<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>System-Design on My AI Digest</title><link>https://ai-digest.derricklin.net/tags/system-design/</link><description>Recent content in System-Design on My AI Digest</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 05 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-digest.derricklin.net/tags/system-design/index.xml" rel="self" type="application/rss+xml"/><item><title>System Design Collaborative Document Editing Summary</title><link>https://ai-digest.derricklin.net/career/system-design-collaborative-document-editing-summary/</link><pubDate>Tue, 05 May 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-collaborative-document-editing-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Collaborative Document Editing&lt;/p&gt;
&lt;p&gt;Scope: Design a Google Docs-style collaborative editor for text documents that supports concurrent editing, revision history, permissions, offline recovery, and low-latency presence without losing user edits.&lt;/p&gt;
&lt;p&gt;Source grounding: Grokking&amp;rsquo;s Dropbox chapter supplies the interview frame for sync, offline edits, client metadata, long polling, queues, chunking, dedupe, metadata partitioning, and bottleneck analysis; Alex Xu&amp;rsquo;s Google Drive chapter adds sync conflicts, revision history, notification service, offline backup queue, strong metadata consistency, and failure handling; DDIA supplies the deeper model for multi-leader-style collaboration, write conflicts, causality, version vectors, operational transformation, CRDTs, total ordering, append-only logs, stream-derived state, and the distinction between timeliness and integrity. Modern refresh: current &lt;a href="https://docs.yjs.dev/" target="_blank"&gt;Yjs&lt;/a&gt; and &lt;a href="https://automerge.org/docs/hello/" target="_blank"&gt;Automerge&lt;/a&gt; documentation show practical CRDT-backed collaborative application patterns, while &lt;a href="https://developer.mozilla.org/en-US/docs/Web/API/WebSocket" target="_blank"&gt;MDN&amp;rsquo;s WebSocket documentation&lt;/a&gt; is a useful reminder that browser WebSocket APIs do not provide built-in backpressure.&lt;/p&gt;</description></item><item><title>System Design URL Shortener Analytics Summary</title><link>https://ai-digest.derricklin.net/career/system-design-url-shortener-analytics-summary/</link><pubDate>Sun, 03 May 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-url-shortener-analytics-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: URL Shortener Analytics&lt;/p&gt;
&lt;p&gt;Scope: Design the analytics subsystem for a URL shortener: capture click events, compute useful aggregates, and serve owner dashboards without slowing down redirects.&lt;/p&gt;
&lt;p&gt;Source grounding: Grokking frames analytics as an extended URL shortener requirement and calls out the danger of updating a shared counter row on every popular redirect; Alex Xu highlights that redirect choice affects analytics fidelity; DDIA supplies the storage and processing lens for event logs, OLTP versus OLAP separation, materialized aggregates, stream windows, partitioning, replication, and idempotent recovery.&lt;/p&gt;</description></item><item><title>System Design Distributed Queue Summary</title><link>https://ai-digest.derricklin.net/career/system-design-distributed-queue-summary/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-distributed-queue-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Distributed Queue&lt;/p&gt;
&lt;p&gt;Scope: Design a durable multi-tenant queue service for asynchronous jobs, service decoupling, retries, and fan-out-style workflows, with at-least-once delivery as the default and per-key ordering where callers explicitly need it.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design a distributed message queue&amp;rdquo; interview problem. Grokking&amp;rsquo;s interview process is the right starting point: clarify whether the interviewer wants a task queue, a pub/sub broker, or a retained event log before debating Kafka, SQS, or RabbitMQ. Alex Xu&amp;rsquo;s system design framing treats a message queue as the durability and decoupling layer between producers and consumers, and his notification, news feed, web crawler, chat, and video chapters show the same pattern repeatedly: put slow, unreliable, or bursty work behind a queue so producers and consumers can scale independently. DDIA adds the critical storage lesson: a queue is not just an in-memory list. It is a data system with ordering, replication, durability, retention, acknowledgements, and replay tradeoffs.&lt;/p&gt;</description></item><item><title>System Design Payment System Summary</title><link>https://ai-digest.derricklin.net/career/system-design-payment-system-summary/</link><pubDate>Wed, 29 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-payment-system-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Payment System&lt;/p&gt;
&lt;p&gt;Scope: Design a card-first online payment platform that creates payment intents, authorizes and captures funds, issues refunds, maintains an auditable ledger, and reconciles asynchronous processor outcomes without double-charging customers.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design a Stripe / Adyen / PSP-style payment platform for merchant checkout&amp;rdquo; interview problem. Grokking&amp;rsquo;s interview flow still applies directly: define the API contract early, size the system before arguing about storage, and then walk through the write path, read path, partitioning, caching, and failure handling. Alex Xu&amp;rsquo;s usual system design patterns also fit well here: stateless API servers on the edge, durable primary storage for transactional state, caches for read-heavy metadata, and queues or streams for slow or retryable side effects. DDIA adds the most important payment-specific lesson: integrity matters more than timeliness. A payment status can be briefly stale, but money must not be created, lost, or charged twice.&lt;/p&gt;</description></item><item><title>System Design Ad Click Aggregation Summary</title><link>https://ai-digest.derricklin.net/career/system-design-ad-click-aggregation-summary/</link><pubDate>Mon, 27 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-ad-click-aggregation-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Ad Click Aggregation&lt;/p&gt;
&lt;p&gt;Scope: Design a high-throughput ad event aggregation pipeline that ingests impression and click events, deduplicates them, produces near-real-time campaign rollups, and supports slower reconciled reporting for analytics, optimization, and billing-adjacent use cases.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This interview problem is less about serving ads and more about turning a firehose of raw events into trustworthy aggregates. Grokking and Alex Xu push the same early habit here: clarify scope, keep the write path simple and durable, and decouple slow downstream consumers with queues or logs. DDIA adds the deeper constraint: aggregated counters are derived data, so correctness depends on event ordering, idempotence, late-event handling, and replay, not on pretending one synchronous write magically updates every store exactly once.&lt;/p&gt;</description></item><item><title>System Design Video Streaming Platform Summary</title><link>https://ai-digest.derricklin.net/career/system-design-video-streaming-platform-summary/</link><pubDate>Sun, 26 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-video-streaming-platform-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Video Streaming Platform&lt;/p&gt;
&lt;p&gt;Scope: Design a global video-on-demand platform that supports resumable creator uploads, transcoding, metadata/search, and adaptive-bitrate playback on web, mobile, and TV clients; exclude recommendations, ads, and live streaming from the first version.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design YouTube / Netflix-style VOD platform&amp;rdquo; interview. Grokking is useful for the interview flow: clarify scope, define APIs early, size uploads versus reads, and separate large media blobs from metadata. Alex Xu adds the practical serving path: object storage for source and encoded media, an asynchronous transcoding pipeline, CDN-based delivery, and a metadata control plane. DDIA sharpens the deeper answer: treat search indexes, counters, and playback-ready state as derived data maintained from durable logs and background workflows, not as one giant transactional database.&lt;/p&gt;</description></item><item><title>System Design ChatGPT Summary</title><link>https://ai-digest.derricklin.net/career/system-design-chatgpt-summary/</link><pubDate>Sat, 25 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-chatgpt-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5.4&lt;/p&gt;
&lt;p&gt;Selected problem: ChatGPT&lt;/p&gt;
&lt;p&gt;Scope: Design a text-first ChatGPT-style assistant that supports multi-turn conversations, streamed responses, document-backed answers, bounded tool use, and durable conversation history for consumers and teams.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This interview problem combines ideas from several classic designs instead of mapping neatly to one old chapter. Grokking&amp;rsquo;s framework still applies: clarify scope, define APIs, estimate scale, pick a data model, then explain the high-level design and bottlenecks. Alex Xu&amp;rsquo;s chat-system chapter is useful for session handling, streaming delivery, and multi-device conversation sync; the YouTube chapter is useful for large-object storage and asynchronous processing; DDIA adds the deeper foundation for logs, caches, replication, partitioning, derived data, and backpressure. The key modern shift is that the assistant is not only a chat transport problem. It is a stateful product sitting on top of an expensive inference pipeline.&lt;/p&gt;</description></item><item><title>System Design Distributed File Storage Summary</title><link>https://ai-digest.derricklin.net/career/system-design-distributed-file-storage-summary/</link><pubDate>Fri, 24 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-distributed-file-storage-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Distributed File Storage&lt;/p&gt;
&lt;p&gt;Scope: Design a cloud file storage and sync service that stores large files durably, keeps file and folder metadata consistent, and propagates changes across devices and shared workspaces.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic Dropbox / Google Drive interview problem. Grokking and Alex Xu both push the same core split early: separate the metadata and synchronization plane from the binary file storage plane. DDIA adds the more durable framing: the namespace, versions, and permissions are the system of record, while notifications, sync cursors, search indexes, thumbnails, and audit views are derived data built from change streams.&lt;/p&gt;</description></item><item><title>System Design Distributed Job Scheduler Summary</title><link>https://ai-digest.derricklin.net/career/system-design-distributed-job-scheduler-summary/</link><pubDate>Fri, 24 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-distributed-job-scheduler-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Distributed Job Scheduler&lt;/p&gt;
&lt;p&gt;Scope: Design a multi-tenant scheduler that supports one-off and recurring jobs, dispatches them reliably to worker fleets, and gives operators clear control over retries, overlap, and execution history.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This interview problem sits between a cron service and a workload orchestrator. Grokking and Alex Xu repeatedly lean on the same building blocks across crawler, notification, and media-processing systems: stateless control planes, durable metadata, queues, workers, and explicit retry paths. DDIA adds the harder part: lease-based coordination, partition ownership, hot-spot avoidance, and clear delivery semantics.&lt;/p&gt;</description></item><item><title>System Design Metrics Logging Pipeline Summary</title><link>https://ai-digest.derricklin.net/career/system-design-metrics-logging-pipeline-summary/</link><pubDate>Fri, 24 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-metrics-logging-pipeline-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Metrics/Logging Pipeline&lt;/p&gt;
&lt;p&gt;Scope: Design a centralized telemetry pipeline that ingests high-volume metrics and logs from many services, powers near-real-time dashboards and alerts, and retains searchable history without letting the write path collapse under bursts.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;Grokking and Alex Xu both push the same interview habit first: clarify scope before naming technology. Here the system is not &amp;ldquo;the dashboard product&amp;rdquo;; it is the backend write and query path behind centralized observability.&lt;/p&gt;</description></item><item><title>System Design Ride-Sharing Dispatch Summary</title><link>https://ai-digest.derricklin.net/career/system-design-ride-sharing-dispatch-summary/</link><pubDate>Fri, 24 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-ride-sharing-dispatch-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Ride-Sharing Dispatch&lt;/p&gt;
&lt;p&gt;Scope: Design the real-time backend that ingests rider requests and driver location updates, finds good matches within seconds, assigns exactly one driver to a rider, and keeps the trip state and live map updated until completion.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design Uber/Lyft dispatch&amp;rdquo; interview problem: a city-scale marketplace where the hard parts are not just finding a nearby driver, but doing so with fresh-enough location data, realistic ETAs, strong assignment correctness, and graceful handling of retries, timeouts, cancellations, and partial failures.&lt;/p&gt;</description></item><item><title>System Design Chat Messaging System Summary</title><link>https://ai-digest.derricklin.net/career/system-design-chat-messaging-system-summary/</link><pubDate>Thu, 23 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-chat-messaging-system-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Chat/Messaging System&lt;/p&gt;
&lt;p&gt;Scope: Design a text-first chat platform for one-to-one and small-to-medium group conversations with presence, multi-device sync, offline delivery, and durable message history.&lt;/p&gt;
&lt;p&gt;Also see &lt;a href="https://wiki.derricklin.net/software-development/System%20Design%20Interview/#chat-system" target="_blank"&gt;https://wiki.derricklin.net/software-development/System%20Design%20Interview/#chat-system&lt;/a&gt;&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic interview problem of designing a real-time chat system without over-promising impossible guarantees. Grokking and Alex Xu both frame the core problem similarly: keep message delivery low-latency, keep history durable, handle offline users, and make multiple devices converge on the same conversation state. DDIA adds the deeper constraint: ordering, replication, partitioning, and delivery semantics must be chosen deliberately instead of hand-waving toward &amp;ldquo;global consistency.&amp;rdquo;&lt;/p&gt;</description></item><item><title>System Design News Feed Summary</title><link>https://ai-digest.derricklin.net/career/system-design-news-feed-summary/</link><pubDate>Thu, 23 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-news-feed-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: News Feed&lt;/p&gt;
&lt;p&gt;Scope: Design a personalized feed service that lets users publish posts, reads recent and ranked posts from accounts they follow, and keeps feed retrieval fast while handling fanout skew, stale caches, media, and eventual consistency.&lt;/p&gt;
&lt;p&gt;Also see &lt;a href="https://wiki.derricklin.net/software-development/System%20Design%20Interview/#news-feed" target="_blank"&gt;https://wiki.derricklin.net/software-development/System%20Design%20Interview/#news-feed&lt;/a&gt;&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design a Facebook News Feed / Twitter timeline / Instagram home feed&amp;rdquo; interview problem. Grokking and Alex Xu frame it as two linked flows: feed publishing, where a new post enters storage and is propagated to followers, and feed retrieval, where a user gets a fast, paginated, hydrated list of feed items. DDIA&amp;rsquo;s Twitter home-timeline example explains the central tradeoff: doing more work at write time makes reads fast, but high-follower authors create fanout storms; doing more work at read time avoids write amplification, but every feed read becomes an expensive merge.&lt;/p&gt;</description></item><item><title>System Design Search Autocomplete Summary</title><link>https://ai-digest.derricklin.net/career/system-design-search-autocomplete-summary/</link><pubDate>Thu, 23 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-search-autocomplete-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Search Autocomplete&lt;/p&gt;
&lt;p&gt;Scope: Design a low-latency autocomplete service that returns top suggestions for a typed prefix, learns from query activity over time, and handles hot prefixes, moderation, and multilingual growth without rebuilding everything on every keystroke.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design Google search suggestions&amp;rdquo; or &amp;ldquo;typeahead&amp;rdquo; interview problem. Alex Xu frames it around a brutally simple user-facing requirement: every keystroke can trigger a request, so suggestion reads must be extremely fast. Grokking&amp;rsquo;s interview style also applies cleanly here: clarify prefix-only versus infix matching, top-K size, language scope, freshness needs, and latency targets before arguing about data structures. The interview answer usually starts with a trie or prefix tree, but DDIA adds the more durable framing: autocomplete is a derived read model built from query logs, aggregation, filtering, and ranking pipelines, so the real design question is how to keep that read model fresh, cheap, and rebuildable.&lt;/p&gt;</description></item><item><title>System Design Web Crawler Summary</title><link>https://ai-digest.derricklin.net/career/system-design-web-crawler-summary/</link><pubDate>Thu, 23 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-web-crawler-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Web Crawler&lt;/p&gt;
&lt;p&gt;Scope: Design a large-scale, HTML-first web crawler for search indexing that discovers new URLs, respects host politeness and robots rules, deduplicates content, and keeps important pages reasonably fresh.&lt;/p&gt;
&lt;p&gt;Also see &lt;a href="https://wiki.derricklin.net/software-development/System%20Design%20Interview/#web-crawler" target="_blank"&gt;https://wiki.derricklin.net/software-development/System%20Design%20Interview/#web-crawler&lt;/a&gt;&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design Googlebot&amp;rdquo; interview question. Grokking emphasizes the minimum crawler loop: start from seed URLs, fetch pages, extract links, dedupe, and repeat. Alex Xu frames the real interview difficulty more accurately: the hard parts are URL frontier design, politeness, prioritization, freshness, robustness, and avoiding bad content. DDIA supplies the missing systems lens: frontier queues, dedupe tables, document storage, and downstream indexing are separate dataflow stages, so the design should favor partitioned state, replayable logs, and idempotent consumers rather than pretending the entire pipeline is one giant transaction.&lt;/p&gt;</description></item><item><title>System Design API Rate Limiter Summary</title><link>https://ai-digest.derricklin.net/career/system-design-api-rate-limiter-summary/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-api-rate-limiter-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: API Rate Limiter&lt;/p&gt;
&lt;p&gt;Scope: Design a distributed server-side rate limiter that enforces per-user, per-IP, per-tenant, and per-route API quotas with very low decision latency across many stateless services.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic server-side API rate limiter from Grokking and Alex Xu: every request should get a cheap admission decision before it reaches expensive application logic.&lt;/p&gt;</description></item><item><title>System Design Distributed Cache Summary</title><link>https://ai-digest.derricklin.net/career/system-design-distributed-cache-summary/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-distributed-cache-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Distributed Cache&lt;/p&gt;
&lt;p&gt;Scope: Design a distributed in-memory cache service that provides very low-latency &lt;code&gt;GET&lt;/code&gt;/&lt;code&gt;SET&lt;/code&gt;/&lt;code&gt;DELETE&lt;/code&gt;/&lt;code&gt;INCR&lt;/code&gt; operations with TTLs, eviction, sharding, and failover for many stateless application services.&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design Memcache / Redis-class cache&amp;rdquo; interview problem: build a shared cache tier that is much faster than the system of record, scales horizontally, and fails gracefully instead of turning into a new bottleneck.&lt;/p&gt;</description></item><item><title>System Design Notification System Summary</title><link>https://ai-digest.derricklin.net/career/system-design-notification-system-summary/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-notification-system-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: Notification System&lt;/p&gt;
&lt;p&gt;Scope: Design a soft real-time notification platform that accepts events from internal services, renders user-facing messages, respects user preferences, and dispatches push, email, SMS, and in-app notifications reliably at large scale.&lt;/p&gt;
&lt;p&gt;Also see &lt;a href="https://wiki.derricklin.net/software-development/System%20Design%20Interview/#notification-system" target="_blank"&gt;https://wiki.derricklin.net/software-development/System%20Design%20Interview/#notification-system&lt;/a&gt;&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic &amp;ldquo;design a notification system&amp;rdquo; interview problem: build the shared infrastructure that product teams use whenever they need to notify users about security alerts, messages, payments, delivery updates, marketing campaigns, or feed changes. The core challenge is not just calling APNs, FCM, an SMS gateway, or an email provider. The harder parts are durable intake, preference filtering, fanout, queue isolation, retries, deduplication, rate limiting, observability, and honest delivery semantics.&lt;/p&gt;</description></item><item><title>System Design TinyURL Summary</title><link>https://ai-digest.derricklin.net/career/system-design-tinyurl-summary/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-digest.derricklin.net/career/system-design-tinyurl-summary/</guid><description>&lt;p&gt;Generated by Codex with gpt-5&lt;/p&gt;
&lt;p&gt;Selected problem: TinyURL&lt;/p&gt;
&lt;p&gt;Scope: Design a highly available URL shortening service that creates compact aliases, resolves them with very low latency, and records click analytics without putting the redirect path at risk.&lt;/p&gt;
&lt;p&gt;Also see &lt;a href="https://wiki.derricklin.net/software-development/System%20Design%20Interview/#url-shortener" target="_blank"&gt;https://wiki.derricklin.net/software-development/System%20Design%20Interview/#url-shortener&lt;/a&gt;&lt;/p&gt;
&lt;h2 id="problem-framing"&gt;Problem framing&lt;/h2&gt;
&lt;p&gt;This is the classic URL shortener problem: map a short code to a long URL, make redirects fast, and survive a very read-heavy workload with occasional viral hot keys.&lt;/p&gt;</description></item></channel></rss>