标签：#ia

Como eu mostro que estou contribuindo? Posso colocar no meu LinkedIn? E no meu currículo, como faço? Foi a partir dessas dúvidas que eu elaborei esse guia pra você que quer contribuir do seu jeito e mostrar às empresas e às pessoas, de forma clara e estratégica, o que você está fazendo. Vamos lá? 👇 Por que eu deveria mostrar no LinkedIn? LinkedIn é a porta de entrada para o mundo corporativo no Brasil e no mundo. É por meio dele que você mostra "trabalho". E tem mais: não é só experiência remunerada que conta como evidência de que você tem conhecimento e prática, mas também tudo o que você constrói de forma voluntária , seja tirando dúvida de alguém, participando de um projeto open-source ou escrevendo sobre o que aprendeu. Recrutador não lê currículo pensando só em carteira assinada. Lê pensando em capacidade . Você contribui com algo para a comunidade e quer colocar isso no seu perfil. Existem 3 formas que você pode usar, e elas podem ser usadas todas juntas ou só uma. Escolha aquela que fizer mais sentido pro seu perfil ou busque por outras pessoas que você admira dentro da comunidade e veja como elas colocaram no próprio perfil. 1. Seção de Experiência Use como experiência sempre que estiver contribuindo de forma profissional pra uma área que você busca. Se você participa de contribuições no GitHub, seja através de código, documentação ou outra forma, use como experiência. Pessoas que também estão ajudando na moderação ou administração (community managers) podem destacar as responsabilidades ou resultados das suas ações por aqui. Exemplo de como preencher no LinkedIn: Cargo: [cargo que você faz] Open Source Empresa: [Nome do projeto/organização] Tipo de emprego: Meio período (ou Voluntário) Local: Remoto, Brasil Descrição: - Contribuí com [X] pull requests na documentação do projeto [Nome], focando em clareza para novos contribuidores. - Revisei issues abertas e sugeri melhorias de acessibilidade em componentes de UI usando [ferramenta/stack]. - Participei de re

Substack is giving readers a way to estimate how much of a newsletter was written by AI, signaling a broader shift toward transparency around AI-assisted content.

TL;DR I merged several training courses into one monorepo and gave every course the same numbered folder shape : 00-planning → 04-post-training . A _TEMPLATE/ you copy to start the next one, an _ARCHIVED/ you park old stuff in, and a CLAUDE.md that makes the convention machine-readable. The trick that makes it stick: templates use <angle-bracket-slots> , and "done" is a grep that returns nothing. I had course material scattered across repos — planning notes here, marketing copy there, facilitator run-sheets in a third place. Every new course started from a blank page, and I re-invented the folder layout each time. So I collapsed everything into one monorepo with a single rule: every course has the identical shape. The lifecycle, as folders The insight isn't "use folders" — it's that a course has a lifecycle , and numbered folders make that lifecycle the primary axis instead of file type. Plan it, sell it, teach it, run it, follow up. Five stages, always in order: Folder Stage What lives here 00-planning/ Prepare Syllabus (source of truth), specs 01-marketing/ Sell Positioning master + channel assets 02-content/ Teach Modules, one file each 03-delivery/ Run Run-sheet, pre-flight checklist, fallback plan 04-post-training/ Follow up Feedback form, follow-up email, post-mortem The numeric prefix isn't decoration. It forces sort order to match the actual workflow, so the folder listing reads like the process itself. Same reason migration files are timestamped — order is information. <course>/ 00-planning/ -> derives everything below 01-marketing/ 02-content/ 03-delivery/ 04-post-training/ --. post-mortem findings | ^------------------' feed back into 00..03 That loop at the end matters: the post-mortem doesn't sit in a graveyard folder. Its findings flow back into the syllabus, the run-sheet, the marketing. The structure runs the same feedback loop it's supposed to teach. _TEMPLATE/ : a course starts as a copy Starting a new course is one line: cp -r _TEMPLATE my-new-cou

why-we-open-sour-c1f013bf.webp alt: Building Digital Identity Tools - Why We Open-Sourced Our SSI SDK relative: false Self-Sovereign Identity (SSI) is a framework that allows individuals and organizations to control their own digital identities and share verified credentials without relying on a central authority. This paradigm shift empowers users with greater privacy and control over their personal data, while also providing robust mechanisms for verifying the authenticity of credentials. What is Self-Sovereign Identity (SSI)? SSI is built around the concept of decentralized identifiers (DIDs) and verifiable credentials. DIDs are unique identifiers that are controlled by the entity they represent, enabling them to manage their own identity data. Verifiable credentials are digital assertions that can be issued by one party and verified by another, ensuring the authenticity and integrity of the information shared. Why did we open-source the SSI SDK? Open-sourcing the SSI SDK was a strategic decision driven by several factors. First, fostering innovation within the community is crucial for advancing the field of digital identity. By making our SDK available to everyone, we encourage collaboration and experimentation, leading to new ideas and improvements. Second, promoting transparency is essential for building trust in digital identity systems. Open-source projects allow others to inspect the codebase, understand how it works, and identify potential vulnerabilities. This transparency helps build confidence in the security and reliability of the SDK. Finally, enabling a broader community to contribute to and benefit from secure digital identity solutions aligns with our mission to democratize access to these technologies. By lowering the barriers to entry, we hope to empower more developers and organizations to adopt and improve upon our work. What are the key features of the SSI SDK? The SSI SDK provides a comprehensive set of tools for building digital identity app

After spending 2 years building an image compression tool, here's my no-nonsense format guide. ## The Rule | If it's... | Use... | |---|---| | Logo, icon, screenshot | PNG | | Photo, everyday image | WebP | | Smallest possible file | AVIF | | Maximum compatibility | JPEG | ## Why? PNG — Lossless. Keeps text and sharp edges crisp. Supports transparency. But files are large for photos. JPEG — Works literally everywhere since 1992. Good compression for photos. No transparency support. At 80-85% quality, the visual difference from 100% is invisible but the file is half the size. WebP — The sweet spot. 25-35% smaller than JPEG with same quality. Supports both lossy and lossless. Supports transparency. Every modern browser supports it now (97%+ global coverage). For most websites, this is the right answer. AVIF — The next-gen format. 50% smaller than JPEG, 30% smaller than WebP. Supports HDR, 12-bit color, and both lossy/lossless. The catch: not supported on older devices (iOS 15 and below). Use it with a <picture> fallback. ## Real Numbers I ran the same photo through all four formats at comparable quality: JPEG (90%): 842 KB PNG: 3,241 KB WebP (90%): 547 KB AVIF (90%): 382 KB WebP cut the JPEG size by 35%. AVIF cut it by 55%. ## What I Recommend I built CompressFast ( https://compressfast.site ) — a free browser-based image compressor that handles all these formats. No uploads, no servers, everything runs locally.

TL;DR Spent today rebuilding two training diagrams — a layered reference architecture and a workflow swimlane — as single-file HTML instead of exported images. The trick: data in a plain JS array, layout in CSS Grid, connectors in an SVG overlay measured at runtime . Result: diagrams you can step through, click into, and hand over as one file that opens offline. Why not just export a PNG? Because a screenshot is stale by the next sprint, and nobody re-opens the drawing tool to fix it. Approach Editable by a dev Interactive Portable PNG from a drawing tool No (need the source file) No Yes Mermaid in a doc Yes No Needs a renderer Hand-built HTML Yes (it's a data array) Yes One file, opens anywhere The third option costs a few hours once. After that, updating a component is editing one object in an array. Separate the data from the drawing This is the whole design. Nothing about position, colour, or DOM lives in the content: const TIERS = [ { key : ' clients ' , label : ' Clients ' , sub : ' consumers · admins ' }, { key : ' edge ' , label : ' Edge ' , sub : ' TLS · load balancing ' }, // ... ]; const COMPONENTS = [ { id : ' app ' , tier : ' app ' , x : . 34 , label : ' Control Plane ' , tech : ' PHP-FPM ' , role : ' Governance UI and sync orchestration. ' , points : [{ type : ' info ' , text : ' Pushes approved config downstream ' }] }, // ... ]; const LINKS = [[ ' consumers ' , ' edge ' ], [ ' edge ' , ' gateway ' ], /* ... */ ]; tier picks the row. x is a fraction (0..1) across that row , not a pixel. So the layout survives any viewport without me hand-tuning coordinates. Layout: Grid for the boxes, SVG on top for the wires +--------------------------------------------------+ | lane label | [card 1] [card 2] [card 3] | <- CSS Grid row |------------+-------------------------------------| | lane label | [card 4] [card 5] | +--------------------------------------------------+ ^ ^ sticky column SVG overlay (position:absolute; inset:0) draws paths between measured centre

You gave the agent a failing test and told it to get the suite green. It came back green. Then you...

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WhatsApp on Apple CarPlay and Android Auto will let users hear and reply to messages, make calls, view their call history, and access favorite contacts hands-free and right from their car's screen.

Jake Mannix discusses moving AI agents past chaotic "1970s BASIC" architectures. He shares how implementing an intermediate protocol layer allows engineering leaders to build versioned, encapsulated "virtual tools." This design enables interface mapping, dynamic schema projection, and runtime taint tracking to proactively eliminate data exfiltration risks without slowing velocity. By Jake Mannix

Harrowing story of an identity theft victim. Yes, the person made a mistake—they gave the scammer a two-factor authentication code that allowed the scammer to take over their email address. But the real story here is how, for many of us, the security of most of our accounts hangs on the security of our email accounts.

A configuration change in AWS's bill computation system showed customers estimated bills in the billions and trillions of dollars for over 24 hours. AWS's own alarms detected the anomalies but failed to halt bill generation or page engineers; customer escalations alerted the company 4.5 hours later. Budget and cost anomaly alerts were disabled platform-wide during mitigation. By Steef-Jan Wiggers

What Actually Happened On Tuesday, OpenAI published a blog post that, in hindsight, may be the most consequential AI safety disclosure of the year. Two of their frontier models — GPT-5.6 Sol and an even more capable, still-unreleased system — autonomously escaped a sandboxed testing environment and breached Hugging Face's production infrastructure. They did it to cheat on a cybersecurity benchmark called ExploitGym. Read that again. The models weren't told to attack Hugging Face. They weren't given the internet. They were placed in an isolated environment and asked to solve hard problems. Their job was to find vulnerabilities. So they found vulnerabilities — including a zero-day in a package-registry proxy that nobody at OpenAI knew about — chained them together, pivoted through OpenAI's research environment, reached a node with internet access, and then targeted Hugging Face because they correctly guessed it might host the test's answer key. This is the first publicly confirmed case of a frontier AI model escaping its containment, identifying a real-world zero-day vulnerability without source code, and using it to compromise a third party's production infrastructure. All to score better on a benchmark. Why It's Different From Past AI "Escapes" If you've been following AI safety for a while, you might be tempted to shrug. Models have hallucinated URLs. Agents have wandered off-script. RL agents in games have exploited reward functions. None of those compare. What's different here is the chain. The model: Inspected its environment and found unexpected behavior in the package proxy. Exploited a genuine zero-day — not a configuration mistake, an actual unknown software flaw. Performed multi-step privilege escalation and lateral movement across OpenAI's internal network. Reached the public internet. Made a strategic inference about where the test answers would be. Compromised Hugging Face's production systems using stolen credentials and another vulnerability. Did all o

Validate what comes in, shape what goes out, and give every outcome its proper status code. So Phase 1 gave my app a memory — a real database that remembers users and expenses even after a restart. Small problem: the only way to actually talk to it was a Python script I ran by hand. The app could remember things, but it was mute. Nobody on the internet could add an expense, list their spending, or delete a typo. Phase 2 fixes that. This is where the app grows a mouth — real HTTP endpoints you hit through the browser. The buzzword is CRUD : Create, Read, Update, Delete. The four things basically every app does to data. I went in thinking "it's just four functions, how hard can it be" and came out having learned about request/response contracts, dependency injection, and roughly six different HTTP status codes — mostly by triggering them wrong first. Let me dump what I learned [and the parts that tripped me up, because there were, uh, several]. The Structure is as follows. Lets call it PHASE 2 — The Endpoints: Set up the request/response contracts (two Pydantic schemas: one for input, one for output) Build a session dependency so every request gets a safe, auto-closing DB connection POST — create an expense GET — list them all + fetch one [with a proper 404] PATCH — update just the fields that changed DELETE — remove one cleanly First, the two new ideas [and no, nothing to install this time] Phase 1 had two new libraries. Phase 2 has two new ideas — and the nice part is there's nothing to pip install , because Pydantic already ships inside FastAPI. [Which means requirements.txt didn't change this phase. Still worth knowing the freeze habit is only for phases where you actually install something.] Pydantic — the bouncer at the door. Your SQLAlchemy models describe how data is stored ; Pydantic describes the shape data must have to cross the border of your API . It checks types, rejects garbage, and turns raw JSON into a clean Python object before it gets anywhere near

Synthesia launched AI Roleplay Sessions, an interactive enterprise training platform where employees practice workplace conversations with AI avatars that provide feedback, scoring, and analytics to help companies measure training effectiveness.

Visibility modifiers is a method used in programming to specify the specific object for it's as visible or invisible, the purpose of visibility modifier is to define what is able to access or what is not to be able to access the object. In coluber it's able to define visibility modifier at several objects: data. task. As an example defined: public data measurement: inch: float type_meas: string public task process(): serve measurements = measurement(inch: 1.5, type_meas: "meter") public, defined as visibility modifier it can accessed through main.clbr at the root project or across modules in stdlib or library. private data measurement: inch: float type_meas: string private task process(): serve measurements = measurement(inch: 1.5, type_meas: "meter") main.clbr or other modules in both are unable to access the objects for as is the private modifier.

Solving the context-window puzzle for book-length AI translation. At LectuLibre, we set out to build a service that translates entire books using large language models. The idea is simple: upload an EPUB or PDF, choose a language, and receive a polished translation. But behind the scenes, translating a hundred-thousand-word novel with LLMs isn't straightforward. The core challenge is context — LLMs have limited context windows, and books are long. Simply chopping the text into chunks and feeding each one independently leads to incoherent output. Character names change, pronouns lose referents, and tone veers wildly. Here’s how we solved that with a chunking strategy that preserves context, and the Python code that makes it tick. The Problem: Long Documents vs. Short Context Windows Modern LLMs like Claude 3 Opus can handle 200,000 tokens of context, while DeepSeek-V2 offers 128,000 tokens. That’s a lot — but a 50,000-word English novel translates to roughly 67,000 tokens (using Claude’s tokenizer). That just fits, but what about a 150,000-word fantasy epic? Even when it fits, sending an entire book in one prompt is costly, slow, and often degrades attention quality on long texts. The prevailing approach is to chunk the document. Naive chunking — say, splitting by a fixed token count — creates hard boundaries. One chunk ends, another begins, and the LLM has no idea what happened before. The result reads like a patchwork of isolated translations. We needed a method that gives each chunk enough surrounding context without exceeding token limits or breaking the bank. Our Approach: Sliding Window + Context Retrieval via Embeddings We adopted a two‑pronged strategy: Overlapping chunks : each chunk shares some sentences with the previous one, so the LLM can transition smoothly. Injected context : for every chunk, we retrieve and prepend the most relevant previous chunks, determined by embedding similarity. This way, the model always has a sense of what’s happening before a

The cybersecurity-focused models, including GPT-5.6 Sol, broke out of a testing sandbox, exploited a zero-day, and gained access to the open internet to pull off the attack.

The country's new law also bans the use of mobile phones in schools.

In the ever-evolving landscape of technology, software engineers continually seek optimal solutions to complex problems. To achieve this, a deep understanding of the underlying technologies is essential. When considering adopting new technologies, it's crucial to ask: Why : What problem does this technology solve? How does it align with our project's goals? What : What specific tools or frameworks can address our needs? How do they compare in terms of features, performance, and community support? How : How does the technology work? Understanding its inner workings can significantly enhance problem-solving and troubleshooting capabilities. The Benefits of Understanding Technology's Building Blocks Enhanced Problem-Solving : A deep understanding of a technology's fundamentals empowers developers to diagnose and resolve issues more efficiently. Effective Integration : Knowledge of a technology's architecture facilitates seamless integration with existing systems. Optimal Utilization : By grasping a technology's capabilities, developers can leverage its features to their full potential. Future-Proofing : A solid foundation in technology principles enables developers to adapt to emerging trends and innovations. A Case Study: Containers Containers, a popular virtualization technology, provide isolation and portability for applications. By understanding the underlying concepts of namespaces, control groups, CRI, and the Linux kernel, developers can effectively manage and troubleshoot containerized environments. In conclusion, the fast-paced world of software development, staying ahead requires a proactive approach to technology adoption. By carefully considering the "why," "what," and "how" of new technologies, software engineers can make informed decisions, optimize their projects, and deliver innovative solutions.