Recent Posts
Hands-On Agentic AI for *Product Leaders*
What happens when 52 Silicon Valley builders, executives, and product leaders spend 90 minutes actually building AI agents, no code required. A recap of TEAMCAL AI's sold-out Palo Alto workshop.
AI Is Having Its||*HTML Moment*
Every decade or so, a new primitive rewires the whole stack. In 1993 it was the hyperlink. Now it is the prompt. And if history is any guide, we are only at the beginning of a very long wave.
Hands on Agentic AI and LLMs for Product Leaders — Let's Build Something
In Part 1 of this bootcamp series, we covered the big ideas behind Generative AI, LLMs, and Agentic AI. We talked about what these technologies are, why they matter, and how forward-thinking companies are already using them to move faster.
AI Isn’t Just for Chatbots — It’s Changing How Physical Products Are Built
AI is transforming manufacturing by helping factories learn from the massive amounts of data generated on production lines. By spotting patterns and detecting issues earlier, AI improves efficiency, reduces defects, and prevents costly downtime. The result is a shift from reactive problem-solving to proactive, smarter production.
Understanding the AI Market: The Tools Everyone Is Racing to Master
This text serves as a strategic breakdown of the modern AI landscape, advocating for a shift from using a single chatbot to building a "composed intelligence" stack. By categorizing ChatGPT as the versatile everyday assistant, Gemini as the integrated operations layer for Google Workspace, and Claude as the precision tool for deep engineering, it argues that a user's competitive advantage no longer comes from finding the "best" model, but from knowing exactly which specialized ecosystem to deploy for a given task. Ultimately, the piece positions AI as the new foundational infrastructure for developers and founders, where the fastest builders are those who can seamlessly orchestrate these different layers of intelligence to eliminate workflow friction.
From Sigmoid to Attention: Why Transformers Changed Text Classification
Imagine you are building a system that reads short movie reviews and answers one yes/no question: "Is this review positive?" This is called binary classification. In this article we will walk through one single review, first with an older, simple neural-network approach, and then with a transformer approach. You will see the same final step at the end (a sigmoid that produces a probability), but you will also see why transformers are so useful before that final step.
Agentic AI Isn’t Just Better Prompting — It’s Better Context
Agentic AI shifts the focus from crafting better prompts to designing better context, where models operate in multi-step loops that use tools, memory, and state to make decisions. While prompt engineering improves individual responses, context engineering determines what information the model sees and how it reasons over time.
