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# Tavily Search Results: Geneva **Query:** AI coding agents updates — Claude Code, OpenAI Codex, Cursor, GitHub Copilot, Gemini Code Assist **Topic:** general **Results:** 5 **Date:** 2026-04-21 14:08:52 --- ## 1. Using AI Coding Agents: Claude Code vs. Copilot CLI vs. Cursor – A Practical Guide **URL:** [https://www.linkedin.com/pulse/using-ai-coding-agents-claude-code-vs-copilot-cli-cursor-rahmanian-a3uzc](https://www.linkedin.com/pulse/using-ai-coding-agents-claude-code-vs-copilot-cli-cursor-rahmanian-a3uzc) **Score:** 1.0000 ### Summary Cursor Rules: Project-specific prompt templates (.cursor/rules) that always apply (e.g. “Always use company style guide” or “When I mention ‘the repo’, use URL X”). Like Claude’s CLAUDE.md, these baked-in instructions ensure consistency. Memory Banks: Cursor supports a “memory bank” (via npx cursor-bank init). This creates a /memory\_bank with markdown files (project overview, tech context, architecture patterns, etc.). The agent uses these as persistent context. This parallels Claude’s memory concept and GitHub’s memory, but is open-source/community-driven. [...] Article content ### Conclusion AI coding assistants have matured into specialized tools. There is no single “best” – it’s about fit. Claude Code is a powerful terminal agent with rules, hooks, and SDK to script sophisticated workflows. Cursor offers the best day-to-day IDE experience with project memory and plan/act discipline. GitHub Copilot CLI is a GitHub-native ally with seamless PR integration and robust session management. In practice, savvy teams often combine them: e.g. write in Cursor, validate with Copilot, and offload heavy automation to Claude. [...] Use SDK or Automation for Repetitive Workflows: If you find yourself repeating “inspect repo, apply change, run tests”, consider scripting it. The Claude Agent SDK can turn that into a programmatic agent. For example, a custom agent could take a Jira ticket ID, fetch the issue, open the repo, and attempt a fix (see pseudo-code below). This is like having a 24/7 junior engineer. Combine Tools for Strengths: Write code in your IDE (Cursor) for fluid editing. Then do a second review pass in Copilot CLI or GitHub (for PR context and memory). Claude Code is great for complex refactors or infra (e.g. updating config across services). Cross-check AI outputs: if Cursor or Copilot flags a bug in Claude’s code, fix it, and vice versa. This “cross-review” often catches issues. ### Full Content Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s [User Agreement](/legal/user-agreement?trk=linkedin-tc_auth-button_user-agreement), [Privacy Policy](/legal/privacy-policy?trk=linkedin-tc_auth-button_privacy-policy), and [Cookie Policy](/legal/cookie-policy?trk=linkedin-tc_auth-button_cookie-policy). ![]() ## Sign in to view more content Create your free account or sign in to continue your search or New to LinkedIn? [Join now](https://www.linkedin.com/signup/cold-join?session_redirect=%2Fpulse%2Fusing-ai-coding-agents-claude-code-vs-copilot-cli-cursor-rahmanian-a3uzc&trk=pulse-article_contextual-sign-in-modal_join-link) By clicking Continue to join or sign in, you agree to LinkedIn’s [User Agreement](/legal/user-agreement?trk=linkedin-tc_auth-button_user-agreement), [Privacy Policy](/legal/privacy-policy?trk=linkedin-tc_auth-button_privacy-policy), and [Cookie Policy](/legal/cookie-policy?trk=linkedin-tc_auth-button_cookie-policy). LinkedIn LinkedIn is better on the app Don’t have the app? Get it in the Microsoft Store. ![]()  # Using AI Coding Agents: Claude Code vs. Copilot CLI vs. Cursor – A Practical Guide ![Meysam Rahmanian]() ### Meysam Rahmanian Executive Summary: AI coding assistants are maturing rapidly. Claude Code (Anthropic) is a terminal-first coding agent with powerful automation (hooks, monitors, MCP support) and explicit project memory ([CLAUDE.md](https://www.linkedin.com/redir/redirect?url=http%3A%2F%2FCLAUDE%2Emd&urlhash=onXc&trk=article-ssr-frontend-pulse_little-text-block)). GitHub Copilot CLI is a GitHub-centered CLI agent with “virtually infinite” session memory (thanks to automatic context compaction and checkpoints) and new persistent Copilot Memory for repos. Cursor is an IDE-focused assistant offering smooth editing, rich context rules, and community “Memory Banks” (project-level .md files). Each tool has trade-offs: larger context windows cost more, session “compaction” can drop detail, and persistent memory features vary. We map tasks (debugging, exploration, PR review, refactors, long infra tasks) to the best tool. We also share 12 practical tips (prompting, monitoring, tests, documentation, combining tools, etc.) and examples (e.g. Claude hook JSON, agent SDK pseudo-code, Copilot CLI commands, Cursor plan/act refactor). This guide synthesizes documentation, expert insights (e.g. Scott Hanselman) and user experience to help developers and tech leaders choose and use these tools effectively. ### Claude Code: Agentic Coding with Hooks, Monitors, SDK, and Memory Claude Code (Anthropic) is designed as an “autonomous coding agent”. It operates in your terminal or IDE, reads the repo context, edits files, runs commands/tests, and remembers project rules. Key features: * Hooks: User-defined scripts or commands triggered on agent events (e.g. PreToolUse, PostToolUse). Hooks enforce deterministic actions – for example, automatically running a formatter after any file edit or linting changed files. Hooks can also block dangerous commands (e.g. a “PreToolUse” hook that denies rm -rf on critical folders). Hooks ensure repeatable workflows. Example hook (JSON) below. * Monitors: Long-running watchers. Claude’s /monitor tool can tail logs, watch file changes, poll CI or APIs in the background, and alert you (e.g. notify on a test failure). Unlike hooks (event-driven), monitors continuously observe state and trigger on changes. * Memory & Rules: Claude uses [CLAUDE.md](https://www.linkedin.com/redir/redirect?url=http%3A%2F%2FCLAUDE%2Emd&urlhash=onXc&trk=article-ssr-frontend-pulse_little-text-block) or .claude/ rule files for project instructions. You can write memory or guidelines in markdown (similar to a knowledge base). On session start or lazy load, Claude injects these into context. This explicit memory system helps the agent remember tech stack, architecture notes, or coding standards. * Agent SDK: Anthropic provides a Claude Agent SDK (Python/TypeScript) for custom agents. You can programmatically orchestrate multi-step tasks (inspect repos, edit, test, summarize). For example, a Python snippet below shows using the SDK to query Claude on a bug report, run commands, and collect the answer. The SDK supports custom tools (via in-process MCP servers) and hook functions, letting you build an internal “assistant-as-a-service” or Slack bot. In short, Claude Code shines for deep, multi-step engineering tasks, local automation, and projects where you want tight control over tools and environment. ### GitHub Copilot CLI: GitHub-Centric, Long Sessions, and Persistent Memory GitHub Copilot CLI brings the Copilot AI experience into the termin *[Content truncated...]* --- ## 2. Claude Code: AI Coding Assistant Explained | CodingNomads **URL:** [https://codingnomads.com/claude-code-overview](https://codingnomads.com/claude-code-overview) **Score:** 0.9999 ### Summary The agentic loop at the heart of Claude Code. Unlike autocomplete tools that react once to your cursor, Claude Code iterates through this cycle until the task is genuinely complete. Let's start with the distinction that matters most. Tools like GitHub Copilot and Codeium are fundamentally predictive. They watch what you're typing and suggest the next token, line, or block. That's useful. But it's also bounded: the tool reacts to your cursor position and a slice of your open file. [...] ## Summary Claude Code is an agentic coding assistant: it plans, executes, observes, and iterates rather than just predicting the next token. It differs from autocomplete tools like GitHub Copilot by operating over entire workflows, not just cursor context. Core capabilities include codebase exploration, multi-file editing, git integration, and CI/CD automation. It runs across five surfaces: terminal CLI, IDE extensions (VS Code, JetBrains), desktop app, web/iOS, and Slack. Pricing tiers range from Pro ($20/month) to Enterprise, with an API pay-as-you-go option for teams and pipelines. The same `CLAUDE.md` configuration and settings work across all deployment surfaces. Enterprise users can deploy through Amazon Bedrock or Google Cloud Vertex AI. [...] Multi-file editing. Real features span files. Claude Code can update an API endpoint, adjust the corresponding TypeScript types, add a test, and update the OpenAPI spec in a single task. Git integration. It stages changes, writes commit messages, creates branches, and opens pull requests. This isn't a gimmick. In CI, you can point it at a GitHub issue and get a PR back. CI/CD automation. You can run Claude Code headlessly in GitHub Actions or GitLab CI/CD. It can do nightly dependency audits, analyze test failures, or auto-triage issues. No human in the loop required. ### Full Content 1. Welcome to Claude Code: What It Is and Why It Matters Lesson # What Is Claude Code? AI Coding Assistant Overview Copy Link LinkedIn Message Facebook Email X 15 min to complete · By Ryan Desmond ## Contents 1. [Introduction](#intro) 2. [What Is Claude Code? The Agentic Coding Assistant Explained](#what-is-claude-code-the-agentic-coding-assistant-explained) 3. [The Core Idea: Agentic vs. Autocomplete](#the-core-idea-agentic-vs-autocomplete) 4. [What Claude Code Can Actually Do](#what-claude-code-can-actually-do) 5. [The Five Deployment Surfaces](#the-five-deployment-surfaces) 6. [Pricing: Choosing the Right Plan](#pricing-choosing-the-right-plan) 7. [How This Compares to Simpler Tools](#how-this-compares-to-simpler-tools) 8. [Our Running Example: HealthTracker](#our-running-example-healthtracker) 9. [What's Coming Next](#whats-coming-next) 10. [Summary](#summary) ## What Is Claude Code? The Agentic Coding Assistant Explained Welcome to the course! If you've been hearing about AI coding assistants and wondering whether they're actually useful or just autocomplete with better PR, you're in exactly the right place. Claude Code is Anthropic's agentic coding assistant, and by the end of this lesson you'll understand what makes it categorically different from the AI tools you may have already tried, where you can run it, and whether it fits your situation. ## The Core Idea: Agentic vs. Autocomplete  The agentic loop at the heart of Claude Code. Unlike autocomplete tools that react once to your cursor, Claude Code iterates through this cycle until the task is genuinely complete. Let's start with the distinction that matters most. Tools like GitHub Copilot and Codeium are fundamentally *predictive*. They watch what you're typing and suggest the next token, line, or block. That's useful. But it's also bounded: the tool reacts to your cursor position and a slice of your open file. Claude Code works differently. It's *agentic*, meaning it plans a sequence of actions, executes them, observes the results, and adjusts. When you ask it to fix a failing test, it doesn't just edit the test file. It reads the test output, traces the failure through the call stack, edits the implementation, reruns the tests, and confirms the fix actually worked. That loop (gather context, take action, verify results) is the engine behind everything Claude Code does. We'll dig into the mechanics in the next lesson, but knowing the loop exists is enough to frame everything else here.  Anthropic's approach to agentic AI is shaped by their research into safe, controllable systems. Claude Code's permission modes (covered in the next lesson) are a direct result of that work, giving you fine-grained control over what the agent can and cannot do autonomously. ## What Claude Code Can Actually Do Most people's mental model of an AI coding assistant is "it writes functions for me." Claude Code does that, but the more interesting capabilities are the ones that replace entire workflows. **Codebase exploration.** Drop Claude Code into an unfamiliar repo and ask it to explain the architecture. It uses agentic search to read file trees, trace imports, and build a coherent picture without you manually selecting which files to include. **Multi-file editing.** Real features span files. Claude Code can update an API endpoint, adjust the corresponding TypeScript types, add a test, and update the OpenAPI spec in a single task. **Git integration.** It stages changes, writes commit messages, creates branches, and opens pull requests. This isn't a gimmick. In CI, you can point it at a GitHub issue and get a PR back. **CI/CD automation.** You can run Claude Code headlessly in GitHub Actions or GitLab CI/CD. It can do nightly dependency audits, analyze test failures, or auto-triage issues. No human in the loop required. ``` # Example: pipe recent logs into Claude Code for anomaly analysis tail -200 app.log | claude -p "Slack me if you see any anomalies" # Example: automate translations in CI claude -p "translate new strings into French and raise a PR for review" ``` Those two lines represent hours of work that would otherwise fall on a developer. That's the real value here. ## The Five Deployment Surfaces  All five Claude Code deployment surfaces share the same underlying engine and CLAUDE.md configuration, so your project context and settings follow you wherever you work. Claude Code isn't one thi *[Content truncated...]* --- ## 3. The 10 Best Vibe Coding Tools in 2026: Our Choices **URL:** [https://roadmap.sh/vibe-coding/best-tools](https://roadmap.sh/vibe-coding/best-tools) **Score:** 0.9998 ### Summary AI tool Category Best for Pros Cons Claude Code Terminal AI agent Developers who work on complex codebases Deep codebase context, agent mode, high code quality Terminal-only, steep learning curve Gemini CLI Terminal AI agent Developers using the Google ecosystem Generous free tier, 1M token context window Newer AI coding tool, smaller community OpenAI Codex AI coding assistants Developers who build apps with ChatGPT Parallel tasks, isolated cloud execution Less IDE-native than Cursor Cursor AI-assisted editor Developers on existing code bases Multi-file editing, model flexibility, and strong support for existing code bases Costs can rise fast on large projects [...] Skill level and tool category When choosing a tool for vibe coding, the first thing to consider is who will be using it. The tools fall into two categories, as I mentioned earlier: app builders (Lovable, Replit, v0 by Vercel) and AI coding assistants (Cursor, Claude Code, Windsurf AI, GitHub Copilot, Gemini CLI, OpenAI Codex). AI coding assistants work inside your existing workflow as code editors, plugins, or terminal agents. App builders are great for people without a coding background, such as non-developers, founders, and product teams. [...] The vibe coding workflow with Claude Code starts with a natural-language request. It can work from high-level instructions without step-by-step guidance. You give it an initial prompt like "Build me a user dashboard with dark mode," and Claude Code starts building from there. For developers who prefer a visual editor, Claude Code offers a VS Code extension. It adds a workspace to your IDE that provides visual feedback on changes and lets you review and approve before anything runs. Image 4: Claude Code Features Agent loop to gather context, edit files, and run tests for verification. Plan mode to research code and outline changes before you edit files. Dual-model power using Opus 4.6 for deep reasoning and Sonnet 4.6 for fast tasks. ### Full Content # The 10 Best Vibe Coding Tools in 2026: Our Choices We value your privacy We use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking "Accept All", you consent to our use of cookies. Customise Reject All Accept All Customise Consent Preferences We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below. 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It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and se *[Content truncated...]* --- ## 4. 1 Introduction - arXiv **URL:** [https://arxiv.org/html/2604.17460v1](https://arxiv.org/html/2604.17460v1) **Score:** 0.9998 ### Summary A complementary line of work has emerged in the open-source community. Everything Claude Code (Affaan, 2025) provides a production-oriented configuration system comprising 16 specialized agents, 65+ skills, and 40+ commands that encode best practices for working with Claude Code effectively. OpenClaw (OpenClaw Contributors, 2025) takes a broader approach, offering an open-source personal AI assistant platform with extensible skills and multi-channel integration. Superpowers (Vincent, 2025) provides a structured workflow framework (brainstorming, planning, TDD, code review) that runs across multiple agentic tools, including Claude Code, Cursor, Codex, and Gemini CLI, demonstrating that structured agentic skill frameworks can be tool-agnostic. These projects collectively demonstrate the [...] These categories are converging: Copilot has added agent mode with autonomous multi-file editing and terminal access; Cursor has evolved from a chat-based assistant to a full agentic coding environment; and a growing number of IDEs, including Amazon Kiro and Google’s Antigravity, are shipping agentic capabilities as core features. As agentic features become standard across tools, the need for pedagogical frameworks that teach compositional tool mastery grows correspondingly. An agentic system with dozens of interacting features cannot be learned through casual use alone, regardless of whether it runs in a terminal, an IDE, or the cloud. Our curriculum framework addresses this need; the current implementation targets Claude Code, but the pedagogical architecture is designed to be [...] #### Agentic coding tools. Claude Code (Anthropic, 2025a) and OpenAI Codex (OpenAI, 2025) shifted the developer’s role from driver to reviewer. These tools operate autonomously, planning multi-step tasks, executing shell commands, reading and writing files, managing git workflows, and iterating on their own output. Claude Code exposes an extensible architecture of hooks, skills, subagents, MCP servers, and evaluation frameworks that compose into complex workflows. #### Convergence. ### Full Content ##### Report GitHub Issue Content selection saved. Describe the issue below:  Agentic Education: Using Claude Code to Teach Claude Code Zain Naboulsi Principal AI Engineer, Sparq zain.naboulsi@teamsparq.com ![[Uncaptioned image]](2604.17460v1/figures/sparq-logo.png) Keywords: AI coding assistants, Claude Code, curriculum design, prompt engineering, auto-updating curricula, gradual release of responsibility, adaptive learning, agentic coding tools, self-efficacy ## 1 Introduction The landscape of AI coding tools has expanded across several distinct categories in a short span. Early tools like Tabnine and Kite offered AI-powered code completion using smaller models, and GitHub Copilot (GitHub, [2022](#bib.bib12); Chen et al., [2021](#bib.bib8)) brought LLM-based completion to mainstream adoption. Cursor (Cursor, Inc., [2024](#bib.bib10)) extended this with codebase-aware context and chat-based interaction within a full IDE. More recently, *agentic coding tools* such as Claude Code (Anthropic, [2025a](#bib.bib3)) and OpenAI Codex (OpenAI, [2025](#bib.bib20)) introduced a different paradigm: they operate autonomously in terminal or cloud environments, planning multi-step tasks, executing shell commands, reading and writing files, and iterating on their own output (Wang et al., [2024](#bib.bib27)). These categories are converging, with Copilot adding agent mode and Cursor gaining agentic capabilities, making the need for pedagogical frameworks for agentic tools increasingly broad. Developers face a widening gap between the availability of powerful AI agents and the learning pathways needed to use them effectively. The prevailing approach to learning AI coding tools is ad hoc and, critically, perishable. Official documentation describes features in isolation without progressive learning paths. Video tutorials and blog posts cover narrow use cases but date quickly. Agentic tools ship breaking changes on a rapid cadence measured in days. We observed nominally current third-party tutorials referencing deprecated or modified features within days of publication. Even vendor-produced training courses, including Anthropic’s own course catalog (Anthropic, [2025b](#bib.bib4)), can lag behind the tool’s shipping pace, leaving learners to reconcile instructional materials with a product that has already moved on. None of these resources provides a progressive, hands-on curriculum that takes a developer from first contact through advanced multi-agent orchestration, and none addresses the *content decay* problem inherent in rapidly evolving tooling. This gap likely limits adoption of features that require compositional understanding, such as hook pipelines, skill systems, and subagent architectures. We present cc-self-train, a curriculum framework designed to address this gap. While the current implementation targets Claude Code, a terminal-based agentic coding tool, the underlying pedagogical architecture (persona progression, unified curricula, step-pacing, and auto-updating design) is designed to generalize to any agentic environment, whether the tool is a coding assistant, a research agent, or a domain-specific AI workflow, and whether it operates in a terminal, an IDE, or the cloud. The curriculum currently comprises 50 module files organized as 10 progressive modules across 5 project domains. Rather than describing features abstractly, each module teaches Claude Code capabilities through the construction of a real software project, such as a portfolio website, a CLI toolkit, an API gateway, a code analyzer, or the learner’s own existing project. While the curriculum provides sequenced scaffolding, controlling *what* is taught and *when*, the actual learning experience is exploratory and agent-driven. The learner is not clicking through slides or following a fixed script; they are conversing with an AI agent that responds to their specific project, questions, and mistakes, making each path through the material unique. The curriculum’s pedagogical design draws on the author’s 18 years of experience as a Microsoft Certified Trainer. The curriculum is designed to stay current: when a learner begins, the onboarding agent checks whether its teaching materials match the learner’s installed tool version and updates them if needed. This addresses the content decay problem at the point of use rather than relying on manual maintenance cycles. #### Contributions. This paper makes the following contributions: A persona progression model (Section [4](#S4 "4 Persona Progression Model")) that adapts instructor tone and scaffolding across four stages, mapping the Gradual Release of Responsibility (GRR) framework (Fisher and Frey, [2013](#bib.bib11)) to AI-mediated instruction. An adaptive learning system (Section [5](#S5 "5 Adaptive Learning System")) that observes learner engagement quality through hook-based heuristics and adjusts scaffoldin *[Content truncated...]* --- ## 5. OpenAI Codex gains desktop control to rival Claude Code | The Tech Buzz **URL:** [https://www.techbuzz.ai/articles/openai-codex-gains-desktop-control-to-rival-claude-code](https://www.techbuzz.ai/articles/openai-codex-gains-desktop-control-to-rival-claude-code) **Score:** 0.9994 ### Summary OpenAI just dropped a major Codex update that lets its coding agent take control of your desktop apps, setting up a direct collision with Anthropic's Claude Code. The new capabilities - including background app operation, multi-agent workflows, image generation, and persistent memory - signal OpenAI's determination to dominate the rapidly heating agentic coding assistant market. For developers who've watched these AI coding tools evolve from autocomplete helpers to full development partners, this marks another leap toward truly autonomous software creation. [...] The real winners might be developers who stay platform-agnostic, using whichever tool best fits each specific task. Codex for complex multi-step workflows requiring desktop control, Claude for safety-critical code that needs extra scrutiny, GitHub Copilot for quick autocomplete - the future looks increasingly like a multi-tool approach rather than loyalty to any single vendor. What we're watching unfold is the formation of a new software category: agentic development environments where AI doesn't just suggest code but actively participates in building, testing, and deploying applications. The companies that win this race will define how the next generation of software gets created. [...] OpenAI is making its boldest move yet in the agentic coding wars. The company just unveiled a sweeping Codex upgrade that brings desktop app control, multi-agent orchestration, and persistent memory to its development platform - features that put it in direct competition with Anthropic's Claude Code. Codex can now operate desktop applications on your computer, OpenAI announced in a blog post detailing the updates. The system works in the background, so it won't interrupt your workflow in other apps, and multiple agents can run simultaneously. "This is helpful for testing and iterating on frontend changes, testing apps, or working in apps that don't expose an API," the company explains. ### Full Content OpenAI Codex gains desktop control to rival Claude Code | The Tech Buzz [## the tech buzz](/) OpenAI Codex gains desktop control to rival Claude Code [AI](/topics/ai)/[developer tools](/topics/developer-tools) # OpenAI Codex gains desktop control to rival Claude Code OpenAI upgrades Codex with computer control and multi-agent work, escalating war with Anthropic by [The Tech Buzz](/authors/the-tech-buzz) PUBLISHED: Thu, Apr 16, 2026, 5:53 PM UTC| UPDATED: Thu, Apr 16, 2026, 5:53 PM UTC [Add as a preferred source on Google](https://www.google.com/preferences/source?q=techbuzz.ai)  * ■ [OpenAI](https://openai.com) launches desktop app control for Codex, enabling it to operate macOS apps in the background while multiple agents work in parallel * ■ New features include image generation, persistent memory across sessions, and the ability to test apps without API access - targeting developer workflows [Anthropic](https://anthropic.com) currently dominates * ■ The update positions Codex as a direct competitor to Claude Code's computer use capabilities, escalating the AI coding assistant arms race * ■ Desktop control starts rolling out to Codex app users signed into macOS, with broader availability expected in coming weeks [OpenAI](https://openai.com) just dropped a major Codex update that lets its coding agent take control of your desktop apps, setting up a direct collision with [Anthropic's](https://anthropic.com) Claude Code. The new capabilities - including background app operation, multi-agent workflows, image generation, and persistent memory - signal OpenAI's determination to dominate the rapidly heating agentic coding assistant market. For developers who've watched these AI coding tools evolve from autocomplete helpers to full development partners, this marks another leap toward truly autonomous software creation. [OpenAI](https://openai.com) is making its boldest move yet in the agentic coding wars. The company just unveiled a sweeping Codex upgrade that brings desktop app control, multi-agent orchestration, and persistent memory to its development platform - features that put it in direct competition with [Anthropic's](https://anthropic.com) Claude Code. Codex can now operate desktop applications on your computer, [OpenAI announced](https://www.theverge.com/ai-artificial-intelligence/913034/openai-codex-updates-use-macos) in a blog post detailing the updates. The system works in the background, so it won't interrupt your workflow in other apps, and multiple agents can run simultaneously. "This is helpful for testing and iterating on frontend changes, testing apps, or working in apps that don't expose an API," the company explains. The timing isn't coincidental. [Anthropic](https://anthropic.com) has been aggressively promoting Claude's computer use capabilities, positioning itself as the go-to solution for developers who need AI that can actually interact with software interfaces. Now OpenAI is firing back with features that match - and in some cases exceed - what Claude offers. What makes this particularly potent is the multi-agent architecture. While one Codex instance tests frontend changes in a browser, another could simultaneously verify backend API responses or run security checks in a terminal window. That parallel processing capability transforms Codex from a coding assistant into something closer to an entire development team working in concert. Advertisement The desktop control feature starts rolling out to Codex desktop app users on macOS, according to [The Verge's reporting](https://www.theverge.com/ai-artificial-intelligence/913034/openai-codex-updates-use-macos). OpenAI hasn't specified an exact timeline for broader availability, but the phased rollout suggests they're testing stability before opening the floodgates. Beyond computer control, OpenAI packed this update with features that address real developer pain points. Codex now generates images on demand - useful when you need placeholder graphics or want to visualize UI concepts without leaving your coding environment. More critically, it remembers context from past sessions, eliminating the frustrating need to re-explain your project architecture every time you start a new conversation. That persistent memory capability hints at where this technology is headed. Today's coding assistants lose context between sessions, forcing developers to repeatedly provide background information. A Codex that remembers your codebase structure, coding preferences, and project goals becomes exponentially more useful - and more competitive with human developers. The feature set reveals OpenAI's strategy: build a coding agent that handles the full development lifecycle, not just code generation. By controlling desktop apps, C *[Content truncated...]* ---