# System Design Published articles for System Design. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Choosing among write-through, write-behind, and write-around caching strategies DevFeed: [Choosing among write-through, write-behind, and write-around caching strategies]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Databases](), [Redis](), [consistency](), [Latency]() Tags: [cache](), [caching](), [consistency](), [database](), [latency](), [orders](), [postgres](), [redis](), [strategies](), [system-design](), [write](), [write-behind](), [write-through]() ### AI overview This article compares write-through, write-behind, and write-around caching strategies. It explains their trade-offs among database latency, cache hit rates, consistency, and the risk of losing acknowledged writes when a process fails. ### Source excerpt The three write strategies differ in what happens between the cache write and the database write, and that gap is where correctness lives. Write-through updates both before acknowledging, so it is safe and pays the full database latency. Write-behind acknowledges after the cache write and flushes later, which is fast and can lose acknowledged writes if the process dies. Write-around skips the cache entirely on write, which avoids caching data nobody reads and costs a miss on the next read. ## How to Learn Low-Level Design: A Roadmap for Study and Interviews DevFeed: [How to Learn Low-Level Design: A Roadmap for Study and Interviews]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-08-09T10:46:58Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Architecture & Design](), [Software Engineering](), [Programming](), [interfaces](), [Polymorphism]() Tags: [article](), [code](), [how-to](), [learn](), [programming](), [software-design](), [system-design]() ### AI overview This tutorial presents a step-by-step roadmap for learning low-level design (LLD), including what to study, the order of topics, interview preparation, and linked learning resources. It explains how LLD differs from high-level system design and describes common interview formats such as object-oriented design, machine coding, and concurrency design. ### Source excerpt In this article, I'll give you a complete step-by-step roadmap to learn Low-Level Design or LLD from scratch covering exactly what to learn, the right order to learn it, and how to prepare effectively for low level design interviews. ## Why CRUD APIs Stop Teaching New Lessons After Basic Production Problems Are Solved DevFeed: [Why CRUD APIs Stop Teaching New Lessons After Basic Production Problems Are Solved]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-02T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [CRUD](), [REST API](), [Back end](), [SQL](), [Latency](), [Network](), [Redis]() Tags: [api](), [backend](), [cache-invalidation](), [career](), [crud](), [latency](), [learning](), [network](), [schema](), [schema-design](), [system-design]() ### AI overview A small CRUD API teaches backend fundamentals such as validation, schema design, migrations, query behavior, connection pooling, and cache invalidation. After those issues are understood, developers may need more complex failure domains involving cross-service contracts, event ordering, and slow downstream calls to continue learning. ### Source excerpt A todo CRUD API teaches real things, including validation, schema design, the N+1 query, index selection, pool sizing, and cache invalidation, and then it goes quiet. Boredom arriving right after you fix those once is not laziness; it is the signal that the problem stopped presenting decisions you have not already seen. The move is up the stack, to a failure domain with cross-service contracts, event ordering, and downstream calls that hang. ## Promotions are Proactive: The 3P Framework for Pitching Your Next Level DevFeed: [Promotions are Proactive: The 3P Framework for Pitching Your Next Level]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-02T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Back end](), [Latency](), [Caching](), [connection pool](), [migration]() Tags: [backend](), [caching](), [career](), [career-growth](), [connection-pool](), [engineering](), [latency](), [leadership](), [migration](), [scope](), [system-design]() ### AI overview This article presents the 3P framework--Problem, Proof, and Proactive pitch--for making an engineer's promotion case legible before review. It argues that promotion reflects the scope or blast radius of decisions, supported by system evidence and an explicit pitch. ### Source excerpt Promotion committees do not promote effort, they promote scope, and scope has to be legible before the vote rather than explained after it fails. Strip the leveling-guide language and the question is blast radius: a mid-level engineer's bad call fails one deploy unit, a senior's saturates a pool at 2 AM across teams. The 3P framework makes that visible: Problem at org scale, Proof in system metrics, and a Proactive pitch. ## 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. ## AI speeds up code writing, but coordination across distributed services remains a system-design challenge DevFeed: [AI speeds up code writing, but coordination across distributed services remains a system-design challenge]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-02T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Artificial Intelligence](), [distributed-systems](), [backends](), [Microservice](), [migration](), [OpenAPI Specification]() Tags: [ai](), [alignment](), [architecture](), [backend](), [distributed-systems](), [migration](), [openapi](), [services](), [system-design]() ### AI overview The article argues that AI assistants have accelerated implementation work while leaving coordination between teams and services largely unchanged. It presents ownership boundaries, contract stability, migration coordination, and failure semantics as central system-design concerns because failures often occur between otherwise correct components. ### Source excerpt AI made the write path fast and left the coordination path untouched, so failures now live in the gaps between services: two event schema versions across two regions, three teams, and one assumption that someone else handled the migration window. Alignment work, deciding ownership boundaries, contract stability, and failure semantics before anyone opens an IDE, is system design, and it produces concrete network cost when skipped. ## 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. ## How to be useful as a software architect DevFeed: [How to be useful as a software architect]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-07-25T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [engineering-culture](), [Code review](), [Automation](), [Software]() Tags: [architecture](), [automation](), [code-review](), [culture](), [software](), [system-design]() ### AI overview The article argues that useful software architects improve the future of a codebase by building systems that let teams move quickly and safely. It emphasizes culture, feedback loops, automation, code review, system design review, testing, and shepherding work into production without becoming a bottleneck. ### Source excerpt Software architects have a weird role: Think about the code _tomorrow_. Today's code is what it is, how do we make it good tomorrow? Without slowing down, losing business, or falling off a cliff. ## Top System Design Performance Metrics DevFeed: [Top System Design Performance Metrics]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-07-14T08:36:41Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [benchmarking](), [Availability](), [Architecture & Design](), [Scalability](), [Load Balancing](), [health checks](), [Concurrency](), [Disaster Recovery](), [Database](), [sharding](), [IO](), [API]() Tags: [api](), [availability](), [blocking](), [concurrency](), [database](), [disaster-recovery](), [health-checks](), [load-balancing](), [performance](), [performance-metrics](), [scalability](), [sharding](), [system-design]() ### AI overview A tutorial on system design performance metrics, focusing on availability and throughput. It explains how these metrics are measured and outlines techniques such as load balancing, health checks, failover, redundancy, disaster recovery, query optimization, sharding, and asynchronous processing. ### Source excerpt Must Know Metrics ## Engineering levels aren't about skill anymore DevFeed: [Engineering levels aren't about skill anymore]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-05-19T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Job](), [systems](), [Code]() Tags: [code](), [engineering](), [hiring](), [interviews](), [system-design]() ### AI overview This commentary argues that engineering levels have shifted from a straightforward progression of coding skill to role expectations that vary by level. It describes responsibilities from entry-level engineering through staff-level work and says hiring decisions increasingly assess candidates through interviews, system design, and behavioral evaluation, with coding treated as a baseline. ### Source excerpt Traditional engineering levels were about skill progression. I think this has changed. Everyone now is a mini staff engineer. ## AI-assisted coding can outpace code review, increasing the need for smaller PRs and clearer architecture context DevFeed: [AI-assisted coding can outpace code review, increasing the need for smaller PRs and clearer architecture context]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-05-17T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [AI-assisted coding](), [Code review](), [Pull Request](), [Latency](), [Network](), [Hardware](), [Database](), [Concurrency]() Tags: [ai](), [architecture](), [branch](), [cloud](), [code-review](), [cost](), [hardware](), [incident](), [latency](), [network](), [production](), [productivity](), [review](), [system-design](), [writing-code]() ### AI overview AI-assisted coding can make implementation much faster than review. The article explains that reviewers must reconstruct context, trace data flow, and assess hardware, network, latency, connection-pool, and architecture implications. It recommends keeping AI-written PRs small, documenting operational costs, and showing network boundaries early. ### Source excerpt AI made writing code a local, fast operation while review stayed cold and global: the reviewer must reload architecture, trace data flow, and mentally simulate hardware and network cost. A 400-line PR can take twice as long to review as it took to generate. Keep AI-written PRs under 100 lines, state endpoint count and expected latency in the description, and show the network boundary up front. ## The Two Abstractions of System Design: Hide or Reduce DevFeed: [The Two Abstractions of System Design: Hide or Reduce]() Original publisher: [Read original article]() Author: Murat (noreply@blogger.com) Published: 2026-05-08T23:27:00Z Content type: article Language: en Sources: [Metadata]() Topics: [abstraction](), [Formal methods](), [interfaces](), [Software Engineering]() Tags: [abstraction](), [design](), [formal-methods](), [interfaces](), [software-engineering](), [system-design](), [tla]() ### AI overview The article distinguishes two meanings of abstraction in system design: modularity abstraction, which hides implementation details through interfaces and boundaries, and modeling abstraction, which reduces a system to the behaviors relevant to a property under investigation. It presents this distinction as important for formal modeling and reasoning. ### Source excerpt When talking about TLA+, I keep referring to "abstraction" as the most important thing to learn. And it is about the hardest to learn as well. But a contradiction has been bugging me. Aren't CS people already supposed to be good at abstraction? Isn't abstraction supposed to be at the root of OS, networking, software engineering? Abstract Data Types (ADTs) are a staple of every in CS curriculum. So why do I (and every other formal methods/modeling person) see such a large skill gap in abstraction, and flag it as the core, make-or-break skill for modeling? I think I finally get to the root of this cognitive disonance. There are two kinds of "abstraction" conflated under the same umbrella term. Modularity abstraction: This is the traditional abstraction taught in CS curricula as ADTs, APIs, layered design, etc. It is all about encapsulation, drawing boundaries, and hiding internals. Modeling abstraction: This is what I talk about when I talk about abstraction in the context of modeling. This is the same sense of abstraction mathematicians and physicists when building models for thinking and reasoning. The goal is to find the minimal and most elegant description that preserves the property you care about. It is all about cutting away everything orhtogonal to the essence of that property. These two couldn't be further apart in terms of their goal! Let me try to explain in the next two sections. Modularity abstraction hides. Modeling abstraction reduces. Modularity abstraction is about interfaces that hide internals. Modeling abstraction is about behaviors, and about reducing a system to its minimal behavioral skeleton for the property you care. Modularity abstraction encapsulates, draws a vertical boundary, and hides the layer below. Modeling abstraction is crosscutting: it slices the system along a behavioral plane and keeps only what is absolutely relevant to the property under investigation, and even then in the form of "what", not "how". This slice usually looks noth ## How Big Tech Interview Decisions and Rounds Work DevFeed: [How Big Tech Interview Decisions and Rounds Work]() Original publisher: [Read original article]() Published: 2026-04-21T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Code](), [Architecture & Design](), [test-coverage](), [Scalability]() Tags: [api-integration](), [bug](), [career](), [clean-code](), [dsa](), [faang](), [interview](), [interviews](), [system-design](), [tech](), [test-coverage]() ### AI overview The article explains how big tech interviews are evaluated. Interviewers assess specific signals independently across coding, system design, and project-focused rounds, then discuss the combined feedback in a panel. Communication, code quality, edge-case handling, problem-solving instincts, scalability, and trade-off analysis can affect the decision, not just whether a problem is solved. ### Source excerpt Having worked at a few big tech companies and having interviewed candidates for them as well, here are some things I wish more candidates knew going in. How decisions are actually made Each interviewer is assigned a round and for every round, there are specific signals they need to collect... ## Top 10 API Gateway Use Cases in System Design DevFeed: [Top 10 API Gateway Use Cases in System Design]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-04-12T12:00:32Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Amazon API Gateway](), [Architecture & Design](), [Microservices](), [API]() Tags: [api-gateway](), [microservices](), [monolith](), [system-design](), [use-cases]() ### AI overview An article about ten API gateway use cases in system design, particularly as systems evolve from monoliths to microservices. The supplied evidence does not detail the individual use cases. ### Source excerpt As your system evolves from a monolith to microservices, a pattern quickly emerges: every service starts rebuilding the same things. ## Software engineer interviews for the age of AI DevFeed: [Software engineer interviews for the age of AI]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-03-25T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Artificial Intelligence](), [coding](), [SRE](), [systems]() Tags: [ai](), [coding](), [interviews](), [software-engineer](), [sre](), [system-design]() ### AI overview This opinion article argues that software engineer interviews should account for AI-assisted coding while still testing practical experience, depth of project knowledge, system design, and willingness to take responsibility for reliable production systems. It recommends repeatable, low-noise evaluation processes with multiple interviewers and detailed follow-up questions. ### Source excerpt Maybe AI will replace engineers, I don't know. Self-driving cars were just around the corner for 50 years. Until then we've got shit to do and engineers to hire. ## The RUM Conjecture: You Cannot Optimize Reads, Updates, and Memory at Once DevFeed: [The RUM Conjecture: You Cannot Optimize Reads, Updates, and Memory at Once]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-17T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [systems](), [Databases](), [Apache Cassandra](), [rocksdb](), [MySQL](), [PostgreSQL]() Tags: [b-tree](), [capacity](), [cassandra](), [database](), [databases](), [dram](), [indexing](), [latency](), [memory](), [mysql](), [node](), [performance](), [rocksdb](), [storage-engine](), [system-design]() ### AI overview The article explains the RUM Conjecture, which describes a tradeoff among read overhead, update overhead, and memory overhead in database indexes and storage engines. It compares B-Trees, LSM-Trees, and hash indexes to show how each optimizes different tradeoffs. ### Source excerpt You can strictly optimise at most two of read overhead, update overhead, and memory overhead; the third will be expensive. B-Trees optimise reads and memory and pay on writes. LSM-Trees optimise writes and memory and pay on reads. Hash indexes optimise reads and writes and pay in RAM. The useful question is not whether a database is good but which corner it optimises and what you are willing to pay for the other two. ## Microservices Deep Dive: Architecting for Scalability and Resilience DevFeed: [Microservices Deep Dive: Architecting for Scalability and Resilience]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [microservices architecture](), [Scalability](), [Resilience](), [Architecture & Design](), [Domain-driven design (DDD)](), [distributed-systems](), [Deployment]() Tags: [architecture](), [ci-cd](), [cloud](), [complexity](), [deployments](), [devops](), [distributed-systems](), [domain-driven-design-ddd](), [incident-response](), [independent](), [isolation](), [layer](), [microservices](), [observability](), [resilience](), [scalability](), [services](), [system-design]() ### AI overview This article explains how microservices replace a tightly coupled monolith with independently deployable services aligned to business capabilities. It covers benefits such as independent scaling, fault isolation, team autonomy, and technology flexibility, while noting the added costs of communication, observability, operations, and data management. It recommends using microservices when domain complexity and scale justify the operational cost, organizing services around bounded contexts, and extracting one domain at a time. ### Source excerpt Microservices trade deployment simplicity for independent scaling, fault isolation, and team autonomy, and they only pay off once domain complexity and scale justify the operational cost. Align services to bounded contexts, give each its own data store, and extract one domain at a time rather than splitting by technical layer. ## System Design: Principles for Maintainability, Scalability, and Reliability DevFeed: [System Design: Principles for Maintainability, Scalability, and Reliability]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Maintainability](), [reliability](), [Scalability](), [Architecture & Design](), [data](), [Testing](), [observability](), [CRUD](), [Latency]() Tags: [architecture](), [crud](), [data-systems](), [debug](), [latency](), [maintainability](), [observability](), [reliability](), [scalability](), [system-design]() ### AI overview This article presents production system design around reliability, scalability, and maintainability. It discusses data building blocks beyond CRUD, fault containment, isolation, testing, recovery, observability, latency versus response time, and vertical versus horizontal scaling. ### Source excerpt Reliability, scalability, and maintainability rest on one distinction: a fault is a component-level anomaly, a failure is the system no longer delivering its service. Design to contain faults so they never become failures, and stay precise that latency is waiting time while response time is the full client-visible number. ## HTTP/2 System Design: How It Fixes HTTP/1.1 DevFeed: [HTTP/2 System Design: How It Fixes HTTP/1.1]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [HTTP](), [Latency](), [Concurrency](), [Compression]() Tags: [compression](), [concurrency](), [http](), [http1-1](), [http2](), [latency](), [networking](), [parallel](), [performance](), [protocols](), [reliability](), [rest](), [system-design](), [tcp](), [tls](), [web-performance]() ### AI overview This article explains how HTTP/2 addresses HTTP/1.1 limitations through binary framing, multiplexed streams over one TCP connection, HPACK header compression, server push, and stream prioritisation. It also describes how HTTP/2 preserves HTTP semantics while improving latency and reducing application-layer head-of-line blocking. ### Source excerpt HTTP/1.1 hit concurrency limits that forced hacks like domain sharding and sprite sheets. HTTP/2 keeps HTTP semantics but replaces the wire format with binary framing, multiplexed streams over a single TCP connection, HPACK header compression, server push, and stream prioritisation, which removes most head-of-line blocking at the HTTP layer. ## Transitioning from REST to gRPC: System Design and Tradeoffs DevFeed: [Transitioning from REST to gRPC: System Design and Tradeoffs]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [gRPC](), [Remote Procedure Call (RPC)](), [Code](), [HTTP](), [JSON]() Tags: [api-design](), [client](), [grpc](), [http](), [json](), [microservices](), [protobuf](), [rest](), [rpc](), [schema](), [server](), [system-design]() ### AI overview This tutorial explains how gRPC uses .proto service definitions and Protocol Buffers to generate typed client and server code, serialize messages, and communicate over HTTP/2. It compares gRPC with REST and recommends gRPC for internal service-to-service calls needing low latency, schema-first contracts, or streaming, while retaining REST with JSON at the edge for browsers and external consumers. ### Source excerpt gRPC defines services in a .proto file, compiles them into typed client and server code, and transports binary protobuf over HTTP/2. Choose it for internal service-to-service calls that need low latency, schema-first contracts, or streaming, and keep REST with JSON at the edge where browsers and external consumers live. ## How UUIDv4 Primary Keys Affect Database Insert Performance DevFeed: [How UUIDv4 Primary Keys Affect Database Insert Performance]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Database](), [MySQL](), [ordering]() Tags: [b-tree](), [database](), [database-performance](), [fragmentation](), [innodb](), [latency](), [mysql](), [performance](), [system-design](), [uuid]() ### AI overview This article explains how random UUIDv4 primary keys can reduce insert performance in clustered indexes. Random inserts land in the middle of B+ tree pages, causing page splits, fragmentation, additional writes, and buffer-pool churn. It recommends UUIDv7 or ULID, or an auto-increment clustered key with UUIDs in a unique secondary index. ### Source excerpt The problem with UUIDv4 primary keys is not uniqueness, it is that random keys fight the storage engine's physical layout. In a clustered index the primary key is the table, so random inserts land mid-page, trigger page splits, leave pages half-full, and can inflate a 50 GB dataset to roughly 100 GB while wasting buffer pool on empty space. Use UUIDv7 or ULID, or keep an auto-increment clustered key with the UUID in a unique secondary index. ## Shazam finds songs by voting on time offsets, not by comparing audio DevFeed: [Shazam finds songs by voting on time offsets, not by comparing audio]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Algorithm](), [hash](), [Databases]() Tags: [algorithm](), [algorithms](), [audio-fingerprinting](), [databases](), [hash](), [indexing](), [lookup](), [music-recognition](), [query](), [system-design]() ### AI overview The article explains how Shazam recognizes songs from short, noisy recordings. Instead of comparing audio similarity, it extracts spectrogram peaks, combines nearby peaks into hashes, and uses an inverted index to find tracks whose hash matches share a common time offset. The production system beyond the public 2003 paper is noted as unavailable. ### Source excerpt Shazam does not compare audio. It reduces each track to spectrogram peaks, pairs nearby peaks into ~32-bit hashes, and looks those up in an inverted index. A match is declared when many hashes from the sample agree on a single time offset into one track. The offset histogram is the whole trick: noise scatters offsets randomly, a real match stacks them into a spike. ## Consistency Models in Azure Cosmos DB: From Strong to Eventual DevFeed: [Consistency Models in Azure Cosmos DB: From Strong to Eventual]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [consistency](), [Databases](), [Availability](), [Latency](), [Replication](), [Azure]() Tags: [availability](), [cloud-architecture](), [consistency](), [cosmosdb](), [database-design](), [databases](), [distributed-systems](), [latency](), [linearizable](), [pacelc](), [replication](), [semantics](), [system-design]() ### AI overview This article explains how Azure Cosmos DB uses five consistency levels to expose PACELC trade-offs between consistency, availability, latency, and read freshness. It describes Strong consistency, bounded staleness, and Session consistency, including their operational trade-offs and suitable use cases. ### Source excerpt Cosmos DB exposes the PACELC trade-off as five explicit levels instead of forcing a strong-or-eventual choice. Strong gives linearizable reads at the cost of write latency and availability. Session, the practical default for user-facing apps, gives read-your-writes within a session via per-partition session tokens while staying highly available. ## Building AI agents that overcome the complexity cliff DevFeed: [Building AI agents that overcome the complexity cliff]() Original publisher: [Read original article]() Author: Ethan Ruhe Published: 2026-03-10T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [systems](), [Framework](), [Artificial Intelligence]() Tags: [agents](), [ai](), [building](), [challenges](), [complexity](), [infrastructure](), [system](), [system-design](), [temporal-voices]() ### AI overview This article presents a framework for understanding how AI agent capability grows with execution time and the number of tools and systems an agent can use. It argues that greater capability also increases the likelihood of failures and slows iteration, creating a "complexity cliff" where conventional frameworks may break down and Durable Execution becomes necessary. ### Source excerpt A principled framework for assessing agent capability -- why advanced agents need different infrastructure. [Next page]()