Rethinking open source for AI
What are the license rights and restrictions for large language models? Do they cover weights and deep neural network architectures?
View ArticleA new way of thinking about open source sustainability
Go beyond paying developers to maintain the software your business depends on. Pay the companies that pay the developers and watch the whole ecosystem thrive.
View ArticleTesting the limits of generative AI
As part of the learning curve with AI and LLMs, experiment all you want, but take the results with some skepticism, especially if you’re using it to write your code.
View ArticlePeople and Python in AI
If you want to squeeze the most value from your data, teach your employees Python and Excel instead of specialised programming languages.
View ArticleLinux distros need to take more responsibility for security
Between the rapid release of open source software, and modern OSes preloaded with packages, enterprises are vulnerable to attacks they aren’t even aware of.
View ArticleMaking sure open source doesn’t fail AI
The lessons learned from cloud are spurring a proactive examination of what it means to be 'open source' in the rapidly evolving world of AI.
View ArticleWhat AI won’t replace in your programming
Generative AI is great at handling tedium and finding errors, but the expertise and intuition of programmers will always be essential.
View ArticleThe clouds can’t afford to forget developers
In the rush to AI, vendors should remember that developers have a lot of clout with IT spending. Helping developers will translate to growth at the bottom line.
View ArticleKubeCon points to the future of enterprise IT
eBPF and Cilium mature into de facto cloud infrastructure, Tetragon tackles new security domains, while OTel topples walled gardens in telemetry data.
View ArticleGitHub’s all-in bet on AI may overlook Git
Not everyone wants AI to do everything for them. Will the risk of losing transparency and visibility into code change how GitHub made collaborative coding so powerful?
View ArticleReining in the BS in AI
Large language models trained on questionable stuff online will produce more of the same. Retrieval augmented generation is one way to get closer to truth.
View ArticleAI hype isn’t helping anyone
We've got real issues with AI, and security is top of the list. Prompt injecting allows hackers to trick your private LLM, and that's a serious problem.
View ArticleGenerative AI is off to a rough start
As the big vendors overstate the capabilities of their generative AI projects, maybe it’s time to use open source to keep them honest.
View ArticleThe biggest bottleneck in large language models
Rate limits mean everyone is waiting for better compute resources or different generative AI models.
View ArticleIs AI making our code stupid?
AI-generated code has transformed software development forever. That’s not necessarily good. A solid review process can shrink bloat and attack surfaces.
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