Matt Asay is a Vice President of Developer Marketing at MongoDB. With a focus on AI and machine learning, software development, and data management, Asay explores themes such as data architecture, MLOps, and generative AI. He is featured in Channel Asia, InfoWorld, CIO Online, TechRepublic, and Oracle.
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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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When a client overpaid for AI compute and then had nowhere to resell it, I realized the cloud resale market had quietly disappeared.
Open source software gives us a paradigm for open-weight models, reducing dependence on the model providers and expanding what application companies can build.
Even hardware companies like Nvidia know they can sell more products to more people if they give software developers what they want.
Install Python package dependencies without the packages, learn the ins and outs of making standalone Python apps, and install Python on Windows the easy way.
AI makes it too easy to create slide decks, reports, blog posts, and software that nobody cares about and nobody needs. Use it when it matters.
Windows 3.1, modems, CompuServe, AOL, and bulletin board systems made writing software fun. You could even earn a little profit.
Retrieval-augmented generation lets a model consult your data, but a learning loop lets the system learn from it. That makes open-weight models a very big deal.
Open weights benefit vendors seeking disruption, companies that don't need expensive frontier models, and any business that doesn't want to be locked in to any particular provider.
You know what else makes coding mistakes, writes bugs, and never writes the same code twice when prompted? Humans. We know how to deal with it.
We want to give agents enough authority to be useful but with strong guardrails to curb the damage they can do. New financial limits from AWS are a good start.
