A technology entrepreneur in Sydney has helped develop an experimental personalized cancer vaccine for his dog using artificial intelligence tools and genomic analysis.

Paul Conyngham turned to AI after his dog Rosie was diagnosed with cancer in 2024. Initial treatments, including chemotherapy and surgery, failed to stop the tumors from progressing.

Conyngham, an electrical and computing engineer and co-founder of Core Intelligence Technologies, began researching alternative options. Using AI tools, he developed a strategy for identifying potential immunotherapy targets.

ChatGPT helped suggest immunotherapy approaches and pointed him toward researchers at the University of New South Wales Ramaciotti Centre for Genomics. Conyngham later worked with scientists at the university to sequence Rosie’s genome and analyze the mutations driving the cancer.

He also used AlphaFold, an AI system developed by Google DeepMind, to analyze mutated proteins and identify potential targets for treatment.

Development of a Personalized mRNA Vaccine

After analyzing the genomic data, Conyngham collaborated with researchers at UNSW’s RNA Institute to design a custom mRNA cancer vaccine tailored to Rosie’s specific tumor mutations.

The vaccine was developed in less than two months by Pall Thordarson, director of the institute and a specialist in nanomedicine and RNA technologies.

According to Thordarson, the case represents one of the first known examples of a personalized mRNA cancer vaccine created specifically for a dog.

“This is still at the frontier of where cancer immunotherapeutics are,” he said. “What Rosie is teaching us is that personalized medicine can be very effective, and done in a time-sensitive manner, with mRNA technology.”

Rosie received her first injection of the vaccine in December and a booster dose in February. Researchers reported that many of the tumors have since shrunk significantly, although some have not responded to the treatment.

Implications for AI in Medicine

While Rosie’s condition has not been fully cured, her health has improved considerably. According to Conyngham, the dog regained energy within weeks of receiving the treatment.

Researchers caution that the experimental therapy is still at an early stage and may not represent a universal solution for cancer treatment. However, the case demonstrates how AI tools can help accelerate the design of personalized therapies by analyzing genomic data and identifying potential biological targets.

The project also highlights the growing role of AI in biomedical research. Technologies such as protein-structure prediction and large language models are increasingly being used by scientists to analyze complex datasets and propose potential treatment strategies.

Some technology leaders say stories like Rosie’s illustrate how AI could transform the pace of medical discovery. As AI-assisted research tools become more widely available, researchers expect personalized medicine approaches to become faster and more accessible in both veterinary and human healthcare.

Anthropic has introduced the Claude Partner Network, a new initiative designed to support organizations helping enterprises deploy its Claude AI models. The company said it will commit an initial $100 million to the program in 2026, funding partner training, technical assistance, and joint market development.

The program targets consulting firms, professional services companies, and AI specialists that guide enterprises through the adoption of AI systems. These partners typically help businesses identify high-value use cases for AI and implement production-ready applications within complex corporate environments.

Anthropic said the initiative reflects growing enterprise demand for structured support in adopting generative AI tools.

“Anthropic is the most committed AI company in the world to the partner ecosystem—and we’re putting $100 million behind that this year to prove it,” said Steve Corfield, Anthropic’s head of global business development and partnerships. “Our partners are instrumental in getting enterprises from proof of concept to production with Claude.”

Claude is currently available across all three major cloud providers: Amazon Web Services, Google Cloud, and Microsoft Azure, allowing partners to deploy the model across multiple enterprise environments.

Training, Certification, and Implementation Support

The Claude Partner Network will provide a range of resources designed to help organizations build services around Anthropic’s AI platform.

Partners will receive access to technical training through Anthropic Academy, along with dedicated engineering support for enterprise deployments. The company plans to expand its partner-facing team significantly, providing applied AI engineers and technical architects to assist with complex customer implementations.

Anthropic is also introducing its first technical certification, called Claude Certified Architect, Foundations. The certification is designed for solution architects developing production applications using Claude models.

Additional certifications for developers, architects, and sales professionals are expected to launch later this year. Partners joining the network will receive priority access to these programs.

In addition, Anthropic has released a code modernization starter kit intended to help enterprises migrate legacy software systems using Claude’s agentic coding capabilities. The company said modernizing older codebases and reducing technical debt is one of the most common enterprise AI use cases.

Growing Demand for Enterprise AI Deployment

Anthropic said the partner network will help enterprises move from experimental AI deployments to large-scale production systems. Many organizations have begun testing AI models but require technical and operational guidance to integrate them into existing workflows.

Members of the program will gain access to Anthropic’s partner portal, which includes sales playbooks, training materials, and co-marketing resources. Qualified partners will also appear in a public services directory where enterprises can find firms experienced in implementing Claude-based solutions.

The company said the Claude Partner Network is open to any organization involved in deploying Claude AI systems, and membership will be free of charge. Applications for the program opened today.

The partner program also comes as Anthropic expands its broader research and policy efforts around advanced AI. The company recently launched the Anthropic Institute, an initiative that brings together engineers, economists, and social scientists to study the societal, economic, and governance implications of increasingly powerful AI systems.

Amazon and Cerebras Systems have announced a partnership to integrate their computing chips into a new artificial intelligence inference service hosted on Amazon Web Services (AWS). The service aims to accelerate AI workloads such as chatbots, coding assistants, and other generative AI applications.

Under the agreement, Cerebras processors will be installed inside AWS data centers and connected with Amazon’s Trainium3 custom AI chips through proprietary networking technology. The companies did not disclose the financial terms of the partnership.

Cerebras, valued at roughly $23.1 billion, has developed a unique AI chip architecture designed to compete with Nvidia’s processors. Unlike many AI accelerators, Cerebras chips do not rely on high-bandwidth memory, a costly component used in Nvidia’s flagship GPUs.

Cerebras Chief Executive Andrew Feldman said the partnership could expand access to the company’s technology through AWS’s large customer base.

“Every customer large or small is on AWS, from individual developers to the largest banks in the world,” Feldman said. “This will make it easy as a click to get on Cerebras.”

Divide-and-Conquer Inference Architecture

The joint service will focus on inference, the stage where trained AI models generate responses to user prompts. Instead of running the entire inference workload on a single chip architecture, Amazon and Cerebras plan to split the process between two specialized systems.

Amazon’s Trainium3 chips will handle the “prefill” stage, which converts user input into tokens that AI systems process. Cerebras processors will manage the “decode” stage, where the model generates output tokens to produce responses.

According to Feldman, this “divide and conquer” approach is intended to improve speed and efficiency for AI inference workloads.

The strategy reflects a broader industry trend toward specialized AI infrastructure where different processors handle distinct parts of AI pipelines to improve performance and cost efficiency.

Competition in AI Infrastructure

The partnership comes amid intensifying competition in AI hardware and infrastructure. Nvidia currently dominates the AI accelerator market, but several companies are attempting to develop alternatives to its GPU-based architecture.

Analysts expect Nvidia to unveil a similar multi-chip strategy soon. Reports indicate the company may combine its GPUs with chips from startup Groq, which Nvidia acquired in a deal valued at $17 billion late last year.

Amazon said its service, expected to launch in the second half of the year, could offer better price-performance compared with GPU-based solutions.

The company added that its Trainium chip roadmap, including the upcoming Trainium4 processor, is designed to provide an alternative to third-party AI hardware by optimizing cost and efficiency for AWS customers.