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!

If I had to keep only two Custom AI Agents in my workspace, it's these two 👇

1️⃣ Post Converter: Every time I publish a newsletter, it reads the script, rewrites it as an X post, and creates a new page in my content database automatically. 20 minutes saved every single time

2️⃣ Daily Reporter: Euns every morning and gives me yesterday's tasks progress, my good and bad habits streaks one by one, actions I missed, and compares it to the same day last week. Everything is logged in a database, so I can always go back.

Both running every day. Both actually useful — not just cool to build 🔥

Wow, what app did you use to record/edit the video?