primitives
Beyond Market Intelligence keeps primitives in one place: 3 stories so far. The section currently leads with “Three Primitives to Build Your Future with Harness Engineering”, “Orchestrate AI Agents: Google Open-Sources AX for Enhanced Efficiency”, and “From Python to weights: a compiler proves what transformers can express”. Harness engineering isn't another buzzword, it's a practical framework for building systems that actually work at scale. Google has open-sourced AX, an orchestrator designed to keep autonomous AI agents from idling away resources. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every primitives story on Beyond Market Intelligence, newest first.
Three Primitives to Build Your Future with Harness Engineering
Harness engineering isn't another buzzword, it's a practical framework for building systems that actually work at scale. This hands-on introduction breaks down three essential primitives that can future-proof how you approach data workflows. No fluff, just the fundamentals. If you're tired of brittle pipelines and want something that adapts, start here. And for a deeper look at how these concepts play out in real teams, explore our piece on coordinating AI agents across your business.
Orchestrate AI Agents: Google Open-Sources AX for Enhanced Efficiency
Google has open-sourced AX, an orchestrator designed to keep autonomous AI agents from idling away resources. By treating agents as stateful actors on its Agent Substrate runtime, AX suspends tasks during downtime and resumes them on demand, trimming latency without sacrificing performance. The control plane's Kubernetes-style primitives make managing agent workloads feel familiar, not foreign. It's a practical step toward efficient, scalable AI operations.
From Python to weights: a compiler proves what transformers can express
Most people assume a transformer's capabilities come from training. Rob's compiler challenges that assumption directly. He built a tool that takes a computation graph written in ordinary Python and outputs the weights for a standard Phi-3 model, loadable in vanilla Hugging Face with zero training involved. It's a clever way to isolate what these architectures can express, separate from what they can learn. For a deeper look at how he constructs these proofs, the write-up is worth your time.