CDOQ

At the last Figma conference , two Google employees presented their updated design principles.  One of classic design principles that most designers follow, including those at Google, is "form follows function." Google has now updated this to "form follows feelings." The change can be observed in the latest Google product releases. The relevant employees feel good about releasing the products that do not function well. 😀 

People don’t buy inferior products just because they are good for the environment.

With a career spanning T-Mobile, National Semiconductor, Facebook, and multiple hi-tech startups, I have firsthand experience in taking innovative ideas from conception to market success, generating billions of dollars and reaching hundreds of millions of users. Aditionally, I have advised hundreds of startups, gaining valuable insights into common pitfalls and effective strategies. Whether building AI-driven enterprise solutions, climate-tech B2B products, or consumer e-commerce mobile apps, the MVP (Minimum Viable Product) approach and the post-MVP journey have been instrumental in reducing risk and accelerating development. Let's explore how you can leverage the MVP framework to not only speed up your development process but also create products that resonate deeply with your users at scale. Minimum Viable Product (MVP) is a methodology to reduce risk and increase speed in development. The concept has been around for over 20 years. MVP is a milestone in the new product developm

Immature leaders tend to solve hypothetical problems at the expense of real problems. 

The excitement around Artificial Intelligence (AI) is contagious. Ever Curious Corporation, a startup I co-founded in 2015, was based on AI. A lot has changed since then. One of the most significant technical achievements has been the development of Transformer technology , which enabled the creation of Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer). I’d like to take a moment to offer a cursory explanation of how LLMs work.  In simple terms, when you ask an LLM a question, it understands the question using the Transformer model, deep neural networks , and other methods for context, syntax, and semantics. It then responds based on this understanding. Before LLMs can be useful to humans, they have to be trained. There are three main techniques for training and enhancing the performance of an LLM: Fine-tuning: This process involves further training a pre-trained LLM on a specific dataset to improve its performance in particular tasks or domains. Humans often r

Practical self-interest often prevails over abstract notions of identity.

Eventually, substance outshines the sparkle.