Matt Asay
Contributor
Matt Asay runs developer relations at MongoDB. Previously. Asay was a Principal at Amazon Web Services and Head of Developer Ecosystem for Adobe. Prior to Adobe, Asay held a range of roles at open source companies: VP of business development, marketing, and community at MongoDB; VP of business development at real-time analytics company Nodeable (acquired by Appcelerator); VP of business development and interim CEO at mobile HTML5 start-up Strobe (acquired by Facebook); COO at Canonical, the Ubuntu Linux company; and head of the Americas at Alfresco, a content management startup. Asay is an emeritus board member of the Open Source Initiative (OSI) and holds a J.D. from Stanford, where he focused on open source and other IP licensing issues.
Red Hat ends the RHEL clones’ free lunch
Red Hat is forcing companies to choose a successor to CentOS Linux. Think carefully about the foundation of your infrastructure and who will support it long-term.
Why isn’t Apple talking about AI?
Apple has been innovating with AI for a long time, but it focuses on the magic of the user experience, not the tech. There's a lesson here, especially since GenAI isn't always the right tool.
How Grafana made observability accessible
Now 10 years old, the open source passion project that made observability open and composable continues to simplify life for developers.
Serverless is the future of PostgreSQL
To differentiate the many flavors of PostgreSQL, the few truly serverless offerings promise better engineering and faster development.
ChatGPT’s parasitic machine
What do ChatGPT and other large language models owe to the human creators who provide the information they train on? What if creators stop making their insights publicly available?
Are large language models wrong for coding?
When the goal is accuracy, consistency, mastering a game, or finding the one right answer, reinforcement learning models beat generative AI.
Kubernetes costs less, but less than what?
Sure, compared to traditional IT, Kubernetes is great, but not much will beat public cloud in the long run.
Somehow OpenSearch has succeeded
The Elasticsearch fork from AWS stands as proof that the company has committed to contributing to open source.
The era of cloud optimization is upon us
As everyone prepares to jump headlong into generative AI and large language models, cloud will continue its strong performance.
Amazon’s quiet open source revolution
After years of getting a free ride from open source projects, the company is developing its own obsession with contributing.
Large language models are the new cloud battleground
Perhaps the biggest thing since open source or Google, LLMs may have companies fighting for supremacy, but it’s the developers who come out ahead.
The AI singularity is here
The time to figure out how to use generative AI and large language models in your code is now.
If you want a career in AI, learn Python
Skills with artificial intelligence, machine learning, and large language models are very much in demand across a variety of industries.
AI and the future of software development
Maybe you’re not ready to let AI write your code, but it’s quite useful for testing and analyzing code.
Docker’s bad week
Instead of focusing on the poorly communicated decision to sunset Free Teams, look at the company’s overall direction to focus on what developers want.
The problem with development speed
Instead of focusing on output, think about increasing testing and research and being willing to scrap projects that don't seem likely to succeed.
Companies can’t stop using open source
Even though open source can be more expensive than proprietary software, the time savings and ability to free up developers to innovate are worth it.
Embrace and extend Excel for AI data prep
Combining machine learning and Excel can get you the data transformation you need while data scientists are scarce.
AI still requires human expertise
AI generates a lot of answers and saves a lot of time, but it’s too often incomplete or untrustworthy.