WordPress.com maker Automattic is expanding into education with a new, free product designed for teachers and their classes called WordPress.com Education. The suite for classrooms includes a full WordPress.com domain for each student, plus free domain names (on the .blog or .art domains) and plug-in support.

Teachers can provide students with access to the program for free for the first year, without having to put a credit card down or enter a trial.

This allows teachers to use the technology in courses that teach students how to build websites, or for other classroom needs such as group projects that incorporate website-building.

Unveiling the product at its annual WordCamp US conference on Monday, the company noted that the new Student plan isn’t a stripped-down version of its product. It includes 6 GB of storage, backups, staging sites, and other tools, as well as support for plugins, SFTP/SSH, phpMyAdmin, and Studio Sync.

Automattic’s best known for its website and blog hosting service, WordPress.com, which runs the open-source WordPress software that powers about 43% of all sites on the Internet. Despite its ubiquity, though, WordPress is not necessarily the go-to platform of choice these days for young people establishing their web presence for the first time. Instead, those users often opt to simply set up social media profiles.

More importantly for Automattic, the student plan will let WordPress be integrated into the classroom to train the next generation of website professionals. And, because it’s free, it can make inroads in underfunded schools as well.

When the first year of use has ended, students can choose to subscribe to the service for $2 per month ($24/year). If they don’t, their work won’t disappear; their site will just drop down to a free WordPress.com site on a free domain. All their web pages, posts, comments, and uploaded media will stay intact.

The company says it piloted the program with 5,000 students across 27 countries, and the majority of educators (88.9%) said access to the program improved their students’ employability. A further 81.5% apparently said the program improved students’ entrepreneurial capacity.

The impact of AI on software development has been both profound and ever-evolving. Last year, I wrote about AMD’s plans to use AI not just for generating new lines of code, but also for other steps in the software development lifecycle (SDLC), such as triaging problems, debugging code, and testing the software. At the time, we were hoping for a 25 percent productivity boost from AI use over the course of two or three years.

But with each new release, the capabilities of Large Language Models (LLMs) improve dramatically—accelerating software development, increasing the quality of AI-generated code, and fundamentally reshaping how software is engineered. Now, just one year later, we have surpassed our productivity target, achieving a 30 percent overall productivity boost through AI. On top of that, we are rethinking not only how we use AI within the SDLC, but the structure of the SDLC itself.

We believe that the biggest AI revolution in software engineering is still ahead. So far, we have largely been teaching AI how we perform tasks and asking it to mimic existing workflows. In many ways, this constrains AI to human patterns of thinking. The next transformation will come from collaborative swarms of AI agents capable of discovering solutions independently.

Agents of today

AMD began developing AI systems for code generation, testing automation, bug analysis, and code review in 2024. At the time, our objective was to achieve 25 percent AI-generated production code by 2027 while gradually automating larger portions of the SDLC.

Measuring productivity is inherently challenging, but from the outset we have consistently tracked one objective metric: the percentage of source code generated by AI. Importantly, we count only code that passes all reviews and testing and is ultimately included in the final product. While AI-generated code is certainly not the only contributor to productivity gains, it is one of the few metrics that can be measured objectively and consistently.

By this metric, we have crossed the 20 percent mark at the beginning of this year and are now progressing toward 50 percent across entire codebase. In some software components, more than 80 percent of the code is now generated using AI.

Agentic AI has enabled us to include AI in every step of the lifecycle: For code analysis and triage, agents are trained to analyze problem reports, identify and group similar requests, and highlight which code snippets are likely to need modification. For debugging and code generation, agents are directed to analyze a bug request and implement required code changes. For testing, the agents generate unit tests, and if those are passed, identify necessary integration and product-level tests. And finally, for the approval and release stage, agents prepare architecture summary, code change review and full test results for engineers’ review and approval and if approved, integrate the changes into the next release.

Agents of tomorrow

Today, engineers create AI agents in their own image: they teach AI what they know about the system, how they would fix an issue, and how they would implement a change. This is already a major technological advancement. Engineers can create multiple “AI versions” of themselves, allowing these agents to work in parallel, scaling their expertise far beyond the limits of individual productivity. The limitation, however, is that these AI agents are still constrained by human thinking and human-defined approaches.

AMD

We believe the next major transformation in software engineering will occur when collaborative AI agent swarms can independently identify and develop solutions, guided by humans on what to solve rather than constrained by human assumptions about how the job should be done. Instead of providing detailed instructions on how to solve a problem, engineers will define the issue, the desired outcome and the quality, performance, and system constraints allowing AI agents to determine the optimal path to a solution.

A swarm of AI agents will then work in parallel to generate, evaluate, and refine multiple solution approaches. These agents will automatically validate correctness, measure performance, test trade-offs, and compare alternative implementations against defined success criteria. Finally, AI agents will prepare ranked solution options, along with validation results and performance metrics, for engineer review and approval. The agents won’t be enhancing each step of the SDLC—they will be rewriting the SLDC themselves.

To get to this point, we need to change how agents are trained. Today, improvement occurs one engineer and one agent at a time: an engineer reviews the output, refines the prompt, and repeats the process. To scale beyond this model, agents must continuously learn from one another, reuse successful strategies, and improve collaboratively across projects and teams.

We are already moving in this direction by using multi-agent workflows extensively through agentic harnesses, such as Codex and Claude Code, while simultaneously developing our own internal multi-agent systems to support the next generation of AI-driven software engineering.

A good example is our AI-driven effort to resolve issues in our Radeon Software eXperience (RSX). RSX is a user interface component that allows users to configure and monitor graphics driver behavior. In October 2025, we began using AI agents to automatically debug and fix reported RSX issues. Out-of-the-box AI tools delivered limited results, resolving only 6% of issues.

The percentage of software issues fixed automatically by AI agents in AMD’s Radeon Software eXperience (RSX) has been growing steadily, reaching 75 percent in June 2026.

As we analyzed failures and identified ways to improve, we built a learning loop—initially a largely manual process—to understand where the agents were falling short and how to improve them. Rather than retraining the underlying models, we refined the objectives given to the agents, allowing them to iteratively explore multiple approaches, evaluate the results against defined success criteria, and converge on better solutions. At the same time, advances in models and agent runtimes further increased effectiveness. Together, these improvements significantly increased our resolution rate from 6 percent to more than 75 percent of RSX issues resolved by agentic loop.

To make agents and agent swarms truly productive, we need a continuous learning loop that feeds errors and human interventions back into future agent workflows. The opportunity is to engineer this loop around clear, measurable goals. Each cycle captures new insights, making the entire AI engineering workflow smarter and more effective. Over time, this self-reinforcing loop—not just the underlying model—will become a key driver of AI progress.

The evolving role of human engineers

At AMD, we view AI as a means of increasing productivity, improving quality, and enabling employees to focus on higher-value work. Our goal is to empower our workforce with AI, not to reduce headcount.

To support this transformation, we are investing heavily in AI education and training across the company. The way we work is evolving rapidly, and we want every AMD employee to be prepared to leverage AI confidently, responsibly, and effectively.

As AI agents continue to improve, engineers will spend less time manually implementing solutions, focusing more on defining specifications, validating outcomes, and making the strategic decisions that drive innovation.

Uber is investing in, and partnering with, drone delivery company Zipline with the goal of making one million deliveries per day using the startup’s drones by the end of 2029.

Zipline drones will make the first deliveries on the Uber Eats platform by the end of this year, the companies said on Monday. These deliveries will start in Zipline’s existing markets, and the companies want to expand into “dozens of U.S. cities.”

The companies didn’t disclose the investment amount.

Uber has been taking on multiple drone delivery partners as it looks for ways to keep growing Uber Eats. The ride-hail giant is replicating the early business model it’s adopted for robotaxis and other services built around autonomous vehicles, which is to essentially bring as many companies on to its platform as possible.

This approach has helped Uber stay at the forefront of these new technologies despite selling off its own programs like the aerial ride-sharing service, Uber Elevate, and Uber Autonomous Technologies Group, which was working on autonomous vehicles. Investments have been a huge part of the strategy, with Uber committing more than $10 billion to dozens of autonomous vehicle providers.

The strategy is not a panacea, though. Uber recently clashed with one of its highest-profile partners so far, Waymo, and the companies are now expected to walk away from each other when their contracts expire in 2028. Uber and Waymo are also on different sides of a growing fight over autonomous vehicle regulation.

The ride-hail giant had tested the waters of drone delivery when it still had its Elevate division. The company dipped back into the idea late last year when it announced a partnership with Israeli startup Flytrex, which also came with a minor investment.

Uber thinks Zipline’s drones can fulfill orders on Uber Eats within five to ten minutes. “Truly quick commerce is proving to be an even bigger market than the original food market was,” Uber CEO Dara Khosrowshahi told the Wall Street Journal in an interview. “We think this can be an enormous tailwind for the next leg of growth for Eats.”

Zipline, based in San Francisco, recently closed an extended Series H funding round of $800 million, pushing its valuation to $7.6 billion.

“Every great transportation revolution has changed where people live, how businesses operate, and how economies grow,” Zipline co-founder Keller Cliffton said in a statement. “Together with Uber, we’re taking the next step toward building a world where getting what you need is as fast and effortless as sending a text, no matter where you are.”