标签：#r

Last month, an AI coding agent handed me a beautiful fix. Five lines. Elegant. It reused an existing helper, matched the codebase style, compiled on the first try. Exactly the kind of diff we've all learned to praise since "make the agent write less code" became the standard advice. It was also completely untested, and it sat on a password-recovery path. That diff taught me something I now consider the central problem of AI-assisted coding in 2026: we've spent a year teaching agents to write less code, and almost no time teaching them to prove the code they kept actually holds. The two failure modes Every AI coding agent fails in one of two directions. Failure mode #1: the over-build. You ask for a date comparison; you get a new dependency, a ValidationService class, and a config layer. This one is well known — it's why minimal-code prompts and skills became popular, and they genuinely work on it. Failure mode #2: the confidently small diff. Minimal, clean, written after reading half the flow, verified never — dropped onto a path that handles money, auth, or user data. It compiles. It demos. It detonates in week three. Here's the uncomfortable part: fixing #1 aggressively makes #2 more likely. When the objective function is "shortest diff," the first things to quietly disappear are edge-case handling, failure-path tests, and the guard clause that looked optional. The diff gets smaller. The blast radius doesn't. A five-line change to a payment path is more dangerous than a four-hundred-line internal script that runs once. Code size is not risk. Blast radius is risk. Yet almost every skill and prompt in this category optimizes for size alone. What a guard does differently This is why I built Guardsman 💂 — an open-source skill that behaves less like a minimalist and more like the royal guard in front of the palace: nothing passes the post unchallenged, and the level of challenge depends on what's behind the gate. Three duties, on every task: 1. Read the standing orders

For a full quarter, our feature velocity significantly dropped after we re-implemented a Go service using Rust. The performance improvements actually happened. Why we did it in the first place We are a small startup. Each engineer is important, and each week is even more important. Our backend was built using Go, which was performing well. It was fast, reliable, and we could easily find resources to hire. However, we became infected with that fever. The phrase "Rewrite it in Rust" was being used in all kinds of situations, and it sounded very appealing with its promises of memory safety, no garbage collector pauses, and blazing speed. We told ourselves it was an investment in the future. What we actually bought was a quarter of silence. The numbers nobody warns you about I may not have the exact metrics we use internally, but I can direct you to an individual who shared accurate calculations transparently. In a retrospective from November 2025, engineering manager Noah Byteforge wrote that a Node.js-to-Rust backend rewrite "dropped API response times from 340ms to 28ms. That's 12.1x faster." And the other metric. A 65% decrease in sprint velocity. They didn't deliver a single story point for three weeks. The time it took to send out new features increased by 185%. The time it took for pull requests to be processed increased by 320%. Additionally, scores from the "I feel productive" survey dropped from 8.2 to 4.1. Most importantly, the kicker is what he says in his own words: "We'd won the technical battle and lost the war that actually mattered." He also admits that if he had been forthright about the 6-12 month per engineer ramp, "the business case would've fallen apart immediately." That retrospective was so relatable, it read like our own diary. The battles with the borrow checker and the compile times just snuck entire weeks away from us. The wins were real. That's the trap. I must give credit to Rust because the safety benefits are not exaggerated. The rewrite

Introdução Validar dados é uma responsabilidade que pode ficar na aplicação, no banco de dados, ou em ambos. Deixar tudo na aplicação é arriscado: diferentes sistemas podem acessar o mesmo banco, migrações podem rodar diretamente, um bug pode deixar passar um valor inválido. Constraints são regras definidas no próprio banco de dados — uma camada de proteção que age independente de quem está escrevendo os dados. PRIMARY KEY A chave primária identifica cada linha de forma única. Ela combina duas restrições implicitamente: NOT NULL e UNIQUE . Nenhuma linha pode ter o mesmo valor de chave primária, e nenhuma pode tê-la nula. CREATE TABLE clientes ( id INT PRIMARY KEY , nome VARCHAR ( 100 ) NOT NULL ); Quando a chave primária envolve mais de uma coluna, ela é declarada separadamente: CREATE TABLE matriculas ( aluno_id INT , curso_id INT , PRIMARY KEY ( aluno_id , curso_id ) ); Na maioria dos bancos, é comum usar uma chave primária auto-incremental para não precisar gerenciar os IDs manualmente: -- PostgreSQL id SERIAL PRIMARY KEY -- MySQL id INT AUTO_INCREMENTPRIMARY KEY -- SQL padrão (suportado por ambos) id INT GENERATED ALWAYS AS IDENTITY PRIMARY KEY FOREIGN KEY A chave estrangeira garante integridade referencial : um valor só pode existir numa coluna se ele existir como chave primária na tabela referenciada. É o que torna os relacionamentos entre tabelas confiáveis. CREATE TABLE pedidos ( id INT PRIMARY KEY , cliente_idINT REFERENCES clientes ( id ) ); Tentar inserir um pedido com cliente_id = 99 quando não existe cliente com esse id resulta em erro imediato. O banco rejeita a operação antes mesmo de ela chegar ao disco. O comportamento quando o registro referenciado é deletado pode ser configurado: CREATE TABLE pedidos ( id INT PRIMARY KEY , cliente_id INT REFERENCES clientes ( id ) ON DELETE CASCADE -- deleta os pedidos junto com o cliente ON UPDATE CASCADE -- atualiza o cliente_id se o id do cliente mudar ); As opções disponíveis são: Opção Comportamento RESTRICT

Interactive Rive mascots can react, think, talk, and connect to real AI, SaaS, web, and mobile products. Your SaaS Mascot Should Do More Than Just Sit There 👀 A lot of products have mascots. They look great on landing pages. Maybe they wave. Maybe they blink. Maybe there is a small looping animation. And that's it. But I think a product mascot can do much more. What if your mascot actually knew what was happening inside your product? That's the idea I've been exploring with Mascot Engine . I don't just want to animate characters. I want to build interactive mascot systems that connect to real products . From a mascot animation to a product system Imagine you're building an AI app. A user opens the app. The mascot is idle . The user sends a message. The mascot starts thinking . The AI begins responding. The mascot switches to talking . The task completes. The mascot celebrates . Something goes wrong? The mascot reacts to the error . The flow could look like this: User Action ↓ Product State ↓ Runtime Input ↓ Rive State Machine ↓ Mascot Reaction This isn't a video. It isn't a GIF. It isn't a pre-rendered animation playing randomly. The product controls the mascot at runtime. That's where things become interesting. A mascot can understand product states Well... not literally understand them 😄 The application still owns the logic. But we can expose a small runtime contract from the Rive file. For example: emotion = 2 isTalking = true lookX = 40 lookY = -10 celebrate = trigger error = false The developer controls these values from the application. The Rive State Machine handles the character behavior. The application controls what happened . The mascot system controls how the character reacts . I really like this separation. Why I use Rive for interactive mascots Traditional animation tools are great for videos and motion design. But product animation has different requirements. The character needs to react to application events. The animation may need runtime values. De

Introdução Consultas individuais respondem perguntas como "qual o email do cliente 42?". Mas as perguntas mais valiosas em qualquer sistema são de outro tipo: "qual o produto mais vendido?", "qual a receita média por pedido?", "quantos clientes se cadastraram esse mês?". Para responder isso, o SQL oferece as funções de agregação — operações que recebem um conjunto de linhas e devolvem um único valor resumido. Para os exemplos a seguir, considere esta tabela: pedidos: | id | cliente | produto | categoria | quantidade | valor | |----|------------|-------------|--------------|------------|--------| | 1 | Ana Lima | Notebook | Eletrônicos | 1 | 3500.00| | 2 | Ana Lima | Mouse | Periféricos | 2 | 80.00| | 3 | Bruno Melo | Teclado | Periféricos | 1 | 150.00| | 4 | Bruno Melo | Notebook | Eletrônicos | 1 | 3500.00| | 5 | Carla Nunes| Monitor | Eletrônicos | 2 | 1200.00| | 6 | Carla Nunes| Mouse | Periféricos | 1 | 80.00| As Funções de Agregação COUNT Conta o número de linhas — ou de valores não nulos em uma coluna específica. -- Total de pedidos SELECT COUNT ( * ) AS total_pedidosFROM pedidos ; -- Resultado: 6 -- Clientes distintos que fizeram pedidos SELECT COUNT ( DISTINCT cliente ) AS clientes_unicosFROM pedidos ; -- Resultado: 3 COUNT(*) conta todas as linhas, incluindo as que têm nulos. COUNT(coluna) conta apenas as linhas onde aquela coluna não é nula. COUNT(DISTINCT coluna) conta valores únicos — útil para saber quantos clientes, produtos ou categorias distintos aparecem no resultado. SUM Soma os valores de uma coluna numérica. -- Receita total SELECT SUM ( valor ) AS receita_total FROM pedidos ; -- Resultado: 8510.00 -- Total de itens vendidos SELECT SUM ( quantidade ) AS itens_vendidos FROM pedidos ; -- Resultado: 8 AVG Calcula a média aritmética dos valores. -- Valor médio por pedido SELECT AVG ( valor ) AS ticket_medio FROM pedidos ; -- Resultado: 1418.33 AVG ignora valores nulos automaticamente — calcula a média apenas sobre os registros que têm valor preenchid

...now, I pride myself on being good at my job, I'm fast at catching mistakes, I'm efficient at...

Hello Hacker News photographers! Yes, this is essentially my take on photo colling and management, similar to the features that Lightroom and Darktable already have. Because I shoot in JPEG+RAW, my workflow looks like me going through my JPEG images and then eliminating the JPEGs I don't like afterwards in addition to their corresponding RAWs. At least on my old MacBook, the JPEGs were faster to load than the RAW photos (probably a combination of CR2, the file my EOS 40D, not having embedded JPE