The App Store Won’t Survive the Age of Agents
When Steve Jobs launched the iPhone in 2007, there was no App Store. His plan was for developers to build web apps accessed through Safari. That lasted about a year. Developers demanded native access, and in 2008 Apple launched the App Store — bundling discovery, distribution, trust, and payment into a single controlled layer.
That bundle has generated hundreds of billions of dollars. But it was built for humans who browse, tap, and swipe. AI agents don’t do any of that. And this mismatch is about to reshape the platform economy. — Read More
Daily Archives: March 25, 2026
Beyond Analytics: The Silent Collection of Commercial Intelligence byTikTok and Meta Ad Pixels
TikTok and Meta’s tracking pixels are quietly harvesting personal data, granular checkout interactions, and detailed commerce intelligence from the websites that implement them. The collection is going far beyond what ad attribution requires, creating serious privacy compliance risks and competitive disadvantages for the businesses involved.
Jscrambler conducted a runtime analysis of the ad pixels used by TikTok and Meta on actual websites, revealing that their default behavior requires immediate attention from every organization that employs them. The analysis focused on large companies in the retail, hospitality, and healthcare sectors. However, it’s worth noting that most businesses with an online presence use these tracking pixels on their websites. — Read More
To Thrive Today, You Have to Become An Agentic Deployment Expert. But So, So Few Actually Are.
Phase one: most of 2023. You had to be technical. The models were there but they hallucinated constantly. You needed to be deeply technical to get anything useful out of a raw LLM API. Most of us — myself included — weren’t equipped. I remember being at SaaStr Annual 2023, talking with David Sacks, asking how he was thinking about AI at Craft. He said they wanted 80% of investments to be AI. I asked to see the great ones already in market. His answer: they’re all proof of concepts. We’re all in anyway. That was the right call if you were investing at the LLM layer. I wasn’t smart enough to play there, let alone deploy AI B2B agents then.
Phase two: 2024 into early 2025: the weird prompt engineer era. You could torture these tools into doing something useful, but you had to craft these elaborate, convoluted prompts that made no sense to anyone else. “Prompt engineer” became the hottest job on the planet for about a year. That job is now dead.
Phase three — which is right now — is the era where ordinarily smart generalists can make AI agents and AI tools do genuinely magical and useful things. No contorted prompts. No engineering degree. Just software deployment skills you probably already have. Some of it is the profound leap forward of Opus 4.5+. Some of it is the agentic products themselves just have gotten better. It’s both. It’s now. — Read More
Designing Agentic AI Systems
How do you build an agentic system that works? And how do you spot potential problems during development that can snowball into massive headaches for future you when they go into production?
To answer these questions, you need to break agentic systems into three parts: tools, reasoning, and action. Each layer comes with its own challenges. Mistakes in one layer can ripple through the others, causing failures in unexpected ways. Retrieval functions might pull irrelevant data. Poor reasoning can lead to incomplete or circular workflows. Actions might misfire in production.
An agentic system is only as strong as its weakest link and this guide will show you how to design systems that avoid these pitfalls. The goal: build agents that are reliable, predictable, and resilient when it matters most. Read More:
Part 1 – Architecture
Part 2 – Modularity
Part 3 – Agent 2 Agent Interactions
Part 4 – Data & RAG
Part 5 – Vectorize MCP
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