A few months ago, I joined a team building two AI-agent products.
My first week, I opened a Jupyter notebook out of habit. Then I closed it. There was no training set, no features to engineer, no model.fit(X_train, y_train) waiting to be called. The agents orchestrated foundation models. The “intelligence” came from a model someone else trained. The entire codebase was TypeScript. No notebooks, no model, no Python. The toolbox I’d spent years filling was, on its surface, irrelevant.
So what, exactly, was I supposed to do?
The answer turned out to be hiding in a simple framework.
Every AI agent has three layers. The foundation model provides raw intelligence. The engineering provides the body: tools, APIs, orchestration, and product surfaces. But the behavior of the agent – what it actually does when a user shows up – is shaped by the context, prompts, policies, schemas, and guardrails that surround the model. That’s the brain of the system. Not the neural network itself, but the cognitive architecture built on top of it.
Someone needs to own the quality of that brain; to make it legible, to understand its failure modes, measure its consistency, map its weaknesses, and create the feedback loops that systematically make it smarter. That someone, it turns out, is the data scientist. Not as a model trainer, but as the team’s methodologist. — Read More
Recent Updates Page 37
Future Casting the Modern Data Stack
After writing an article a few years ago called “Big Data is Dead,” it feels a bit clichéd to call things “dead.” So I won’t say any such thing about the Modern Data Stack. It does, however, appear very, very sleepy. Someone should go and poke it with a stick.
The Modern Data Stack – deceased or just drowsy?
While we’re all dead in the long run, one thing that is different now is that AI is bringing the “long run” a lot closer than it has ever been. In the last couple of years, AI has forever changed a number of professions that were once thought to be safe from disruption. From art to software engineering, AI is changing how people get things done, and changing things much faster than you’d expect.
… The interesting question to me is, “What comes next?” If we assume models continue to get better, companies capitalize on the opportunities, things get tied together in a nice bow, what does the world look like? What could it look like? Let’s start with what we know. — Read More
Announcing Arm AGI CPU: The silicon foundation for the agentic AI cloud era
Today, Arm is announcing the Arm AGI CPU, a new class of production-ready silicon built on the Arm Neoverse platform and designed to power the next generation of AI infrastructure.
For the first time in our more than 35-year history, Arm is delivering its own silicon products – extending the Arm Neoverse platform beyond IP and Arm Compute Subsystems (CSS) to give customers greater choice in how they deploy Arm compute – from building custom silicon to integrating platform-level solutions or deploying Arm-designed processors. It reflects both the rapid evolution of AI infrastructure and growing demand from the ecosystem for production-ready Arm platforms that can be deployed at pace and scale. — Read More
App Store | Age of Agent
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
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
Japan’s Team Mirai Uses Tech to Bolster Democracy, Not Undermine It
Japan’s election last month and the rise of the country’s newest and most innovative political party, Team Mirai, illustrates the viability of a different way to do politics.
In this model, technology is used to make democratic processes stronger, instead of undermining them. It is harnessed to root out corruption, instead of serving as a cash cow for campaign donations.
Imagine an election where every voter has the opportunity to opine directly to politicians on precisely the issues they care about. They’re not expected to spend hours becoming policy experts. Instead, an AI Interviewer walks them through the subject, answering their questions, interrogating their experience, even challenging their thinking. – Read More
Versatile Editing of Video Content, Actions, and Dynamics without Training
Controlled video generation has seen drastic improvements in recent years. However, editing actions and dynamic events, or inserting contents that should affect the behaviors of other objects in real-world videos, remains a major challenge. Existing trained models struggle with complex edits, likely due to the difficulty of collecting relevant training data. Similarly, existing training-free methods are inherently restricted to structure- and motion-preserving edits and do not support modification of motion or interactions. Here, we introduce DynaEdit, a training-free editing method that unlocks versatile video editing capabilities with pretrained text-to-video flow models. — Read More
Vibe physics: The AI grad student
There has been a lot of recent hype about AI scientists doing end-to-end research autonomously. In August 2024, Sakana AI released their AI Scientist, a system designed to automate the entire research lifecycle—from generating hypotheses to writing papers. In February 2025, Google released an AI co-scientist built on Gemini, promising to help researchers generate and evaluate hypotheses at scale. And in August 2025, the Allen Institute for AI (Ai2) launched the open-source Asta ecosystem, featuring tools like CodeScientist and AutoDiscovery to find patterns in complex datasets. Since then, a new entrant has appeared every few months—FutureHouse’s Kosmos, the Autoscience Institute’s Carl, the Simons Foundation’s Denario project, among others—each promising some version of end-to-end autonomous research. Even as these approaches are visionary, their successes to date seem a bit forced: run hundreds or thousands of trials and define the best one as interesting. While I believe we are not far from end-to-end science, I’m not convinced we can skip the intermediate steps. Maybe LLMs need to go to graduate school before advancing straight to the Ph.D.
… What about theoretical physics? End-to-end AI scientists have found their footing in data-rich domains, but theoretical physics is not one of them. Unlike mathematics, theoretical physics problems can be more nebulous—less about formal proof search and more about physical intuition, choosing the right approximations, and navigating a landscape of subtleties that often trip up even experienced researchers. Even so, there are problems in physics where AI might be better suited. Not yet the paradigm-shifting questions at the frontier, but those where the conceptual framework is established and the goal well-defined. To find out if AI can solve these types of theory problems, I supervised Claude through a real research calculation at the level of a second-year grad student. — Read More
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