Back in the ancient days of machine learning, before you could use large language models (LLMs) as foundations for tuned models, you essentially had to train every possible machine learning model on all of your data to find the best (or least bad) fit. By ancient, I mean prior to the seminal paper on the transformer neural network architecture, “Attention is all you need,” in 2017.
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Back in 2014, when the wave of containers, Kubernetes, and distributed computing was breaking over the technology industry, Torkel Ödegaard was working as a platform engineer at eBay Sweden. Like other devops pioneers, Ödegaard was grappling with the new form factor of microservices and containers and struggling to climb the steep Kubernetes operations and troubleshooting learning curve.
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“AI models currently shine at helping so-so coders get more stuff done that works in the time they have,” argues engineer David Showalter. But is that right? Showalter was responding to Santiago Valdarrama’s contention that large language models (LLMs) are untrustworthy coding assistants. Valdarrama says, “Until LLMs give us the same guarantees [as programming languages, which consistently get computers to respond to commands], they’ll be condemned to be eternal ‘cool demos,’ useless for most... Читать дальше...
