Most teams we work with want to say their product is AI-powered. Few have asked whether their organisation is ready to support that claim. We have observed a recurring pattern where a team commits to building a machine learning system and, after months, discovers that their data isn't clean enough to train on. The infrastructure then fails to reliably serve a model. In such cases, our diagnosis has often concluded that the foundation is the problem. Machine learning readiness is a precondition, and one must begin with a straightforward question - "Do we have the data pipeline, infrastructure and cross-functional alignment to make this sustainable beyond a PoC?" That question is what prompted our first article titled "The Machine Learning Readiness Checklist". We cover everything a team needs, from data and infrastructure to the right kind of team members. All of this must be done before committing serious engineering time and budgets. If you're a founder, engineering lead or a decision maker within your organisation who wants to build things correctly, we encourage you to read the full piece here:

https://www.algorithmic.co/blogs/the-machine-learning-checklist We'll be sharing more each week. Follow along if this is relevant to what you're building.

P.S. This article is from late 2025 but more than ever relevant.

Love the vibe of this!

Hey everyone, this is my submission for the @Rive HMI Challenge: NEURAL ROBOT CONTROL.

This project explores a system where a human can remotely operate a maintenance robot inside a radioactive reactor core — an environment too dangerous for direct interaction. The robot acts as a physical extension of the operator.

The experience is built as a clear flow: connection → system sync → diagnostics → navigation → scan → operation

The AR interface serves as the main point of view, overlaying essential information directly onto the environment. It shows navigation guidance, target location, and system feedback while keeping the view minimal and unobstructed.

The robot includes a self-diagnostic HUD for maintenance teams, displaying real-time data such as battery, CPU temperature, mechanical stability, and radiation exposure. It also features a small onboard display that communicates system states like connecting, loading, warnings, errors, and movement direction.

The focus of this project is how motion communicates system behavior. Each transition reflects a state change, helping users understand what the robot is doing at any moment.

Built using State Machine, Data Binding, and Layout in Rive.

Excellent work mate! Love it