product

! Welcome back to the “EDA in Public” series! That is Part 2 of the series; when you haven’t seen Part 1 yet, read it here. Here’s a recap of what we conquered. In Part...

I-day streak on LinkedIn Games. Yes, LinkedIn also has games, and so they’ve been around for over a 12 months. Sometimes, I notice recent games, design tweaks, and recent features being rolled out. As...

For SaaS (software as a service) corporations, monitoring and managing their product data is crucial. For individuals who fail to grasp this, by the point they notice an incident. Damage is already done. For...

You call your “AI Strategy Playbook” a set of mental models that help teams align on what to construct and why. Which models most frequently unlock clarity in executive rooms, and why do they...

under uncertainty is a central concern for product teams. Decisions large and small often must be made under time pressure, despite incomplete — and potentially inaccurate — information concerning the problem and solution...

You’ve written extensively about agentic AI and frameworks like smolagents and LangGraph. What excites you most about this emerging space? I first began exploring generative AI largely out of curiosity and, admittedly, a little bit...

Meta has used artificial intelligence (AI) to automate the product risk assessment procedure that has been conducted in improving the function of the platform and modifying algorithms. In consequence, development efficiency is anticipated to...

In fashion, visuals are every little thing. But behind every product description page is data. From the cut of a hem to the colour name in a dropdown, product data dictates how items are...