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SQL: Normalização e Formas Normais

Introdução Imagine uma planilha onde cada linha de um pedido guarda também o nome do cliente, o email, a cidade, o nome do produto e a categoria — tudo junto. Funciona no começo. Mas conforme o sistema cresce, começam a aparecer problemas silenciosos: dados duplicados, inconsistências difíceis de rastrear, operações que quebram coisas inesperadas. A normalização existe para eliminar esses problemas sistematicamente. Proposta por Codd nos anos 1970 e formalizada ao longo das décadas seguintes,

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Data Manipulation Language (DML)

Introdução DML é o conjunto de comandos que os usuários e aplicações usam no dia a dia: consultar, inserir, atualizar e deletar dados. Se o DDL define o esquema, o DML é o que acontece dentro dele ao longo de toda a vida do sistema. SELECT O comando mais usado em SQL. Consulta dados de uma ou mais tabelas. SELECT nome, email FROM clientes WHERE criado_em>= '2024-01-01' ORDER BY nomeASC; O SELECT é composto por cláusulas que se encaixam numa ordem lógica: Cláusula Função FROM Define d

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Data Definition Language (DDL)

Introdução DDL é o conjunto de comandos responsável por definir e modificar a estrutura do banco de dados: criar tabelas, alterar colunas, remover objetos. Operações DDL geralmente são irreversíveis — um DROP TABLE não pede confirmação, ele simplesmente executa. CREATE TABLE Cria uma nova tabela com suas colunas, tipos de dados e restrições. CREATE TABLE clientes ( id INT PRIMARY KEY, nomeVARCHAR(100) NOT NULL, emailVARCHAR(150) UNIQUE, criado_em DATE DEFAULT CURRENT_DATE ); Neste e

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I Turned My Football Group Chat Arguments Into an Actual Scoreboard!

This is a submission for Weekend Challenge: Passion Edition Called It! is a social football prediction platform. You predict match outcomes before kickoff, earn points based on how accurate you were, and compete with friends. No squad management, no transfers, no budgets. Just your ability to read a game. The problem is something I've felt for years. I have a group chat where we all throw out predictions before big matches. Someone says 2-1 with a specific goalscorer. Someone else says 3-0. Then

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RNAValidate: CPU-only validator for AI-predicted 3D RNA structures

RNAValidate: CPU-only validator for AI-predicted 3D RNA structures A predictor-agnostic linter that checks AI-predicted 3D RNA structures against experimental data (FRET, cryo-EM, SHAPE/DMS probing, designability) — and flags the ones that will hydrolyze before you can measure them. Predictors and designers (AlphaFold-3, RiboSphere, RoseTTAFold-RNA) are plentiful; an open-source, reproducible layer that audits a predicted 3D RNA structure against experimental evidence is not. Validating befor

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cryoval: CPU-only validator for cryo-EM/ET maps

cryoval: CPU-only validator for cryo-EM/ET maps A reconstructor-agnostic linter that checks cryo-EM/ET maps for overfitting, particle-picking bias, and local map-noise hallucination — before you trust a deposited or generated map. Reconstruction ecosystems (RELION, Scipion, Topaz, IsoNet) are plentiful; an open-source, reproducible layer that audits a produced map against the data is not. As of the 2026-07-11 batch scan, the GitHub count for a "cryo-EM map validation tool" was 0. cryoval read

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The benchmark that built the tools

How we measured a markdown knowledge graph as agent memory — lost to grep, rebuilt our search and editing primitives, retracted our own best number, and ended with a $4.50 curator whose store reads back at 96% of a hand-built ceiling in a single call. Every AI agent product eventually hits the same wall: the model forgets. Context windows end, sessions restart, and everything the agent learned about you evaporates. An industry has grown up around this — hosted "memory layers" that ingest your co

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