3+ hour, 13+ min ago (191+ words) If you've ever built a React application that fetches data from an API, you know the challenges of managing server state. Between loading states, error handling, caching, and synchronization, data management often becomes complex. Enter React Query " the missing piece for data fetching in React applications. In this guide, we'll explore how React Query transforms your development experience and improves app performance. React Query (now TanStack Query) handles server state management in React applications. It provides hooks for fetching, caching, synchronizing, and updating server data without complex "global state" solutions. With just a few lines, we get the same functionality plus automatic caching, background updates, and error handling! Here's how you might use React Query in a dashboard component: React Query eliminates boilerplate code, provides excellent user experiences through smart caching, and offers powerful devtools for debugging. Whether you're building…...

3+ hour, 19+ min ago (937+ words) Life would have been easy if the world were filled with monolithic functions. Simple functions that execute once, and if they crash, we could try again. But we build distributed systems and life isn't that easy (but it is fun). A classic scenario is : A user on our system clicks "Pay Now. The backend needs to do two things : This looks simple enough in code. But what if the database commits the transaction but the network acts up before the event is published onto the communication queue? The user is charged, but the email is never sent, and so the warehouse never ships the item. And if we try reversing it, the email is sent, but we do not get the payment. This is the Dual Write Problem, and it is the silent killer of data integrity in microservices. To…...

4+ hour, 15+ min ago (150+ words) Machine Learning engineers often celebrate higher accuracy, better architectures, newer models " but there's another equally powerful lever that rarely gets attention: Training Efficiency " how fast you can experiment, iterate, and improve. In real engineering environments, speed = productivity. Faster model training means: So instead of upgrading to bigger GPUs or renting expensive cloud servers, I ran an experiment to explore how far we can optimize training using software-level techniques. Caching + Prefetching alone nearly cut training time in half. In smaller datasets, data loading " GPU idle time is often the bottleneck. Fix the pipeline, not the model. There is no perfect technique. There are only informed trade-offs. The best engineers choose based on the actual bottleneck. You don't always need a bigger GPU. You need smarter training. Efficiency engineering matters " especially at scale. What's the biggest training speed improvement you've ever achieved,…...

4+ hour, 17+ min ago (403+ words) While this normalized structure kept our data clean and avoided redundancy, it created a performance bottleneck for search operations. To execute a single search query, we needed to perform 2-3 layers of lookups across collections, followed by text regexp searches. The result? Unacceptably slow search performance that degraded user experience. We implemented a centralized search solution using a dedicated collection called userSearch. This collection stores denormalized data in string format, combining: By leveraging MongoDB Atlas Search, we created indexes that could query this consolidated data efficiently, dramatically improving search performance. Creating the search index was straightforward. The difficult part was ensuring data consistency. Our userSearch collection needed to remain synchronized with source data across 4-5 different collections, with updates reflected within 5 seconds to maintain a smooth user experience. This is where things got interesting. We built a CDC pipeline using MongoDB Change…...

7+ hour, 37+ min ago (446+ words) Your bundle is 2MB and users wait 8 seconds for first paint! You're shipping EVERYTHING in one bundle. Users download code for routes they never visit. This ONE weird trick makes React skip re-rendering entirely. The Hidden React Behavior: PS: Don't add bail-out condition using JSON.stringify initially itself while writing code as comparing using JSON.stringify is slower for larger objects. Use this as an optimization step. If you can calculate it from existing state/props, DON'T put it in useState. This bug pattern creates sync issues that haunt production. Storing calculated/derived values in state creates two sources of truth. They get out of sync, causing difficult-to-debug issues. Your codebase has 1000+ unused files and imports you don't even know about! Knip finds them ALL in 10 seconds. This tool is a game-changer! Dead code accumulates over time. Unused files, unused exports,…...

9+ hour, 33+ min ago (353+ words) Chegamos ao final da nossa jornada pela Complexidade de Algoritmos! Vimos o poder do Merge Sort O(N log N) e a velocidade da Busca Bin'ria O(log N). Neste post final, vamos: Revisite o problema: Dada uma lista j' ordenada nums, retorne o lista dos quadrados dos n'meros, tamb'm ordenado. A solu'o ing'nua seria elevar ao quadrado O(N) e depois ordenar O(N log N), totalizando O(N log N). O truque ' usar a informa'o de que a entrada j' est' ordenada. Os maiores quadrados estaro sempre nas pontas do list de entrada, devido aos n'meros negativos com alto valor absoluto. A solu'o O(N) usa a t'cnica dos Dois Ponteiros para construir o resultado de tr's para frente (do maior para o menor): Em Elixir, usamos o Enum.reduce para simular o movimento dos ponteiros e o [square | acc] (prepend) para construir o…...

9+ hour, 36+ min ago (361+ words) Nos posts anteriores, conquistamos a velocidade da Busca Bin'ria O(log N) e a efici'ncia Linear O(N). Agora, vamos ao padr'o-ouro para o Ordena'o: a complexidade O(N log N) (Linearithmic). Todo algoritmo de ordena'o que precisa escalar para grandes volumes de dados (como Merge Sort ou Quick Sort) mira nessa complexidade, pois ela combina o poder de dividir o problema log N com a efici'ncia de processar os resultados de forma linear N. O Merge Sort (Ordena'o por Mesclagem) " um exemplo perfeito de Divide and Conquer (Dividir para Conquistar). Ele funciona em duas fases, que correspondem diretamente " sua complexidade O(N log N): O algoritmo divide recursivamente a lista em metades, continuando a divis'o at" que restem apenas listas de um "nico elemento (que, por defini'o, est'o ordenadas). " aqui que o trabalho pesado " feito. Em cada n'vel da recurs'o (os log N n'veis),…...

10+ hour, 27+ min ago (319+ words) No Post 1 - O que " Big O? e o Vil'o O(N^2), vimos que a complexidade O(N^2) " causada por um la'o aninhado que obriga o sistema a varrer a lista inteira (N) para cada elemento (N). Se um algoritmo tem que fazer N x N opera'es, ele n'o vai escalar. A chave para migrar de O(N^2) para O(N) (Linear) " garantir que cada elemento da entrada seja visitado um n'mero constante de vezes. Objetivo: Transformar a opera'o de "procurar um elemento na lista" de O(N) para O(1). Para obter uma busca ou inser'o em tempo constante O(1), n'o podemos usar a lista padr'o de Elixir. Precisamos de uma estrutura que responda "Este item j" existe?" de forma instant'nea, sem precisar iterar. Em Elixir, essa estrutura " o MapSet (Conjunto) ou o Map (Mapa). Eles utilizam uma t"cnica chamada Hashing para transformar a…...

10+ hour, 27+ min ago (282+ words) Se voc" programa em Elixir ou qualquer outra linguagem e j" viu seu c'digo funcionar perfeitamente com 10 itens, mas travar com 10.000, voc" esbarrou na Complexidade de Algoritmos. Em Elixir, a estrutura de dados mais fundamental " a Lista Ligada Simples (Singly Linked List). Entender como ela funciona nos d" a base para o O(1) e o O(N). O tempo de execu'o " sempre o mesmo, no importa se sua lista tem 10 elementos ou 1 milho. Essa " a opera'o mais comum em c'digo funcional e a razo pela qual a cria'o de listas em Elixir " to r"pida. O tempo de execu'o cresce proporcionalmente ao tamanho da entrada. Se a lista dobra de tamanho, o tempo dobra. A complexidade O(N^2) " o seu pior inimigo para escalabilidade. Se a entrada (N) dobra de tamanho, o tempo de execu'o quadruplica (2^2 = 4). Causa: A presena de laos de…...

12+ hour, 16+ min ago (390+ words) This article serves as a comprehensive guide, covering the fundamental techniques for selecting and modifying elements, as well as advanced strategies for optimizing performance and ensuring code quality. The DOM represents the HTML document as a hierarchical tree of objects, where each HTML tag, attribute, and piece of text is a node [2]. To begin manipulating this structure, the first step is always to gain a reference to the desired element. Historically, developers relied on specific methods to select elements. While still valid, modern methods offer superior flexibility and power by leveraging CSS selectors [7]. It is a best practice to cache selected elements in variables to avoid repeatedly querying the DOM, which is an inefficient operation [4]. Furthermore, when selecting descendants, limit the scope of the query by calling querySelector or querySelectorAll on a parent element instead of the entire document [4]. Once…...