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We cover 17 best practices for optimizing cost with Pinecone, specifically for the newcomers to vector databases as target. These practices will save you potentially tens of thousands of dollars. The advice is grouped into four buckets: 1) general tips, 2) application-level best practices, 3) infrastructure-level best practices, as well as 4) advice specific to the paid tier.

Stanford University's release of the Alpaca model - which basically showed that an inferior foundational LLM can mimic a superior model cheaply - has massive business and science implications. Though "self-instruct" - i.e. the pattern of fine-tuning models with auto-generated demonstrations/training data - is nothing new, Stanford showed that it could be done at the scale of LLMs, and that it only costs $600! If Stanford's results are valid (and that's a big "if"), here are the major implications.

Is AI adoption actually beneficial for tech stocks? While enthusiasm for AI has piqued investor interest in tech stocks again, i the long-term impact of AI on tech valuations may not be as positive as initially thought. In this article, we'll explore why AI adoption may actually be bearish for the average tech stock, particularly for IaaS, PaaS, and SaaS companies. We'll delve into the risks associated with rapid AI advance, such as execution risk, reduced cash flow visibility, and a shift in data and compute gravity. Additionally, we'll discuss how the rise of AI may reduce the demand for white-collar workers, leading to fewer seats and less revenue for SaaS companies. Ultimately, while the impact of AI on tech valuations may not be immediate, strategic mishaps will inevitably lead to re-pricings over multiple earnings cycles.

Are you interested in AI but feeling overwhelmed by the technical jargon? Learn the foundational ideas behind ChatGPT, GPT3, and Large Language Models (LLMs) in plain English. Discover how these models generate text and the "secret sauce" that makes them so powerful, and how InstructGPT model differs from GPT3.