The Convergence of Data, AI, and Agents: Are You Prepared?

Subscribe • Previous Issues Autonomous Agents are Here. What Does It Mean for Your Data? By Ciro Greco and Ben Lorica. A striking factoid emerges from Anthropic’s latest Economic Index report: directive AI usage, where users delegate complete tasks to Claude, has surged from 27% to 39% on Claude.ai in just eight months.  Among API customers buildingContinue reading “The Convergence of Data, AI, and Agents: Are You Prepared?”

Escaping Prototype Purgatory: A Playbook for AI Teams

We’re living through a peculiar moment in AI development. On one hand, the demos are spectacular: agents that reason and plan with apparent ease, models that compose original songs from a text prompt, and research tools that produce detailed reports in minutes. Yet many AI teams find themselves trapped in “prototype purgatory,” where impressive proofs-of-conceptContinue reading “Escaping Prototype Purgatory: A Playbook for AI Teams”

Think Smaller: The Counterintuitive Path to AI Adoption

Subscribe • Previous Issues Escaping Prototype Purgatory: A Playbook for AI Teams We’re living through a peculiar moment in AI development. On one hand, the demos are spectacular: agents that reason and plan with apparent ease, models that compose original songs from a text prompt, and research tools that produce detailed reports in minutes. Yet many AIContinue reading “Think Smaller: The Counterintuitive Path to AI Adoption”

A Better Way to Build and Refine Agents

Modern AI applications have evolved far beyond single models. Many systems orchestrate multiple specialized agents — planners that decompose tasks, extractors that gather data, generators that create content — all coordinating through external tools and APIs. This architectural shift creates a fundamental optimization problem: the entire workflow becomes non-differentiable, making traditional gradient-based training methods impossibleContinue reading “A Better Way to Build and Refine Agents”

Beyond RL: A New Paradigm for Agent Optimization

Subscribe • Previous Issues A Better Way to Build and Refine Agents Modern AI applications have evolved far beyond single models. Many systems orchestrate multiple specialized agents — planners that decompose tasks, extractors that gather data, generators that create content — all coordinating through external tools and APIs. This architectural shift creates a fundamental optimization problem: theContinue reading “Beyond RL: A New Paradigm for Agent Optimization”

A Tiered Approach to AI: The New Playbook for Agents and Workflows

A Small Language Model (SLM) is a neural model defined by its low parameter count, typically in the single-digit to low-tens of billions. These models trade broad, general-purpose capability for significant gains in efficiency, cost, and privacy, making them ideal for specialized tasks. While I’ve been cautiously testing SLMs, their practical value is becoming clearer.Continue reading “A Tiered Approach to AI: The New Playbook for Agents and Workflows”

Top 10 Open-Source Projects in the Large Model Ecosystem

This leaderboard ranks the ten most influential open-source projects in the AI development ecosystem using OpenRank, a metric that measures community collaboration rather than simple popularity indicators like stars. The list spans the entire technology stack, from foundational infrastructure such as PyTorch for training and Ray for distributed compute, to high-performance inference engines like vLLM,Continue reading “Top 10 Open-Source Projects in the Large Model Ecosystem”

Is your LLM overkill?

Subscribe • Previous Issues A Tiered Approach to AI: The New Playbook for Agents and Workflows A Small Language Model (SLM) is a neural model defined by its low parameter count, typically in the single-digit to low-tens of billions. These models trade broad, general-purpose capability for significant gains in efficiency, cost, and privacy, making them ideal forContinue reading “Is your LLM overkill?”

Rethinking Databases for the Age of Autonomous Agents

As the AI community buzzes with the potential of autonomous agents, I’ve been pondering a less glamorous but critical question: what does this mean for our data infrastructure? We are designing intelligent, autonomous systems on top of databases built for predictable, human-driven interactions. What happens when software that writes software also provisions and manages itsContinue reading “Rethinking Databases for the Age of Autonomous Agents”

Why Your Database Can’t Handle the Coming Agent Swarm

Subscribe • Previous Issues Rethinking Databases for the Age of Autonomous Agents As the AI community buzzes with the potential of autonomous agents, I’ve been pondering a less glamorous but critical question: what does this mean for our data infrastructure? We are designing intelligent, autonomous systems on top of databases built for predictable, human-driven interactions. What happensContinue reading “Why Your Database Can’t Handle the Coming Agent Swarm”