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Stop using agents for everything
For anyone working with data — analysts, scientists, engineers — at any level.
I’ve seen companies and teams scrambling to adapt to the age of AI. The mandate arrives fast: use agents, build and share skills, move autonomously. The pressure is real, and so is the enthusiasm.
Why I stopped using logistic regression for churn
A few years ago, I built a churn model for a B2B SaaS product. Logistic regression, binary label, 30-day prediction window. It performed fine. The business used it. I moved on.
What bothered me was a question the model couldn’t answer: how long does a customer actually stay?
The Data Analyst's Survival Guide to the Agentic Era
·3 mins
I need to say something that makes some data analysts uncomfortable: the job is changing. Not disappearing — changing. And the analysts who understand the change will thrive. The ones who don’t will spend the next five years fighting it.
From Monolith to Modular: Rebuilding a Billing Data Pipeline From Scratch
Earlier this year I shipped a pipeline rewrite I’m genuinely proud of. It replaced a 2,200-line SQL monolith — one of those files that everyone’s afraid to touch — with a clean layered architecture that handles 14 products, runs daily, and can be extended by adding a handful of config files.
Open Source Won the AI Agent War — Here's What That Means for Data Teams
·2 mins
In January 2024, Hugging Face published a benchmark that most people in the data world missed. They compared open-source LLMs against GPT-3.5 and GPT-4 on agent tasks — using a dataset that requires web search and calculator use, the fundamentals of any analytics agent.