# Algorithms, Complexity Computer science discipline focused on designing, implementing, and analyzing algorithms, including their time and space complexity. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Minimizing the length of regular expressions, in practice DevFeed: [Minimizing the length of regular expressions, in practice]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-05-23T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Algorithms, Complexity](), [Regular expression](), [Programming](), [cloud-infrastructure]() Tags: [algorithms](), [cdn](), [configuration](), [engineering](), [google](), [hosting](), [infrastructure](), [news](), [re](), [repository](), [software-engineering]() ### AI overview Craig Silverstein explains how Khan Academy needed a compact regular expression to distinguish static URLs from dynamic URLs for CDN routing. The article describes why simple patterns were unsuitable, reports that a naive generated expression reached 402,025 characters, and introduces a second approach that reduced the length to 2,400 characters. ### Source excerpt By Craig Silverstein The problem Software engineering interviews tend to be full of "algorithms" questions, because they're easy ... Read more ## IBM Releases GENCO and the GridFM Development Framework for Electric Grid Analysis DevFeed: [IBM Releases GENCO and the GridFM Development Framework for Electric Grid Analysis]() Original publisher: [Read original article]() Author: Peter Hess Published: 2026-08-11T13:00:40Z Content type: release Language: en Sources: [IBM Research]() Topics: [Artificial Intelligence](), [Algorithms](), [foundation-models](), [Open Source](), [Algorithms, Complexity](), [LLM evaluation / benchmarking](), [linux foundation]() Tags: [ai](), [algorithms](), [benchmarking](), [climate-and-sustainability](), [foundation-models](), [linux-foundation](), [open-source](), [release](), [research]() ### AI overview IBM Research and collaborators announce GENCO, an open-source neural solver for three steady-state electric-grid analysis tasks, alongside the GridFM Development Framework for building and benchmarking neural grid solvers. ### Source excerpt GENCO is a neural solver that, alongside the GridFM Development Framework, unifies three core electrical grid analysis tasks. ## The search for quantum advantage in differential equations DevFeed: [The search for quantum advantage in differential equations]() Original publisher: [Read original article]() Author: Robert Davis Published: 2026-08-03T13:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [Algorithms, Complexity](), [Complex Systems]() Tags: [algorithms](), [complex-systems](), [ibm](), [mathematical-sciences](), [physics](), [q-a](), [quantum](), [quantum-algorithms](), [research](), [systems]() ### AI overview IBM researcher Hari Krovi discusses quantum algorithms for solving certain differential equations and their potential to scale beyond classical methods in selected applications. ### Source excerpt New quantum algorithms could unlock faster ways to model the complex systems behind circuits, fluids, finance, and more. ## Turing Award Winner: Early AI, LLM Predictions, Causality | Judea Pearl DevFeed: [Turing Award Winner: Early AI, LLM Predictions, Causality | Judea Pearl]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-07-27T13:06:14Z Content type: opinion Language: en Sources: [The Developing Dev]() Topics: [Artificial Intelligence](), [Networks](), [Algorithms, Complexity](), [Learning]() Tags: [ai](), [algorithms](), [artificial-intelligence](), [learning](), [networks]() ### AI overview A podcast interview with Judea Pearl about his career, early artificial intelligence, causal reasoning, Bayesian networks, alpha-beta pruning, physics, and approaches to science education. ### Source excerpt When I went to UCLA, I had heard professor Pearl's name here and there from the other professors. ## The power of collaboration: How we can reduce traffic congestion DevFeed: [The power of collaboration: How we can reduce traffic congestion]() Original publisher: [Read original article]() Published: 2026-07-07T16:42:08Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Algorithms, Complexity](), [Optimization](), [Routing (disambiguation)](), [App](), [Artificial Intelligence]() Tags: [algorithms](), [algorithms-theory](), [climate-sustainability](), [collaboration](), [data-mining-modeling](), [driving](), [google](), [optimization](), [reduce](), [routing](), [transportation](), [us]() ### AI overview Google Research describes a large-scale routing experiment in 10 major US cities. By guiding a small fraction of trips toward alternative routes, the study reports improved overall traffic conditions, faster driving speeds, and reduced emissions. ### Source excerpt Algorithms & Theory ## Optimizing OPA performance: From arrays to objects DevFeed: [Optimizing OPA performance: From arrays to objects]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-07-07T14:25:30Z Content type: tutorial Language: en Sources: [Capital One Tech]() Topics: [opa](), [Open Policy Agent](), [rego](), [Data structures](), [Optimization](), [Algorithms, Complexity]() Tags: [algorithms](), [cloud-native](), [data-structures](), [kubernetes](), [opa](), [open-policy-agent](), [optimization](), [performance-tuning](), [rego](), [software-development](), [software-engineering](), [software-supply-chain]() ### AI overview This article explains how to improve Open Policy Agent performance by choosing appropriate data structures for Rego policies. It focuses on replacing nested arrays with keyed objects to avoid inefficient array traversal when evaluating large datasets. ### Source excerpt Achieve 99% faster Rego policy execution through optimization. Note: This post focuses on one aspect of performance tuning Rego policies and datasets evaluated by OPA-arrays vs. objects. The Rego Style Guide and Regal Rego linter are very helpful resources for learning Rego best practices and avoiding code smells in Rego policies. There is also the OPA performance tuning documentation. In 2018, I started using open policy agent (OPA) as a solution for controlling and preventing unwanted behaviors in our Kubernetes Clusters. OPA, along with Kubernetes Dynamic Admission Control, provided a means to build preventive controls. Since then, I have worked with several PaC solutions. I have always stayed close to the OPA tool set because of how well it supports multiple use cases. OPA is domain agnostic and can be used with virtually any use case, as long as you supply the correct data and policies. To that end, OPA use cases have expanded throughout several technical disciplines, such as cloud-native computing and software supply chain management. OPA performance engineering OPA enables us to unify PaC solutions across multiple use cases and systems, using the same languages and tools. However, there is always room for improvement and performance engineering policies and the execution thereof. In addition, optimizing data that policies evaluate and mutate should be part of our focus when we deliver OPA-based solutions. Recently I was asked to help with OPA performance issues. I made several recommendations, but I overlooked one simple and glaring issue: the poor performing policy was processing a large data set using nested-arrays, instead of the best practice of using keyed-objects. Later, something was bothering me about my interaction and I realized that while I gave decent architectural level advice, I completely missed the best engineering advice. Rego policies and data should be optimized just like other algorithms and relative data, and part of that optimization is ## A Career Journey from Cryptanalysis Research to SRE and Chief Editor at Microsoft DevFeed: [A Career Journey from Cryptanalysis Research to SRE and Chief Editor at Microsoft]() Original publisher: [Read original article]() Author: Alexandra Savelieva Published: 2026-07-07T07:16:01Z Content type: opinion Language: en Sources: [Data Science at Microsoft]() Topics: [Data Science](), [Microsoft](), [Cryptography](), [site-reliability-engineering](), [Algorithms, Complexity](), [audit trail]() Tags: [ai](), [algorithms](), [audit-trail](), [careers](), [crack](), [data-science](), [logs](), [machine-learning](), [microsoft](), [paper](), [sre]() ### AI overview The author reflects on becoming chief editor of Microsoft's Data Science + AI online publication and recounts a career spanning cryptanalysis research, Bing Ads R&D, and site reliability engineering. ### Source excerpt Thoughts on taking the helm of DS@MImage by the author (generated with ChatGPT). This week marks an important chapter of Data Science + AI at Microsoft, as well as in my own professional journey, as I step into the role of chief editor of this online publication after the farewell of its wonderful founder, Casey Doyle. This change is not something that I had planned -- rather it's a combination of unexpected circumstances have come together to make it happen, like many other things that have shaped my career and enabled this opportunity. If you read Casey's farewell article from last week, you may see why a metaphor of navigating the ocean came to mind when I was thinking about what's next for DS@M now that I'm "captaining the ship." The first chapter of my journey took place in 2010 as a Ph.D. intern in Microsoft Research. Under the supervision of Dmitry Khovratovich, I studied block hash functions. The paper that I coauthored ended up making a big splash in cryptanalysis (see Biclique attack -- Wikipedia), and a fun fact is that it took two years and several rejections at conferences and workshops for it to be recognized. Our approach involved surprisingly simple math and deterministic algorithms to crack a problem that was previously considered a "puzzle" requiring some craft with a bit of luck to solve. This was my main takeaway from the internship: twist and dissect the complex problems until they get reduced to an intuitively understood form, so that solving them becomes a matter of applying the right calculus. I returned in October 2012 as a full-time employee in Bing Ads R&D. I was expecting an applied research job and ended up as an SRE (Site Reliability Engineer) in the Audit Trail service working with logs collected for customer ads. It was "type 2" fun work -- absolutely not fun in the moment, but exciting when I look back. It served as a practical crash course that left the "bible" of SRE imprinted in my brain: how to design services for reliability, how t ## Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart DevFeed: [Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart]() Original publisher: [Read original article]() Author: Benjamin Knight Published: 2026-06-29T16:06:24Z Content type: article Language: en Sources: [Instacart]() Topics: [benchmarking](), [math](), [Algorithms, Complexity](), [Software](), [Programming](), [Routing (disambiguation)]() Tags: [benchmarking](), [bias](), [cardinality](), [data-science](), [delivery](), [efficiency](), [estimator](), [fixed-effects-model](), [instacart](), [linear-regression](), [measurement](), [memory](), [precision](), [pyfixest](), [regression](), [routing](), [speed](), [statistics](), [techniques]() ### AI overview This Instacart article explains why ordinary least squares regression becomes computationally impractical for marketplace experiments with high-cardinality categories. It presents the mathematical basis for using Fixest and Pyfixest, discusses switchback experiment designs for addressing treatment spillover, and describes benchmarks comparing processing speed, memory efficiency, and estimator precision. ### Source excerpt Benjamin S. Knight Scaling Marketplace experiments requires specialized statistical techniques. We examine why standard ordinary least squares regression (OLS) becomes computationally intractable when controlling for high-cardinality categories. We then dive into the underlying math and demonstrate how modern packages -- specifically Fixest and Pyfixest -- bypass these limitations. We conclude by benchmarking these methods to show their real-world impact on processing speed, memory efficiency, and estimator precision. At Instacart we strive to give our customers access to all the fresh foods and ingredients that they would normally get from a trip to the grocery store, but without the hassle of driving, finding parking, waiting in line, etc. Instacart's Marketplace team is responsible for surfacing customers' orders to shoppers, aligning Instacart's delivery windows with shoppers' projected availabilities as efficiently as possible. This entails a careful balancing act. If we offer delivery windows that are sooner / more popular, then we risk overextending shoppers' ability to fulfill those orders on time. If we are too conservative in our delivery option offerings, then we risk losing potential orders. Accurately measuring the impact of changes in our batching and routing algorithms requires thoughtful experiment design and software. Better predictions of future demand / time-to-fulfill allow Instacart to offer more convenient delivery windows.Experimentation on Marketplace One of our primary concerns in Marketplace is treatment spillage. For example, if we adjust our batching algorithm and increase the rate at which multiple orders are combined into batches in Brooklyn and Queens, then we face a real risk of also influencing the rate of batch creation / completion in Staten Island, the Bronx, and Manhattan. In this case the treatment impacts the control group -- a classic source of measurement bias as a consequence of violating the Stable Unit Treatment Value Assumpt ## How to Take an 11 Month Sabbatical DevFeed: [How to Take an 11 Month Sabbatical]() Original publisher: [Read original article]() Author: Zac Sweers Published: 2026-06-29T05:54:30Z Content type: opinion Language: en Sources: [Zac Sweers]() Topics: [Programming](), [Slack](), [compilers](), [Algorithms, Complexity]() Tags: [2025](), [algorithms](), [compilers](), [slack](), [work]() ### AI overview The author reflects on leaving Slack after nearly six years to take an 11-month sabbatical. During the break, they focused on running, open-source work on Metro, fostering dogs, travel, and revisiting subjects such as compilers and algorithms through practical projects. ### Source excerpt A sequel to the last one, but a bit longer. On Leaving Slack After nearly 6 years, I left Slack in August of 2025. It was for a combination of reasons that added up to "it's time". The biggest reason was that Salesforce (who acquired Slack ## Turing Award Winner: P vs NP, Zero-Knowledge Proofs, Quantum Computation | Avi Wigderson DevFeed: [Turing Award Winner: P vs NP, Zero-Knowledge Proofs, Quantum Computation | Avi Wigderson]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-06-01T10:02:09Z Content type: article Language: en Sources: [The Developing Dev]() Topics: [Algorithms, Complexity](), [Computer science](), [Programming]() Tags: [algorithm](), [algorithms](), [computer-science]() ### AI overview An interview with Avi Wigderson explains the P versus NP problem as a question about which problems computers can solve efficiently and which solutions can be efficiently checked. The discussion connects these ideas to algorithms and real-world applications such as navigation. ### Source excerpt His field of study & life's work ## Quantum Computers Are Not a Threat to 128-bit Symmetric Keys DevFeed: [Quantum Computers Are Not a Threat to 128-bit Symmetric Keys]() Original publisher: [Read original article]() Author: Filippo Valsorda Published: 2026-04-20T15:21:12Z Content type: article Language: en Sources: [Filippo Valsorda]() Topics: [Post-Quantum](), [Cryptography](), [Algorithms, Complexity](), [Security]() Tags: [algorithm](), [algorithms](), [cryptography](), [post-quantum](), [quantum](), [quantum-algorithms](), [security]() ### AI overview The article argues that quantum computers do not require larger symmetric key sizes during the post-quantum transition. It explains why Grover's algorithm does not reduce the practical security of AES-128 and SHA-256 enough to justify replacing 128-bit symmetric keys, while asymmetric cryptography remains affected by Shor's algorithm. ### Source excerpt There is no need to update symmetric key sizes as part of the post-quantum transition, due to the details of how Grover's algorithm scales. Most authorities agree. ## Sorting Algorithms Series DevFeed: [Sorting Algorithms Series]() Original publisher: [Read original article]() Author: baeldung Published: 2026-04-01T15:54:31Z Content type: article Language: en Sources: [Baeldung - CS]() Topics: [Sorting](), [Algorithms](), [Algorithms, Complexity](), [Computer science]() Tags: [algorithms](), [analysis](), [comparison](), [complexity](), [efficiency](), [guide](), [memory](), [series](), [series-sorting](), [sorting](), [time]() ### AI overview A guide to sorting algorithms, covering comparison-based methods such as merge sort, quicksort, and heapsort alongside adaptive and specialized non-comparison algorithms. It focuses on trade-offs involving time complexity, stability, memory requirements, and practical applicability. ### Source excerpt This guide covers the full spectrum of sorting: from foundational comparisons and merge sort to quicksort, heapsort, adaptive algorithms, and specialized non-comparison sorts. The post Sorting Algorithms Series first appeared on Baeldung on Computer Science. Related Stories Natural Language Processing (NLP) Series Cycle Sort Algorithm LaTeX Series ## TurboQuant: Redefining AI efficiency with extreme compression DevFeed: [TurboQuant: Redefining AI efficiency with extreme compression]() Original publisher: [Read original article]() Published: 2026-03-24T19:54:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [quantization](), [Compression](), [large-language-models](), [Language models](), [AI search](), [Caching](), [Optimization](), [Algorithms, Complexity](), [Google]() Tags: [ai](), [ai-models](), [algorithms](), [algorithms-theory](), [cache](), [compression](), [efficiency](), [generative-ai](), [google](), [iclr](), [iclr-2026](), [language-models](), [large-language-models](), [machine-intelligence](), [memory](), [model](), [optimization](), [performance](), [quantization](), [research](), [search]() ### AI overview Google Research introduces TurboQuant, a theoretically grounded compression algorithm for large language models and vector search. It targets vector-quantization overhead and key-value cache bottlenecks, aiming to reduce model size and memory costs while preserving accuracy. ### Source excerpt Algorithms & Theory ## From games to biology and beyond: 10 years of AlphaGo's impact DevFeed: [From games to biology and beyond: 10 years of AlphaGo's impact]() Original publisher: [Read original article]() Author: Demis Hassabis Published: 2026-03-09T13:52:36Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Algorithms](), [Algorithms, Complexity](), [Programming](), [Optimization](), [Open Source](), [Reinforcement learning](), [Language models](), [Mathematics](), [Quantum Computing]() Tags: [ai](), [algorithms](), [code](), [coding](), [data](), [language-models](), [model](), [models](), [open-source](), [optimization](), [quantum-computing](), [reasoning](), [reinforcement-learning](), [research](), [science]() ### AI overview AlphaGo's search-based approach is presented as a catalyst for scientific discovery beyond Go. The article highlights AlphaFold 2's impact on protein-structure prediction, AlphaProof and AlphaGeometry 2's mathematical reasoning, Gemini's performance at the IMO, and AlphaEvolve's use of code search for algorithm discovery and optimization. ### Source excerpt Ten years since AlphaGo, we explore how it is catalyzing scientific discovery and paving a path to AGI. ## Good technology blogs: a reading list for the holidays DevFeed: [Good technology blogs: a reading list for the holidays]() Original publisher: [Read original article]() Author: Alexey Milovidov Published: 2025-12-29T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [Programming](), [Algorithms, Complexity](), [Data structures](), [Databases](), [Computer science](), [Parsing](), [systems](), [Networks](), [Web Development](), [Artificial Intelligence]() Tags: [blog](), [compilers](), [data-structures](), [databases](), [hardware](), [networks](), [performance](), [programming](), [systems](), [web-development]() ### AI overview A holiday reading list of favorite technology blogs covering performance optimization, data structures and algorithms, database development, compilers, operating systems, programming languages, computer science, mathematics, hardware, networks, web development, computer graphics, and AI. It highlights authors and libraries connected to high-performance software and ClickHouse, including simdjson, Roaring bitmap, USearch, StringZilla, Hyperscan, Miniselect, LZ4, and zstd. ### Source excerpt A collection of my favorite technology blogs ## Four variants of array-shuffle algorithms DevFeed: [Four variants of array-shuffle algorithms]() Original publisher: [Read original article]() Published: 2025-12-25T23:45:02Z Content type: tutorial Language: en Sources: [Tony Finch's blog]() Topics: [Algorithms](), [Algorithms, Complexity]() Tags: [algorithm](), [permutations]() ### AI overview The article examines four symmetric variants of an array-shuffling algorithm. It distinguishes sampling-based and permutation-based approaches, and places the common Durstenfeld shuffle among the variants. ### Source excerpt Here's a pearlescent winter holiday gift for you! There are four variants of the algorithm for shuffling an array, arising from two independent choices: whether to swap elements in the higher or lower parts of the array whether the boundary between the parts moves upwards or downwards The variants are perfectly symmetrical, but they work in two fundamentally different ways: sampling or permutation. The most common variant is Richard Durstenfeld's shuffle algorithm, which moves the boundary downwards and swaps elements in the lower part of the array. Knuth describes it in TAOCP vol. 2 sect. 3.4.2; TAOCP doesn't discuss the other variants. (Obeying Stigler's law, it is often called a "Fisher-Yates" shuffle, but their pre-computer algorithm is arguably different from the modern algorithm.) the four variants In the pseudocode below, min and max are the inclusive bounds on the array to be shuffled; the arguments to rand() are the inclusive bounds on its return value; and the loop bounds are inclusive too. I chose this style to make the symmetries more obvious. In all variants, it's possible for the indexes this (the boundary between the parts of the array) and that (chosen at random) to be the same, in which case the swap is a no-op. I could have written the loop bounds as min and max instead of min+1 and max-1 to make the variants look as similar as possible, but it's more realistic to omit the loop iterations when this and that are guaranteed to be equal. It should be clear that rand() is invoked for spans of each size between 2 and N (where N = max - min + 1) so the algorithms produce N! possible permutations as expected. boundary moves down, pick from lower shuffle(a, min, max) for this = max to min+1 step -1 that = rand(min, this) swap a[this] and a[that] boundary moves up, pick from higher shuffle(a, min, max) for this = min to max-1 step +1 that = rand(this, max) swap a[this] and a[that] boundary moves down, pick from higher shuffle(a, min, max) for this = max-1 t ## Some Fun Software Facts DevFeed: [Some Fun Software Facts]() Original publisher: [Read original article]() Author: Hillel Wayne Published: 2025-12-10T18:45:37Z Content type: article Language: en Sources: [Newsletter feed for Hillel Wayne's Newsletter]() Topics: [Software](), [Algorithms, Complexity](), [Simulation](), [ASCII](), [Vim](), [Cloudflare]() Tags: [algorithms](), [ascii](), [cloudflare](), [simulation](), [software](), [vim]() ### AI overview A year-end newsletter shares miscellaneous software facts, including a Game of Life implementation of Tetris, leap-second handling, Vim's computational capabilities, ASCII history, impractical faster algorithms, Cloudflare's lava-lamp randomness, and historical sorting-algorithm details. ### Source excerpt Last newsletter of the year! First some news on Logic for Programmers. Thanks to everyone who donated to the feedchicago charity drive! In total we raised $2250 for Chicago food banks. Proof here. If you missed buying Logic for Programmers real cheap in the charity drive, you can still get it for $10 off with the holiday code hannukah-presents. This will last from now until the end of the year. After that, I'll be raising the price from $25 to $30. Anyway, to make this more than just some record keeping, let's close out with something light. I'm one of those people who loves hearing "fun facts" about stuff. So here's some random fun facts I accumulated about software over the years: In 2017, a team of eight+ programmers successfully implemented Tetris as a game of life simulation. The GoL grid had an area of 30 trillion pixels and implemented a full programmable CPU as part of the project. Computer systems have to deal with leap seconds in order to keep UTC (where one day is 86,400 seconds) in sync with UT1 (where one day is exactly one full earth rotation). The people in charge recently passed a resolution to abolish the leap second by 2035, letting UTC and UT1 slowly drift out of sync. Vim is Turing complete. The backslash character basically didn't exist in writing before 1930, and was only added to ASCII so mathematicians (and ALGOLists) could write /\ and \/. It's popular use in computing stems entirely from being a useless key on the keyboard. Galactic Algorithms are algorithms that are theoretically faster than algorithms we use, but only at scales that make them impractical. For example, matrix multiplication of NxN is normally O(N^2.81). The Coppersmith Winograd algorithm is O(N^2.38), but is so complex that it's vastly slower for even 10,000 x 10,000 matrices. It's still interesting in advancing our mathematical understanding of algorithms! Cloudflare generates random numbers by, in part, taking pictures of 100 lava lamps. Mergesort is older than bubblesor ## ClickHouse Release 25.11 DevFeed: [ClickHouse Release 25.11]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-12-05T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Optimization](), [TLS (Transport Layer Security)](), [Algorithms, Complexity]() Tags: [clickhouse](), [configuration](), [optimization](), [performance](), [post](), [release](), [tls]() ### AI overview ClickHouse 25.11 is a monthly release with 24 new features, 27 performance optimizations, and 97 bug fixes. Highlights include automatic TLS certificate provisioning through ACME providers, parallel merging for small GROUP BY operations, and projections as secondary indices. ### Source excerpt ClickHouse 25.11 is here! In this post, you will learn about parallel merge for small GROUP BY, projections as secondary indices, and more! ## Book early-access discount code DevFeed: [Book early-access discount code]() Original publisher: [Read original article]() Author: ericlippert Published: 2025-11-17T19:48:52Z Content type: release Language: en Sources: [Eric Lippert]() Topics: [Data structures](), [Algorithms, Complexity]() Tags: [algorithms](), [book](), [data-structures](), [uncategorized]() ### AI overview The author announces that Fabulous Adventures In Data Structures And Algorithms is available through Manning's Early Access Program, with six chapters currently published. A 50% discount is available through November 27 using the code MLLippert. ### Source excerpt Hey everyone, I first want to say a heartfelt thank you so much for the warm comments and supportive feedback I've gotten since I announced that I'm in process of writing Fabulous Adventures In Data Structures And Algorithms. It means ... Continue reading -> ## Differentially private machine learning at scale with JAX-Privacy DevFeed: [Differentially private machine learning at scale with JAX-Privacy]() Original publisher: [Read original article]() Published: 2025-11-12T15:32:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Machine Learning & Artificial Intelligence](), [Algorithms, Complexity](), [Deep learning](), [distributed-training](), [data](), [Google](), [High-Performance Computing](), [Programming]() Tags: [algorithms](), [algorithms-theory](), [data](), [deep-learning](), [distributed-training](), [google](), [high-performance-computing](), [libraries](), [machine-learning](), [release](), [research](), [responsible-ai](), [scalability](), [security-privacy-and-abuse-prevention](), [training]() ### AI overview Google announces JAX-Privacy 1.0, a library for differentially private machine learning built on JAX. The release is intended to help researchers and developers implement, audit, and scale private training workflows for deep learning models using large datasets and distributed training. ### Source excerpt Algorithms & Theory ## Murat and Aleksey Read Papers: "Barbarians at the Gate: How AI is Upending Systems Research" DevFeed: [Murat and Aleksey Read Papers: "Barbarians at the Gate: How AI is Upending Systems Research"]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2025-10-17T22:08:59Z Content type: opinion Language: en Sources: [Aleksey Charapko]() Topics: [Artificial Intelligence](), [Large Language Model](), [systems](), [Algorithms, Complexity]() Tags: [ai](), [algorithm](), [distributed-systems](), [evaluation](), [llm](), [llms](), [other-thoughts](), [paper-review-and-summary](), [research](), [systems]() ### AI overview This commentary examines the "Barbarians at the Gate: How AI is Upending Systems Research" paper and its proposed AI-Driven Research for Systems approach. The approach places LLM agents in an iterative loop that refines candidate solutions and evaluates them, but the article questions whether it addresses problem formulation and other essential research steps. ### Source excerpt The "Barbarians at the Gate: How AI is Upending Systems Research" paper by Audrey Cheng, Shu Liu, Melissa Pan, Zhifei Li, Bowen Wang, Alexander Krentsel, Tian Xia, Mert Cemri, Jongseok Park, Shuo Yang, Jeff Chen, Lakshya Agrawal, Aditya Desai, Jiarong Xing, Koushik Sen, Matei Zaharia, Ion Stoica from Berkeley has recently made a splash in [...] ## ClickHouse Release 25.9 DevFeed: [ClickHouse Release 25.9]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-10-02T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Databases](), [Algorithms, Complexity](), [Streaming]() Tags: [bug-fixes](), [clickhouse](), [database](), [features](), [new-features](), [performance](), [release](), [streaming]() ### AI overview ClickHouse 25.9 introduces 25 new features, 22 performance optimizations, and 83 bug fixes. Highlights include automatic global join reordering, streaming for secondary indices, and a new text index. ### Source excerpt ClickHouse 25.9 is available. In this post, you will learn about new features, including a new text index, join reordering, streaming for secondary indices, and more. ## Software Performance Begins with Time Complexity and Memory DevFeed: [Software Performance Begins with Time Complexity and Memory]() Original publisher: [Read original article]() Author: Furkan Kolcu Published: 2025-09-10T17:39:37Z Content type: tutorial Language: en Sources: [Furkan Kolcu - Software Engineer Blog]() Topics: [Software](), [Algorithms, Complexity](), [Scalability](), [Programming](), [Data structures](), [Go](), [C]() Tags: [big-o-notation](), [binary-search](), [c](), [complexity](), [go](), [memory](), [performance](), [scalability](), [software-engineering](), [sorting](), [space-complexity](), [technology](), [time-complexity]() ### AI overview This tutorial explains how time complexity and memory usage affect application performance as input size, system scale, and traffic increase. It introduces Big O notation, compares common complexity classes, and discusses trade-offs involving algorithms, data structures, and memory, with examples in Go and C. ### Source excerpt Memory usage and time complexity shape the performance of every application. This post explains why they matter, how they affect scalability, and how to think about the trade-offs behind every decision. ## HoliPaxos: Towards More Predictable Performance in State Machine Replication DevFeed: [HoliPaxos: Towards More Predictable Performance in State Machine Replication]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2025-08-12T20:42:29Z Content type: article Language: en Sources: [Aleksey Charapko]() Topics: [Replication](), [Protocol (disambiguation)](), [Algorithms](), [Algorithms, Complexity]() Tags: [consensus](), [management](), [network](), [other-thoughts](), [paper-review-and-summary](), [partitions](), [paxos](), [performance](), [protocol](), [replication]() ### AI overview The article introduces HoliPaxos, a paper proposing orthogonal optimizations to the classical MultiPaxos state machine replication protocol. The changes target more stable performance during slow-node conditions, network partitions, and log management while preserving MultiPaxos behavior in the common case. ### Source excerpt I will be presenting our new paper, "HoliPaxos: Towards More Predictable Performance in State Machine Replication," at the VLDB'25. Feel free to ping me if you are there and want to chat! This paper explores several orthogonal optimizations to the classical MultiPaxos state machine replication protocol to improve its performance stability in the presence of [...] [Next page]()