VP, Developer Relations @MongoDB. Formerly @AWScloud. Last name pronounced "Ay-see." Arsenal apologist. Regularly write for InfoWorld/TechRepublic.
Matt Asay's coverage focuses on the intersection of technology, open source, and software development. He seems to be particularly interested in AI, cloud computing, and the role of open-source software in these domains.
Given his high percentage of citations of data and expert commentary along with opinion editorials, he is likely to respond well to pitches that provide access to current research or experts who can offer unique insights into the latest trends and developments at the intersection of AI, cloud computing, and open-source technologies.
Since Matt does not have a specific geographic focus but operates out of the United States, sources from diverse locations may find relevance if they are able to contribute valuable insights into global tech trends or offer expert opinions on emerging technologies.
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Software freedom. User freedom. Why do we have to choose? Licensing hasn’t kept pace with the reality of software in the cloud and AI.
AI coding tools such as Amazon CodeWhisperer and GitHub Copilot boost the productivity of pro and novice developers alike. They’re only the beginning.
Is the complexity of billing better handled by buying software or building it? Lago offers developers a chance to get back to solving core business problems.
When the Open Source Definition isn't applied to cloud-distributed software, and the General Public License allows cloud companies to grow rich without contributing much, everyone loses.
The inherent weaknesses of large language models are reason enough to explore other technologies, such as reinforcement learning or recurrent neural networks.
Apache Airflow is a great data pipeline as code, but having most of its contributors work for Astronomer is another example of a problem with open source.
Yes, a tiny number of companies have relicensed their open source code. Let’s worry about actual problems, like security and megacompanies that contribute almost nothing.
Without skilled developers supervising AI coding assistants, they are likely to break your code rather than write it. Right now, only people can fine-tune and evaluate AI.
Take a look at this unscientific survey to discover some ways to keep developers happy. A few are even simple.
AI can’t replace bad developers because it only works for good developers. Recognizing when code from an LLM will fail requires skill and experience.
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