A New Era for the Digital Resale Market
Technology and clearer regulation are finally making it possible for companies to earn a share of every resale.
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The Complete Guide to Using Pydantic for Validating LLM Outputs
Large language models generate text, not structured data.
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The cost of thinking
Large language models (LLMs) like ChatGPT can write an essay or plan a menu almost instantly. But until recently, it was also easy to stump them. The models, which rely on language patterns to respond to users’ queries, often failed at math problems and were not good at complex reasoning. Suddenly, however, they’ve gotten a lot better at these things. A new generation of LLMs known as reasoning models are being trained to solve complex problems. Like humans, […]
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Does the Fourth Amendment require a garage? The Moses Case
How an “automobile” can become a talisman before which the Fourth Amendment fades. The post Does the Fourth Amendment require a garage? The Moses Case appeared first on Downsize DC.
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Being Adaptable Isn’t Enough. You Have to Demonstrate It.
Even the most capable leaders can unintentionally signal rigidity or complacency.
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Zohran Mamdani’s “Democratic Socialist” Plans For New York City
New York’s future does not lie in further centralization or state control. Its vitality has always derived from individual freedom, entrepreneurial energy, and the rule of law. The Big Apple became great because it allowed people to build, innovate, and prosper—not because government directed them.
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Differences between transformer-based AI and the new generation of AI models
I frequently refer to OpenAI and the likes as LLM 1.0, by contrast to our xLLM architecture that I present as LLM 2.0. Over time, I received a lot of questions. Here I address the main differentiators. First, xLLM is a no-Blackbox, secure, auditable, double-distilled agentic LLM/RAG for trustworthy Enterprise AI, using 10,000 fewer (multi-)tokens, no vector database but Python-native, fast nested hashes in its original version, and no transformer to generate the structured output to a prompt. […]
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How AI Cuts Costs and Adds Value for Data Science Workflows (Sponsored)
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Researchers discover a shortcoming that makes LLMs less reliable
Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study. Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks. The researchers found that models can mistakenly link certain sentence patterns to specific topics, so an LLM might give a convincing answer by recognizing familiar phrasing instead of understanding the […]
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