a generic capability available to everyone. There are no layoffs or plant closures. The hollowing out happens silently, in every usage cycle, in every operation the model records and leverages. Where there was once exclusive know-how, there is now a standard resource. "You can outsource a task, or even a job. But you can never outsource the learning." Microsoft CEO Warns That AI Winners Could Hollow 'Entire Industries' - Business Insider AI models are hoovering up corporate knowledge, and that's leaving one big loser, says Satya Nadella. businessinsider.com The Invisible Asset / The Identity This warning is not directed at employees but at executives. The risk Nadella identifies is not the loss of an individual position, but the erosion of the organisation's collective intellectual property, its processes, and the judgment a team spends years building. To name this, he introduces a term that was not in the business management vocabulary until now: "Token Capital" . This represents the layer of agentic capability that a firm builds and owns when it connects its real workflows with the AI models it uses. It is not software or a database. It is a system that learns with eve

Outro dia, vi nos stories do Instagram uma amiga pesquisadora contando que estava usando o Claude para ressuscitar uma plataforma criada anos antes por parceiros. Ela não é da área de desenvolvimento de software. O projeto estava praticamente parado. Não havia mais recurso para manter tudo como estava. Com a ajuda da IA generativa, ela conseguiu migrar serviços, reduzir custo, melhorar performance, redesenhar partes da experiência e voltar a implementar coisas que estavam no backlog havia muito tempo. Achei aquilo inspirador! Mas também um pouco assustador. Não por ela estar usando IA generativa. Pelo contrário: acho fascinante que pessoas que não programam profissionalmente estejam conseguindo recuperar autonomia sobre projetos que antes ficavam dependentes de verba, disponibilidade de terceiros ou uma fila infinita de prioridades. O ponto que me acendeu uma luz amarela foi outro: em certo momento da conversa ela comentou que o projeto tinha dados de usuários e pagamentos via Stripe . Antes de seguir, uma ressalva: eu não gosto muito do termo "vibe coding" . Vou usar o termo aqui porque ele pegou, e porque todo mundo entende mais ou menos o que ele quer dizer: criar software com muita ajuda de IA generativa, muitas vezes sem dominar profundamente a linguagem, o framework ou a arquitetura por trás do projeto. Mas o termo me incomoda porque parece diminuir a responsabilidade envolvida. Se você está criando código, alterando código e colocando esse código no ar, você é sim uma pessoa desenvolvedora. Talvez iniciante. Talvez insegura. Talvez dependente demais da IA generativa. Talvez uma pessoa desenvolvedora ruim ou medíocre, como todos nós somos em algum recorte. Mas é. E isso traz responsabilidades. Vibe coding em uma página pessoal é uma coisa. Vibe coding em um sistema com login, dados pessoais, áreas administrativas, arquivos, integrações externas ou pagamento é outra. E aqui existe uma tensão interessante. Eu trabalho com desenvolvimento de software há bastante

When was the last time you deleted an email account on purpose? For most teams the answer is never, and that tells you something. We treat mailboxes the way we treated servers in 2008: hand-built, carefully named, kept alive indefinitely because recreating one is painful. They're pets. Meanwhile every other piece of our infrastructure — compute, queues, databases — became cattle: numbered, provisioned by code, destroyed without sentiment when the job ends. Email is finally catching up. With Nylas Agent Accounts (in beta), a mailbox is created with one call and destroyed with another, and that symmetry is the whole point. The full lifecycle in two commands Provisioning, from the CLI: nylas agent account create signup-agent@agents.yourdomain.com Or via POST /v3/connect/custom with "provider": "nylas" — no OAuth, no refresh token, just an address on a domain you've registered. Teardown is equally unceremonious: nylas agent account delete signup-agent@agents.yourdomain.com --yes (The API equivalent is a DELETE on the grant.) The signup automation recipe treats this as a loop: provision a fresh inbox, point a third-party signup form at it, catch the verification email through a message.created webhook, follow the confirmation link, delete the grant. No human inbox involved at any step, and nothing left behind. The middle of that loop is about twenty lines of webhook handler, and the recipe's version filters hard before acting — right grant, right sender, right URL shape: const { grant_id , id : messageId , from } = event . data . object ; if ( grant_id !== AGENT_GRANT_ID ) return ; const sender = from [ 0 ]?. email ?? "" ; if ( ! sender . endsWith ( " @saas-you-care-about.example.com " )) return ; const match = /https: \/\/ saas-you-care-about \. example \. com \/ confirm \? token= [^ " \s < ] +/ . exec ( message . body , ); if ( match ) await fetch ( match [ 0 ]); Nylas fires message.created within a second or two of mail arriving, so the whole signup round-trip typical

By spring 2026, the division of labor between human engineers and AI had become precise enough to describe. Not speculate about. Describe. Delegate these 7 immediately: Boilerplate generation: CRUD scaffolding, config files, standard patterns. Near-human accuracy. Review required is a naming scan, not a logic audit. Test generation: 40-60% faster test development with no measurable decline in coverage quality, provided the tests are reviewed by someone who understands the domain. Documentation: 67% of companies rely on AI-assisted doc generation in 2026. The first draft is a solved problem. Your job is verifying and contextualizing. Code translation: Python to TypeScript. React to Vue. Framework migrations that once consumed sprint cycles now take hours. Routine bug fixing: Claude Code, Devin, BugBot can resolve 60% of reported bugs autonomously. Resolution time down 30-50%. Automated code review: First-pass filter before human review. Misses context issues. Doesn't replace human review. Eliminates noise so you focus on signal. Commit hygiene: Messages, PR summaries, changelog entries. Fully automatable. No meaningful error rate. Never delegate these 6: Architecture and system design: AI proposes. You decide. The tradeoffs require organizational context, team capability assessment, and long-horizon thinking no model possesses. Business context translation: The spec says "export to CSV." You ask: which users, under what conditions, with what compliance implications? AI cannot know the specification is wrong. You can. Security architecture: AI generates vulnerabilities as readily as it detects them. Adversarial thinking is not statistical. It is human. Long-horizon product thinking: What to build and why. Not how. Multi-stakeholder navigation: The politics, the relationships, the conversation with the PM that keeps the sprint on track. No model has stakes in the outcome. Agent orchestration: Designing, managing, and correcting the AI systems themselves. This is the ne

Something feels different about security incidents lately. Breaches, leaks, account takeovers, phishing campaigns they're not new. But their frequency, sophistication, and scale seem to be growing at a pace that feels genuinely alarming. Instagram accounts hacked overnight. Corporate systems compromised in hours. Phishing emails that sound disturbingly human. As someone studying AI & Big Data, I can't help but ask: is AI responsible for this? And if so, how? I think the honest answer is: yes but in two very different ways. The two faces of AI in cybersecurity When we talk about AI and cyberattacks, most people imagine one scenario: hackers using AI to attack systems faster and smarter. That's real. But it's only half the picture. The other half is something we talk about far less: the vulnerabilities that come from integrating AI into systems in the first place. These are two very different problems. And conflating them leads to the wrong solutions. Problem 1: AI is expanding the attack surface Every time a platform integrates an AI feature, they're adding something new to their infrastructure. And new infrastructure means new potential vulnerabilities. AI systems require: Massive data pipelines more data flowing through more systems APIs connecting multiple services more endpoints that can be exploited Third-party models and tools more external dependencies, more trust relationships Real-time processing less time to detect anomalies before damage is done Many organizations are integrating AI features faster than their security teams can audit them. And the consequences are already visible. In June 2026 , hackers reportedly manipulated AI-powered support systems to gain unauthorized access to Instagram accounts. The attack didn't target traditional software vulnerabilities it targeted the AI system itself , exploiting the automated account recovery flow that Meta had built with AI. This is the new reality: attackers are no longer just targeting your code. They're ta

Last quarter our OpenAI bill went from $620 to $2,480 in 23 days. No new features shipped. No traffic spike. Zero error alerts. Deployment logs were clean. Just a number climbing in silence while five engineers stared at dashboards that gave us totals and nothing else. This is what we found. And why "cost monitoring" is completely the wrong mental model. The dashboard that answers the wrong question First thing I did was open the OpenAI usage dashboard. It showed me a total. A graph going up. A model breakdown. I knew we spent $2,480. I still had no idea which feature spent it, which service triggered it, or which user was responsible. The dashboard was answering "how much" while we were desperately asking "what caused it." Those are completely different questions. Almost every cost tool on the market only answers the first one. That distinction matters more than most engineering teams realise until they are staring at a bill like ours. Three features, zero visibility We had three features hitting GPT-4o: A document summariser, triggered manually by users An inline suggestion engine, triggered on keystrokes A batch report generator, triggered on export Any one of them could be the problem. Or all three. Or one specific tenant hammering one endpoint in a loop nobody noticed. Without attribution at the feature, service, and user level, we were just guessing. So I did what most engineers do: optimised the feature that felt most expensive. Added caching to the one that ran most often. Two weeks later the bill was still climbing. Guessing at cost problems without attribution data is exactly like debugging a performance issue without a profiler. You move things around and hope. 48 hours of real data A teammate dropped CostReveal in our Slack. I set it up that evening. The Node.js SDK wraps your existing provider calls. You instrument each one with a feature name, service context, and user or tenant ID. That is the entire integration for the base case: import { CostReveal

AI for API Design & Testing in 2026: Speakeasy vs Swagger AI vs Postman AI — I Built 3 APIs with Each API design is one of the most tedious parts of backend development. Writing OpenAPI specs, generating SDKs, testing endpoints, maintaining documentation — it's all hours of work that could be automated. In 2026, three tools are fighting for your API workflow: Speakeasy , Swagger AI , and Postman AI . I spent 4 weeks building the same REST API with each one, tracking time savings, code quality, and actual developer experience. Here's what actually happened. The Test Setup: Building a Real API I built a simple but realistic ecommerce API (GET/POST products, orders, user management) three separate times, measuring: Time to spec — writing the OpenAPI definition Time to SDK generation — generating client libraries Time to testing — setting up test cases and running them Documentation quality — readability, examples, completeness Iteration speed — how fast you can modify the API and regenerate everything All three tools were given the same requirements. Same laptop, same environment, same skill level (senior backend engineer). Speakeasy: The SDK Generation King Setup time: 8 minutes (CLI install, auth, first config) Learning curve: Low — clear docs, straightforward CLI Speakeasy is laser-focused on one problem: turning your API spec into production-ready SDKs. It generates TypeScript, Python, Go, Java, and more from a single OpenAPI definition. The Good SDK quality is exceptional. The generated TypeScript SDK was production-grade immediately — proper error handling, retry logic, request/response typing, retries built in. No cleanup work needed. Multi-language generation is instant. After writing the OpenAPI spec once, I had Python, Go, and TypeScript SDKs in < 2 minutes total. Each one was framework-idiomatic (using popular libraries in each language). Versioning is smart. Speakeasy automatically versioned SDKs and generated changelogs. When I modified the spec, it detect

Stop letting the prompt be your state machine You shipped an LLM feature six months ago. Now the same user input produces wildly different outputs depending on... nothing you can point to. Something in the sampling? The time the context filled up and a chunk got dropped? Nobody knows. This is what happens when the prompt becomes your runtime. The trap: the prompt as an accidental runtime Here is what the trap looks like in TypeScript: async function handleUserRequest ( input : string ): Promise < string > { const prompt = ` You are a helpful assistant. The user said: ${ input } Previous context: ${ someGlobalContext } Decide what to do, gather any information you need, format the response, and return it. ` ; return llm . complete ( prompt ); } The model is doing everything here: deciding the intent, gathering data, formatting output, choosing what to persist. That is a footgun. You handed the runtime to a stochastic function. Gartner attributes many failed agentic AI projects to unclear value and inadequate risk controls. Deterministic, testable workflows address both. The fix is not a better prompt. The fix is to stop using the prompt as an architecture. What "deterministic" can and cannot mean here Be honest about what you can and cannot control. You cannot control: the model's exact output. It is probabilistic by design. You can control: The shape of the output (structured output plus schema validation) The steps that run before and after the model call What data enters the model What happens when the output fails validation Whether a human reviews the result before it commits to anything irreversible Determinism here means: the same inputs, the same workflow steps, the same guardrails every time. Not the same tokens every time. That is a realistic and achievable target. It is also the thing teams skip when they are moving fast. Typed workflow steps around the model call Break the work into discrete typed steps. Each step has a clear input type and a clear output

What Recruiters Can't See On My GitHub If you spend about 30 seconds looking at my GitHub profile, you might think I'm all over the place. React. Python. Healthcare. AI. Scrapers. Automation. Marketing tools. Job bots. Honestly, that's something I've worried about. I have over 100 repositories. Recruiters can see most of them, but not all of them. Some are private because they're client work. Some are private because they're unfinished. Some are private because they contain ideas I've spent years developing and I'm not quite ready to throw the blueprints onto the internet. From the outside, it can look random. But recently I realized something. All of those projects are solving the same problem. I hate repetitive work. My GitHub is here: https://github.com/ashb4 The Job Application That Broke Me I've applied to thousands of jobs over the years. Thousands. And one thing has always driven me absolutely insane. You upload your resume. Then the company immediately asks you to type your entire resume into fifteen different boxes. Your work history. Your education. Your skills. Everything. The computer already has the information. The resume is right there. Yet somehow I'm sitting on page seven of an application retyping information that already exists. It feels inefficient. It feels stupid. And most of all, it feels like a waste of time. Eventually I got annoyed enough to start building tools to help. Then I Noticed a Pattern At first I thought I was building unrelated projects. A job application helper. A content scheduler. A healthcare platform. An AI framework. A browser automation system. But when I stepped back, I noticed the same motivation behind almost all of them. Every project started with some version of: "There has to be a better way to do this." Take PostPunk. Most people see a social media scheduler. I see hours of repetitive posting that I never want to do again. I like creating content. I do not like manually posting the same content everywhere. So I buil

The company said the cuts were part of a restructuring meant to help scale to profitability. Rivian recently pushed back its profitability goal to invest in autonomy.

Nimble’s thoughtfully designed SharePower is a modular power bank you can share with a friend.

Snap CEO Evan Spiegel lays out the company’s vision for its augmented-reality smart glasses, arriving later this year.