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Models are improving quickly, but the cheaper options are often good enough.

As part of my learning journey with Django, I wanted to build a project that would challenge me beyond the basics. I decided to create RecipeHub, a web application where users can create, manage, and share recipes while exploring recipes from other users. The project started from a Django starter template, but I customized it by adding new features, redesigning the interface, and deploying it online. Features RecipeHub allows users to: Register and log in Create, edit, and delete recipes Browse recipes by category Save favourite recipes Upload recipe images Access a personal dashboard Use the application in both light and dark mode The application is fully responsive, making it easy to use on both desktop and mobile devices. Technologies Used I built the project using: Python Django Django Allauth PostgreSQL Tailwind CSS DaisyUI HTMX Vite Gunicorn Render GitHub was used for version control throughout the project. Challenges One of the biggest challenges was deployment. While everything worked locally, deploying to Render required configuring PostgreSQL, environment variables, and static files correctly. I also encountered an issue with uploaded recipe images. Since the application is hosted on Render's free tier, uploaded media is stored on an ephemeral filesystem, meaning uploaded images are lost after redeployment. Learning why this happens gave me a better understanding of the difference between development and production environments. Another challenge was redesigning the dashboards. I wanted them to feel clean and modern instead of looking like a default Django application, so I spent time improving the layout, spacing, and responsiveness. What I Learned This project helped me improve my understanding of: Django project structure Authentication and user management CRUD operations Database relationships Responsive UI design Git and GitHub workflows Deploying Django applications Debugging real-world issues More importantly, it taught me how to troubleshoot proble

Jotai v2.20.0 has been released, focusing on improved performance for high-throughput scenarios. Key changes involve adjustments to internal building blocks and addressing previous performance regressions. While the API for everyday use remains unchanged, library authors should note some deprecations as the groundwork for Jotai v3 begins. By Daniel Curtis

I Built a Manga Reader That Works on Every Platform — Here's How Nyora is a free, open-source manga/manhwa/manhua reader for Android, iOS, macOS, Windows, Linux, Web, and even Docker — with AI-powered on-device translation and cross-platform sync. The Problem Every manga reader makes you choose: Free but ad-riddled (most Android readers) Polished but paywalled (commercial apps) Powerful but single-platform (Tachiyomi, Aidoku) I wanted one library — same titles, same progress, same bookmarks — on my phone, laptop, and browser. No ads. No account required. So I built it. What Nyora Does Every Platform, One App Platform Distribution Android APK (sideload) iOS/iPadOS IPA via AltStore/SideStore macOS .dmg or brew install --cask nyora Windows .exe (x64 + ARM64) Linux .deb , .rpm , or curl installer Web web.nyora.xyz — zero install Docker Single container, self-hosted No account needed to read. Cloud sync is opt-in. AI Translation That Understands Manga This is the flagship feature. Instead of dumping translated text over the artwork: Detects text baked into speech bubbles and captions Translates using on-device ML Typesets the result back over the original artwork Each platform uses the best local engine: Android : Google ML Kit + ONNX Runtime iOS : Apple Intelligence + Google Translate macOS : Apple Vision + MangaOCR CoreML Windows : Windows OCR Linux : Tesseract There's also an Ensemble AI Narrative Engine that tracks character names and speaking styles across chapters so translations stay consistent. 1,100+ Sources The Android app pulls from 1,100+ manga sources via 35 generic engine templates (Madara, FoolSlide, MMRCMS, etc.). Web has ~390 live, health-checked sources. Desktop ports are growing toward parity. Free Cloud Sync Sync library, categories, reading history, bookmarks, and exact page progress across all six platforms. Two sign-in methods: Google OAuth Nyora Cloud (email + password, free) Self-hostable — the backend is just Supabase/PostgreSQL with row-level s

In the age of "Cloud Everything," our most sensitive data—our heartbeat, our sleep cycles, our stress levels—often ends up on a server somewhere in Northern Virginia. But what if we could keep that data where it belongs? On your device. Today, we're diving deep into Edge AI and On-device LLMs . We will build a privacy-centric health coach that uses MLX-Swift to run Llama-3 directly on your iPhone's Apple Silicon. We’ll be pulling real-time Heart Rate Variability (HRV) data from the HealthKit API and generating semantic health summaries without a single byte ever leaving your phone. 🚀 Why Edge AI? 🛡️ When dealing with Private AI and sensitive medical metrics, the "Cloud-First" approach is a liability. By leveraging MLX-Swift and the Unified Memory Architecture of the A17 Pro/A18 chips, we achieve: Zero Latency : No round-trip to a server. Total Privacy : Your data stays in the Secure Enclave. Offline Capability : Health insights in the middle of the woods? Yes. The Architecture 🏗️ The data flow is simple but powerful. We fetch raw samples from HealthKit, preprocess them into a prompt-friendly format, and feed them into a quantized Llama-3 model managed by the MLX framework. graph TD A[iPhone HealthKit Store] -->|Fetch HRV Samples| B(Swift Data Controller) B -->|Normalize & Format| C{MLX-Swift Engine} D[Llama-3-8B-4bit Model] -->|Load Weights| C C -->|Local Inference| E[Neural Engine / GPU] E -->|Semantic Summary| F[SwiftUI Dashboard] F -->|User Feedback| A Prerequisites 🛠️ To follow this advanced tutorial, you'll need: Xcode 15.4+ and a physical iPhone (iPhone 15 Pro or newer recommended for 8GB+ RAM). MLX-Swift : Apple's framework for machine learning on Apple Silicon. Llama-3-8B (4-bit quantized) : To fit within the iOS memory footprint. HealthKit Permissions : Configured in your Info.plist . Step 1: Accessing HealthKit Data 💓 First, we need to grab that juicy HRV data. Heart Rate Variability is a key indicator of autonomic nervous system stress. import HealthKit c

~7 min read · Tutorial I look at a lot of MUI admin templates. material-kit-react from the minimals people is one I keep going back to. Clean, typed, and the folder structure makes sense. Repo: minimal-ui-kit/material-kit-react . But every time I just want to see it, or change one color to check something, it's the same ritual. git clone , npm install , wait, npm run dev , wait more, tab over to localhost. Few minutes gone, a few hundred MB of node_modules on disk. All that to look at a dashboard. So this time I skipped the build. Opened the folder in CrossUI Studio , rendered src/main.tsx directly. No install, no Vite, no localhost. Below is what I did, including the bits that made me stop and think. One honest note first. This does not replace your dev server. You still need the real thing for tests, prod builds, actual feature work. It's good for the look-and-tweak loop. Evaluating a template, recoloring something, showing a client. The stuff where booting the whole toolchain costs more than the task itself. Quick note : local folder support requires a Pro account. To test it out, use the code in the original blog for a free upgrade. No credit card required, available while it lasts. Your browser does not support video. Watch on YouTube . 1. Clone to local disk (don't open it straight from GitHub) Studio can mount a GitHub repo directly. For a small repo that's the nicest path. For this one I cloned to disk first: git clone https://github.com/minimal-ui-kit/material-kit-react The reason is boring. src/ alone is ~130 files, ~245 in the whole project, spread over sections/ , components/ , layouts/ , theme/ , routes/ . Opening a project means the tool has to pull the files it touches. Over the GitHub API, on demand, that's a lot of small requests. It works, just not snappy, and you can hit the rate limit if you poke around. A local folder is only the filesystem, so it's instant. For a template this size, local wins. No npm install here. I only cloned the source. The

1. Introduction: What is TanStack Query? TanStack Query (formerly React Query) is a framework-agnostic state management library designed specifically to manage Server State —handling data fetching, caching, background updating, and cache invalidation. 2. Server State vs. Client State & The Memory Reality Client State: Owned and controlled entirely by the browser (e.g., isModalOpen , selected UI theme). Server State: Owned by the remote backend database (e.g., user profiles, posts, cart items). The browser only holds a read-only temporary snapshot . Where is data physically stored? Physical Location: By default, cached data lives strictly in the browser tab's JavaScript RAM (In-Memory) . Backend ("The Server"): Refers to your remote API/database (regardless of whether it runs on Kubernetes, Docker containers, serverless functions, or bare metal). Optional Persistence: You can opt to sync this RAM cache to localStorage , sessionStorage , or IndexedDB using TanStack Query Persisters. import React , { useState } from ' react ' ; import { QueryClient , QueryClientProvider , useQuery , useMutation , useQueryClient , } from ' @tanstack/react-query ' ; // 1. Initialize QueryClient (manages the RAM cache) const queryClient = new QueryClient ({ defaultOptions : { queries : { staleTime : 10000 , // Data stays fresh in RAM for 10 seconds }, }, }); // Mock API functions async function fetchPost ( postId ) { const res = await fetch ( `[https://jsonplaceholder.typicode.com/posts/$](https://jsonplaceholder.typicode.com/posts/$){postId}` ); if ( ! res . ok ) throw new Error ( ' Network error ' ); return res . json (); } async function createPost ( newPost ) { const res = await fetch ( ' [https://jsonplaceholder.typicode.com/posts](https://jsonplaceholder.typicode.com/posts) ' , { method : ' POST ' , headers : { ' Content-Type ' : ' application/json ' }, body : JSON . stringify ( newPost ), }); return res . json (); } // 2. Query Component (Fetching & Reading Data) function PostViewe

QCon AI New York 2026 (Dec 15-16) has opened registration at The Westin Jersey City Newport. Six tracks on production AI, chaired by Eder Ignatowicz with Faye Zhang and Wes Reisz. First sessions announced in August, full program by November. By Artenisa Chatziou

Ink & Switch published bijou64, a variable-length integer encoding where every number has exactly one byte representation, closing the canonicality bug class behind attacks on PKCS#1, JWT libraries, and Bitcoin. The design also decodes two to ten times faster than LEB128. Community ports to Elixir, Go, Perl, and Java followed, while HN commenters debated SIMD performance and residual range checks. By Steef-Jan Wiggers

Every morning, an online store receives the previous day’s orders from a marketplace partner. The file comes in JSON format. The company needs to add those orders to its main MongoDB orders collection. The sales manager also needs a CSV report that can be opened in Excel. That sounds like a small task. Import the file, copy the documents, export the report. But in practice, a few things can break the process. A date can be imported as a string. A field can have the wrong name. One batch may use total , while the main collection uses totalAmount . A temporary collection can keep old records and trigger duplicate key errors. A CSV export can create null values because the mapping points to fields that do not exist. And then there is customer data. The manager may need the sales numbers, but they probably do not need real customer names or internal customer IDs. This article walks through a real daily workflow: Import marketplace JSON ↓ Store the batch in a temporary MongoDB collection ↓ Copy the orders into the main orders collection ↓ Mask customer fields during export ↓ Create a CSV report The goal is not just to move data from JSON to CSV. The goal is to make the process repeatable, easier to check, and safer to share. The workflow The workflow has three jobs: Import Yesterday Orders ↓ Add Orders to Main ↓ Export Daily Sales Report The important part is the parent relationship between the jobs. Add Orders to Main depends on Import Yesterday Orders , so it only runs after the JSON file is imported successfully. Export Daily Sales Report depends on Add Orders to Main , so the CSV is created only after the main orders collection has been updated. This prevents the report from being generated when data is missing or incomplete. The incoming JSON file The partner sends a file with yesterday’s completed orders. A single order looks like this: { "orderId" : "ORD-2026-07-201" , "customerId" : "CUST-1003" , "customerName" : "Sofia Rossi" , "orderDate" : "2026-07-21T08:20:00

Meta's React library has integrated a Rust version of the React Compiler into its main repository, aimed at enhancing build speed and compatibility with the Rust-based JavaScript toolchain. This port, which memoizes components automatically, demonstrates significant performance improvements, boasting up to 50% faster compilation. The public API remains unchanged to facilitate easy upgrades. By Daniel Curtis

I love InertiaJS to the point where it's becoming a personality trait I tend to want to use it for everything, but adding Inertia to an existing Laravel API gets awkward fast. Same thing happens in the other direction: you start with a full Inertia frontend and then realize you want to expose some of that data as a public API too. The naive solutions are: Sprinkle if ($request->wantsJson()) into your controllers Maintain two separate routes that return the exact same data Neither feels right. So I made inertia-split Starting fresh with Inertia: serve both from the same controller class ProjectController extends Controller { use HasHybridResponses ; public function index () { return $this -> respond () -> component ( 'Projects/Index' , [ 'projects' => Project :: all (), ]); // Inertia request → renders the Svelte/Vue/React component // API request → returns JSON } } The controller doesn't check anything. Inertia requests get an Inertia response, API clients get JSON. Existing API? Don't touch it If you just want to make an existing API method Inertia-aware, one annotation is enough: #[InertiaComponent('Users/Show')] public function show ( User $user ): array { return [ 'user' => $user ]; } The method body stays exactly as it was. Inertia requests get the component rendered with your data as props. Everything else gets the same JSON as before. Methods without the annotation are completely unaffected. Wait, how does this even work? The package can out Inertia's ResponseFactory for its own in the service provider (opt-in): $this -> app -> singleton ( ResponseFactory :: class , HybridResponseFactory :: class ); // checks if it's an Inertia request and returns appropriate response Good old OOP. Thank you polymorphism. Wrap-up Whatever the direction of your problem, making Inertia and API endpoints use the same controller is a big win. You're still responsible for writing routes and wiring middlewares, but this should save a lot of time and effort. Still in beta, use accor

Shipped this week Workflows are now a first-class Bizbox primitive — PR #86 · v2026.603.0 The biggest drop this week. @DennisDenuto landed Workflows as a company-scoped concept that sits alongside issues and routines — not shoehorned into either. What that means in practice: Google ADK-backed execution — workflow pipelines run as ADK agents, with phase state persisted as run records. Human handoffs baked in — pipelines can pause and wait for a human before resuming. Deliverables that survive — artefacts from each run are persisted and surfaced in the UI. A pipeline graph in the UI — topologically ordered, showing live phase state and console output. This is the foundation. More on what we can build on top of it below. Workflow human-handoffs now route through ClickUp — PR #91 · v2026.605.0 The day after Workflows landed, @angelofallars wired up the last kilometre: when ADK Python code calls input() inside a pipeline, Bizbox now intercepts that call and sends a ClickUp message to collect the human reply — instead of blocking the process forever. A few things that were fixed along the way: input() monkey-patching now works consistently across Python environments (was silently failing in some setups). Failed workflow runs no longer submit deliverables. You only see artefacts from runs that actually completed. ClickUp awaiting-human bridge adapter ships as a pure plugin — PR #78 · v2026.601.0 This one technically crossed the line on the last day of May (23:56 UTC, 31 May), so it's in scope. @ralphbibera ported the ClickUp transport and adapter as a genuine plugin — implementing the AwaitingHumanBridgeAdapter registry interface — without touching bridge core at all. What that gives you: ClickUp works through the same provider-agnostic layer as any future provider (Slack, Discord, whatever comes next). The core doesn't know ClickUp exists. Included: send/poll/reaction transport, message templates for request_confirmation and ask_user_questions interactions, brain_is_think

I'm AlanWu. I'm in junior high. I've written a lot of bad code. Here's what I changed to make it less bad. 1. Name things like a human // Don't do this int d ; // days? distance? damage? int cnt = 0 ; // "cnt" — you know, the classic vector < int > v ; // v of what // Do this int daysUntilDeadline ; int errorCount = 0 ; vector < int > studentScores ; Full words, no abbreviations. idx instead of i in loops is fine. But sz for size, cnt for count, ptr for pointer — just type the word. You're not being charged by the character. 2. Functions should do one thing If you need the word "and" to describe a function, split it. // Bad: does two things, name lies void loadAndValidateConfig () { readFile (); checkSyntax (); } // Better Config loadConfig ( string path ) { return parseConfig ( readFile ( path )); } bool validateConfig ( const Config & cfg ) { return cfg . width > 0 && cfg . height > 0 ; } My rule of thumb: if a function is longer than what fits on one screen, break it. If I can't describe what it does in one sentence without "and", break it. 3. Comments explain WHY, not WHAT // Bad — tells me what the code already says // Loop through all students for ( auto & s : students ) { s . score += 5 ; } // Good — tells me WHY, which the code can't // Extra credit: 5 points for submitting early for ( auto & s : students ) { s . score += 5 ; } If you're writing a comment that just restates the next line of code, delete it. The only comments worth keeping are the ones that answer "why did I do it this way?" 4. Don't nest too deep // Bad — 3 levels deep, I've already forgotten what the top level was for ( auto & student : students ) { if ( student . hasSubmitted ()) { for ( auto & answer : student . answers ) { if ( answer . isCorrect ()) { score ++ ; } } } } // Better — flatten with early exits for ( auto & student : students ) { if ( ! student . hasSubmitted ()) continue ; for ( auto & answer : student . answers ) { if ( ! answer . isCorrect ()) continue ; score ++ ; } } Al

Rspack, developed by ByteDance, has released version 2.0, featuring enhanced performance, reduced dependencies, and a focus on modern ECMAScript modules. Key updates include a pure ESM core, improved static analysis, and support for RSC. Performance benchmarks show big improvements in build times. The project has experienced substantial growth, with npm downloads exceeding 5 million weekly. By Daniel Curtis

DISCLAIMER: This post was written entirely by me! I used AI for a little research, spelling, grammar, and comprehension checks. This post was originally shared on Hackernoon Entertain me for a moment, let's appreciate where we are today by understanding where we've been… or at least, where I've been. I remember when I first learned to code. I was bad, like really bad. But I was so curious! It all started by writing some VBA in an MS Access Database to create an IT Inventory app in the late 90s. Then I learned JavaScript, ASP (without the .Net), then C#, .Net ( I still have my .Net for Dummies book, see below) , jQuery, Python, Java, Angular, ReactJS, and Python again (yeah, had to relearn that one for some reason), and I'm sure there are others in there I forgot about. Learning to write code was rewarding! There were those days I'd spend hours on a bug, only to realize I didn't initialize the variable or forgot a semicolon. I learned .Net over a weekend thanks to the above book. I never became an artist of the craft, like some of my colleagues have (you know who you are: Josh, Kevin, and many others), but I knew how to build anything. I loved that ability: I could build anything. Pure joy! If you haven't had the joy of learning to code, do your best to learn it, because you can't prompt your way to being a Senior Engineer . As I progressed in my career, I became an architect and senior lead. I started off by leading a single engineering team, and now I support large programs and teams. All the while, I never let go of hands-on-code. I still love coding for work and my myriad of side projects. Then, a few years ago, this GenAI thing showed up. Put me in that group of: oh-no-there-goes-my-joy. Joy, yes, not my job. I love my job because I get to do what I love. I loved the dramatic rollercoasters: architecting a perfect solution, realizing it's wrong, getting to write every line of code, chasing impossible bugs, panicking with deadlines, late nights chasing hot fixes,

GenPage is a generative AI system developed by Netflix to replace its traditional multi-stage recommendation pipeline by directly generating personalized user homepages. GenPage leverages user history and request context as a prompt to produce the entire page, resulting in improved user engagement and reduced serving latency. By Sergio De Simone

What if you could take an existing class and say "I want everything this already does, plus a few...

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

Why some records should be created by your system, not your users, and how to do it safely in .NET. A support ticket lands on your desk: "The Teams page is empty. I added a member, but no team shows up." You check the API. It's behaving exactly as written: { "items" : [], "totalCount" : 0 } Nothing is broken. And that's the problem. The system is faithfully returning nothing, because the row that the page reads from was never created. Somewhere in your design, you assumed a human would create it first. This article is about a small, recurring design decision that quietly causes empty dashboards, confused users, and "is this a bug?" tickets: who is responsible for creating derived records the user, or the system? and how to let the system do it without introducing duplicate rows or race conditions. The problem Let's use a fictional product: a collaboration tool called Loop . In Loop, the important entities are: An Organization (a paying customer). A Member (a person invited into an organization under a plan). A Team a grouping that members belong to, keyed by (OrganizationId, PlanCode) . The admin dashboard lists Teams . Each team card shows a member count. Here's the catch in the original design: creating a Member wrote a member row. Creating a Team was a separate, manual step an admin was expected to do first. If an admin invited members without first creating the matching team, the dashboard showed nothing even though the members clearly existed. From the user's point of view, they did everything right. From the system's point of view, a required row simply didn't exist. Why it matters The Team record isn't independent information. It is fully derivable from the first member invited under a plan. When one entity's existence is implied by another, forcing a human to create it manually is a design smell. It leads to: Empty states that look like outages. Users can't tell "no data" from "misconfigured." Support load. Every skipped step becomes a ticket. Silent data dr