标签：#Performance

📺 Prefer to watch? 90-second YouTube Short · 💬 Telegram Originally published on software-engineer-blog.com . You fixed the CSS. You deployed. You opened the site and checked it yourself — perfect. Then a customer sends a screenshot of last week's layout. Nothing is broken. No deploy failed, no CDN is lying to you, no file is corrupt. The browser is doing exactly what you told it to do, several days ago, in a header you probably never wrote by hand. This is the part of web performance that gets skipped, because caching looks like a setting rather than a contract. It is a contract. And like any contract, the interesting part is not what it gives you — it is what you can no longer do once you have signed it. Throughout this post I will use one running example: PlantPal , a small plant shop. One stylesheet ( app.css ), one logo ( logo.png ), one API endpoint ( /api/products ). The floor: a page load is not one thing Before caching means anything, you have to see what it is acting on. Loading PlantPal's homepage is not a request. It is roughly 40 separate requests — the HTML, the stylesheet, a few fonts, the logo, a dozen product images, the JavaScript bundle, the product API. Each one is a full round trip: DNS is probably warm, but you still pay connection setup, the request, the server's think time, and the bytes coming back. The numbers for a first visit: ~40 requests 1.2 MB transferred 2.1 s to a usable page Which gives us the only sentence in this post that you actually need to remember: The fastest request is the one the browser never sends. Not a faster server. Not a closer edge node. Not a smaller file. No request at all. Everything below is a way of getting closer to that. max-age: buying silence The blunt instrument is Cache-Control : HTTP / 1.1 200 OK Content-Type : text/css Cache-Control : max-age=31536000 31536000 is one year in seconds. You are telling every browser that receives this response: keep this copy and use it for a year without asking me again. O

Go 1.27 landed in August 2024, and while it doesn’t introduce earth-shattering changes, it polishes the language in ways that add up. If you’re maintaining production services or building new ones, these updates can save you time and headaches. Let’s cut through the noise and focus on what actually affects your code. Performance: Faster Without Changing a Line The compiler and runtime received several under-the-hood optimizations. Benchmarks show a 3-5% speedup in typical server workloads, with some microbenchmarks hitting 10%. This isn’t magic, it’s the result of better inlining decisions and reduced memory allocation overhead. The best part? You get this for free. Just recompile your existing code with Go 1.27 and measure the difference. One standout improvement is in garbage collection. The GC now handles large heaps more efficiently, which matters if you’re running services with hundreds of gigabytes of live data. Latency spikes during GC cycles should be less pronounced, though you’ll still want to monitor this in production. Language Tweaks: Small but Useful Go 1.27 introduces a few language changes that simplify common patterns. The most notable is the addition of the new built-in function clear. It works on slices, maps, and type parameters, letting you reset collections without reallocating them. This is particularly handy for pooling or reusing buffers. For slices, clear sets all elements to their zero value and truncates the slice to length zero. For maps, it removes all entries, leaving the map empty but with the same capacity. For type parameters, it behaves based on the underlying type, useful for generic code. Another small but welcome change is the ability to use //go:linkname with methods. This was previously restricted to functions, which made certain low-level optimizations awkward. Now you can link methods directly, which is useful for writing highly optimized libraries or interfacing with C code. Tooling: Better Debugging and Dependency Manageme

Java 11 New Features and Performance Improvements Released in September 2018, Java 11 is a Long-Term Support (LTS) release, making it one of the most important milestones since Java 8. For teams planning migrations, Java 11 offers a compelling mix of new language features, API enhancements, and under-the-hood performance improvements. In this post, we'll explore the most impactful changes and how they affect real-world applications. Why Java 11 Matters Java 11 is the first LTS release after Java 8, meaning it receives extended support and security updates. Unlike the non-LTS releases (9 and 10), it's designed for production stability, which is why many enterprises skipped straight from 8 to 11. Language and API Enhancements 1. Local-Variable Syntax for Lambda Parameters Java 10 introduced var for local variables. Java 11 extends this to lambda parameters, allowing you to apply annotations consistently. // Now valid in Java 11 list . forEach (( var item ) -> System . out . println ( item )); // Useful for annotations list . forEach (( @Nonnull var item ) -> process ( item )); 2. New String Methods The String class gained several convenient methods that reduce boilerplate. // Check if a string is blank (empty or whitespace only) " " . isBlank (); // true // Strip leading/trailing whitespace (Unicode-aware) " hello " . strip (); // "hello" " hello " . stripLeading (); // "hello " " hello " . stripTrailing (); // " hello" // Repeat a string "ab" . repeat ( 3 ); // "ababab" // Stream lines "line1\nline2" . lines (). forEach ( System . out :: println ); Note: strip() differs from trim() because it uses Character.isWhitespace() , correctly handling Unicode whitespace characters. 3. Files Read/Write Convenience Methods Reading and writing strings to files is now a one-liner. import java.nio.file.Files ; import java.nio.file.Path ; Path path = Path . of ( "example.txt" ); // Write Files . writeString ( path , "Hello, Java 11!" ); // Read String content = Files . readString (

Every video card on our category grids was hotlinking a 1280x720 JPEG from a third-party CDN and then letting CSS scale it down to about 320 device-independent pixels. That is roughly 90 KB of wasted transfer per card, 24 cards per page, across eight regional page variants that each carry their own cache key. Mobile LCP on the busiest category pages sat at 4.1s, and the largest single contributor was an image we did not host, could not resize, and could not re-encode to WebP. The fix was not clever CSS. It was owning the frame. We built a small Go service that takes a source video (a partner preview MP4, or a poster frame that arrives at the wrong dimensions), pulls a representative frame with FFmpeg, encodes it at three widths in WebP, and writes the result to a content-addressed path the front end links directly. That service now feeds the same multi-region cron that runs TrendVidStream , and the generated files ride the same FTP mirror as the rest of the deploy. What follows is the part that actually mattered: the FFmpeg invocations, the Go concurrency model that keeps a 2-core build box from melting, and how a stateless Go daemon hands work to a PHP 8.4 + SQLite front end that cannot run a daemon at all. Why this is not a PHP job Our front end is PHP 8.4 on LiteSpeed shared hosting with SQLite (FTS5 for search) as the only datastore. It is a genuinely good fit for a read-heavy discovery site: no database server to babysit, page cache on disk, cron jobs pulling regional feeds every 2-7 hours depending on the site. It is a terrible fit for thumbnail extraction: Shared hosting caps max_execution_time at 180s. A cold FFmpeg decode of a 4-minute 1080p preview can burn 20-40s. Do 200 of them in one cron tick and you are wearing a hard timeout. shell_exec is frequently disabled, and when it is not, you get one process per request with no way to bound total concurrency. There is no shared memory between PHP requests, so two cron ticks racing on the same video ID will ha

Imagine opening a website from India while its servers are located in the United States. You request an image. Your request travels thousands of kilometers to the server, the server processes it, and the response travels all the way back to you. It works. But what happens when millions of users around the world do the same thing? This is where a CDN (Content Delivery Network) comes in. A CDN helps websites deliver content from servers that are geographically closer to users, reducing latency, improving performance, and taking load away from the main server. In this article, we'll understand how CDNs work, why they're important, and how they're used in large-scale systems. What Is a CDN? A Content Delivery Network is a globally distributed network of servers that stores and delivers frequently requested content closer to users. Without a CDN, requests might look like this: User ↓ Main Server ↓ Content With a CDN, a distributed layer is added between users and the origin server: ┌── CDN Edge Server ── User (India) │ Origin Server ────┼── CDN Edge Server ── User (Europe) │ └── CDN Edge Server ── User (USA) The main server is called the origin server . The distributed servers are commonly called edge servers or Points of Presence (PoPs) . Why Do We Need a CDN? Without a CDN, users from different parts of the world may have to communicate with the same origin server. For example: User in India ───────┐ User in Germany ─────┤ User in USA ─────────┼──→ Origin Server User in Japan ───────┘ As traffic grows, this creates several problems: Higher latency More traffic reaching the origin Increased server load Slower image and video delivery Poor performance for users far away from the server A CDN solves this by distributing frequently requested content geographically. How Does a CDN Work? Suppose your website contains an image: /images/product.jpg A user in India requests it. Instead of immediately contacting your origin server, the request goes through the CDN: User ↓ CDN ↓

Let's be honest about why you're here. You have a 4MB animated GIF that's slowing down a product page, bouncing back from an email attachment limit, or getting rejected by an ecommerce backend that caps images at 2MB. Or maybe a client sent you a loop that's 15MB and you need it under 1MB for a Slack header. The good news: you can usually cut a GIF's file size by 70–90% without anyone noticing the difference. The bad news? You have to stop thinking about GIFs as images and start thinking about them as video. Here's the practical guide to compressing GIFs in 2026, using only free browser-based tools. No uploads to shady servers, no software installs, just math and smart tradeoffs. Why GIFs get so big (and why your 4MB file is normal) GIF is a 1987 format. It was designed for simple graphics on dial-up internet, not for 4K animated logos. To understand why it bloats, you need one mental model: A GIF is a video pretending to be an image. Here's what happens under the hood: Limited palette: A GIF can only store 256 colors per frame. That's 8-bit color. Your screen displays millions of colors, so the GIF has to approximate. The real problem is how it stores those colors. Frame-by-frame storage: Unlike MP4, which stores only the changes between frames, a GIF stores every single frame as a full image . A 100-frame animation at 800x600px is 100 full-size images stacked on top of each other. Uncompressed data: GIF uses LZW compression, which is weak by modern standards. It works well on flat colors but fails on gradients, noise, or photographic content. A 10-second screen recording with a subtle gradient? That's a 20MB GIF waiting to happen. The math: A 500x500px, 30fps, 3-second GIF has 90 frames. Each frame is roughly 500x500x3 bytes (RGB) = 750KB raw. Before compression, that's 67.5MB of raw data. LZW might get it down to 4–8MB. That's why your file is huge. It's not a bug; it's the format being honest about its limitations. The three real levers to shrink a GIF You can't

ASP.NET Core Output Caching: How to Make Web APIs Faster in .NET When an API receives the same request repeatedly, performing the same database query and rebuilding the same response every time can waste valuable resources. For example, imagine this endpoint: GET /api/products If thousands of users request the same product catalog, your application might repeatedly: HTTP Request ↓ Controller ↓ Database Query ↓ Business Logic ↓ JSON Response For data that doesn't change frequently, this can create unnecessary database load. ASP.NET Core provides Output Caching to help solve this problem. Instead of executing the complete request pipeline every time, the application can temporarily store the generated response and reuse it for subsequent requests. In this tutorial, we'll look at how Output Caching works, how to configure it, how to invalidate cached responses, and when you should avoid using it. What Is Output Caching? Output caching stores the generated response from an endpoint. For example: First request ↓ GET /api/products ↓ Execute controller ↓ Query database ↓ Generate response ↓ Store response in cache Later: Second request ↓ GET /api/products ↓ Cached response ↓ Return immediately The database doesn't need to be queried again while the cached response is valid. Output Caching vs Response Caching These two concepts are often confused. Response Caching Response caching mainly relies on HTTP caching semantics and headers. Output Caching Output caching is controlled by ASP.NET Core and allows your application to decide which responses should be cached and for how long. Output caching provides more control over server-side response caching. 1. Add Output Caching Start by registering the output-cache services. var builder = WebApplication.CreateBuilder(args); builder.Services.AddControllers(); builder.Services.AddOutputCache(); var app = builder.Build(); app.UseOutputCache(); app.MapControllers(); app.Run(); The important pieces are: AddOutputCache() ↓ Configure cac

Performance testing is essential for ensuring your applications can handle expected user loads without bottlenecks or failures. Apache JMeter remains one of the most popular open-source tools for load, stress, and performance testing. Here is a quick guide to getting your JMeter environment set up and executing your first load test. 1. Prerequisites JMeter requires Java to execute. Ensure you have JDK 11 or higher installed on your system. Verify your Java installation: java -version 2. Download and Installation Download the latest binary zip/tgz file from the Official Apache JMeter Site. Extract the archive into your preferred local directory. Launch JMeter from the bin directory: Windows: Double-click jmeter.bat macOS/Linux: Open terminal and run ./jmeter.sh 3. Install the Plugins Manager The Plugins Manager simplifies adding listeners, graph generators, and custom samplers. Download jmeter-plugins-manager.jar from JMeter Plugins. Move the file into your JMeter lib/ext directory. Restart JMeter. Access the Plugins Manager under Options > Plugins Manager. 4. Building Your First Test Plan Set up a basic HTTP test using the GUI interface: Thread Group: Right-click Test Plan > Add > Threads (Users) > Thread Group. Configure your target virtual users, ramp-up time, and loop count. HTTP Request Defaults: Right-click Thread Group > Add > Config Element > HTTP Request Defaults. Set your target server domain/IP and port. HTTP Sampler: Right-click Thread Group > Add > Sampler > HTTP Request. Define the API path and request method. Listeners: Right-click Thread Group > Add > Listener > View Results Tree or Summary Report (use these GUI listeners primarily for test script validation). 5. Running Tests in Non-GUI Mode Never run actual heavy load tests through the JMeter GUI as it consumes significant local system resources. Use CLI mode for accuracy: jmeter -n -t /path/to/testplan.jmx -l /path/to/results.jtl -e -o /path/to/html-report-folder -n: Non-GUI execution -t: Path to y

I spent a while building browser-based hardware diagnostics and came away with a much clearer sense of where the web platform is genuinely capable and where it quietly lies to you. Notes below, with live demos for each API so you can poke at them yourself. Refresh rate: requestAnimationFrame is the only signal you get There's no screen.refreshRate . The only approach is timing requestAnimationFrame callbacks and inferring the rate from the median frame delta: const deltas = []; let last = performance . now (); function tick ( now ) { deltas . push ( now - last ); last = now ; if ( deltas . length < 180 ) requestAnimationFrame ( tick ); else { const sorted = deltas . slice (). sort (( a , b ) => a - b ); console . log ( Math . round ( 1000 / sorted [ sorted . length >> 1 ])); } } requestAnimationFrame ( tick ); Two gotchas that cost me time. Use the median , not the mean — a single dropped frame wrecks an average. And browsers throttle rAF in background tabs, so the measurement is meaningless unless the tab is visible; gate it on document.visibilityState . ( live version ) Screen dimensions: four different answers, all "correct" screen.width , window.innerWidth , window.devicePixelRatio and screen.availWidth measure genuinely different things, and the one people usually want — actual native panel resolution — is screen.width * devicePixelRatio . Except that's still CSS-pixel derived, so on a scaled display it can disagree with what the panel physically is. The browser simply does not expose true hardware resolution. ( demo ) Keyboard: event.code vs event.key , and the keys you never receive event.key is layout-dependent, event.code is physical position — for a hardware tester you want code . The real limitation is that some keys never reach JS at all: PrintScreen often doesn't fire keydown , Meta combinations get swallowed by the OS, and Fn isn't a browser-visible key on most laptops. N-key rollover testing works surprisingly well though, since you just track the siz

If you're running Magento 2 with MSI (Multi-Source Inventory) enabled — and since Magento 2.4 it's the default — you have a silent performance killer lurking in your database. The inventory_reservation table grows without bound, and every single cart operation hits it. This post walks through why this table becomes a bottleneck, how to measure the impact, and concrete steps to fix it. How Inventory Reservations Work When a customer adds a product to their cart, Magento doesn't immediately decrement stock. Instead, it creates a reservation — a record in inventory_reservation that says "this quantity is tentatively reserved for this order." The actual stock deduction happens later, when the order is placed and the shipment is processed. The flow looks like this: Add to cart → placeReservation writes a negative reservation record Place order → reservation is linked to the order Ship order → inventory_source_item is decremented, reservation should be compensated Compensation reservation → a positive record that cancels out the original negative one In theory, reservations are transient. They exist to bridge the gap between cart and shipment. In practice, they accumulate forever. The Problem: Unbounded Growth Here's what happens in production: Orders that are canceled leave orphaned negative reservations Orders that fail during checkout leave reservations that are never compensated Partial shipments create partial compensation records Quote conversions that error out mid-process leave dangling reservations Re-indexing, re-stocking, and admin edits can create duplicate records After 6–12 months of moderate traffic, the inventory_reservation table routinely hits several million rows . I've seen tables with 10M+ rows on stores doing 200 orders/day. SELECT COUNT ( * ) FROM inventory_reservation ; -- 4,872,341 rows on a store running 8 months SELECT COUNT ( * ) FROM inventory_reservation WHERE created_at < DATE_SUB ( NOW (), INTERVAL 30 DAY ); -- 4,710,882 — 96.7% of rows are

For the past few months, I've been working on a project that translates online meetings in real time. At first, I thought the problem would mostly be about choosing the right speech recognition model and finding a good translation API. It turned out those were the easy parts. The real challenge was making everything feel instant. People don't wait for subtitles. If the translation appears two or three seconds after someone finishes speaking, the conversation has already moved on. Technically it works, but from a user's perspective, it feels broken. That completely changed how I approached the project. Instead of optimizing only for accuracy, I had to optimize the entire pipeline for latency: Capturing audio continuously Streaming audio to ASR Performing speech recognition incrementally Translating partial sentences Updating subtitles without flickering Handling corrections when the speech recognizer revised previous words Every stage might only take a few hundred milliseconds, but together they determine whether the experience feels "real-time." Another lesson surprised me even more. Translation quality isn't just about picking a better LLM. Spoken language is messy. People interrupt each other, change direction halfway through a sentence, use filler words, and rarely speak in complete grammatical sentences. A model that performs well on benchmarks can still struggle in a live conversation if the input arrives one fragment at a time. That forced me to rethink prompts, buffering strategies, and when to display or revise translated text. Building this project also gave me a new appreciation for streaming systems in general. Real-time applications are fundamentally different from batch processing. Instead of asking, "How accurate is the result?", you're constantly balancing three competing goals: Latency Stability Accuracy Improving one often makes another worse. I'm still learning every day, and there are plenty of problems left to solve. Over the next few weeks, I'd

If you’ve managed web servers or applications for any length of time, you’ve probably seen this happen: a new feature or campaign goes live, traffic suddenly spikes, and Website Load Testing becomes critical when your website starts returning 503 errors at exactly the moment you need it to perform. What happens next is usually a scramble, SSH into a server you haven’t checked in months, inspect running processes, restart services, and make infrastructure changes based on guesswork. Eventually, the traffic settles, the site recovers, and the immediate crisis is over. But that kind of incident is often preventable. Load testing helps you find your website’s limits before your users do. In this guide, we will cover what load testing is, why it matters at every scale, how to run your first test using loader.io (the most accessible free tool available), what your results actually mean, how to find and fix bottlenecks, and how to make load testing a normal part of how you ship software. TL;DR Load testing answers one critical question: how many concurrent users can your server handle before it falls over? Without it, you’re guessing about capacity, and guessing wrong right when it matters most loader.io is the simplest free tool to get started: no install, browser-based, generous free tier Your three essential numbers: concurrent user target, response time threshold, and peak traffic window Run load tests before every major deployment, not after your site goes down What Load Testing Actually Is Let me clear up some confusion first, because “load testing” gets thrown around interchangeably with a few related terms that mean different things. Load testing is specifically about simulating concurrent users hitting your site and measuring how your server behaves under a expected load. You’re asking: “When 500 people are on this site at the same time, what happens?” Stress testing pushes beyond that, you keep adding users until something breaks, then you figure out exactly wher

We all know the Big-O complexity of basic data structures. Arrays are O(n) for search. Hash maps are O(1). Linked lists are... well, complicated. But when I set out to build hashbrowns — a C++17 benchmarking suite comparing arrays, linked lists, and hash maps — I discovered that theory and practice are very different beasts. Here's what I learned building this project from scratch, and why you should probably benchmark before you optimize. 🎯 The Goal Was Simple (Ha!) I wanted a clean, educational project that would: Implement dynamic arrays, linked lists, and hash maps from scratch Benchmark insert, search, and remove operations Find the "crossover points" where one structure beats another Export everything to CSV for analysis Sounds straightforward, right? Four months later, I had written a custom memory tracker, implemented multiple hash map strategies, added statistical bootstrapping for confidence intervals, and learned more about CPU caches than I ever wanted to know. 📚 Lesson 1: Polymorphism Has a Price (But It's Worth It) My first architectural decision was creating a common DataStructure interface: class DataStructure { public: virtual void insert ( int key , const std :: string & value ) = 0 ; virtual bool search ( int key , std :: string & value ) const = 0 ; virtual bool remove ( int key ) = 0 ; virtual size_t memory_usage () const = 0 ; virtual std :: string type_name () const = 0 ; // ... }; This made benchmarking elegant — I could write generic code that tested any data structure: for ( auto & structure : structures ) { timer . start (); structure -> insert ( key , value ); timer . stop (); } But virtual function calls have overhead. In tight loops, that vtable lookup adds up. I spent a whole weekend convinced my hash map was slower than expected... until I realized I was measuring the cost of polymorphism, not the data structure itself. The fix? I kept the clean interface for the benchmarking harness but used templates internally where performance-cri

When a user closes a tab, submits a form, or clicks an external link, you often need to send one last...

Small business websites rarely fail because of one catastrophic bug. They fail from an accumulation of small, fixable problems — a slow hero image here, an unlabeled form field there, a broken tab order that quietly locks out keyboard users. None of it looks dramatic in a screenshot. All of it adds up to lost conversions. Working across client rebuilds and audits at Alynox, the same handful of issues show up repeatedly, regardless of industry. Here are ten of the most common, with the practical, mostly low-effort fixes that address them. Unoptimized Images Dragging Down Load Time The single most common performance killer on small business sites is still oversized images — a 4MB PNG hero banner exported straight from a design tool, served at full resolution to a phone screen 400px wide. Fix: html src="hero-800.webp" srcset="hero-400.webp 400w, hero-800.webp 800w, hero-1600.webp 1600w" sizes="(max-width: 600px) 400px, (max-width: 1200px) 800px, 1600px" alt="Interior of the workshop showing custom furniture in progress" loading="lazy" width="1600" height="900" /> Convert to WebP or AVIF, generate a handful of responsive sizes, lazy-load anything below the fold, and always set explicit width/height to reserve space and avoid layout shift. No Real Mobile-First Design A lot of "responsive" small business sites are really desktop layouts that get squeezed with media queries until they technically fit a phone screen. Buttons end up too small to tap accurately, text wraps awkwardly, and nav menus overlap content. Fix: Design and build mobile-first — base styles for small screens, then progressively enhance with min-width media queries for larger viewports: css .card { padding: 1rem; } @media (min-width: 768px) { .card { padding: 2rem; } } Tap targets should be at least 44×44px (per WCAG and Apple/Google HIG guidance), with enough spacing between interactive elements to prevent mis-taps on smaller screens. Accessibility Treated as an Afterthought Missing alt text, low-contras

If you work with library data, you work with MARC21 — the length-prefixed binary record format catalogues have run on since the 1960s, complete with a directory of field offsets, subfield delimiters, and a pre-Unicode character encoding called MARC-8 that needs a lookup table with thousands of entries to decode. In Python that problem is solved: pymarc is mature, complete, and pleasant to use. In Go it wasn't. gomarc is a port of pymarc to Go. It covers the binary MARC21 transmission format, MARC-8 to Unicode conversion, MARCXML, and MARC-in-JSON — and on real catalogue exports it runs 4x to 11x faster than the library it was ported from. go get github.com/beyto1974/gomarc@v0.1.0 It reads like pymarc If you know pymarc, you already know this API. Iterate records, pull the fields you want: reader := marc . NewReader ( f ) for { record , err := reader . Next () if errors . Is ( err , io . EOF ) { break } if err != nil { log . Println ( err ) // permissive: bad records are skipped, not fatal continue } title , _ := record . Title () fmt . Println ( title ) } Title , Author , ISBN , ISSN , Subjects , Publisher , PubYear and more are there as methods. For anything else, go at the tag and subfield directly: value , ok := record . Get ( "245" ) . Subfield ( "a" ) for _ , f := range record . GetFields ( "650" ) { fmt . Println ( f ) } Build records, modify them, write them back: record . Get ( "245" ) . SetSubfield ( "a" , "The Zombie Programmer : " ) writer := marc . NewWriter ( out ) writer . Write ( record ) And convert to the formats the rest of your stack can actually read — both use UTF-8 throughout instead of MARC-8, so standard tooling works: s , err := record . AsJSON () // MARC-in-JSON records , err := marc . ParseXML ( r ) // MARCXML Large MARCXML files stream one record at a time via marc.NewXMLReader rather than loading into memory. The numbers Two real catalogue exports — 138,076 records, 166 MB. AMD Ryzen 5 3600, Go 1.25.12, CPython 3.13.5, gomarc v0.1.0, pym

I spent a few days writing a satellite ground station daemon in Crystal, with an empty dependency list and a hard rule against third-party code. It works, it ships as one file, and it sits at 1.9 MB of memory at rest. This is what the language was like to use, and what it cost. The project is kozai : it reads orbital elements, propagates them with SGP4/SDP4, predicts passes over a ground station, serves a JSON API and an offline web interface, and drives a rotator and a radio through hamlib. About 9,000 lines of source and 6,400 lines of specs, on Crystal 1.21.0. None of that matters here except as the load under which the language was tested — this is a report on the tool, not on the satellites. What the language actually delivers The headline claim of a compiled language with a garbage collector is that you get Ruby's ergonomics and a binary at the end. In 2026 that claim holds, and the numbers are the part worth quoting: Docker image, FROM scratch 7.41 MB Static binary, musl, arm64 6.9 MB Dynamic binary, release 1.9 MB Memory at rest, 2 satellites 1.9 MB Memory at rest, 97 satellites 4.3 MB Memory after a day of serving, 97 satellites 19.3 MB, flat Build steps before crystal build none Runtime files outside the binary none The last two rows are the ones that changed how the project was built. There is no Node in this repository, no bundler, no asset pipeline, and no postinstall . The web interface — HTML, CSS, JavaScript, and a 66 KB SVG of the world's coastlines — is read at compile time by {{ read_file(...) }} and lives inside the executable ( src/assets.cr ). Deploying is scp . The standard library covered the whole surface of a network daemon with six imports: http/server , http/client , json , log , socket , option_parser . That list is not an aspiration; CI fails if a seventh appears. The type system earned its keep in the numerical core. Predicting a week of passes for a hundred satellites is on the order of ten million propagator calls, and the hot loop a

Evolusi Pemrosesan Data: Dari Batch ke Real-Time Transformasi infrastruktur data mendorong transisi dari pemrosesan batch statis ke arsitektur streaming. Integrasi LLM kini menjadi komponen inti sistem data terdistribusi, di mana kecepatan pemrosesan informasi menentukan relevansi dan akurasi output AI secara instan. Tantangan Latensi pada Integrasi LLM Langsung Menyematkan LLM dalam pipeline real-time memicu tantangan sinkronisasi state dan overhead komunikasi antar-node. Bottleneck utama biasanya terjadi pada transfer KV cache dan keterbatasan bandwidth memori, yang menghambat performa inferensi pada skala terdistribusi. Strategi Optimasi: TensorRT-LLM dan Arsitektur Asinkron Optimasi melalui TensorRT-LLM krusial untuk menekan latensi token-to-token melalui optimalisasi kernel CUDA dan manajemen memori yang lebih efisien. Selain itu, arsitektur asinkron seperti Pathways memungkinkan eksekusi grafik dataflow dinamis, meminimalkan idle time pada akselerator GPU/TPU. Kapan Memilih Pendekatan Batch Tradisional? Pendekatan batch tetap superior untuk tugas non-sensitif waktu. Efisiensi biaya (cost-efficiency) dan throughput tinggi menjadikan metode ini pilihan utama untuk pemrosesan dataset masif, seperti pelatihan ulang model (retraining) atau analisis historis skala besar. Masa Depan: Arsitektur Hibrida untuk Skala Besar Sistem masa depan akan mengadopsi model hibrida: inferensi kritis latensi dijalankan di edge atau buffer lokal, sementara pemrosesan berat tetap berada pada jalur batch. Strategi ini memaksimalkan data-locality dan mengoptimalkan alokasi sumber daya komputasi.

Meryem Arik discusses strategies for designing low-cost LLM inference architectures for high-volume, non-real-time workloads. She explains how software architects and engineering leaders can achieve order-of-magnitude cost reductions by making critical trade-offs across hardware, inference runtimes, speculative decoding, and smart queue reordering. By Meryem Arik

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