I just got back from San Francisco, where I gave a talk at Undercurrent, a small, intimate data engineering event put on by Confluent. I shared the stage with some legends (Maxime Beauchemin, Josh Wills, Holden Karau, Shinji Kim. The attendees were also stacked, with lots of talented and storied engineers and leaders. I talked to one guy who built and modernized the data warehouses at both LinkedIn and Uber. The Bay Area is like that. Legends everywhere. Conversations like this are the reason I still get on the road.
But the real reason I’m writing today is some new data. I closed the March 2026 Practical Data Pulse Survey on March 21st and used its results as the backbone of my Undercurrent talk. 194 data professionals responded. These are mostly data engineers, some analytics engineers, and some leaders – all people using AI tools in their data engineering work.
The TL;DR? AI has changed everything except the hard parts. — Read More
Recent Updates Page 35
AI PM at Netflix, Amazon and Meta – Here’s How to Become an AI PM
…Before you write a resume, update a portfolio, or prep for a single interview, you need to answer two questions.
What type of AI PM role are you targeting? And where in the stack do you want to sit?
Get these wrong and you’ll spend months preparing for interviews that test completely different skills than what you studied.
Two axes of AI PM roles: Traditional PM with AI features, AI native PM — Read More
As the US Midterms Approach, AI Is Going to Emerge as a Key Issue Concerning Voters
In December, the Trump administration signed an executive order that neutered states’ ability to regulate AI by ordering his administration to both sue and withhold funds from states that try to do so. This action pointedly supported industry lobbyists keen to avoid any constraints and consequences on their deployment of AI, while undermining the efforts of consumers, advocates, and industry associations concerned about AI’s harms who have spent years pushing for state regulation.
Trump’s actions have clarified the ideological alignments around AI within America’s electoral factions. They set down lines on a new playing field for the midterm elections, prompting members of his party, the opposition, and all of us to consider where we stand in the debate over how and where to let AI transform our lives. – Read More
This World Model Learns Physics by Watching Videos
Yann LeCun’s team just taught an AI to imagine the future from raw video. On one GPU. With a model smaller than most apps on your phone.
You know how you can close your eyes and imagine what happens when you push a coffee cup off the edge of a table? You don’t need to actually do it. Your brain just… knows. Gravity. Impact. Shattered ceramic. Coffee everywhere.
hat is a world model. An internal simulation of how reality works. AI researchers have been trying to build the same thing for machines. Not by programming physics rules manually, but by letting the AI watch videos and figure it out on its own. If a robot can imagine the consequences of its actions before taking them, it can plan. It can reason. It can avoid stupid mistakes. The problem? Building these things has been an absolute nightmare. Read More
A foundation model of vision, audition, and language for in-silico neuroscience
Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. Here, we introduce TRIBE v2, a tri-modal (video, audio and language) foundation model capable of predicting human brain activity in a variety of naturalistic and experimental conditions. Leveraging a unified dataset of over 1,000 hours of fMRI across 720 subjects, we demonstrate that our model accurately predicts high-resolution brain responses for novel stimuli, tasks and subjects, superseding traditional linear encoding models, delivering several-fold improvements in accuracy. Critically, TRIBE v2 enables in silico experimentation: tested on seminal visual and neuro-linguistic paradigms, it recovers a variety of results established by decades of empirical research. Finally, by extracting interpretable latent features, TRIBE v2 reveals the fine-grained topography of multisensory integration. These results establish artificial intelligence as a unifying framework for exploring the functional organization of the human brain. — Read More
GitHub
How Agentic RAG Works?
The main problem with standard RAG systems isn’t the retrieval or the generation. It’s that nothing sits in the middle deciding whether the retrieval was actually good enough before the generation happens.
Standard RAG is a pipeline where information flows in one direction, from query to retrieval to response, with no checkpoint and no second chance. This works fine for simple questions with obvious answers.
However, the moment a query gets ambiguous, or the answer is spread across multiple documents, or the first retrieval pulls back something that looks good but isn’t, RAG starts losing value.
Agentic RAG attempts to fix this problem. It is based on a single question: what if the system could pause and think before answering? — Read More
The LiteLLM Supply Chain Attack: A Complete Technical Breakdown Of The AI Ecosystem’s Darkest Hour
On March 24, 2026, the artificial intelligence development community experienced an unprecedented security catastrophe. LiteLLM, an essential open-source Python library used to route and manage API calls across hundreds of large language models, was weaponized in a highly sophisticated supply chain attack. Threat actors known as TeamPCP successfully published two malicious versions of the package (1.82.7 and 1.82.8) directly to the Python Package Index (PyPI).
With LiteLLM averaging 97 million monthly downloads and serving as a foundational dependency for industry titans like Stripe, Netflix, and Google alongside major AI frameworks such as CrewAI, DSPy, and MLflow, the magnitude of this compromise is staggering. — Read More
The Death of model.fit(): What Data Scientists Actually Do in the Age of AI Agents
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
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
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