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Effortlessly deploy cloud software factories. Discussion | Link

One memory for every AI you use Discussion | Link

Introdução A visão original de Kay para OOP não era "objetos com dados públicos que outros manipulam", era objetos que trocam mensagens e decidem sozinhos o que fazer com elas. é Tell dont ask é basicasmente um resgate dessa ideia original, porquer com o tempo muita gente passou a usar OOP como “structs com getters e setters”, pedendo o encapsulamento de verdade. Ideia Central Exemplo do Cliente e Carteira Ask Eu PERGUNTO o saldo, e EU decido o que fazer com ele if ( cliente . carteira . saldo >= 50 ) { cliente . carteira . saldo -= 50 ; } else { console . log ( "saldo insuficiente" ); } Tell Eu DIGO pro cliente pagar, e ELE decide o que faze // "Tell" — eu DIGO pro cliente pagar, e ELE decide o que fazer cliente . pagar ( 50 ); class Cliente { carteira : Carteira ; pagar ( valor : number ) { if ( this . carteira . saldo < valor ) { throw new Error ( "saldo insuficiente" ); } this . carteira . saldo -= valor ; } } Repare a diferença de responsabilidade: No "Ask", quem chama o código precisa saber a regra ("se o saldo for menor, não pode pagar") e tomar a decisão sozinho. No "Tell", o próprio objeto conhece sua regra e decide por dentro. Quem chama só diz o que quer que aconteça. Por que "perguntar" é perigoso Pensa no "Ask" espalhado pelo sistema: toda tela, todo botão, todo endpoint que cobra do cliente vai ter que copiar essa mesma verificação de saldo: if ( cliente . carteira . saldo >= valorDoCarrinho ) { ... } if ( cliente . carteira . saldo >= valorDaAssinatura ) { ... } if ( cliente . carteira . saldo >= valorDoBoleto ) { ... } Se um dia a regra mudar (por exemplo, "clientes VIP podem ficar com saldo negativo até -R$100"), você precisa caçar todos esses lugares e mudar um por um. É praticamente garantido que algum lugar vai ser esquecido — e aí seu sistema tem um bug de regra de negócio inconsistente. Com "Tell", a regra mora em um lugar só ( Cliente.pagar ). Mudar uma vez, resolve todo o sistema. Como isso conecta com Law of Demeter Os dois princípios andam

How machine learning supports aircraft maintenance using operational data. Key Takeaways Predictive maintenance estimates component health before failure. Data quality determines model performance. Explainable models support maintenance decisions. Human review remains part of every maintenance action. Model performance requires continuous validation. Introduction Aircraft produce large volumes of operational data. Machine learning converts this data into maintenance support inspection planning and fault detection. What Is Predictive Maintenance? Predictive maintenance estimates the condition of aircraft components using historical and real-time data. The goal is to identify early signs of degradation before a failure affects operations. Traditional maintenance often follows fixed inspection intervals. Data-driven maintenance adds condition-based recommendations using operational evidence. Data Sources Model quality depends on reliable data. Common sources include: Engine sensor readings Flight data recorder information Maintenance records Aircraft utilization history Environmental conditions Component replacement history Incomplete or inaccurate data reduces prediction accuracy. Machine Learning Workflow A typical workflow includes: Collect operational and maintenance data. Remove errors and missing values. Create features from sensor measurements. Train the prediction model. Validate performance using unseen data. Monitor prediction accuracy after deployment. Retrain the model as new data becomes available. Model Selection Different problems require different algorithms. Common choices include: Random Forest XGBoost LightGBM Support Vector Machine Long Short-Term Memory (LSTM) Transformer-based time-series models Model selection depends on the prediction task, dataset size, and operational requirements. Engineering Challenges Data Quality Sensor failures, missing records, and inconsistent maintenance logs reduce model reliability. Class Imbalance Aircraft failures

AxonASP is a high-performance Classic ASP engine written in Go — but here's the twist: it runs JavaScript (JScript) natively on the server side. You get a synchronous, predictable execution model, full ECMAScript 5/6+ support, and zero dependency on Node.js or any third-party JavaScript runtime. And yes — you can build an entire production website with it. Why AxonASP Changes the Game for Server-Side JS Most developers associate server-side JavaScript exclusively with Node.js. And Node.js is great — until you're drowning in async/await chains, package.json conflicts, and the 47th minor version bump of a dependency that broke your build. AxonASP takes a fundamentally different approach. Instead of wrapping everything in an event loop and forcing asynchronous patterns everywhere, AxonASP's JavaScript engine executes code synchronously by default . You write your server logic the same way you write your frontend logic — line by line, top to bottom. It compiles through a high-performance AST parser and runs directly on a custom Go-based virtual machine. The result? Cleaner code, simpler debugging, and a massive reduction in cognitive overhead. What You Get Out of the Box Full ES5 + ES6 support (classes, arrow functions, template literals, destructuring, proxies, for...of , Map , Set , Symbol , typed arrays — 37+ modern features documented) Synchronous execution — no callback pyramid, no promise chains for basic I/O ASP intrinsic objects — Request , Response , Session , Application , Server — all accessible directly from your JS code No npm install required — just write .asp or .js files and point the server at them CLI execution — run JavaScript files from the command line for automation, batch processing, or testing The Philosophy: Simplicity Over Complexity Here's a hard truth: most web applications don't need 2,000 npm modules. They need to read a database, render HTML, handle form submissions, and maybe serve a JSON API. That's it. The modern JavaScript ecosystem ha

The pricing formulas in Motor, the estimating engine I built for a water feature shop, did not come from the manual. I pulled 32 of them out of the JavaScript behind Aquascape's contractor calculator, the tool contractors actually use to bid jobs. The manual was sitting right there, official and free. Ignoring it was the best design decision in the whole system. A vendor never ships a sloppy calculator Why trust the calculator over the manual? Because of what happens when each one is wrong. If the manual sizes a pump wrong, a reader shrugs and moves on. If the calculator sizes a pump wrong, a contractor bids a job at that number, wins it, and loses money on the install. Then the phone rings. So calculators get fixed and manuals drift. Give it ten years and the two quietly disagree, and everyone in the trade knows which one to trust without anyone saying so. A vendor will ship a sloppy PDF. They will never ship a sloppy calculator. Documentation is what a domain says about itself. The artifacts money flows through are what it actually believes. Once you see that split, you cannot stop seeing it. The other half was in old invoices Formulas only get you to cost. What a shop charges on top of cost is a belief about its market, and no vendor document holds that number. So I pulled 132 historical quotes out of the shop's CRM. Real quotes, sent to real customers, most of them paid. I calibrated Motor's markup against those, then checked its output against what the shop had actually charged. The result: Calibrated against 132 real quotes, Motor's estimates landed within 5 percent of what the shop actually charged, with no pricing rule taken from documentation. I could have just asked the owner what his markup was. But what an owner says and what his invoices show are rarely the same number, and the invoices are the ones customers paid. When the two disagree, believe the invoices. The same bug in a different industry I build and run systems in several industries, and the sur

In 1898, years before radio broadcasting existed and decades before anyone used the word "electronics," Nikola Tesla stood in front of a crowd at Madison Square Garden and did something that looked like magic. In a large pool of water sat a small iron-hulled boat. With no wires connecting them, Tesla sent commands through the air and the boat obeyed, turning, stopping, and blinking its lights on demand. Spectators were so unprepared for the idea that some accused him of hiding a trained monkey inside the hull, or of controlling it with his mind. What Tesla had actually built was the first wireless remote control, and it is the direct ancestor of every connected device we make today. A machine that took commands through the air Tesla called his invention a "teleautomaton," from the Greek for "remote" and "self-acting." The boat carried a radio receiver, a set of relays, and a battery driving its motor and rudder. From a control box on the side of the pool, Tesla transmitted radio signals that the receiver decoded into physical actions. Press a control, and a coherer-based circuit closed a relay, which in turn stepped the boat's steering and switching mechanism to a new position. The patent behind the demonstration, US Patent 613,809, "Method of and Apparatus for Controlling Mechanism of Moving Vessels or Vehicles," was granted in November 1898. Read today, it is startling how modern the thinking is. Tesla was not just wiggling a boat around a pool for show; he was describing a general system for sending control signals to a remote machine and having that machine act on them without a human physically present. That is the exact problem statement behind modern IoT , just with vacuum-era hardware. Why nobody knew what to do with it Tesla saw enormous potential. He imagined remotely piloted vessels, automated vehicles, and machines that could carry out instructions from miles away. He even pitched the concept to the US military as a radio-controlled torpedo. The receptio

Introdução O nome do princípio vem do próprio nome do projeto de pesquisa (que remete a Deméter, deusa grega da agricultura — a metáfora era "cultivar" software que cresce de forma incremental e adaptável, não do princípio de acoplamento em si). O projeto Demeter investigava como reduzir o custo de manutenção de sistemas orientados a objetos observando que boa parte das mudanças de software quebrava código muito distante do ponto onde a mudança real acontecia — um efeito cascata causado por classes que conheciam profundamente a estrutura interna de outras classes. Essa observação foi confirmada empiricamente alguns anos depois: em 1994, Chidamber & Kemerer publicaram as famosas métricas CK ( A Metrics Suite for Object Oriented Design ), nas quais o CBO (Coupling Between Objects) — quão acoplada uma classe é a outras — se tornou um dos preditores mais fortes de defeitos e esforço de manutenção em estudos empíricos posteriores de engenharia de software. Ou seja: a intuição por trás da Law of Demeter (menos acoplamento = menos bugs ao mudar código) tem respaldo em dados de décadas de pesquisa empírica em qualidade de software. Definição Também chamada de "Principle of Least Knowledge" , a formulação clássica é: Um método M de um objeto O só deve chamar métodos de: O próprio O Os parâmetros recebidos por M Qualquer objeto que M crie/instancie internamente Os componentes diretos de O (seus atributos/campos) Variáveis globais acessíveis a O Resumo popular: "use apenas um ponto" — evite código como: pedido . getCliente (). getEndereco (). getCidade (). getNome () Isso é conhecido como "train wreck" (trem de vagões) — cada . é um vagão acoplado ao anterior. Se a estrutura interna de Cliente ou Endereco mudar, todo código que fez essa travessia quebra, mesmo estando em um módulo completamente não relacionado. Porque isso importa na prática? Quando o método M faz objeto.getX().getY().metodo() , ele passa a depender da estrutura interna de X e Y , não só da interface pública d