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How to Leverage NLP for Product Data Science
A practical guide to strengthening churn, CLV, and other traditional models with text
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Towards Data Science 人工智能 全文
We Specified an AI Agent in 2016. We Called It Analytics.
Re-reading my own IBM patent on real-time concert feedback as what it actually was: an agentic loop with multimodal perception, memory-grounded inference, and a human in the loop. What it got right, and what a decade of hindsight teaches…
Towards Data Science 人工智能 全文
I Built a Trap for AI Image Judges. They Walked Into a Different One.
Three vision AI models ignored the pretty image, yet GPT-4o invented answers and Claude kept answering B.
Towards Data Science 人工智能 全文
A Practical Guide to OpenAI's New Decisions API
Is this the JEV killer?
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Agentic Systems: A Practitioner's Guide to 6 Advanced Architectural Patterns
Beyond basic ReAct loops: six production-oriented architectures for building autonomous, reliable, and scalable LLM agents.
Towards Data Science 人工智能 全文
How Can AI Agents Read Untrusted Sources Safely?
Add architectural guardrails around how agents read untrusted sources and use them in the workflow.
Towards Data Science 人工智能 全文
Why Temperature 0 Isn't Deterministic
A one-line formula for how often an LLM's top token flips, and why identical completions fall apart after about a hundred tokens
Towards Data Science 人工智能 全文
Can TypeSafe's Jev Make AI Agents Safer Without Another LLM?
How a decision-first model can catch risky tool calls before they turn into real-world actions
Towards Data Science 人工智能 全文
Where Does the Money Go Across Long-Running Coding Agents?
The cost of control
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10 Things I’m Learning Beyond AI to Become More Technologically Fluent
Part 1: Understanding the technologies shaping our future
Towards Data Science 人工智能 全文
Your Model's MSE Is Lying to You: Part II
Autoregressive rollout and uncertainty propagation. Second in a series on probabilistic forecasting for physical signals.
Towards Data Science 人工智能 全文
RAG Isn't an Agent — I Built the Layer Between Retrieval and Action
RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems.
Towards Data Science 人工智能 全文
Jev vs. LLMs: When AI Moves from Generation to Decision-Making
I tested TypeSafe AI’s Jev on 3,080 classification tasks to see how its accuracy, latency, calibration, and confidence compare with LLMs — and whether it works as a practical decision layer for AI systems.
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How to Maximize Your Coding Agent Subscriptions
Get more out of your coding agent subscriptions
Towards Data Science 人工智能 全文
Beyond RAGs: Building Actually Truthful AI Harnesses
Retrieval is not evidence. How to build AI that proves its own claims.
Towards Data Science 人工智能 全文
Towards Spec-Driven Test Automation: Part 1
Why a green test suite can mean nothing
Towards Data Science 人工智能 全文
When the Correct Answer Is Nothing, What Does Your Pipeline Return?
The reliability mechanisms we add to LLM pipelines are often the ones that make them confidently wrong.
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