DevToolsDigest: Issue #300
This week's digest includes news and resources from Applied LLMs, Mistral, and more.
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The Week in Developer Tools
The Open (Source) Question in AI
In the sitcom that is Silicon Valley, the debate surrounding artificial intelligence has reached a fever pitch, and nothing seems to divide people more strongly than the future of open-source AI. Depending on where in the valley you work, open-source is either a futile endeavor or God's greatest gift to mankind. The truth likely lies somewhere in the middle.
What We’ve Learned From A Year of Building with LLMs
Over the past year, LLMs have become “good enough” for real-world applications. Furthermore, provider APIs have made LLMs more accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. Nonetheless, while the barrier to entry for building with AI has been lowered, creating products and systems that are effective—beyond a demo—remains deceptively difficult.
Industry Research
The Technical History of Kubernetes
"We actually started work on Kubernetes back in 2013, and Kubernetes was based fairly directly on R&D that was done over the few years leading up to that. Of course, there were precursors farther back, including Linux Containers, Borg, Workqueue, and Babysitter, but I’m going to focus on efforts I was directly involved with." Read more from Brian Grant, the original lead architect of Kubernetes.
Situational Awareness: The Decade Ahead
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. There are perhaps a few hundred people that have situational awareness. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen.
Developer Venture News
Mistral AI Lands Huge $640M Round
Mistral had used a “little more than 1,000” of the high-powered graphics processing units chips needed to train AI systems and spent “just a couple of dozen millions” of euros to build products that can rival those built using much bigger budgets by some of the richest companies in the world, including OpenAI, Google and Meta. Its technology differs from OpenAI because many of its AI systems are available as “open source” software, meaning its source code is available for anybody to examine or customise.