This paper presents a multi-agent AI architecture that achieves 92.1% reliability by organizing specialized AI agents into teams with opposing roles and hierarchical oversight, similar to corporate organizational …
GEPA introduces a novel prompt optimization approach that uses natural language reflection and Pareto-based evolutionary search to optimize compound AI systems, achieving superior performance compared to reinforcement …
A randomized controlled study examining how AI assistance affects skill formation in software development, finding that while AI can improve productivity, it significantly impairs conceptual understanding, code reading, …
Stripe Launches Tempo: A New L1 Blockchain for Stablecoin Infrastructure Stripe has entered the blockchain space with Tempo, a Layer 1 blockchain designed specifically for stablecoin payments and real-world business …
Engineering Effective AI Evaluations: Lessons from Production LLM Deployments Building robust AI applications requires more than good prompts—it demands systematic evaluation frameworks that enable rapid iteration and …
The Bitter Lesson: Why Computational Scale Beats Human Knowledge in AI Seventy years of AI research reveals a fundamental truth: general computational methods ultimately outperform approaches based on human knowledge. …
Constraint Solvers: The Overlooked Solution to Complex Programming Problems Many challenging algorithmic problems that stump developers in interviews and real-world scenarios become surprisingly manageable when …
AI Agents for Economic Research: A Comprehensive Guide to Building Autonomous Research Systems AI agents represent a transformative leap from traditional chatbots to autonomous systems that plan, use tools, and execute …
Building ChatGPT: A Comprehensive Guide to Large Language Models When you type a question into ChatGPT and press enter, what exactly happens behind that text box? This guide takes you through the entire pipeline of how …
Building Character-Level Language Models: From Bigrams to GPT-2 Character-level language models predict the next character in a sequence by learning patterns from training data. This tutorial builds these models …
Disciplined AI Software Development: A Structured Methodology for AI-Human Collaboration AI-assisted development often produces bloated code, architectural inconsistencies, and context drift across sessions. The …
The Mathematics Behind Large Language Models: A Physics Perspective Physics training provides an unexpected advantage for understanding Large Language Models. The mathematical foundations that seemed irrelevant in …
Defeasibility in Answer Set Programs with Defaults and Argumentation Rules Defeasible reasoning allows logical axioms to be true “by default” while permitting other conflicting axioms to undercut or negate …
