In this tutorial, we build a universal long-term memory layer for AI agents using Mem0, OpenAI models, and ChromaDB. We design a system that can extract structured memories from natural conversations, store them semantically, retrieve them intelligently, and integrate them directly into personalized agent responses. We move beyond simple chat history and implement persistent, user-scoped memory with full CRUD control, semantic search, multi-user isolation, and custom configuration. Finally, we construct a production-ready memory-augmented agent architecture that demonstrates how […]

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Database Functions Changed How I Write Backend Code (A Beginner’s Guide) TL;DR: Database functions let you move certain logic from your application code into the database itself. This guide explains what database functions are, why they matter for backend development, and how to write practical PostgreSQL functions you can start using immediately. What Is a Database Function? A database function is a named, reusable block of code that lives inside your database. You define it once and call […]

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arXiv:2603.05522v1 Announce Type: new Abstract: Recent advances in vision language models (VLMs) have shown strong potential for spatial reasoning and 3D scene layout generation from open-ended language instructions. However, generating layouts that are not only semantically coherent but also feasible for interaction by embodied agents remains challenging, particularly in physically constrained indoor environments. In this paper, RoboLayout is introduced as an extension of LayoutVLM that augments the original framework with agent-aware reasoning and improved optimization stability. RoboLayout integrates […]

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arXiv:2405.00081v5 Announce Type: replace-cross Abstract: We introduce the concept of an imprecise Markov semigroup (mathbf Q). It is a tool that allows us to represent ambiguity around both the transition probabilities and the invariant measure of a continuous-time Markov process via a collection of Markov semigroups, each associated with a (possibly different) Markov process. We use techniques from topology, geometry, and probability to analyze ergodic limits under model uncertainty encoded by (mathbf Q). We establish long-term bounds that […]

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Anthropic published a lawyer-related plugin on 30 January, and within four to five days, software stocks had lost billions of dollars. The pace of the collapse left everyone shocked, as the whole industry had been operating on a fundamental misperception of how the AI economy works. Why Everyone Thought They Were Safe The narrative that dominated the technological field held that AI companies would build baseline models, which would then serve as a foundation for software companies to […]

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We maintain an open-source catalog of cloud GPU offerings (skypilot-catalog, Apache 2.0). It auto-fetches pricing from 20+ cloud APIs every 7 hours. We made it browsable – 50 GPU models, 2K+ offerings, on-demand and spot pricing, historical trends. A few other GPU comparison tools already use our catalog as their data source. Figured we’d make the raw data visible to everyone. submitted by /u/Shot-Patience-9874 [link] [comments]

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In this article, we interview Fyodor Soikin from CollegeVine – an online platform that connects high school students with college admissions guidance and mentorship. We talk about CollegeVine’s use of Haskell and PureScript in production, as well as how they have managed to integrate these languages with a Rails backend. Interview with Fyodor Soikin Could you give our readers a brief introduction to CollegeVine and your role there? CollegeVine is an online platform that offers college admissions guidance […]

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arXiv:2604.07486v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as a powerful tool for synthetic data generation. A particularly important use case is producing synthetic replicas of private text, which requires carefully balancing privacy and utility. We propose Realistic and Privacy-Preserving Synthetic Data Generation (RPSG), which leverages privacy-preserving mechanisms, including formal differential privacy (DP); and private seeds, in particular text containing personal information, to generate realistic synthetic data. Comprehensive experiments against state-of-the-art private synthetic data generation […]

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We study the algorithmic task of testably learning general Massart halfspaces under the Gaussian distribution. In the testable learning setting, the aim is the design of a tester-learner pair satisfying the following properties: (1) if the tester accepts, the learner outputs a hypothesis and a certificate that it achieves near-optimal error, and (2) it is highly unlikely that the tester rejects if the data satisfies the underlying assumptions. Our main result is the first testable learning algorithm for […]

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