# Algorithms Published articles for Algorithms. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## pgBackRest Compression: How Much CPU Is a Smaller Backup Worth? DevFeed: [pgBackRest Compression: How Much CPU Is a Smaller Backup Worth?]() Original publisher: [Read original article]() Author: Agustín Gallego Published: 2026-09-16T21:43:06Z Content type: comparison Language: en Sources: [Blog - Percona]() Topics: [Compression](), [Algorithms](), [PostgreSQL](), [postgresql 18](), [backups](), [Percona]() Tags: [algorithms](), [backup](), [compare](), [compression](), [cpu](), [percona](), [postgresql](), [postgresql-18](), [uncategorized]() ### AI overview This comparison measures pgBackRest compression algorithms and levels on Percona Distribution for PostgreSQL 18.4. It finds that low-level Zstandard offers a favorable balance between CPU use and backup size, with zst(3) near the efficiency curve's shoulder. ### Source excerpt In this blog post, we'll compare pgBackRest's compression algorithms and levels to find where spending more CPU stops buying a meaningfully smaller backup. The short version of the answer, which we'll build up to with real numbers, is that Zstandard at a low level is the sweet spot, and its default (zst(3)) already sits right ... Continued The post pgBackRest Compression: How Much CPU Is a Smaller Backup Worth? appeared first on Percona. ## What's New in OSS at Khan Academy DevFeed: [What's New in OSS at Khan Academy]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-04-03T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Open Source](), [Software](), [React](), [Functional programming](), [JavaScript](), [Algorithms](), [jQuery]() Tags: [algorithms](), [engineering](), [functional-programming](), [javascript](), [news](), [open-source-software](), [oss](), [react](), [web-frontend]() ### AI overview Khan Academy highlights several recent open-source projects, including Mu Lambda, a small JavaScript library of functional programming utilities; React Balance Text, a React wrapper for Adobe's Balance-Text project; Fuzzy Match Utils, which uses string-matching algorithms for filtering; and React Multi Select. ### Source excerpt By Brian Genisio At Khan Academy, we rely heavily on Open Source Software (OSS). The majority of our ... Read more ## 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 ## Pinterest's Manas Search Platform Uses Quantization and SSD-Based Serving DevFeed: [Pinterest's Manas Search Platform Uses Quantization and SSD-Based Serving]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-16T06:06:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [quantization](), [AI search](), [webgpu]() Tags: [algorithms](), [architecture-design](), [development](), [infrastructure](), [news](), [optimization](), [performance](), [pinterest-search](), [quantization](), [search](), [ssd](), [vector]() ### AI overview Pinterest Engineering enhanced its Manas distributed search platform with scalar and product quantization, SSD-based serving, and late-interaction retrieval. The reported evaluations describe trade-offs among index size, recall, throughput, latency, and serving cost. ### Source excerpt Pinterest Engineering has enhanced its Manas search platform to manage vast data, improving efficiency in search and discovery functions. By applying Scalar and Product Quantization, memory usage decreased significantly while maintaining high recall rates. The platform utilizes SSDs for optimized performance, and it is transitioning to multi-vector models for refined relevance matching. By Olimpiu Pop ## Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection DevFeed: [Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection]() Original publisher: [Read original article]() Author: Osher Jacob Published: 2026-09-14T10:00:01Z Content type: article Language: en Sources: [Unit 42]() Topics: [AWS CloudTrail](), [Cloud](), [threat detection](), [Threat Research](), [SIEM, Security, Observability](), [Machine learning](), [Algorithms](), [SQL](), [IAM](), [identity and access management](), [Amazon Web Services]() Tags: [algorithms](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [cloud](), [cloud-cybersecurity-research](), [cloud-detection](), [devops](), [iam](), [identity-and-access-management](), [logs](), [machine-learning](), [post](), [sql](), [threat-detection](), [threat-research]() ### AI overview This article presents a behavioral clustering model for mapping cloud identities to functional roles using activity patterns from audit logs. It applies unsupervised machine learning with UMAP and HDBSCAN to data from more than 40,000 identities across 125 cloud environments, and shows how the resulting map can support automated threat detection. The article also explains how lightweight heuristics extracted from the map can classify identities at scale using standard SQL, reducing the need for continuous resource-intensive machine learning pipelines. ### Source excerpt We designed a behavioral clustering model to map cloud identity roles from audit logs, enabling continuous threat detection using standard SQL queries. The post Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection appeared first on Unit 42. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026 DevFeed: [IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-11T16:25:47Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [ibm](), [Quantum Computing](), [Supercomputing](), [Cloud](), [Algorithms](), [Optimization](), [Simulation and Design](), [Hardware]() Tags: [2026](), [ai](), [algorithms](), [amd](), [chemistry](), [cloud](), [collaboration](), [core](), [enterprise](), [financial-services](), [hardware](), [hub](), [ibm](), [infrastructure](), [manufacturing](), [optimization](), [processors](), [quantum](), [quantum-computing](), [science](), [simulation](), [supercomputing]() ### AI overview IBM and Lockheed Martin are establishing a quantum innovation hub at ETH Zurich, centered on an IBM Quantum System Two planned for installation at the Swiss National Supercomputing Centre by the end of 2026. The system will use IBM's 120-qubit Nighthawk r2 processor and support research in areas including chemistry, materials science, optimization, and financial services. ### Source excerpt IBM and Lockheed Martin are setting up a quantum innovation hub at ETH Zurich, and its core is Switzerland's first IBM Quantum System Two, to be installed at the Swiss National Supercomputing Centre (CSCS) in Lugano by the end of 2026. The hub comes out of an offset agreement with armasuisse, Switzerland's Federal Office for The post IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026 appeared first on StorageReview.com. ## Union body tells UK government workers must get a say before AI clocks in DevFeed: [Union body tells UK government workers must get a say before AI clocks in]() Original publisher: [Read original article]() Author: Carly Page Published: 2026-09-11T10:35:22Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Algorithms]() Tags: [ai](), [ai-and-ml](), [algorithms](), [government](), [job-cuts](), [public-sector](), [workers]() ### AI overview The TUC warns that workplace algorithms should not make decisions without workers having a say. ### Source excerpt TUC warns against letting algorithms call the shots in the workplace ## 1.1.1.1 now supports post-quantum DNSSEC, all 2,420 bytes of it DevFeed: [1.1.1.1 now supports post-quantum DNSSEC, all 2,420 bytes of it]() Original publisher: [Read original article]() Author: Bas Westerbaan Published: 2026-09-10T13:00:00Z Content type: article Language: en Sources: [Cloudflare Blog]() Topics: [Cryptography]() Tags: [1-1-1-1](), [algorithms](), [cryptography](), [dns](), [dnssec](), [post-quantum](), [research](), [security](), [testing](), [tls](), [validation]() ### AI overview Cloudflare's 1.1.1.1 now validates DNSSEC signatures using the post-quantum ML-DSA-44 algorithm. The article discusses testing oversized DNS responses and preventing downgrade to conventional signatures while retaining compatibility with older resolvers. ### Source excerpt 1.1.1.1 now validates DNSSEC signatures using NIST's post-quantum ML-DSA-44 algorithm. Here is how we manage 2,420-byte signatures and downgrade risks at scale. ## Introducing IBM and NASA's new foundation model for the Moon DevFeed: [Introducing IBM and NASA's new foundation model for the Moon]() Original publisher: [Read original article]() Author: Kim Martineau Published: 2026-09-10T12:30:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [lunar foundation model](), [ibm](), [foundation-models](), [data](), [Artificial Intelligence](), [AI Architecture]() Tags: [accelerated-discovery](), [ai](), [ai-architecture](), [algorithms](), [ibm](), [lunar-foundation-model](), [mapping](), [model](), [nasa](), [release](), [science](), [space](), [us]() ### AI overview IBM and NASA are open-sourcing the NASA-IBM Lunar Foundation Model, a multimodal AI model that integrates lunar observations from US and Japanese missions across viewing angles, spatial scales, and measurement types. The model is intended to support lunar mapping, volcanic-history research, and searches for polar ice. ### Source excerpt The multi-modal model could help astronauts navigate craters, investigate ancient lava, and search for ice, as the US plans for a long-term lunar presence. ## From MIT to IBM, expediting AI and quantum deployment DevFeed: [From MIT to IBM, expediting AI and quantum deployment]() Original publisher: [Read original article]() Author: Lauren Hinkel | MIT-IBM Computing Research Lab Published: 2026-09-02T20:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ibm](), [Reinforcement learning](), [Computing](), [LLMs]() Tags: [academic](), [ai](), [ai-agents](), [algorithms](), [alumni-ae](), [aram-harrow](), [artificial-intelligence](), [atari-games](), [careers](), [collaboration](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computing](), [data](), [electrical-engineering-and-computer-science-eecs](), [fair-ai](), [graduate-postdoctoral](), [graduate-students](), [ibm](), [industry](), [irene-ko](), [isaac-chuang](), [language-models](), [learning](), [luca-daniel](), [machine-learning](), [mit-ibm-computing-research-lab](), [mit-ibm-watson-ai-lab](), [mit-schwarzman-college-of-computing](), [pulkit-agrawal](), [quantum-computing](), [quantum-machine-learning](), [research](), [school-of-engineering](), [srinivasan-arunachalam](), [trustworthy-ai](), [vllm-hook](), [zhang-wei-hong]() ### AI overview MIT graduate students and a former postdoc who moved to IBM describe how work with the MIT-IBM Computing Research Lab helped translate rigorous research into industry applications. Their areas include quantum machine learning, reinforcement learning, AI agents, and trustworthy and fair AI. ### Source excerpt MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems. ## The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough DevFeed: [The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-02T17:15:57Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Optimization](), [GPU](), [toolchain](), [debug](), [Instrumentation](), [Algorithms]() Tags: [algorithms](), [benchmark](), [cuda](), [debug](), [developer-tools-techniques](), [gpu](), [how-to](), [image-processing](), [instrumentation](), [memory](), [optimization]() ### AI overview A step-by-step CUDA optimization walkthrough that improves an image-processing pipeline with debugging, benchmarking, optimized algorithms, GPU memory management, pinned transfers, streams, and asynchronous transfers. ### Source excerpt NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing... ## Why a cryptographic inventory is key for addressing the quantum computing threat DevFeed: [Why a cryptographic inventory is key for addressing the quantum computing threat]() Original publisher: [Read original article]() Author: Christopher Day Published: 2026-08-28T14:01:00Z Content type: article Language: en Sources: [Tenable Blog]() Topics: [Cryptography](), [Security, Privacy and Abuse Prevention](), [migration](), [Security Attacks]() Tags: [algorithms](), [attacks](), [cryptographic](), [cryptography](), [encryption](), [migration](), [post-quantum](), [quantum](), [quantum-computing](), [security]() ### AI overview The article explains how a cryptographic inventory and phased migration to quantum-resistant cryptography can address the risk that future quantum computers pose to public-key algorithms. It highlights harvest-now, decrypt-later attacks, the vulnerability of RSA, ECC, and Diffie-Hellman to Shor's Algorithm, and the expected resilience of AES-256. ### Source excerpt When quantum computers become generally available, they'll be able to crack current public-key cryptographic algorithms, putting digitally stored and transmitted data at risk. But the threat already exists, as attackers use the "harvest now, decrypt later" tactic. Discover why building a comprehensive cryptographic inventory and executing a phased operational strategy are critical for protecting your data against quantum computing attacks. Key takeaways Quantum computing risks are an operational threat today due to "harvest now, decrypt later" (HNDL) tactics, in which adversaries actively harvest and store encrypted data to decrypt it retroactively once quantum capabilities mature. When run on a quantum computer that's powerful enough, Shor's Algorithm will break foundational asymmetric infrastructure like the RSA, ECC, and Diffie-Hellman algorithms, although symmetric encryption standards like AES-256 are expected to remain secure against quantum attacks. Globally, more regulatory bodies are starting to mandate a comprehensive cryptographic inventory, making absolute visibility across the digital environment a prerequisite for an orderly post-quantum migration. Transitioning to quantum-resistant cryptography requires a phased operational strategy spanning discovery, prioritization, remediation, and verification. The quantum threat to modern security architecture Future quantum computers will represent a threat to the foundational security architecture that protects digital data. For decades, the global economy, national security apparatus, and critical infrastructure have relied on asymmetric cryptography, specifically RSA and elliptic curve cryptography (ECC), to secure data in transit, authenticate identities, and protect digital signatures. The mathematical difficulty of factoring large integers or solving discrete logarithm problems has long provided a robust shield against cyber attacks launched using conventional computing capabilities. However, the rapid mat ## Project Lighthouse -- Part 3: Introducing project-lighthouse-anonymize DevFeed: [Project Lighthouse -- Part 3: Introducing project-lighthouse-anonymize]() Original publisher: [Read original article]() Author: Adam Bloomston Published: 2026-08-25T17:01:02Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Algorithms](), [Open Source](), [Python](), [Data Quality](), [Code](), [Programming]() Tags: [algorithms](), [architecture](), [code](), [data](), [data-quality](), [engineering](), [open-source](), [privacy](), [python](), [scale](), [technology](), [validation]() ### AI overview Project Lighthouse -- Part 3 introduces project-lighthouse-anonymize, an open-source Python library for privacy-preserving anonymization. The article describes the project's k-anonymity methodology, scalable Core Mondrian algorithm, parallel processing architecture, and data quality validation framework, alongside two new technical papers. ### Source excerpt Project Lighthouse -- Part 3: Introducing project-lighthouse-anonymizeThe data in Project Lighthouse is powered by privacy-preserving anonymization code. We've put this code into open source, and published two new technical papers detailing the scalable algorithms and data quality frameworks behind it. By: Adam Bloomston Introduction In 2020, we launched Project Lighthouse, which we developed in partnership with leading civil rights and privacy organizations. As our 2020 announcement details, Project Lighthouse enables us to measure potential disparities in user experiences. This work uses perceived race data that is never linked to individual accounts; we only use this data for measuring potential disparities, and users who want to opt-out can do so by turning off the data use settings in their account's Privacy page. Our results, shared in 2024, demonstrate how we use these analyses to measure our progress in mitigating those disparities. Earlier this year, we open-sourced project-lighthouse-anonymize, the Python library that powers Project Lighthouse's anonymization process. To provide the full technical foundation for this work, we also published two new papers on arXiv alongside the code release. Together with our original 2020 paper, these three papers form a complete story: the foundational methodology, the scalable implementation, and the quality validation framework. The foundational methodology (2020) Our original 2020 paper established the privacy-by-design approach for Project Lighthouse and provides the rationale for choosing k-anonymity as the technical privacy model to prevent sensitive attribute disclosure at scale. For an introduction to this paper, see our first blog post on p-sensitive k-anonymity and our second blog post on measurement with anonymized data. Core Mondrian: Scalable partition-based anonymization (2025) The first of our new papers, Core Mondrian: Basic Mondrian beyond k-anonymity, presents the k-anonymity algorithm at the heart of th ## Generating scenarios for extreme events, without extreme data DevFeed: [Generating scenarios for extreme events, without extreme data]() Original publisher: [Read original article]() Author: Jennifer Chu | MIT News Published: 2026-08-24T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Machine learning](), [Algorithms](), [Critical Infrastructure](), [data]() Tags: [algorithm](), [algorithms](), [artificial-intelligence](), [center-for-computational-science-and-engineering](), [climate](), [climate-risk-assessment](), [computer-modeling](), [critical-infrastructure](), [data](), [extreme-event-aware](), [extreme-weather](), [fire](), [heat](), [idss](), [kai-chang](), [learning-fefb62e9fa83](), [machine-learning](), [mechanical-engineering](), [mit-meche](), [mit-schwarzman-college-of-computing](), [natural-disasters](), [research](), [risk](), [school-of-engineering](), [storm](), [sustainability](), [themis-sapsis](), [weather](), [weather-prediction]() ### AI overview MIT engineers developed a machine-learning algorithm that generates plausible future extreme-event scenarios without requiring past extreme events in the training data. It learns from available records, filters out implausible weather scenarios, and estimates events' frequency, size, intensity, duration, and area of impact to help planners prepare. ### Source excerpt A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for. ## Vector search in production: index trade-offs, failure modes & what to watch DevFeed: [Vector search in production: index trade-offs, failure modes & what to watch]() Original publisher: [Read original article]() Author: Cedric Turner Published: 2026-08-14T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI search]() Tags: [algorithms](), [data](), [database](), [embeddings](), [production](), [search](), [tech-de](), [vector]() ### AI overview A practical guide to vector search in production, explaining embeddings, nearest-neighbor retrieval, index accuracy-speed trade-offs, and when keyword search is preferable. ### Source excerpt Vector search runs on a simple idea: turn data into coordinates, and treat similarity as distance. An embedding model maps each sentence, image, or document to a point in a few hundred dimensions of space, where items with related meaning land near ea... ## DocLang: a markup language for LLMs DevFeed: [DocLang: a markup language for LLMs]() Original publisher: [Read original article]() Author: Kim Martineau Published: 2026-08-12T12:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [XML](), [Parser](), [Parsing](), [Large Language Model](), [Generative AI](), [HTML]() Tags: [ai](), [algorithms](), [generative-ai](), [llms](), [news](), [parsing](), [xml]() ### AI overview IBM researchers introduce DocLang, a constrained XML sub-language designed to help large language models interpret document structure. The article explains how it represents semantics, layout, bounding boxes, and reading order, and how it may reduce token use, latency, and inference costs in AI pipelines. ### Source excerpt The lead researcher behind IBM's popular document parser, Docling, explains why generative AI needs its own document standard. ## 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. ## Coding Challenge #130 - Sort Visualiser DevFeed: [Coding Challenge #130 - Sort Visualiser]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-08-08T08:01:19Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Sorting](), [Algorithm](), [coding](), [ui](), [implementation]() Tags: [algorithms](), [animation](), [coding](), [implementation](), [sorting](), [tool](), [ui]() ### AI overview Coding Challenge #130 asks readers to build a sorting algorithm visualiser that animates how eight classic algorithms operate on arrays. The project also provides practice with UI rendering, animation timing, and clean abstractions, while allowing choices such as algorithm, sample size, data order, display mode, and animation speed. ### Source excerpt This challenge is to build your own tool to visualise how sorting algorithms work. ## ClickHouse launches ClickHouse Labs with Andy Pavlo as VP of Database Research DevFeed: [ClickHouse launches ClickHouse Labs with Andy Pavlo as VP of Database Research]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-08-03T13:34:03Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [data]() Tags: [algorithms](), [analytics](), [clickhouse](), [data](), [database](), [databases](), [open-source](), [performance](), [postgresql](), [research]() ### AI overview ClickHouse announces ClickHouse Labs, a database research group led by Andy Pavlo. The group will conduct and openly share foundational work on database architectures, query processing, systems performance, and infrastructure for ClickHouse and PostgreSQL. ### Source excerpt Renowned database researcher will lead a new group dedicated to advancing foundational database technology and sharing its work openly with the broader community. ## 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. ## Implementing LFU Cache in O(1) Time: A Hands-on Breakdown DevFeed: [Implementing LFU Cache in O(1) Time: A Hands-on Breakdown]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-02T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Cache](), [Caching](), [Data structures](), [implementation]() Tags: [algorithms](), [cache](), [caching](), [implementing](), [java](), [lfu](), [memory](), [order](), [performance](), [pointers](), [system-design]() ### AI overview This tutorial explains why LRU can evict a frequently requested product during a long-tail burst, then presents an O(1) LFU cache design. It contrasts a naive O(n) eviction scan with an implementation using key-to-node and frequency-to-bucket maps, doubly linked lists, and a minFreq pointer. ### Source excerpt LFU evicts the least frequently used key rather than the least recently used, which protects a hot key during a long-tail burst that would make LRU drop a bestseller. The naive implementation scans every key to find the minimum frequency, which is O(n) per eviction. The O(1) version inverts the index: a key-to-node map, a frequency-to-bucket map of doubly linked lists, and a minFreq pointer. ## I Rebuilt a Minimal BitTorrent Client in Go to Understand Peer-to-Peer Choking Algorithms DevFeed: [I Rebuilt a Minimal BitTorrent Client in Go to Understand Peer-to-Peer Choking Algorithms]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-02T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Algorithms](), [P2P](), [Go Language](), [Programming](), [Network]() Tags: [algorithms](), [bittorrent](), [distributed-systems](), [go](), [golang](), [network](), [networking](), [p2p](), [peer-to-peer](), [protocol](), [system-design]() ### AI overview A hands-on explanation of BitTorrent choking algorithms through rebuilding a minimal client in Go. The article describes choking as a decentralized bandwidth-allocation mechanism, explains the choked and interested states, and shows how upload capacity affects peer prioritization. ### Source excerpt The BitTorrent spec explains choking in two paragraphs; implementing it in Go and watching every peer choke you within 60 seconds is what makes it stick. Choking is a bandwidth allocator running independently on every node, not a courtesy flag. Upload is the scarce resource on an asymmetric link, and each connection carries just two booleans, choked and interested, whose four combinations drive the entire fairness policy of the swarm. ## Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions DevFeed: [Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions]() Original publisher: [Read original article]() Author: Junpeng Niu Published: 2026-07-30T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [AI search](), [AI, ML & Data Engineering](), [Language models]() Tags: [algorithms](), [data](), [engineering](), [graphs](), [knowledge-graph](), [large-language-models-llms](), [llm](), [machine-learning](), [search](), [validation]() ### AI overview The article presents a feedback-driven system for verifying knowledge-graph relationships through live search interactions. It tests unverified parent-child or sibling edges in search suggestions, measures user engagement, and uses greedy exploration to balance data collection with result quality. ### Source excerpt Introduction The efficacy of semantic search relies on the accuracy of the underlying Knowledge Graph (KG). In high-velocity domains like on-demand food delivery or e-commerce, the catalog of entities like dishes, products, and merchants changes rapidly. Current methods for KG construction and maintenance face three critical challenges: Inaccuracy and hallucination from Large Language Models (LLMs): Automated models often infer relationships based on statistical text co-occurrence rather than semantic reality. For instance, an LLM might incorrectly classify "Pho" as a child of "Italian Noodle Soup" due to linguistic similarity, leading to irrelevant search results. Scalability limits of manual verification: Traditional verification relies on human annotators or domain experts. This approach is slow, expensive, and unable to keep pace with dynamic catalogs containing millions of entities. For example, daily changes in restaurant menus or grocery stock keeping units (SKUs). Error propagation in ranking: Inaccurate graph edges propagate errors downstream. If a parent-child relationship is wrong, query expansion algorithms will retrieve irrelevant items, directly degrading Click-Through Rate (CTR) and user trust. We introduce a feedback-driven verification engine that operationalizes the search interface as a validation environment. Key contributions include: User feedback-driven verification: The system treats unverified graph edges as hypotheses. Instead of accepting them as truth, it tests them against live traffic by injecting them into search suggestions and measuring user engagement. Hierarchical relationship refinement: Unlike systems that only validate entities (nodes), this framework validates structural links (edges). It confirms whether entity A is truly a parent, child, or sibling of entity B, ensuring structural integrity. Adaptive exploration: The system employs a greedy exploration policy. It intelligently balances exploitation by showing known good resul [Next page]()