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Follow the cloud money

Led by CIQ, Oracle, and SUSE, the new OpenELA trade association is likely to fail without at least one major cloud vendor on board.

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When the generative AI hype fades

GenAI is a small piece of the artificial intelligence pie, not the whole pie itself. Keep paying attention to deep learning and machine learning.

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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?

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A 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.

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Testing 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.

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People 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.

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Linux 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.

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Making 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.

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What 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.

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The 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.

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KubeCon 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.

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GitHub’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?

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Reining 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.

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AI 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.

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Generative 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.

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