# Linear Published articles for Linear. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Khan Academy Uses Local Storage and Request Queues to Handle Offline Actions DevFeed: [Khan Academy Uses Local Storage and Request Queues to Handle Offline Actions]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-08-17T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [LocalStorage](), [client](), [Code](), [Internet](), [servers]() Tags: [architecture](), [change](), [code](), [engineering](), [function](), [hints](), [linear](), [local](), [news](), [offline](), [queue](), [request](), [server](), [web-frontend]() ### AI overview This article explains how Khan Academy addressed offline hint cheating by changing the client's request architecture. Actions are stored in local storage and placed in a queue for retry when connectivity returns, with linear backoff to avoid repeated requests during outages. ### Source excerpt By Phillip Lemons The problem Recently, a number of students on Khan Academy found a way to cheat ... Read more ## From Atlas experiment to Airlock: extracting agent governance into a product DevFeed: [From Atlas experiment to Airlock: extracting agent governance into a product]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T15:35:36Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [Security](), [Artificial Intelligence](), [App](), [Linear](), [Slack]() Tags: [agent](), [ai](), [ai-agents](), [authorization](), [email](), [integrations](), [linear](), [permission](), [policy](), [product](), [security](), [slack]() ### AI overview The article explains how WorkOS's Airlock grew from the Atlas experiment into a standalone authorization product for governing AI-agent actions in company tools. A demonstration shows policies blocking emails containing financial information and requiring approval for unfamiliar distribution lists. ### Source excerpt Airlock grew out of Atlas to give IT and security teams a shared way to govern agent actions. Aaron Tainter's Agent Night demo shows how it works. ## Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI DevFeed: [Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-09-14T23:05:28Z Content type: article Language: en Sources: [Product Growth]() Topics: [Artificial Intelligence](), [context](), [repo](), [context window](), [Developer experience](), [GitHub](), [Linear]() Tags: [ai](), [context](), [context-window](), [developer-experience](), [github](), [linear](), [product](), [repo](), [team](), [tools]() ### AI overview The article examines Together AI's AI-oriented product team and describes four parts of its working stack: a shared context repository, reusable skills for research and product documentation, an orchestrator for product leaders, and agent evaluations. It explains how shared context files and workflows support collaboration across product areas, including automated weekly status updates using Linear, GitHub, and strategy documents. ### Source excerpt I got a truly AI-pilled product team to demo the 4 key tools in their stack ## 🎙 How I AI: GPT-6 Astra is a banger + Stripe's AI playbook + Grok Bot vs. OpenClaw: why I replaced my entire agent stack DevFeed: [🎙 How I AI: GPT-6 Astra is a banger + Stripe's AI playbook + Grok Bot vs. OpenClaw: why I replaced my entire agent stack]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-07T15:02:41Z Content type: opinion Language: en Sources: [Lenny's Newsletter]() Topics: [Bot](), [OpenClaw](), [Artificial Intelligence](), [Linear](), [Slack](), [soc 2]() Tags: [agent](), [ai](), [linear](), [openclaw](), [slack](), [soc-2]() ### AI overview A podcast episode compares Grok Bot with OpenClaw through Claire's migration of her agent setup. It discusses using specialized bots for inboxes, family coordination, pull-request review, compliance monitoring, customer support, subscription audits, and shopping, along with permissions, multi-account access, and maintaining agent context and routines. ### Source excerpt Your weekly listens from How I AI, part of the Lenny's Podcast Network ## Three ways to let an AI agent call third-party APIs on behalf of a user DevFeed: [Three ways to let an AI agent call third-party APIs on behalf of a user]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-27T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [OAuth](), [Security]() Tags: [agent](), [ai-agent](), [apis](), [hubspot](), [linear](), [oauth](), [patterns](), [proxy](), [security](), [slack](), [third-party](), [tokens]() ### AI overview This article explains three patterns for letting an AI agent access third-party APIs on a user's behalf: storing OAuth credentials yourself, fetching tokens at runtime, or using a proxy that keeps tokens out of the agent-controlled runtime. It compares their operational and security implications and recommends choosing based on where the code runs and the system's requirements. ### Source excerpt Store the token yourself, fetch it at runtime, or never hold it at all. Where the credential ends up in each pattern, what each one costs, and how to pick without guessing. ## When non-devs open PRs DevFeed: [When non-devs open PRs]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-08-25T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [Pull Request](), [Development](), [Linear](), [Artificial Intelligence]() Tags: [ai](), [article](), [code](), [jira](), [linear](), [reviews](), [work]() ### AI overview An opinion article examines the challenges and potential benefits of allowing non-engineers, including product managers and designers, to open pull requests on a software codebase. It discusses review responsibility, boundaries, scaling concerns, and reduced communication cycles, while citing Linear usage data about non-engineer code contributions and AI feature use. ### Source excerpt What to watch out for when letting non-engineers open PRs on your codebase ## The Single Most Undervalued Fact of Linear Algebra DevFeed: [The Single Most Undervalued Fact of Linear Algebra]() Original publisher: [Read original article]() Author: Tivadar Danka Published: 2026-08-24T09:47:15Z Content type: opinion Language: en Sources: [The Palindrome]() Topics: [Graphs]() Tags: [animation](), [arts](), [audio](), [graphs](), [linear](), [linear-algebra](), [matrices](), [video]() ### AI overview The author presents matrices and graphs as related representations and describes remastering an earlier piece into a video with improved animation and audio recording. ### Source excerpt Matrices are graphs and graphs are matrices ## Human judgment doesn't leave the software factory. It relocates. DevFeed: [Human judgment doesn't leave the software factory. It relocates.]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Software](), [coding](), [event driven](), [implementation](), [GitHub](), [Claude Code](), [codex]() Tags: [agent](), [architecture](), [automated](), [batch](), [build](), [claude-code](), [codex](), [event-driven](), [github](), [linear](), [maintainability](), [mutation-testing](), [pull-requests](), [quality](), [scanners](), [slack](), [software](), [testing](), [verification]() ### AI overview A field guide to building a repeatable software factory while keeping humans responsible for product intent, system design, quality standards, code review, and final merge decisions. It recommends early and continuous quality checks, deliberate constraints, and event-driven automation when ordinary coding workflows are no longer sufficient. ### Source excerpt A field guide to building a software factory that still has an owner. ## Automating quality support at scale: AI and human in the loop DevFeed: [Automating quality support at scale: AI and human in the loop]() Original publisher: [Read original article]() Author: Hugo Casademont Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Windmill Blog]() Topics: [Artificial Intelligence](), [Claude](), [Discord](), [Slack](), [GitHub Issues](), [email](), [Linear](), [telemetry](), [WebSocket]() Tags: [ai](), [ai-windmill-support-claude](), [automated](), [claude](), [discord](), [email](), [github-issues](), [linear](), [slack](), [support](), [telemetry](), [windmill]() ### AI overview Windmill describes an internal support-automation pipeline that consolidates Discord, Slack, email, and GitHub issues into a Discord queue. It uses Claude and customer context to triage requests, draft replies and fixes, while requiring human approval before responses are sent. ### Source excerpt How does Windmill automate its own support? A Windmill pipeline funnels Discord, Slack and email tickets into one Discord queue, triages them with Claude, drafts replies, and dispatches fixes. ## How building AI Sessions was shaped by our own experience with Webmux DevFeed: [How building AI Sessions was shaped by our own experience with Webmux]() Original publisher: [Read original article]() Author: Tristan Lécuyer Published: 2026-07-26T00:00:00Z Content type: opinion Language: en Sources: [Windmill Blog]() Topics: [AI-assisted coding](), [dashboards](), [Open Source](), [Git](), [Command-line interface](), [Claude Code](), [codex]() Tags: [ai](), [ai-coding-agents](), [browser](), [claude-code](), [cli](), [codex](), [git](), [linear](), [open-source](), [windmill]() ### AI overview The article explains how Windmill's experience building and using Webmux, an open-source dashboard for supervising parallel AI coding agents, influenced the design of its AI Sessions product. It describes Webmux's worktree, tmux, web interface, Linear integration, terminal forwarding, and support for Claude Code and Codex. ### Source excerpt Before we shipped a single line of AI Sessions, we had already spent months supervising AI coding agents on our own codebase, through Webmux, the open-source dashboard we built for ourselves. Here is how that daily use shaped the product. ## Use My /No-AI-Slop Skill to Remove 20+ Patterns of AI Slop From Your Writing DevFeed: [Use My /No-AI-Slop Skill to Remove 20+ Patterns of AI Slop From Your Writing]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-07-22T14:46:17Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [Artificial Intelligence](), [codex](), [Claude Code](), [Linear](), [MCP]() Tags: [ai](), [ai-writing](), [analysis](), [claude-code](), [code](), [codex](), [how-to](), [linear](), [mcp](), [skills]() ### AI overview The article introduces the author's open-source /no-ai-slop skill, explains how to install and use it in Codex, Claude Code, or another AI harness, and describes its editing and AI-writing detection modes. It also discusses a 25/50/25 approach to using AI during writing and editing. ### Source excerpt Plus my honest reflections on how to use AI to edit without giving in to the dark side ## 🍔🧠 Why Linear Feels So Fast (Technical Breakdown) DevFeed: [🍔🧠 Why Linear Feels So Fast (Technical Breakdown)]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-07-20T15:30:40Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Caching](), [Local-First](), [client](), [WebSocket](), [Software Engineering](), [Vite](), [npm]() Tags: [cache](), [caching](), [javascript](), [linear](), [local-first](), [npm](), [technical](), [vite](), [websocket]() ### AI overview A technical breakdown of how Linear aims to feel fast by minimizing network latency. It describes local-first state updates, asynchronous synchronization over WebSocket, code splitting, parallel module loading, service-worker precaching, and separately cached dependency chunks. ### Source excerpt PLUS: Virtual memory explained 👨💻, Spark retires vector DBs 💾, Backprop explained simply 🧮 ## Vite 8.1 is out! DevFeed: [Vite 8.1 is out!]() Original publisher: [Read original article]() Author: The Vite Team Published: 2026-06-23T00:00:00Z Content type: release Language: en Sources: [Vite]() Topics: [Vite](), [Development](), [React]() Tags: [2026](), [announce](), [browser](), [linear](), [performance](), [release](), [vite]() ### AI overview Vite 8.1 has been released with experimental bundled dev mode, which is intended to improve development performance for large applications. Initial testing reported faster startup and full-page reloads than the unbundled dev server. ### Source excerpt Vite 8.1 is out! June 23, 2026 Vite 8 was released in March with a single unified bundler powered by Rolldown, opening the door to further improvements. It is now seeing 41.6 million weekly downloads, almost reaching the total downloads of Vite 7. Alongside resolving upgrade regressions, we've been working on new features, and we're excited to announce the release of Vite 8.1. Quick links: Docs Translations: 简体中文, 日本語, Español, Português, 한국어, Deutsch, فارسی GitHub Changelog Play online with Vite 8.1 using vite.new or scaffold a Vite app locally with your preferred framework running pnpm create vite. Check out the Getting Started Guide for more information. We invite you to help us improve Vite (joining the more than 1.2K contributors to Vite Core), our dependencies, or plugins and projects in the ecosystem. Learn more at our Contributing Guide. A good way to get started is by triaging issues, reviewing PRs, sending tests PRs based on open issues, and supporting others in Discussions or Vite Land's help forum. If you have questions, join our Discord community and talk to us in the #contributing channel. Stay updated and connect with others building on top of Vite by following us on Bluesky, X, or Mastodon. Features Experimental Bundled Dev Mode Experimental support for bundled dev mode is now available. This was previously called as "Full Bundle Mode". This mode is to improve performance of huge applications that suffer from the number of modules. In our initial testing with an app loading 10,000 React components, bundled dev mode achieved around 15x faster startup and 10x faster full page reloads compared to the unbundled dev server, while keeping HMR instant regardless of the application size. Early testing on real-world applications shows similar gains: the Linear team saw cold start rendering up to 3x faster, full reloads around 40% faster, and 10x fewer network requests. Why bundled dev mode? Vite is known for its unbundled dev server approach, which is a ## Building Gigaboy, An Autonomous Software Engineering Agent Orchestrator DevFeed: [Building Gigaboy, An Autonomous Software Engineering Agent Orchestrator]() Original publisher: [Read original article]() Published: 2026-03-02T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [AI Agent](), [Software Engineering](), [Linear](), [GitHub](), [Go Language](), [pull-requests](), [PostgreSQL](), [Redis](), [function calling]() Tags: [agent](), [ai-agent](), [engineering](), [function-calling](), [github](), [go](), [linear](), [postgresql](), [redis](), [software-engineering]() ### AI overview This article introduces Gigaboy, an autonomous software engineering orchestrator that watches Linear issues, takes tickets marked Todo, explores a repository, makes code changes, opens and iterates on pull requests, and can merge approved work. It explains the limitations of chat-based coding assistants and describes using Linear as the primary interface, with Redis and PostgreSQL supporting the system. ### Source excerpt . [Building Gigaboy: An Autonomous Agent Orchestrator](building-an-ai-agent-orchestrator-cover... ## How to build an AI agent workflow with Linear, MCP, and CLAUDE.md DevFeed: [How to build an AI agent workflow with Linear, MCP, and CLAUDE.md]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-02-07T16:36:17Z Content type: tutorial Language: en Sources: [The AI Engineer]() Topics: [Claude Code](), [Model Context Protocol (MCP)](), [Code](), [Pull Request](), [Terminal]() Tags: [agent](), [agents](), [claude-code](), [code](), [context](), [developer](), [how-to](), [linear](), [markdown](), [mcp](), [project](), [review](), [terminal](), [workflow]() ### AI overview This tutorial explains how to manage AI coding tasks through a Linear board connected to Claude Code with MCP. A CLAUDE.md file defines the workflow, enabling the agent to read tickets, implement changes, run builds, review code, and open pull requests. The approach depends on clear tickets and provides status visibility as the number of tasks grows. ### Source excerpt How to build your agent control plane ## Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026 DevFeed: [Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026]() Original publisher: [Read original article]() Published: 2025-11-29T00:41:00Z Content type: opinion Language: en Sources: [Dragan Djuric]() Topics: [Clojure](), [Artificial Intelligence](), [Machine learning](), [CUDA](), [OpenCL](), [Programming](), [NumPy]() Tags: [2026](), [ai](), [ai-ml](), [algebra](), [apple](), [clojure](), [cuda](), [linear](), [matrices](), [neanderthal](), [opencl](), [programming](), [vectors]() ### AI overview The article outlines a 2026 development and funding plan for the Uncomplicate ecosystem of Clojure libraries for AI, machine learning, and high-performance computing. It describes support for Nvidia GPUs, Apple Silicon, CPUs, CUDA, OpenCL, and several planned library improvements. ### Source excerpt I've applied for Clojurists Together yearly funding in 2026. Here's my application. If you are a Clojurists Together member, and would like to see continued development in this area, your vote can help me keep working on this :) My goal with this funding in 2026 is to continuously develop Clojure AI, ML, and high-performance ecosystem of Uncomplicate libraries (Neanderhal and many more), on Nvidia GPUs, Apple Silicon, and traditional PC. In this year, I will also focus on writing tutorals on my blog and creating websites for the projects involved, which is something that I wanted for years, but didn't have time to do because I spent all time on programming. How that work will benefit the Clojure community This will highly benefit the Clojure community as this is THE AI ecosystem for Clojure, and supporting AI is arguably the main focus on probably all software platforms. Clojure has something to offer on that front, beyond just calling OpenAI API as a web service! Uncomplicate grew to quite a few libraries (of which some are quite big; just Neanderthal is 28,000 lines of highly-condensed, aggresively macroized, and reusable code): Diamond ONNX Runtime, Neanderthal, Deep Diamond, ClojureCUDA, ClojureCPP, Apple Presets, ClojureCL, Fluokitten, Bayadera, Clojure Sound, and Commons. Here's a word or two of how I hope to improve each of these libraries with Clojurists Together funding in 2026. Neanderthal (Clojure's alternative to NumPy, on steroids) In 2025, Neanderthal celebrated its 10th birthday. It started as a humble but fast matrix and vector library for Clojure, but after 10 years of relentless improvements, now it boasts a general matrix/vector/linear algebra API implemented by no less than 5(!) engines for CPUs, GPU (Nvidia CUDA), GPU (OpenCL: AMD, Intel, Nvidia), Apple Silicon (Accelerate), and general CPU (OpenBLAS). And this is not a superficial support for the sake of ticking a check box; each of these engines support much more operations on exotic structure ## Code Review with AI: Best Practices DevFeed: [Code Review with AI: Best Practices]() Original publisher: [Read original article]() Author: Daniel Moka Published: 2025-10-11T05:01:22Z Content type: article Language: en Sources: [Craft Better Software]() Topics: [Code review](), [Pull Request](), [Artificial Intelligence](), [Code](), [ide](), [YAML](), [vs-code]() Tags: [ai](), [code-review](), [ide](), [linear](), [productivity](), [pull-requests](), [quality](), [review](), [vs-code](), [yaml]() ### AI overview A practical guide to using CodeRabbit for AI-assisted code reviews, including repository context, configuration, smaller pull requests, and pre-PR reviews in an IDE. ### Source excerpt Practical tips to use the best AI-powered code review assistant ## Linear MCP Integration for AI Agents - Kevin Galligan DevFeed: [Linear MCP Integration for AI Agents - Kevin Galligan]() Original publisher: [Read original article]() Published: 2025-04-23T00:00:00Z Content type: tutorial Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Model Context Protocol (MCP)](), [Linear](), [AI Agent](), [Artificial Intelligence](), [dev-tools]() Tags: [agents](), [ai](), [ai-agent](), [ai-agents](), [authentication](), [developer-tools](), [integration](), [linear](), [mcp](), [open-source]() ### AI overview Touchlab describes an enhanced, stabilized Model Context Protocol integration for Linear that lets AI agents create detailed, context-aware issues. The article covers configuration for teams, projects, statuses, priorities, and assignees, along with authentication, error reporting, and maintenance improvements. ### Source excerpt Touchlab's enhanced Model Context Protocol (MCP) integration for Linear allows your AI agent to interact directly with Linear, streamlining workflows and saving you time. Building upon existing work, this stabilized tool will create detailed, context-aware issues with significantly less manual effort. ## Leapfrog Probing DevFeed: [Leapfrog Probing]() Original publisher: [Read original article]() Author: Jeff Preshing Published: 2016-03-14T20:24:00Z Content type: article Language: en Sources: [Jeff Preshing]() Topics: [Data structures](), [Caching]() Tags: [alternatives](), [benchmarks](), [data](), [linear](), [search](), [strategy](), [structure]() ### AI overview The article explains hash tables and compares collision-resolution strategies, focusing on open addressing, linear probing, and a new strategy called Leapfrog Probing. ### Source excerpt A hash table is a data structure that stores a set of items, each of which maps a specific key to a specific value. There are many ways to implement a hash table, but they all have one thing in common: buckets. Every hash table maintains an array of buckets somewhere, and each item belongs to exactly one bucket. To determine the bucket for a given item, you typically hash the item's key, then compute its modulus - that is, the remainder when divided by the number of buckets. For a hash table with 16 buckets, the modulus is given by the final hexadecimal digit of the hash. Inevitably, several items will end up belonging to same bucket. For simplicity, let's suppose the hash function is invertible, so that we only need to store hashed keys. A well-known strategy is to store the bucket contents in a linked list: This strategy is known as separate chaining. Separate chaining tends to be relatively slow on modern CPUs, since it requires a lot of pointer lookups. I'm more fond of open addressing, which stores all the items in the array itself: In open addressing, each cell in the array still represents a single bucket, but can actually store an item belonging to any bucket. Open addressing is more cache-friendly than separate chaining. If an item is not found in its ideal cell, it's often nearby. The drawback is that as the array becomes full, you may need to search a lot of cells before finding a particular item, depending on the probing strategy. For example, consider linear probing, the simplest probing strategy. Suppose we want to insert the item (13, "orange") into the above table, and the hash of 13 is 0x95bb7d92. Ideally, we'd store this item at index 2, the last hexadecimal digit of the hash, but that cell is already taken. Under linear probing, we find the next free cell by searching linearly, starting at the item's ideal index, and store the item there instead: As you can see, the item (13, "orange") ended up quite far from its ideal cell. Not great for lookups. ## Smoother Signatures DevFeed: [Smoother Signatures]() Original publisher: [Read original article]() Author: Square Engineering Published: 2012-07-20T16:01:00Z Content type: article Language: en Sources: [Square Corner Blog](), [Square Corner Blog RSS Feed]() Topics: [Algorithms](), [Android]() Tags: [algorithm](), [algorithms](), [android](), [caching](), [engineering](), [image](), [linear](), [points](), [touchscreen]() ### AI overview Square's Android signature rendering was improved with a better spline interpolation algorithm, variable stroke-width rendering, and bitmap caching. The article explains replacing straight-line interpolation with cubic Bezier curves to produce smoother signatures from sampled touchscreen points. ### Source excerpt Capturing even more beautiful signatures on Android.