manage
Beyond Market Intelligence keeps manage in one place: 3 stories so far. The section currently leads with “From ad spend to asset creation, Melius raises $20M to reimagine marketing tools”, “Treat Context Like Code to Scale AI Agents With Control”, and “Run AI Models Locally with Ollama's OpenAI-Compatible Endpoint”. Melius has raised $20 million to build marketing tools that actually generate creative assets and campaigns, rather than just optimizing ad spend. Patrick Debois proposes something quietly radical: treat context like code. 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 manage story on Beyond Market Intelligence, newest first.
From ad spend to asset creation, Melius raises $20M to reimagine marketing tools
Melius has raised $20 million to build marketing tools that actually generate creative assets and campaigns, rather than just optimizing ad spend. That's a meaningful reorientation. For too long, marketers have been asked to do more with spreadsheets and dashboards. Melius is betting on creation over management. It's a bet we find compelling. For deeper coverage on how AI is reshaping practical workflows, see our related piece on building Python AI libraries that deliver.
Treat Context Like Code to Scale AI Agents With Control
Patrick Debois proposes something quietly radical: treat context like code. For engineering leaders wrestling with non-deterministic AI agents, his argument lands with clarity. Apply testing, CI/CD, package managers, and security scanning to context itself. That approach promises control without stifling innovation. It's a practical path to scaling AI agents reliably while building lasting organizational knowledge. For deeper exploration, our coverage of Amazon CloudWatch Omni shows how AI-first observability platforms are already putting similar principles into practice.
Run AI Models Locally with Ollama's OpenAI-Compatible Endpoint
Ollama runs a local HTTP server on port 11434 and hands any client an OpenAI-compatible endpoint pointed at your own machine. That's a practical shift: pulling model weights locally means you control the data, the latency, and the cost. We find this approach refreshingly direct, no cloud dependence, just a clean API you already know. For deeper context management strategies, see our related article on treating context like code to scale AI agents.