标签：#ia

Migrar 100 usuários manualmente no console da AWS é lento, suscetível a erros e impossível de auditar com precisão. Neste artigo você vai ver como automatizar esse processo usando AWS CLI e Shell Script direto no AWS CloudShell — sem instalar nada localmente. O resultado final: usuários criados, alocados nos grupos corretos e com MFA obrigatório, tudo em minutos. O que é o IAM? O AWS Identity and Access Management (IAM) é o serviço que controla quem pode acessar os recursos da sua conta AWS e o que cada pessoa ou serviço pode fazer. Com o IAM você gerencia: Conceito Descrição Usuário Identidade individual com credenciais próprias Grupo Conjunto de usuários que compartilham as mesmas permissões Política Documento JSON que define o que é permitido ou negado Role Identidade temporária assumida por serviços ou usuários A boa prática é nunca conceder permissões diretamente a um usuário — sempre use grupos. Visão geral da solução O fluxo é simples: Criar os grupos IAM no console Montar um arquivo CSV com os dados dos usuários Rodar um shell script no CloudShell que lê o CSV e cria tudo automaticamente Aplicar a política de MFA obrigatório nos grupos Passo 1 — Criar os Grupos IAM Antes de importar os usuários, os grupos precisam existir. No AWS Console , acesse IAM → User groups → Create group e crie um grupo para cada perfil do seu ambiente. Neste exemplo usaremos: RedesAdmin — administradores de rede LinuxAdmin — administradores de servidores Linux DBA — administradores de banco de dados Estagiarios — acesso limitado para estagiários Nomes de grupos suportam até 128 caracteres (letras, números e + = , . @ _ - ), são únicos por conta e não diferenciam maiúsculas de minúsculas. Passo 2 — Montar o arquivo CSV Crie uma planilha com os dados dos usuários e salve como CSV separado por vírgula (UTF-8) . O arquivo deve ter exatamente três colunas: Username , Group e Password . Username , Group , Password joao . silva , LinuxAdmin , Senha @2024 ! maria . souza , DBA , Senha @2024

I just joined DEV to explore the community and get into the habit of writing about what I'm learning. I also set up a blog on Hashnode — figuring out how the two fit together. Here's a quick code block to test formatting: ​ function greet ( name ) { console . log ( `Hello, ${ name } !` ); } greet ( " DEV " ); ​ ``` More to come as I find my way around 👋

Co to jest WCAG 2.1? WCAG 2.1 ( Web Content Accessibility Guidelines ) to międzynarodowy standard techniczny określający, jak tworzyć strony www i aplikacje mobilne, aby były dostępne dla osób z niepełnosprawnościami (wzroku, słuchu, ruchu, poznawczymi). Wersja 2.1 rozszerza wcześniejsze zasady o wytyczne dla urządzeń mobilnych oraz osób słabowidzących. Struktura WCAG 2.1 i poziomy zgodności Standard opiera się na 4 głównych zasadach. Dzielą się one na wytyczne, do których przypisane są konkretne kryteria sukcesu wdrażane na trzech poziomach: Poziom A: Absolutne minimum. Bez niego strona jest całkowicie niefunkcjonalna dla wielu użytkowników. Poziom AA: Standard rynkowy i prawny. Wymagany przez polskie i europejskie przepisy dla sektora publicznego i biznesu. Poziom AAA: Najwyższy stopień dostępności, trudny do wdrożenia w całym serwisie. Zasady POUR – Fundamenty WCAG 2.1 Wszystkie wytyczne WCAG 2.1 opierają się na czterech głównych zasadach tworzących akronim POUR : P erceivable ( Postrzegalność ) - Treść musi być dostarczana w sposób czytelny dla zmysłów użytkownika (wzroku, słuchu). O perable ( Funkcjonalność ) - Interfejs i nawigacja muszą być możliwe do obsługi za pomocą różnych urządzeń (np. samej klawiatury). U nderstandable ( Zrozumiałość ) - Informacje oraz obsługa strony muszą być jasne, logiczne i przewidywalne. R obust ( Solidność ) - Kod strony musi być poprawny i kompatybilny z obecnymi oraz przyszłymi technologiami (przeglądarki, czytniki ekranu). Kto musi spełniać standardy WCAG? Dostępność cyfrowa to już od dawna nie tylko "dobra praktyka", ale twardy wymóg prawny, który stale się rozszerza: Sektor publiczny (Obecnie): W Polsce urzędy państwowe i samorządowe, szkoły, uczelnie, szpitale oraz spółki skarbu państwa mają bezwzględny obowiązek spełniania standardu WCAG 2.1 na poziomie AA. Wynika to wprost z Ustawy z dnia 4 kwietnia 2019 r. o dostępności cyfrowej . Za brak zgodności grożą kary finansowe. Sektor prywatny i biznes: Na mocy Europejskiego Akt

I Built My First Token on Solana — Here's What Actually Surprised Me This week I went from zero tokens to minting, transferring, charging fees, and locking tokens so they can never move. Here's what stuck with me. Tokens don't live in your wallet Coming from Web2, I assumed tokens would just... show up in your account. Nope. On Solana, every wallet needs a separate token account for each token it holds. One mint, one folder. It felt weird at first. Now it makes sense. You can charge fees without writing a single backend The Token Extensions Program has a built-in transfer fee. One flag at mint creation time: spl-token create-token \ --program-id TokenzQdBNbLqP5VEhdkAS6EPFLC1PHnBqCXEpPxuEb \ --transfer-fee-basis-points 100 That's a 1% fee on every transfer, enforced by the blockchain. No middleware. No payment processor. No way to bypass it. You can make a token that literally cannot be transferred spl-token create-token \ --program-id TokenzQdBNbLqP5VEhdkAS6EPFLC1PHnBqCXEpPxuEb \ --enable-non-transferable I minted 10, tried to send 5, and watched the transaction get rejected. Not by my code — by the program itself. Perfect for credentials, badges, or certificates that should belong to one person forever. The biggest shift from Web2: these rules are set at creation and are permanent. You can't add a transfer fee to an existing token. You can't make a transferable token non-transferable later. It forces you to think about token design upfront, which is honestly a good constraint. solana #blockchain #webdev #beginners #100DaysOfSolana

A new reading subscription from Everand offers access to both ebooks and audiobooks, and Fable's book club community.

submitted by /u/SirNirmal [link] [留言]

International Mathematical Union endorses warning about tech industry influence.

The caveat is that one of the world's most famous living directors is using the tech solely for storyboarding.

I recently completed a 5-minute philosophical science fiction short film called The Robot Summit. The story takes place in a future where humanity has disappeared and intelligent machines gather to understand their origins, purpose, and the nature of intelligence itself. As the discussion unfolds, an unexpected human survivor challenges many of their assumptions. This project was developed over several months using a workflow that combined AI image generation, AI video generation, AI voice synthesis, original music composition, and traditional editing in Final Cut Pro. One of the biggest challenges was maintaining visual consistency and narrative coherence across dozens of AI-generated shots while still creating something that felt like a film rather than a technology demonstration. I'm particularly interested in feedback regarding: • Narrative flow and pacing • Visual continuity between scenes • Audio balance between narration and music • Whether the philosophical themes feel natural or overly explicit • Overall effectiveness as a short film I'm also happy to answer questions about the production workflow, tools used, and lessons learned during development. Film: https://www.youtube.com/watch?v=pMeJ7h734vE submitted by /u/renatobotto [link] [留言]

submitted by /u/jameslaney [link] [留言]

The class action lawsuit, filed in Seattle by Virginia resident Charles Sigwalt, claims that Ring's Familiar Faces feature stores images of passersby without consent.

Quantum computing is starting to get pulled into the same conversation as AI, semiconductors and national scientific computing. The federal government is supporting quantum through CHIPS-style incentives, national lab initiatives, and post-quantum cybersecurity regulation. Big tech is also still heavily involved through IBM, Google, Microsoft, Amazon, Nvidia and Honeywell/Quantinuum. But I’m trying to understand the real timeline. AI has immediate commercial demand. Data centers need GPUs right now. Power demand is visible right now. Quantum is different. The potential is huge, but broad commercial quantum advantage still seems uncertain. So is quantum a real near-term AI infrastructure theme, or is it more like a 5-10 year strategic bet? Where do people think the first real commercial use cases show up? Optimization? Chemistry/materials? Cybersecurity? Finance? Drug discovery? AI model training? National labs? Curious what people working closer to the field think. submitted by /u/CalebMitchell840 [link] [留言]

submitted by /u/ThereWas [link] [留言]

Hey everyone, If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster. Most of the "guides" out there are just static, out-of-date tables or dense walls of text. So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently. What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page. You select the model size (8B, 32B, 70B, etc.). You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ). The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy. It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances. It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive) Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide submitted by /u/Outside-Risk-8912 [link] [留言]

Tenho aproveitado meu tempo sem trabalhar pra estudar, enfim a vida de quem trabalha com tecnologia né? E um dos meus maiores focos tem sido IA, seus usos, como ela entra e pode ser aplicada em áreas diferentes, e todas as novidades que saem todos os dias. Hoje vim compartilhar uma coisa bem legal que aprendi no curso AI-Native Engineering Foundations do Addy Osmani , o problema dos 70%. Existe um padrão claro que tenho observado na prática ao acompanhar dezenas de equipes de engenharia: a Inteligência Artificial resolve com impressionante eficiência 70% de quase qualquer tarefa técnica. Falo daquela camada previsível, repetitiva e baseada em padrões exaustivamente documentados na internet. Coisas como código boilerplate, arquivos de configuração, implementações de CRUDs simples, conversão de sintaxe entre linguagens e a escrita de testes unitários básicos. A IA já "viu" milhões de exemplos disso em repositórios públicos e consegue reproduzir o padrão em segundos. Para essa fatia do trabalho, ela é uma aceleradora fantástica. O grande problema, e o motivo pelo qual muitos projetos com IA começam bem mas falham no meio, é que os outros 30% são justamente os que sustentam o software. É nesses 30% que entram as decisões que inteligência nenhuma consegue tomar sozinha: Contexto de Negócio: A IA não sabe por que aquela feature está sendo construída ou como ela impacta o usuário final. Arquitetura e Manutenibilidade: Escrever código que funciona hoje é fácil; escrever código que outra pessoa consegue alterar daqui a seis meses sem quebrar o sistema é outra história. Casos de Borda e Segurança: A IA tende a gerar o "caminho feliz". Tratar falhas de concorrência, vazamento de memória e vulnerabilidades específicas do seu ecossistema exige malícia técnica. Essas questões não se resolvem apenas digitando linhas de código, elas exigem contexto, experiência, histórico de dores passadas e, acima de tudo, julgamento humano. E é exatamente aqui que a IA ainda não entrega. O Parado

submitted by /u/YuriPD [link] [留言]

Opal, the company famous for making a fancy webcam, has pivoted to making other consumer electronics. Fueled by big investments from OpenAI and Samsung, it’s working on an audio gadget first.

The question in the title. I tried to run the same image with different formats and got different result. Also it also depends on whether image is uploaded on PC or phone, so I thought of asking about the stuff behind everything. I know very little about this stuff and would appreciate if you go into details. Thank you! submitted by /u/Neuron_Pixel_4 [link] [留言]

Meta told TechCrunch that it's considering ways to monetize the new feature, but didn't share specifics on what that could look like.

X will now let you 'react with video' to posts.