Days before Anthropic took its most advanced AI models offline, the White House ordered the company to revoke SK Telecom’s access to Claude Mythos over claims of alleged ties to China.

RAMaggedon claims another one.

Maggot therapy lacks robust data, but it has fans and a fail-safe "bacon therapy."

Guys! What’s the best way to gather early adopters to evaluate new dev tool that literally just killed REST, gRPC, Thrift, SignalR, DAPR and GraalVM in one shot? The concept is to shift away from writing code to expose any business logic in any specific integration strategy like create REST controllers or provide proto interfaces or expose via subscription to queue events as well as avoid implementing the integration-specific client code to call that rest or gRPC endpoint. Instead of that the question comes when I call “zip” module from Nuget in .net I just use its public methods and handle exceptions. Why if I want to use it remotely I should expose the same method via REST, map it to routes, strange parameters, http codes and next call that on other end? Wy I cannot just say it will be on this remote node so whenever I ca that method to “unzip” my intention just travel over network and execute there? The potential solution is to bridge runtimes on native level. Think of it like intercepting developer intention at abstract syntax tree so when you perform operation in code before it gets executed and just send it to the remote runtime fulfill job there and return result. Wouldn’t be beautiful if you could just call methods of any module regardless if it’s same tech or different and regardless of its in memory or remotely? Just by calling methods? I know I know there are isolation interfaces etc… but if you apply those concepts design facade properly, decide what should be public, and allow to attach to those calls any headers (to still support JWT, NTLM, api Keys etc) and will add support that if method is static it goes stateless if it’s instance it goes stateful with sticky session and give the DevOps ability to change channel between WebSocket, http/2, tcp/ip or any message bus without touching code as it will be just pure method code. It could really work. Think of it. How clean your codebase would be, how much more design would fit your original uml, how much l

O Event Loop é o mecanismo responsável por decidir quando callbacks e continuidades de operações assíncronas devem ser executados. Ele não executa operações de I/O diretamente, mas organiza a ordem em que elas retornam para o JavaScript. Essa arquitetura permite que o Node.js mantenha uma única thread de execução para JavaScript, enquanto delega operações de rede, disco e sistema operacional para componentes especializados do runtime e do próprio sistema operacional. Início Quando iniciamos um processo Node.js, o runtime carrega o arquivo de entrada da aplicação e executa todo o código síncrono disponível na Call Stack. Somente após essa etapa o Event Loop passa a assumir o controle do fluxo da aplicação, verificando continuamente quais callbacks estão prontos para execução. │ timers │ └─────────────┬─────────────┘ │ v ┌───────────────────────────┐ ┌─>│ pending callbacks │ │ └─────────────┬─────────────┘ │ ┌─────────────┴─────────────┐ │ │ idle, prepare │ │ └─────────────┬─────────────┘ ┌───────────────┐ │ ┌─────────────┴─────────────┐ │ incoming: │ │ │ poll │<─────┤ connections, │ │ └─────────────┬─────────────┘ │ data, etc. │ │ ┌─────────────┴─────────────┐ └───────────────┘ │ │ check │ │ └─────────────┬─────────────┘ │ ┌─────────────┴─────────────┐ │ │ close callbacks │ │ └─────────────┬─────────────┘ │ ┌─────────────┴─────────────┐ └──┤ timers │ └───────────────────────────┘ Trecho retirado da documentação principal. Sobre o Event Loop Durante muito tempo tratei o Event Loop como um dos conceitos mais complexos do Node.js. Depois de estudar a documentação oficial com mais calma, percebi que a dificuldade não está no Event Loop em si, mas na quantidade de conceitos diferentes que normalmente são apresentados ao mesmo tempo: libuv, Call Stack, Promises, Microtasks, Sistema Operacional e I/O. Quando isolamos o papel do Event Loop, ele se torna surpreendentemente simples. Definindo os passos e apresentando o iceberg 🧊 O Event Loop não executa trabalho. Ele agenda tr

Build Governed, Grounded, and Trustworthy Data Agents Discussion | Link

Make the world's documents computable Discussion | Link

Apple is planning to raise prices in response to the ongoing memory shortage. In an interview with The Wall Street Journal, Apple CEO Tim Cook says "price increases are unavoidable:" We're doing our best to mitigate the huge increases that are being passed to us, and we've been trying to shield our customers from the […]

3x Mobile Automation. Same QE Team. Discussion | Link

Most dashboard icon problems do not come from bad icons. They come from good icons used with the wrong meaning. You search for users , pick a clean SVG icon, place it in the sidebar, and move on. Then later you need another icon for: Customers Team members Account owners Permissions Audiences Invited users Admins Suddenly, the same “user” metaphor has to carry too many meanings. That is where SaaS dashboards often start to feel noisy. Not because the icons are ugly. Not because the SVGs are technically wrong. Not because the design system is broken. Because the icon choices were made by keyword instead of meaning. Keyword search is only the first step Most developers choose icons like this: Need an icon for billing? Search billing . Need an icon for users? Search users . Need an icon for analytics? Search chart . Need an icon for settings? Search settings . That works for finding candidates. But it does not solve the real UI problem. A keyword tells you what the icon is related to. It does not tell you what the icon means in your product. For example, search for settings . You might find: A gear Sliders A wrench Control knobs A preferences panel A tune icon They all match the keyword. But they do not say the same thing. A gear usually means global settings. Sliders suggest adjustable preferences or filters. A wrench feels technical or maintenance-oriented. Control knobs suggest fine tuning. A panel icon may suggest a configuration screen. The same keyword can point to different mental models. And in a dashboard, mental models matter more than decorative accuracy. SaaS dashboards are meaning-dense interfaces A marketing website can sometimes get away with decorative icons. A SaaS dashboard cannot. Dashboards are dense. They contain navigation, actions, status indicators, tables, filters, empty states, permissions, billing screens, integrations, reports, and settings. Users do not look at each icon in isolation. They scan. They compare. They move quickly. They expect

Dialogue management is the process of tracking conversational state and deciding what an agent should say or do next. Classical systems split this into isolated modules: natural language understanding, dialogue state tracking, a policy engine, and response generation. Large language models can collapse these boundaries into a single inference step, but doing so reliably requires careful architecture choices. This article examines practical patterns for using LLMs as dialogue managers, with a focus on structured reasoning, tool use, and cost-efficient inference. What Is LLM Dialogue Management? An LLM-based dialogue manager treats conversation as a partially observable decision process where the model itself reasons over history, user intent, and available actions. Instead of hand-written rules or separate slot taggers, the model receives the full transcript, a system prompt defining the task, and optionally a schema of tools it can invoke. The model then emits either natural language or structured JSON representing the next system action. This approach excels in open-domain or rapidly changing domains where maintaining a rigid ontology is impractical. Architecture Patterns for LLM-Based Dialogue Most production implementations fall into one of four patterns. The right choice depends on how much control you need over state transitions and how willing you are to trade complexity for flexibility. End-to-end generation. The LLM receives the full chat history and outputs the next response. It works well for unstructured chit-chat but can hallucinate state or ignore business rules without additional guardrails. Structured state extraction. The LLM is prompted to output a JSON object representing dialogue state, such as slots, user intent, and confirmed facts. A lightweight policy layer reads this state to decide whether to ask a question, call an API, or close the task. This separates reasoning from control and makes debugging easier. Tool-augmented manager. The LLM uses