Spotify's best developers have stopped writing code manually since December and now rely on an internal AI system called Honk that enables remote, real-time code deployment through Claude Code, the company's co-CEO Gustav Soderstrom said during a fourth-quarter earnings call this week. Engineers can fix bugs or add features to the iOS app from Slack on their phones during their morning commute and receive a new version of the app pushed to Slack before arriving at the office. The system has helped Spotify ship more than 50 new features throughout 2025, including AI-powered Prompted Playlists, Page Match for audiobooks, and About This Song. Soderstrom credited the system with speeding up coding and deployment tremendously and called it "just the beginning" for AI development at Spotify. The company is building a unique music dataset that differs from factual resources like Wikipedia because music-related questions often lack single correct answers -- workout music preferences vary from American hip-hop to Scandinavian heavy metal.

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Amazon engineers have been pushing back against internal policies that steer them toward Kiro, the company's in-house AI coding assistant, and away from Anthropic's Claude Code for production work, according to a Business Insider report based on internal messages. About 1,500 employees endorsed the formal adoption of Claude Code in one internal forum thread, and some pointed out the awkwardness of being asked to sell the tool through AWS's Bedrock platform while not being permitted to use it themselves. Kiro runs on Anthropic's Claude models but uses Amazon's own tooling, and the company says roughly 70% of its software engineers used it at least once in January. Amazon says there is no explicit ban on Claude Code but applies stricter requirements for production use.

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About 4% of all public commits on GitHub are now being authored by Anthropic's Claude Code, a terminal-native AI coding agent that has quickly become the centerpiece of a broader argument that software engineering is being fundamentally reshaped by AI. SemiAnalysis, a semiconductor and AI research firm, published a report on Friday projecting that figure will climb past 20% by the end of 2026. Claude Code is a command-line tool that reads codebases, plans multi-step tasks and executes them autonomously. Anthropic's quarterly revenue additions have overtaken OpenAI's, according to SemiAnalysis's internal economic model, and the firm believes Anthropic's growth is now constrained primarily by available compute. Accenture has signed on to train 30,000 professionals on Claude, the largest enterprise deployment so far, targeting financial services, life sciences, healthcare and the public sector. On January 12, Anthropic launched Cowork, a desktop-oriented extension of the same agent architecture -- four engineers built it in 10 days, and most of the code was written by Claude Code itself.

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Amid a push toward AI agents, with both Anthropic and OpenAI shipping multi-agent tools this week, Anthropic is more than ready to show off some of its more daring AI coding experiments. But as usual with claims of AI-related achievement, you'll find some key caveats ahead.

On Thursday, Anthropic researcher Nicholas Carlini published a blog post describing how he set 16 instances of the company's Claude Opus 4.6 AI model loose on a shared codebase with minimal supervision, tasking them with building a C compiler from scratch.

Over two weeks and nearly 2,000 Claude Code sessions costing about $20,000 in API fees, the AI model agents reportedly produced a 100,000-line Rust-based compiler capable of building a bootable Linux 6.9 kernel on x86, ARM, and RISC-V architectures.

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Today, OpenAI announced GPT-5.3-Codex, a new version of its frontier coding model that will be available via the command line, IDE extension, web interface, and the new macOS desktop app. (No API access yet, but it's coming.)

GPT-5.3-Codex outperforms GPT-5.2-Codex and GPT-5.2 in SWE-Bench Pro, Terminal-Bench 2.0, and other benchmarks, according to the company's testing.

There are already a few headlines out there saying "Codex built itself," but let's reality-check that, as that's an overstatement. The domains OpenAI described using it for here are similar to the ones you see in some other enterprise software development firms now: managing deployments, debugging, and handling test results and evaluations. There is no claim here that GPT-5.3-Codex built itself.

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Today, OpenAI launched a macOS desktop app for Codex, its large language model-based coding tool that was previously used through a command line interface (CLI) on the web or inside an integrated development environment (IDE) via extensions.

By launching a desktop app, OpenAI is catching up to Anthropic's popular Claude Code, which already offered a macOS version. Whether the desktop app makes sense compared to the existing interfaces depends a little bit on who you are and how you intend to use it.

The Codex macOS app aims to make it easier to manage multiple coding agents in tandem, sometimes with parallel tasks running over several hours—the company argues that neither the CLI nor the IDE extensions are ideal interfaces for that.

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Software developers have spent the past two years watching AI coding tools evolve from advanced autocomplete into something that can, in some cases, build entire applications from a text prompt. Tools like Anthropic's Claude Code and OpenAI's Codex can now work on software projects for hours at a time, writing code, running tests, and, with human supervision, fixing bugs. OpenAI says it now uses Codex to build Codex itself, and the company recently published technical details about how the tool works under the hood. It has caused many to wonder: Is this just more AI industry hype, or are things actually different this time?

To find out, Ars reached out to several professional developers on Bluesky to ask how they feel about these tools in practice, and the responses revealed a workforce that largely agrees the technology works, but remains divided on whether that's entirely good news. It's a small sample size that was self-selected by those who wanted to participate, but their views are still instructive as working professionals in the space.

David Hagerty, a developer who works on point-of-sale systems, told Ars Technica up front that he is skeptical of the marketing. "All of the AI companies are hyping up the capabilities so much," he said. "Don't get me wrong—LLMs are revolutionary and will have an immense impact, but don't expect them to ever write the next great American novel or anything. It's not how they work."

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