# random Published articles for random. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## What algorithm did Windows XP use to choose your initial user picture? DevFeed: [What algorithm did Windows XP use to choose your initial user picture?]() Original publisher: [Read original article]() Author: Raymond Chen Published: 2026-09-09T14:00:00Z Content type: article Language: en Sources: [Raymond Chen]() Topics: [Algorithm](), [Randomizer](), [Windows](), [Filesystems]() Tags: [algorithm](), [files](), [history](), [old-new-thing](), [random](), [recursion](), [windows]() ### AI overview The article explains that Windows XP selected an initial user picture randomly from the Default Pictures directory using the current time as the random seed. It describes a one-pass reservoir-sampling algorithm, including its efficiency and behavior when files change during selection, with a 100-picture safety limit. ### Source excerpt It's random, really. The post What algorithm did Windows XP use to choose your initial user picture? appeared first on The Old New Thing. ## What part of 'No!' is so hard for the DNS understand? DevFeed: [What part of 'No!' is so hard for the DNS understand?]() Original publisher: [Read original article]() Author: Geoff Huston Published: 2026-09-04T01:06:30Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [servers](), [Caching](), [DDoS](), [Cybercrime](), [Availability](), [Bot](), [Script]() Tags: [apnic-labs](), [attacks](), [availability](), [bots](), [caching](), [ddos](), [dns](), [generate](), [measurement](), [random](), [server](), [servers](), [tech-matters]() ### AI overview The article explains how random name attacks overwhelm authoritative DNS servers by generating queries for nonexistent names, bypassing recursive resolver caches and potentially causing domain availability failures. It also describes APNIC Labs' measurement work on nonexistent-domain responses to improve DNS resilience. ### Source excerpt At APNIC Labs we've been experimenting with understanding how the DNS handles requests to resolve nonexistent names to make the DNS more resilient to random name attacks. ## Let's Use the Emergent CSS random() Function in all the Browsers DevFeed: [Let's Use the Emergent CSS random() Function in all the Browsers]() Original publisher: [Read original article]() Author: Lee Meyer Published: 2026-08-31T15:12:19Z Content type: article Language: en Sources: [CSS-Tricks]() Topics: [CSS](), [Web Development](), [browser](), [JavaScript]() Tags: [articles](), [browser](), [browsers](), [css](), [css-functions](), [javascript](), [polyfill](), [random](), [resource](), [ui](), [ux]() ### AI overview This article explores the emerging CSS random() function and the effort to create a polyfill that works across browsers. It discusses controlled randomness in web layouts and UX, including a custom JavaScript confetti implementation, and considers how CSS could reduce the need for JavaScript or third-party frameworks. ### Source excerpt The journey to create a polyfill for the upcoming CSS random() function that works in all browsers. Let's Use the Emergent CSS random() Function in all the Browsers originally handwritten and published with love on CSS-Tricks. You should really get the newsletter as well. ## Too big to draw, but yet drawable DevFeed: [Too big to draw, but yet drawable]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2026-07-06T09:00:00Z Content type: article Language: en Sources: [Klipse]() Topics: [ordering](), [structure]() Tags: [aboulafia](), [caustics](), [generative](), [math](), [permutation](), [permutations](), [random](), [ranking](), [recursion](), [visualization]() ### AI overview The third article in a series explains how Aboulafia's tserouf orders all permutations of a word and visualizes them by placing the permutations around a circle and connecting each word to its reversal. Because the full permutation space becomes too large to draw, the article samples chords and shows that they form recurring caustic curves visible at multiple scales. ### Source excerpt Aboulafia's Tserouf - Part 3 of 4 <- Previous: An elegant formulation, inspired by Bill Gates - Next: A wheel, the same forwards and backwards -> ## Glitch Effect in Jetpack Compose DevFeed: [Glitch Effect in Jetpack Compose]() Original publisher: [Read original article]() Author: sinasamaki Published: 2025-10-11T08:00:46Z Content type: tutorial Language: en Sources: [sinasamaki]() Topics: [Jetpack Compose](), [Compose](), [Graphics](), [GUI]() Tags: [animation](), [article](), [canvas](), [compose](), [graphics](), [jetpack-compose](), [mutation](), [random](), [scale](), [slices](), [translation]() ### AI overview A tutorial on implementing a glitch effect in Jetpack Compose by drawing composable content to an offscreen buffer, slicing it horizontally, and applying randomized translations, scaling, color overlays, and timed animation. ### Source excerpt Easily apply sci-fi, cyberpunk effects on to your app ## Turbocharging V8 with mutable heap numbers DevFeed: [Turbocharging V8 with mutable heap numbers]() Original publisher: [Read original article]() Author: Victor Gomes, the bit shifter Published: 2025-02-25T00:00:00Z Content type: article Language: en Sources: [V8]() Topics: [Code]() Tags: [benchmark](), [benchmarks](), [internals](), [javascript](), [optimization](), [performance](), [random]() ### AI overview This article explains a V8 optimization for mutable heap numbers that improves JavaScript performance in the JetStream2 async-fs benchmark by reducing allocation pressure and enabling faster integer arithmetic. ### Source excerpt At V8, we're constantly striving to improve JavaScript performance. As part of this effort, we recently revisited the JetStream2 benchmark suite to eliminate performance cliffs. This post details a specific optimization we made that yielded a significant 2.5x improvement in the async-fs benchmark, contributing to a noticeable boost in the overall score. The optimization was inspired by the benchmark, but such patterns do appear in real-world code. The target async-fs and a peculiar Math.random # The async-fs benchmark, as its name suggests, is a JavaScript file system implementation, focusing on asynchronous operations. However, a surprising performance bottleneck exists: the implementation of Math.random. It uses a custom, deterministic implementation of Math.random for consistent results across runs. The implementation is: let seed; Math.random = (function() { return function () { seed = ((seed + 0x7ed55d16) + (seed << 12)) & 0xffffffff; seed = ((seed ^ 0xc761c23c) ^ (seed >>> 19)) & 0xffffffff; seed = ((seed + 0x165667b1) + (seed << 5)) & 0xffffffff; seed = ((seed + 0xd3a2646c) ^ (seed << 9)) & 0xffffffff; seed = ((seed + 0xfd7046c5) + (seed << 3)) & 0xffffffff; seed = ((seed ^ 0xb55a4f09) ^ (seed >>> 16)) & 0xffffffff; return (seed & 0xfffffff) / 0x10000000; }; })(); The key variable here is seed. It's updated on every call to Math.random, generating the pseudo-random sequence. Crucially, here seed is stored in a ScriptContext. A ScriptContext serves as a storage location for values accessible within a particular script. Internally, this context is represented as an array of V8's tagged values. On the default V8 configuration for 64-bit systems, each of these tagged values occupies 32 bits. The least significant bit of each value acts as a tag. A 0 indicates a 31-bit Small Integer (SMI). The actual integer value is stored directly, left-shifted by one bit. A 1 indicates a compressed pointer to a heap object, where the compressed pointer value is incremented by o ## Postgres 17 is Now Available on Neon DevFeed: [Postgres 17 is Now Available on Neon]() Original publisher: [Read original article]() Author: Heikki Linnakangas Published: 2024-09-26T13:32:32Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [SQL](), [JSON](), [synthetic-data]() Tags: [data](), [database](), [features](), [free](), [generate](), [json](), [postgres](), [random](), [series](), [sql](), [updates]() ### AI overview Neon announces that Postgres 17 is available to all Neon users, including Free-plan users. The article highlights MERGE with RETURNING support, ranged random integer generation, bulk test-data generation with generate_series(), and JSON_TABLE for working with JSON data. ### Source excerpt Postgres 17 is out today, and we're here for it. In keeping with our version policy, the latest major version is now available for all Neon users including those on the Free plan. Get your Postgres 17 database and start exploring the latest features immediately. How to speed run... ## Password Complexity, Hash Iterations and Entropy DevFeed: [Password Complexity, Hash Iterations and Entropy]() Original publisher: [Read original article]() Author: Busra Demir Published: 2024-05-21T14:56:51Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [passwords]() Tags: [analysis](), [attacks](), [cpu](), [engineering](), [latency](), [passwords](), [performance](), [random](), [security]() ### AI overview The article examines whether SCRAM password-hashing iterations can be reduced from 4096 to one while preserving security through longer, randomly generated passwords. It explains the trade-off between hashing cost, brute-force resistance, CPU use, and connection latency. ### Source excerpt Introduction Is it possible to safely reduce the number of SCRAM password hashing iterations from 4096 to just one? An engineer recently raised this question about our password generation to optimise CPU usage. Why? In Neon, every database is secured via a password. Verifying the... ## Monte Carlo Simulations: Separating Signal from Noise in Sampled Success Metrics DevFeed: [Monte Carlo Simulations: Separating Signal from Noise in Sampled Success Metrics]() Original publisher: [Read original article]() Author: Tom Lowe Published: 2023-01-03T12:20:27Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Simulation](), [data](), [pandas](), [Python](), [Shopify]() Tags: [confidence-interval](), [cost](), [data](), [guide](), [python](), [random](), [shopify](), [simulation]() ### AI overview This guide explains how Monte Carlo simulations can separate meaningful trends from sampling noise when success metrics cannot be measured across an entire population. It discusses sample size, confidence intervals, metric variability, and the tradeoff between statistical certainty and the cost of collecting more data. The article demonstrates the approach with the Shopify App Store and describes implementation in Python and pandas. ### Source excerpt Check out this guide for using a Monte Carlo simulation to identify the size and confidence percentage of your sampled success metric. ## Cookie for a Thought - How to Manage HTTP Sessions DevFeed: [Cookie for a Thought - How to Manage HTTP Sessions]() Original publisher: [Read original article]() Author: sakshyam.shah@goteleport.com (Sakshyam Shah) Published: 2022-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [HTTP](), [Cookies](), [web applications](), [Access Control](), [Authentication](), [Security](), [TLS (Transport Layer Security)](), [Persistence]() Tags: [access-control](), [authentication](), [best-practices](), [browser](), [cookies](), [cryptographic](), [generate](), [hashing](), [how-to](), [http](), [identifier](), [persistence](), [protection](), [random](), [security](), [tls](), [token](), [web-applications]() ### AI overview This article explains how HTTP sessions use cookies to persist session state and support identity-based access control. It recommends protecting session IDs against guessing, replay, and theft, including the use of cryptographically secure random values and TLS. ### Source excerpt Exploring techniques and best practices to manage HTTP sessions. ## A random number you already have: The stack address DevFeed: [A random number you already have: The stack address]() Original publisher: [Read original article]() Author: Graham King Published: 2022-06-05T23:19:39Z Content type: article Language: en Sources: [Graham King]() Topics: [Linux](), [Rust](), [Python](), [macOS](), [Security](), [Go]() Tags: [go](), [linux](), [macos](), [number](), [performance](), [python](), [random](), [rust](), [security](), [software](), [stack]() ### AI overview This article explains how Address Space Layout Randomization can make a stack variable's memory address serve as a low-cost source of random bits. It describes address-bit handling in Rust and CPython, notes that the approach does not work the same way in Go, and connects the technique to hash-table randomization and denial-of-service resistance. ### Source excerpt Thanks to Address Space Layout Randomization you can use the address of a stack variable as a zero-cost random number. ## "Practical Math" Preview: Collect Sensitive Survey Responses Privately DevFeed: ["Practical Math" Preview: Collect Sensitive Survey Responses Privately]() Original publisher: [Read original article]() Published: 2022-05-14T09:40:49Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [math](), [Statistics](), [Code](), [Software]() Tags: [bias](), [code](), [collect](), [differential-privacy](), [mathematics](), [politics](), [practical](), [practical-math](), [privacy](), [programming](), [python](), [random](), [randomized-algorithm](), [research](), [responses](), [survey]() ### AI overview This chapter preview explains randomized response, a survey technique for estimating aggregate statistics about sensitive questions while preserving respondents' privacy. Respondents use private coin flips to introduce randomization and maintain plausible deniability. ### Source excerpt This is a draft of a chapter from my in-progress book, Practical Math for Programmers: A Tour of Mathematics in Production Software. Tip: Determine an aggregate statistic about a sensitive question, when survey respondents do not trust that their responses will be kept secret. Solution: import random def respond_privately(true_answer: bool) -> bool: '''Respond to a survey with plausible deniability about your answer.''' be_honest = random.random() < 0.5 random_answer = random.random() < 0. ## Libraries I use for generative art DevFeed: [Libraries I use for generative art]() Original publisher: [Read original article]() Author: Stanko Published: 2022-02-28T00:00:00Z Content type: article Language: en Sources: [Stanko Tadić]() Topics: [generative art](), [JavaScript](), [Randomizer](), [plotting](), [WebAssembly](), [Data structures]() Tags: [generative-art](), [javascript](), [plotting](), [programming](), [random](), [webassembly]() ### AI overview A curated list of JavaScript libraries used to create generative art, organized into categories including randomness and noise, geometry, vector fields, 3D, spatial data structures, Axidraw, and other tools. The author selected the libraries based partly on performance and describes their practical uses. ### Source excerpt This is going to be a very straight forward post. Just a list of JavaScript libraries I use in creating my generative art. And I really mean I use (and abuse) them. All of them are used in multiple of my drawings. I'll include couple of photos under the respective libraries. I finally settled with these libraries after trying several other alternatives for each. Performance was one of the key factors (especially as generative work can get brute-forcy sometimes). Huge thank you to all people who created and shared these wonderful tools!Also, as a rule of thumb, when you find a good library, you might want to check author's GitHub profile. In the most cases you are going to find more useful stuff there. That is exactly how I found some of these. Categories: Randomness and noise Geometry Vector fields 3D vector line engines Spatial data structures Axidraw Other Randomness and noise # seedrandom # In generative programming it is super important to be able to repeat the same output. As randomness is heavily involved, we need a way to repeat random values. That's where seedrandom steps in as an excellent seeded random number generator. random-words # For random number generator seeds, I like to use real words instead of randomly generated strings. This library is a simple dictionary of couple of thousands english words. simplex-noise # Fast implementation of Open Simplex noise. poisson-disk-sampling # When you need a random collections of points in space, Poisson sampling is a great tool. It gives us much more natural distribution than just using random values. Geometry # js-angusj-clipper # This library is a lifesaver. For all your polygon clipping and offseting needs. And it is super fast as it is implemented in WebAssembly. voronoi # Sometimes I think I use Voronoi diagrams too much in my work. Anyway, this library efficiently computes Voronoi diagrams. robust-point-in-polygon # For figuring out if the point is inside of the polygon. I tried a couple of other libraries ## Functional Duplications DevFeed: [Functional Duplications]() Original publisher: [Read original article]() Published: 2021-10-28T00:00:00Z Content type: opinion Language: en Sources: [Robert C. Martin](), [The Clean Code Blog]() Topics: [bug](), [debug](), [Test-driven development](), [test-coverage](), [Randomizer]() Tags: [bug](), [debugging](), [random](), [tdd](), [test-coverage]() ### AI overview The article describes debugging a crash in a Space War game after randomly placed bases were duplicated at the same location. Despite using TDD and having test coverage, the defect appeared during gameplay and was traced by adding runtime checks until the responsible low-frequency function was found. ### Source excerpt I broke out my old Space War game a few days ago and decided to make a few changes to speed the game up and make it more fun to play. In so doing I discovered a very interesting bug. One of the changes I made was to populate the initial space with a few random bases scattered here and there. This would allow the player some extra resources with which to battle the Klingons while building up a network of more bases. While I was playing the modified game, it crashed. Hard. Now I wrote this with TDD, and I was very disciplined about the cleanliness of the code, and the test coverage. So this was unexpected. So I dug up all my old debugging skills from the pit in which I had buried them, and started to work out what was going on. It wasn't long before I realized that crash was occuring because a transport was being launched between two bases, but the angle of the velocity vector of the transport was :bad-angle. This can only happen if the two bases exist at the exact same location. Bases don't move around in this game, so there's no chance that two bases will accidentally slide on top of each other. There is a very (very) minor chance that the random number generator will put two bases on top of each other at the start of the game; but the odds are so miniscule that didn't worry about it. In any case, this crash happened well into the game I was playing, so initial values could not have been the cause. Fortunately it's pretty easy to hunt and peck around in the game, so I was quickly able to discover that the two bases in question were duplicates of each other. Something in my code was duplicating bases! Well now that should't be too hard to find. So I wrote a litte function that would examine the world and halt with a message if the world contained two bases at the same location. I called this function in the main update loop, and sure enough after 20 minutes of play the program halted with my message. Unfortunately being able to detect that the duplication occurred di ## Random Animating Pie Button DevFeed: [Random Animating Pie Button]() Original publisher: [Read original article]() Published: 2021-06-16T21:36:11Z Content type: tutorial Language: en Sources: [maiatoday]() Topics: [Compose](), [Canvas](), [Code]() Tags: [animate](), [canvas](), [code](), [color](), [component](), [compose](), [custom](), [data-class](), [exploring](), [fun](), [functions](), [jetpack](), [jetpack-compose](), [random](), [val](), [var]() ### AI overview A Jetpack Compose sample demonstrates a custom pie-chart component that draws with Canvas and animates to a random percentage when a button is clicked. ### Source excerpt I am exploring animations with small sampler functions using Jetpack Compose. This one is a custom component that draws a little pie chart. It will animate a random pie value on the click of the button. data class PieData( val foreground: Color = Color.White, val strokeWidth: Dp = 4.dp, val percentage: Float ) @Composable fun PieStatus( modifier: Modifier = Modifier, pieData: PieData ) { var animationPlayed by remember { mutableStateOf(false) } val currentPercentage = animateFloatAsState( targetValue = if (animationPlayed) pieData.percentage else 0f, animationSpec = tween(1000) ) LaunchedEffect(key1 = true) { animationPlayed = true } Canvas( modifier = modifier ) { val canvasWidth = size.width val canvasHeight = size.height drawCircle( color = pieData.foreground, center = Offset(x = canvasWidth / 2, y = canvasHeight / 2), radius = canvasWidth / 2 - pieData.strokeWidth.toPx(), style = Stroke(width = pieData.strokeWidth.toPx()) ) val arcPadding = pieData.strokeWidth.toPx() * 2 drawArc( color = pieData.foreground, startAngle = -90f, sweepAngle = currentPercentage.value * 360, useCenter = true, topLeft = Offset(arcPadding, arcPadding), size = Size(size.width - (arcPadding * 2f), size.height - (arcPadding * 2f)) ) } } code ## Writing Simulations Using Go DevFeed: [Writing Simulations Using Go]() Original publisher: [Read original article]() Published: 2021-01-13T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Simulation](), [Go Language](), [Programming](), [Randomizer](), [Statistics]() Tags: [ardan-labs](), [blog](), [go](), [go-programming](), [golang](), [how-to](), [programming](), [random](), [simulation](), [statistics]() ### AI overview A practical tutorial on writing simulations in Go using a random number generator. It demonstrates a Catan dice-roll simulation that estimates how often each sum occurs and explains why seeding affects repeated results. ### Source excerpt Introduction If you can write a for-loop, you can do statistics. - Jake Vanderplas A lot of developers shy away from problems which involve statistics or probability. Which is shameful since in today's data-rich environment, you can gain a lot of insights from data. In this blog post, I'll show you how to write a simulation tool which requires no knowledge in statistics or probability. Simulations are easy to write and can be a very effective tool in research. You only need some basic programming skills and a random number generator. ## Queueing theory for fun and practice #2: нагрузка и время отклика DevFeed: [Queueing theory for fun and practice #2: нагрузка и время отклика]() Original publisher: [Read original article]() Author: Ruslan Cheremin (noreply@blogger.com) Published: 2020-07-27T15:13:00Z Content type: tutorial Language: ru Sources: [\>рабочие заметки]() Topics: [queueing theory]() Tags: [fifo](), [queueing-theory](), [random](), [tag-e5017782b67f](), [theory]() ### AI overview This article explains how system load affects response time through internal queues and buffers. It discusses the characteristic J-curve, the difficulty of deriving a general analytical formula, and how queueing discipline, workload distributions, server allocation, and utilization influence waiting time. ### Source excerpt Во храме Божьей Матери Поклонской батюшка Иннокентий принимает исповедь у раба божьего обыкновенно минут за 10, а утешения жаждут около 5-и рабов божьих в час. Много ли стульев надобно поставить во храме, дабы исповеди ожидающие не толпились в праздности пред святым алтарем? "Массовое окормление паствы: пособие для начинающих" (редакция 3-я, неизданная) (Часть 2, начало: ТМО, square ## Technology Preview: Signal Private Group System DevFeed: [Technology Preview: Signal Private Group System]() Original publisher: [Read original article]() Published: 2019-12-09T00:00:00Z Content type: article Language: en Sources: [Signal Blog]() Topics: [online privacy](), [App](), [data]() Tags: [api](), [app](), [data](), [database](), [feature](), [messaging](), [privacy](), [race-condition](), [random](), [retrospective](), [scalability](), [security](), [server](), [technology](), [update](), [updates]() ### AI overview Signal previews a private group system that keeps group memberships and attributes hidden from the service. It discusses the limitations of Signal's earlier client-coordinated group state, including concurrent-update race conditions, while aiming to add administration, access control, and scalability without weakening privacy. ### Source excerpt Groups are inherently social, and Signal is a social app. Whether you're planning a surprise party, discussing last night's book club meeting, exchanging photos with your family, or organizing something important, group messaging has always been a key feature of Signal. Signal provides private groups: the Signal service has no record of your group memberships, group titles, group avatars, or group attributes. We've been working on new private group technology that will enable group administrators and access control, improve group scalability, and set the stage for a much richer group experience - all while maintaining Signal's unique group security and privacy properties. We're moving into the future while keeping what we loved about the past. Read more... ## Calculating the true impact of zip file entries DevFeed: [Calculating the true impact of zip file entries]() Original publisher: [Read original article]() Published: 2019-09-20T00:00:00Z Content type: article Language: en Sources: [Jake Wharton]() Topics: [Zip](), [Java](), [Filesystems](), [mount](), [Streams]() Tags: [api](), [files](), [filesystem](), [java](), [mount](), [open](), [random]() ### AI overview This article examines how to calculate the true contribution of each entry to a ZIP file's total size in Java. It explains why summing compressed entry sizes is insufficient and considers the entry headers and central-directory records that also contribute to the final size. ### Source excerpt How can we determine the impact of each entry on a zip file's size? It seems like a trivial problem, but things quickly don't add up. There's three built-in ways to read information about the contents of zip file in Java: Mount the zip as a FileSystem using FileSystems.newFileSystem and then access its contents using Paths. Open it with ZipInputStream for a one-shot iteration over the zip entries. Open it with ZipFile for random access to the zip entries. The first mechanism is extremely convenient. It allows interacting with the contents of a zip file using the same APIs as normal files. Unfortunately, by virtue of being exposed like regular files, you only have one way to check their size: Files.size(Path). This delegates to an API called BasicFileAttributes.size() which returns size of the file contents. While there is a ZipFileAttributes.compressedSize() for returning the size of the compressed contents, it's internal to the JDK and not available for our use. The other two mechanisms,ZipInputStream and ZipFile, both expose entries using the ZipEntry type. These being zip-centric APIs, many of the properties of the zip file format are directly available. Notably for our use case, there's a getCompressedSize() method. Problem solved? Not exactly... If you sum the compressed size of all entries in a zip the result will not equal the size of the zip file. This isn't entirely unexpected. After all, the zip file format surely requires additional metadata to track per-entry information like the relative path of each compressed file. So if we're looking to calculate the actual size impact of an entry on the final zip, can we do it? Zip file format An overview of the zip file format specification can be found on Wikipedia. It consists of a list of entries which are each defined as header followed by the compressed data (whose length is specified in the header). Finally, at the end, there is a central directory which lists all of the entries available in the file. A slight ## Overview of Inter-Service Authentication Schemes DevFeed: [Overview of Inter-Service Authentication Schemes]() Original publisher: [Read original article]() Published: 2018-06-12T20:27:00Z Content type: article Language: en Sources: [Latacora]() Topics: [Authentication](), [Authorization](), [Security](), [servers](), [passwords](), [OAuth](), [saml](), [API keys](), [Cookies](), [client](), [Randomizer](), [VPC]() Tags: [api-keys](), [authentication](), [authorization](), [cookies](), [oauth](), [password](), [random](), [saml](), [security](), [server](), [vpc]() ### AI overview This article surveys server-to-server authentication and authorization schemes for systems composed of multiple services. It discusses options including bearer tokens, passwords, cookies, API keys, OAuth, and SAML, and notes risks such as token capture or logging. ### Source excerpt Modern applications tend to be composed from relationships between smaller applications. Secure modern applications thus need a way to express and enforce security policies that span multiple services. This is the "server-to-server" (S2S) authentication and authorization problem (for simplicity, I'll mash both concepts into the term "auth" for most of this post). Designers today have a lot of options for S2S auth, but there isn't much clarity about what the options are or why you'd select any of them. Bad decisions sometimes result. What follows is a stab at clearing the question up. ## Balls Into Bins In Distributed Systems DevFeed: [Balls Into Bins In Distributed Systems]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2018-01-01T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [distributed-systems](), [Scalability](), [Algorithm](), [backends](), [systems]() Tags: [backends](), [complexity](), [distributed-systems](), [distribution](), [load-balancer](), [random](), [scalability](), [scale](), [statistics](), [systems](), [work]() ### AI overview This article explains the Balls Into Bins problem and its relevance to hash tables, distributed load balancing, and push-based work allocation. It discusses how request or work-item distributions affect lookup performance, load-balancing strategies, scalability, and statistics such as the maximum, mean, median, variance, and distribution tail. ### Source excerpt Balls Into Bins In Distributed Systems Throwing things can be fun. If you've come across the Balls Into Bins problem, you probably heard about in context of hash tables. When you hash things into a hash table (especially with separate chaining) it's really useful to be able to ask "If I throw 𝑀 balls into 𝑁 bins, what is the distribution of balls in bins?" You can see how this is fundamental to hash tables: the amortized complexity argument for hash tables depends on their being some load factor (i.e. 𝑀/𝑁) for which most bins contain a small number of items. Once this stops being true, lookup and insertion time on hash tables starts to get ugly. So from that perspective it's already clearly an important problem. Load Balancing and Work Allocation Hash tables aren't the only place that the Balls Into Bins problem is interesting. It comes up often in distributed systems, too. For one example, think about a load balancer (in this case a distributor of independent requests) sending load to some number of backends. Requests (𝑀) are balls, and the backends are bins (𝑁) and typically there are multiple requests going to each backend (𝑀 > 𝑁). If we know how to solve for the number of balls in each bin, we can understand the limits of random load balancing, or whether we need a stateful load balancing algorithm like least connections. This is an important question to ask, because sharing consistent state limits scalability, and sharing eventually-consistent state can even make load balancing decisions worse. Load balancing is much easier if it can be done statelessly. A related problem is push-based work allocation. Here, there is some co-ordinator handing out work items to a fleet of workers, and trying to have those workers do approximately equal amounts of work. One way that systems end up with this pattern is if they are using shuffle sharding or consistent hashing to distribute work items (or records). These hashing-based methods can be great for scaling, and so are wid ## Testing Polynomial Equality DevFeed: [Testing Polynomial Equality]() Original publisher: [Read original article]() Published: 2017-04-24T09:00:14Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Testing](), [function]() Tags: [field](), [function](), [polynomials](), [random](), [roots](), [testing](), [variables]() ### AI overview The article explains how to test whether two multivariable polynomial expressions represent the same function. It presents randomized evaluation over a finite subset of a field and uses the Schwartz-Zippel lemma to bound the probability of an incorrect equality judgment. ### Source excerpt Problem: Determine if two polynomial expressions represent the same function. Specifically, if $ p(x_1, x_2, \dots, x_n)$ and $ q(x_1, x_2, \dots, x_n)$ are a polynomial with inputs, outputs and coefficients in a field $ F$, where $ |F|$ is sufficiently large, then the problem is to determine if $ p(\mathbf{x}) = q(\mathbf{x})$ for every $ x \in F$, in time polynomial in the number of bits required to write down $ p$ and $ q$. ## Problème de connexion à Oracle DevFeed: [Problème de connexion à Oracle]() Original publisher: [Read original article]() Author: brice.dutheil@gmail.com (Brice Dutheil) Published: 2015-11-06T22:33:53Z Content type: tutorial Language: fr Sources: [The Coffee Workshop]() Topics: [Java](), [Randomizer](), [Linux](), [Firewall](), [POSIX](), [systems]() Tags: [firewall](), [http](), [java](), [jvm](), [linux](), [posix](), [random]() ### AI overview This article examines two causes of Oracle database connection problems that can lead to HTTP timeouts: insufficient system entropy and firewall connection termination. It explains how Java and POSIX random sources affect blocking behavior and discusses the security trade-off between /dev/random and /dev/urandom. ### Source excerpt Deux problèmes assez courant peuvent survenir sur les connexions à une base de donnée Oracle. Ces problèmes peuvent être à l'origine de timeout sur les connexions HTTP, etc... Ces deux problèmes touchent deux choses totalement différente, l'entropie du système et la coupure de connexion par un firewall. ## Inferring Tweet Quality From Retweets DevFeed: [Inferring Tweet Quality From Retweets]() Original publisher: [Read original article]() Author: Evan Miller Published: 2015-07-18T03:15:00Z Content type: article Language: en Sources: [Evan Miller]() Topics: [X (Twitter)](), [Network](), [Users]() Tags: [follower](), [metric](), [model](), [network](), [random](), [twitter]() ### AI overview This article develops a model for estimating a tweet's quality as the percentage of readers who retweet it. It considers the tweet's age, random user reload behavior, and retweeting beyond the author's immediate followers using publicly available Twitter information. ### Source excerpt Inferring Tweet Quality From Retweets -- Modeling tweets, retweets, and the passage of time. [Next page]()