5 Free Tools to Experiment with LLMs in Your Browser
Discover five free tools that let you run and test large language models directly in your browser without any setup.
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Prompt Compression for LLM Generation Optimization and Cost Reduction
Large language models (LLMs) are mainly trained to generate text responses to user queries or prompts, with complex reasoning under the hood that not only involves language generation by predicting each next token in the output sequence, but also entails a deep understanding of the linguistic patterns surrounding the user input text.
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Insights on The Benevolent Nature of Capitalism
By the “benevolent nature of capitalism,” I mean the fact that it promotes human life and well-being and does so for everyone.
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Harness hits $5.5B valuation with $240M raise to automate AI’s ‘after-code’ gap
Goldman Sachs has led Harness’s Series E round, with participation from IVP, Menlo Ventures, and Unusual Ventures.
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How to Climb the Hidden Career Ladder of Data Science
The behaviors that get you promoted The post How to Climb the Hidden Career Ladder of Data Science appeared first on Towards Data Science.
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A Coding Implementation of a Complete Hierarchical Bayesian Regression Workflow in NumPyro Using JAX-Powered Inference and Posterior Predictive Analysis
In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a probabilistic model that captures both global patterns and group-level variations. Through each snippet, we set up inference using NUTS, analyze posterior distributions, and perform posterior predictive checks to understand how well our model captures the underlying structure. By approaching the tutorial step by step, we build an intuitive […]
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How to design LLMs that don’t need prompt engineering
Standard LLMs rely on prompt engineering to fix problems (hallucinations, poor response, missing information) that come from issues in the backend architecture. If the backend (corpus processing) is properly built from the ground up, it is possible to offer a full, comprehensive answer to a meaningful prompt, without the need for multiple prompts, rewording your query, having to go through a chat session, or prompt engineering. In this article, I explain how to do it, focusing on enterprise […]
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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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16 Google AI tips for stress-free holiday hosting in 2025
Use Gemini, Search, Pixel and more to make holiday planning feel effortless in 2025.
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