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When I first had the idea for this post, I wanted to provide a collection of actionable ways to handle errors caused by API rate limits in your applications. But as it turns out, it’s not that straightforward (is it ever?). API rate limiting is a minefield, and at the time of writing, there are no published standards in terms of how to build and consume APIs that implement rate limiting.

Splunk’s recent update to its Machine Learning Toolkit (MLTK) is a good reason to spend a few paragraphs thinking through the links between Observability and machine learning. First, let us quickly review what Observability and machine learning are.

An LLM hallucination occurs when a large language model (LLM) generates a response that is either factually incorrect, nonsensical, or disconnected from the input prompt. Hallucinations are a byproduct of the probabilistic nature of language models, which generate responses based on patterns learned from vast datasets rather than factual understanding.

If one story stands out in the tech industry for 2023, it was the coming-out party for generative AI. OpenAI’s ChatGPT artificial intelligence chatbot, which was publicly released late in 2022, seized the industry’s imagination in 2023, becoming the fastest-growing consumer application ever. The technology quickly evolved throughout 2023 with a paid subscription service; an API; iOS and Android versions; an enterprise version; plug-ins; and an AI image generator.

In this second article of the “Practical Zephyr” series, we’ll explore the kernel configuration system Kconfig by looking at the printk logging option in Zephyr. We won’t explore the logging service as such in detail but instead use it as an excuse to dive deep into Kconfig. Finally, we’ll create our own little application-specific Kconfig configuration. Like Interrupt? Subscribe to get our latest posts straight to your inbox.