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A linked list is an ordered linear data structure where elements are not stored in sequential memory locations, instead they are stored in nodes that are linked together by a pointers. Linked list are used in data intensive application because linked list offer specific benefits for high frequency data manipulation, this benefits include: Efficient insertion and deletion Adding and removing elements from a linked list is highly efficient, unlike arrays which requires shifting all subsequent elements to maintain indexing. A linked list only requires updating the pointer. Dynamic sizing: linked list can grow or shrink during runtime without needing to pre-allocate memory. Memory management: Nodes in a linked list are only allocated when needed which prevents memory wastage. Flexible Traversal: Doubly and circular list allow you to move forward or backward, which makes them helpful for complex navigation The first node in a linked list is called the head which signifies the start of the list, while the last node is called the tail and has a pointer of null except in a circular linked list. Each node in a linked list has two things which are: the actual data the pointer or reference There are three main types of linked list: Singly linked list Doubly linked list Circular linked list Singly Linked List: Singly linked list are lists where each node has a next pointer that points to the next node. Doubly Linked List: Doubly linked list are list where each node has a next and previous pointer that points to the previous and next node. Circular Linked List: Circular linked list are list where the last node points back to the first node, forming a circle. Table of Contents create node class create linked list class isEmpty and getSize Methods prepend and append Method removeHead and removeTail Methods insert and search Methods getIndex and removeIndex Methods clear and print Methods create node class First let's open our code editor and create a new file called singlyLinkedLi

Stop Shipping 20 Locale Files in React Native: On-Device Translation for Dynamic Language Packs Internationalization in mobile apps usually starts clean and then gets expensive. At first, you keep a couple of JSON files: en.json es.json fr.json That works when your product is small and the set of languages is stable. It breaks down when: you want to support many languages the product team keeps changing copy translated files drift out of sync some languages are only partially used you do not want to run every string through a server-side translation pipeline This is the problem @tcbs/react-native-language-translator is trying to solve. It lets a React Native app keep a source language, translate missing keys on device, and cache the generated language pack locally. Package: @tcbs/react-native-language-translator The problem Many React Native apps treat localization as a static asset problem: keep one JSON file per language ship all of them in the app update all of them whenever English changes That model has real costs. 1. Translation files become operational debt Every new feature adds more keys. Every copy change forces translators to update multiple locale files. Over time, the translation layer becomes a maintenance queue. The result is predictable: missing keys stale translations untranslated fallback strings inconsistent release quality across languages 2. Shipping many locales is wasteful Most users only need one target language. But many apps ship every locale anyway. That increases bundle size and creates a lot of dead weight for users who will never use most of those files. 3. Dynamic product copy is hard to localize well If your app changes quickly, static translation files lag behind. Teams either accept stale translations or build a backend workflow to keep everything synchronized. That is often more infrastructure than the app actually needs. 4. Server-side translation is not always the right tradeoff Calling a translation API at runtime introduces: la

In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution. By Aaditya Chauhan

“Did We Already Watch This?” — Building KindaSeen with FastAPI and Next.js A few months ago, my friends and I kept running into the same question whenever we talked about movies, dramas, anime, or variety shows: “Did we already watch this before?” Sometimes we remembered the title but forgot whether we had finished it. Other times, we completely forgot we had already seen it at all. That simple problem inspired me to build KindaSeen, a full-stack personal media repository designed to help users track and organize the media they’ve consumed in one centralized platform. The goal of the project was not only to create a useful application, but also to gain hands-on experience building a real-world full-stack system with modern web technologies. What KindaSeen Currently Supports User authentication with Supabase CRUD operations for personal media records TMDB-powered search functionality Watchlist system Favorites system Persistent PostgreSQL storage Dockerized backend deployment Separate frontend/backend deployment workflow Tech Stack Frontend Next.js React Tailwind CSS Shadcn/ui Vercel deployment Backend FastAPI PostgreSQL Docker Render deployment External Services Supabase Authentication TMDB API integration One of the main goals of this project was to simulate a more realistic production workflow by using a decoupled frontend/backend architecture instead of building everything inside a single monolithic application. In this article, I’ll share: Why I chose this architecture How I integrated TMDB into the application Challenges I faced during deployment What I Learned From Building KindaSeen Why I Chose This Architecture Instead of building a monolith using Next.js API routes, I decided to decouple the application into a Next.js frontend and a FastAPI backend. This decision was driven by three main factors: AI Compatibility & Future Proofing : While researching the job market, I noticed that most companies building AI products heavily rely on Python. By choosing FastA

When managing production enterprise infrastructure, you rarely have direct root access via SFTP or SSH for security reasons. Instead, you often have to navigate multi-layered permissions—logging in as a standard user, transferring files locally, and escalating privileges via CLI to finalize configurations. In this tutorial, we will walk through the step-by-step procedure to safely renew an Apache SSL certificate under a restricted environment where WinSCP access is limited to a non-root user (sysops), requiring command-line intervention to complete the installation. Prerequisites A target Apache web server (CentOS/RHEL-based configuration using /etc/httpd/). A standard user account (sysops) with sudo privileges. The new SSL certificate (.crt) and CA bundle/chain file ready on your local machine. Step 1: Backup Existing Certificates Before making any changes to production security files, always back up the working configuration. Access the server via PuTTY using the sysops account, and switch to the root user or use sudo to create a backup of your existing keys: sudo cp /etc/httpd/server.crt /etc/httpd/server.crt.bak sudo cp /etc/httpd/server.key /etc/httpd/server.key.bak Step 2: Stage the New Certificates via WinSCP Because your WinSCP session cannot log in directly as root, you must stage the files in a directory your user owns. Open WinSCP and log in using your sysops credentials. Upload your new certificate files (nouveau_certificat.crt and nouveau_certificat_chain.pem) directly into your home directory: /home/sysops/. Step 3: Install and Replace the Certificates Now, return to your terminal session (PuTTY) to move the files from your staging directory to the protected Apache directory using elevated privileges. Copy the new primary certificate sudo cp /home/sysops/nouveau_certificat.crt /etc/httpd/server.crt Copy the new certificate chain / CA bundle sudo cp /home/sysops/nouveau_certificat_chain.pem /etc/httpd/server-ca.crt Step 4: Verify Permissions and Ownersh

Some news: The Vergecast is now a daily podcast! Starting today, we'll be posting every weekday, with even more gadgets and rankings and conversations and feelings and podcasts-within-podcasts. We're excited for all the ways this new schedule lets us tell new kinds of stories, experiment with new tech and new formats, and involve you even […]

The test passed. The runbook completed. Infrastructure came back online inside the RTO window. None of that means the organization can recover from an actual disaster. Disaster recovery testing is designed to succeed. Clean environments, pre-staged dependencies, known failure modes, available staff — each design decision is operationally reasonable. Collectively they remove the conditions that make real recovery hard. What the test validates is test completion, not recovery capability. The Test Is Designed to Pass Every design decision in a standard DR test tilts toward a successful outcome. The test window is pre-announced, so the right engineers are available. The scope is pre-defined, so unexpected systems don't surface mid-exercise. The environment is either isolated or pre-staged, so competing failures don't complicate the recovery sequence. The data state is known and clean, so integrity issues don't slow the restore. The declaration point is assumed, so nobody has to make an ambiguous call under pressure. A test designed to remove the variables that make recovery hard cannot produce evidence about what happens when those variables are present. What Disaster Recovery Testing Actually Excludes Declaration threshold. In a DR test, recovery starts at a pre-agreed time. In a real incident, recovery starts when someone decides the situation has crossed the threshold for declaration — a decision that is rarely clean and routinely delayed 45 minutes to several hours. That delay is inside the real outage window and outside the test clock. Dependency assumptions. DR tests run against known, pre-cleared dependencies. Real incidents surface undocumented dependencies that were never in scope — a configuration service that hasn't been touched in two years, an authentication endpoint that wasn't in the architecture diagram. Data state. Test environments use clean or pre-staged data. Real recovery requires handling whatever state the data was in at the moment of failure — pa

Today I’m talking with Harvey Mason Jr., who is CEO of the Recording Academy — that’s the outfit that puts on the Grammy Awards. I last talked to Harvey in 2024, when it was obvious that generative AI would upend the music industry, but still not exactly clear how that would happen. Well, it’s been […]

Michael Stiefel spoke to Sonya Natanzon, about the intersection of technical and social aspects of software architecture. Understanding the business and how a company operates is more important than the specific technologies used. Effective requirements analysis requires focusing on problems to be solved that describe good and bad outcomes, rather than statements of need or solution statements. By Sonya Natanzon

The Seoul-based rocket startup is developing its own launch vehicles and engines.

PostgreSQL LISTEN/NOTIFY for Real-Time Multi-Tenant Events: Ditching Polling and WebSocket Complexity I've shipped real-time features in CitizenApp using three different approaches: naive polling (embarrassing), Redis pub/sub (overkill), and now PostgreSQL's native LISTEN/NOTIFY. The third option is what I should have started with. Most teams reach for Redis or RabbitMQ the moment they need real-time updates. It's the conventional wisdom. But here's the truth: if you're already running PostgreSQL, you have a battle-tested pub/sub system sitting right there. It handles multi-tenancy correctly, scales to thousands of concurrent connections, and eliminates an entire infrastructure dependency—which matters when you're deploying to Render or Vercel where every added service is friction. Why LISTEN/NOTIFY beats the alternatives Polling is dead. HTTP requests every 2-5 seconds for "new notifications"? That's technical debt masquerading as simplicity. It wastes bandwidth, kills your database with unnecessary queries, and users see stale data. Redis is powerful but expensive. Not just in dollars—in operational overhead. You need to manage connection pools, handle failover, monitor memory usage, and keep another service running in production. At CitizenApp's scale (thousands of concurrent tenants), we were paying $50/month for Redis on top of Render just to broadcast notifications that PostgreSQL could handle natively. WebSockets without a broker are a nightmare. If you're running multiple FastAPI workers (and you should be), a WebSocket connection to Worker A doesn't know about events published by Worker B. You need a message broker to fan-out events across processes. Unless you use PostgreSQL LISTEN/NOTIFY, which handles that automatically. PostgreSQL's pub/sub is: Transactional. Notifications only fire after a transaction commits. Tenant-aware. Use channel names like tenant_123_notifications and broadcast only to the right subscribers. Zero extra infrastructure. It's part

Most RAG tutorials I found were either "pip install langchain and you're done" or 50-page academic papers. I wanted something in between — a pipeline I could actually explain in an interview, where I understood every line. So I built one from scratch. No LangChain, no LlamaIndex, no frameworks. Just FastAPI, FAISS, sentence-transformers, and an LLM API. Here's what I built, what worked, and what broke. The architecture PDF --> extract text (pypdf) --> chunk (500 char, 50 overlap) --> embed (MiniLM-L6-v2) | v question --> embed --> FAISS top-k search --> build prompt with chunks --> LLM --> answer + sources Five Python files, ~300 lines total: File Responsibility main.py FastAPI app, 3 endpoints, prompt engineering pdf_loader.py PDF text extraction via pypdf rag.py Chunking + embedding store.py FAISS vector store wrapper llm.py Swappable LLM client (Groq / OpenAI / Anthropic) How the upload works When you POST a PDF to /upload , three things happen: 1. Text extraction — pypdf reads each page and returns the raw text. Pages with no extractable text (scanned images) are skipped. 2. Chunking — each page is split into ~500-character chunks with 50 characters of overlap. The overlap prevents losing context at chunk boundaries. CHUNK_SIZE = 500 CHUNK_OVERLAP = 50 def chunk_pages ( pages ): chunks = [] chunk_id = 0 for text , page_num in pages : start = 0 while start < len ( text ): end = min ( start + CHUNK_SIZE , len ( text )) chunk_text = text [ start : end ]. strip () if chunk_text : chunks . append ( Chunk ( chunk_id = chunk_id , text = chunk_text , page = page_num )) chunk_id += 1 if end == len ( text ): break start = end - CHUNK_OVERLAP return chunks 3. Embedding — each chunk is embedded into a 384-dimensional vector using all-MiniLM-L6-v2 . This runs locally on CPU, no API call needed. Vectors are normalized so we can use inner product as cosine similarity. def embed_texts ( texts ): model = get_embed_model () # lazy-loaded singleton vectors = model . encode ( texts

AI placement latency is not the problem most teams think they are managing. The default framing treats it as an optimization variable — pick the cheapest compute that meets the SLA, centralize inference, optimize for utilization, revisit locality later when the architecture matures. That framing is wrong in a way that compounds over time. AI placement decisions are not continuously reversible optimization choices. They are architectural commitments that harden incrementally — through inference path configuration, data gravity, routing dependencies, and runtime behavior that normalizes around whatever topology you chose first. By the time latency SLAs begin failing, the placement topology is already embedded across routing, observability, and application behavior. The remediation cost is not an optimization exercise. It is a re-architecture. The First Optimization Becomes the Permanent One Cost is the default optimization axis for AI placement decisions. Centralized GPU clusters are cheaper to operate per token than distributed inference endpoints. Utilization density justifies centralization on paper. Procurement processes reward it. FinOps tooling measures it. So teams centralize. They optimize the compute economics. They defer locality decisions to a later phase when requirements are better understood. That later phase rarely arrives before the architecture has already made the locality decision implicitly — through the inference paths built against a centralized endpoint, the data gravity that formed around it, and the application behavior that normalized against the latency profile it produced. The pattern this creates is latency debt: accumulated runtime latency overhead from placement decisions that optimized for cost before locality requirements were operationally visible. It accrues gradually, stays invisible until something triggers it, and is significantly more expensive to resolve after the fact than it would have been to avoid at design time. It does not

22 Astro Best Practices: The Bookmark-Worthy Tips At QuotyAI I'm using Astro to build landing pages and blog posts, so I have hands-on experience how to use it properly and how to vibe-code without headache. Astro is the best framework for content sites right now - #1 in developer satisfaction in the State of JS 2025 survey, with Cloudflare backing it since January 2026. But like any tool, it rewards people who use it the way it was designed. This is the reference I wish I had when I started. Whether you're building your first Astro project or vibe-coding a blog at 2am, these are the habits worth forming from day one. Heads up on versions: This article covers Astro 6.x (released March 2026) and Astro 6.4 (released May 2026). Some APIs from older tutorials are now deprecated - those are called out explicitly below. Always check the upgrade guide when moving between majors. 🖼️ Assets & Media 1. Use <Image /> instead of <img /> Astro's built-in <Image /> component does a lot of work at build time that plain <img> tags leave on the table: it converts images to WebP, generates the right width and height attributes to prevent layout shift, and compresses everything without you touching a single config file. --- import { Image } from 'astro:assets'; import hero from '../assets/hero.png'; --- <!-- ✅ Optimized: converted to WebP, compressed, no layout shift --> <Image src={hero} alt="Hero image" /> <!-- ❌ Skips all of that --> <img src="/hero.png" alt="Hero image" /> For art-direction scenarios (different images at different breakpoints), reach for <Picture /> instead. 2. Use the Astro 6 Built-in Fonts API Almost every website uses custom fonts, but getting them right is surprisingly complicated - performance tradeoffs, privacy concerns, self-hosting, fallback generation, and preload hints. Astro 6 added a built-in Fonts API that handles all of it for you. Configure your fonts in astro.config.mjs : // astro.config.mjs import { defineConfig , fontProviders } from ' astro/conf

The engineering team at Meta recently outlined how the company migrated a data ingestion platform that transfers several petabytes of MySQL social graph data daily to improve reliability and operational efficiency. The team used techniques like reverse shadowing and continuous checksum monitoring to ensure zero downtime during the transition. By Renato Losio

Most people will never own, drive, or even sit inside a Ferrari Luce. (If you can, or do… hit us up.) There's still no question that Ferrari's first electric vehicle is one of the most interesting, surprising cars of the year. With a decidedly un-Ferrari look, and lots of new technology and designs courtesy of […]

The Cloud Native Computing Foundation has highlighted a new AI-assisted migration approach that enabled engineers to migrate 60 ingress-nginx resources to Higress in roughly 30 minutes, demonstrating how artificial intelligence is increasingly being applied to modernize Kubernetes networking and gateway infrastructure. By Craig Risi

New features coming to YouTube could make it better for listening to podcasts, rolling out to Premium subscribers starting today on Android and coming later to iOS. A new "on-the-go mode" shifts YouTube into an audio-first layout, with larger, simplified playback buttons, a still image in place of the video, and a timeline showing video […]

The update signals YouTube's ongoing efforts to compete with other platforms for podcast audiences.

Today, I’m talking with Wassym Bensaid, the chief software officer at Rivian, and the co-CEO of Rivian’s platform joint venture with Volkswagen, which everyone just calls RV Tech. That joint venture kicked off about a year and a half ago with a nearly $6 billion investment from Volkswagen. It effectively puts Wassym in charge of […]