Grafana turns 10
How an open source passion project made observability open and composable to developers around the world.
How an open source passion project made observability open and composable to developers around the world.
Prompt engineering is still telling a computer what to do. Studying large language models and the limits of generative AI will keep your job security.
It’s time for the open source Rambos to stop fighting and agree that developers care more about software access and ease of use than the purity of its license.
Generative AI is helping us churn out vastly more content at remarkable speed, when what we really need is better content. It’s up to humans to put the focus on quality and value.
The biggest threats to Red Hat’s Linux market share will come from the companies that make it easiest for developers to do their jobs.
While short-sighted companies may look to AI to cut jobs and costs, smart companies will use AI to increase productivity and agility. Just as they did with open source and cloud.
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.
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.
To differentiate the many flavors of PostgreSQL, the few truly serverless offerings promise better engineering and faster development.
Sure, compared to traditional IT, Kubernetes is great, but not much will beat public cloud in the long run.
As everyone prepares to jump headlong into generative AI and large language models, cloud will continue its strong performance.
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 time to figure out how to use generative AI and large language models in your code is now.
Maybe you’re not ready to let AI write your code, but it’s quite useful for testing and analysing code.
AI generates a lot of answers and saves a lot of time, but it’s too often incomplete or untrustworthy.