Your AI Coding Buddy Is Always Available at 2 a.m.

05/15/25 • 20 min

Aja Hammerly, director of developer relations at Google, sees AI as the always-available coding partner developers have long wished for—especially in those late-night bursts of inspiration. In a conversation with Alex Williams at Google Cloud Next, she described AI-assisted coding as akin to having a virtual pair programmer who can fill in gaps and offer real-time support.

Hammerly urges developers to start their AI journey with tools that assist in code writing and explanation before moving into more complex AI agents. She distinguishes two types of DevEx AI: using AI to build apps and using it to eliminate developer toil. For Hammerly, this includes letting AI handle frontend work while she focuses on backend logic. The newly launched Firebase Studio exemplifies this dual approach, offering an AI-enhanced IDE with flexible tools like prototyping, code completion, and automation. Her advice? Developers should explore how AI fits into their unique workflow—because development, at its core, is deeply personal and individual.

Learn more from The New Stack about the latest AI insights with Google Cloud:

Google AI Coding Tool Now Free, With 90x Copilot’s Output

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Learn more from The New Stack about the latest AI insights with Google Cloud:

Google AI Coding Tool Now Free, With 90x Copilot’s Output

Gemini 2.5 Pro: Google’s Coding Genius Gets an Upgrade

Q&A: How Google Itself Uses Its Gemini Large Language Model

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Previous Episode

Google AI Infrastructure PM On New TPUs, Liquid Cooling and More

At Google Cloud Next '25, the company introduced Ironwood, its most advanced custom Tensor Processing Unit (TPU) to date. With 9,216 chips per pod delivering 42.5 exaflops of compute power, Ironwood doubles the performance per watt compared to its predecessor. Senior product manager Chelsie Czop explained that designing TPUs involves balancing power, thermal constraints, and interconnectivity.

Google's long-term investment in liquid cooling, now in its fourth generation, plays a key role in managing the heat generated by these powerful chips. Czop highlighted the incremental design improvements made visible through changes in the data center setup, such as liquid cooling pipe placements. Customers often ask whether to use TPUs or GPUs, but the answer depends on their specific workloads and infrastructure. Some, like Moloco, have seen a 10x performance boost by moving directly from CPUs to TPUs. However, many still use both TPUs and GPUs. As models evolve faster than hardware, Google relies on collaborations with teams like DeepMind to anticipate future needs.

Learn more from The New Stack about the latest AI infrastructure insights from Google Cloud:

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A2A, MCP, Kafka and Flink: The New Stack for AI Agents

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Next Episode

Agentic AI and A2A in 2025: From Prompts to Processes

Agentic AI represents the next phase beyond generative AI, promising systems that not only generate content but also take autonomous actions within business processes. In a conversation recorded at Google Cloud Next, Kevin Laughridge of Deloitte explains that businesses are moving from AI pilots to production-scale deployments. Agentic AI enables decision-making, reasoning, and action across complex enterprise environments, reducing the need for constant human input.

A key enabler is Google’s newly announced open Agent2Agent (A2A) protocol, which allows AI agents from different vendors to communicate and collaborate securely across platforms. Over 50 companies, including PayPal, Salesforce, and Atlassian, are already adopting it. However, deploying agentic AI at scale requires more than individual tools—it demands an AI platform with runtime frameworks, UIs, and connectors. These platforms allow enterprises to integrate agents across clouds and systems, paving the way for AI that is collaborative, adaptive, and embedded in core operations. As AI becomes foundational, developers are transitioning from coding to architecting dynamic, learning systems.

Learn more from The New Stack about the latest insights about Agent2Agent Protocol:

Google’s Agent2Agent Protocol Helps AI Agents Talk to Each Other

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