Towards Data Science
Publish AI, ML & data-science insights to a global community of data professionals.
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Latest posts
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Reflection's Beam Is the New Open-Weight Model Everyone Is Talking About
A 501B model that only activates 23B parameters per token sounds like a bargain, until you look at what it takes to host. The post Reflection's Beam Is the New Open-Weight Model Everyone Is Talking About appeared first on Towards Data Science .
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Our AI Has an Invisible Footprint — Let's see How to Measure It
The hardest part is no longer getting the numbers. It's deciding what to do with them. The post Our AI Has an Invisible Footprint — Let's see How to Measure It appeared first on Towards Data Science .
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Let Decision Models Watch Your Agents
A hands-on guide to detecting behavioral anomalies from an agent’s evolving execution history The post Let Decision Models Watch Your Agents appeared first on Towards Data Science .
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The Enterprise Context Layer: A Practitioner's Guide to 6 Advanced Architectural Patterns
Beyond RAG: How modern architectures engineer precise, cost-efficient context for enterprise AI agents. The post The Enterprise Context Layer: A Practitioner's Guide to 6 Advanced Architectural Patterns appeared first on Towards Data Science .
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Fix the Image, Not the Prompt
Vision models often fail before they even start reasoning. A few well-placed labels on the image fix more than any prompt tweak. The post Fix the Image, Not the Prompt appeared first on Towards Data Science .
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A Neural Network Learned to Read Digits From Pure Noise
A small NumPy reproduction of subliminal learning, and a short argument for why it works The post A Neural Network Learned to Read Digits From Pure Noise appeared first on Towards Data Science .
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How to Build Bug-Free Applications with Claude Code
Use E2E tests to minimize bugs The post How to Build Bug-Free Applications with Claude Code appeared first on Towards Data Science .
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We Taught AI to Answer Questions. Who Taught It to Change Its Mind?
When new evidence kills one conclusion, do the decisions built on it change too? I tested six update strategies, then a local model. The post We Taught AI to Answer Questions. Who Taught It to Change Its Mind? appeared first on Towards Data Science .
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I Told an AI Agent That Its Wrong Answers Were Right. One Model Believed Me.
A research paper claims reward labels barely change how agents learn. I tested that on three models, and one of them did exactly what the wrong labels told it to. The post I Told an AI Agent That Its Wrong Answers Were Right. One Model Believed Me. appeared first on Towards Data…
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Pick a Number Between 1 and 10
A viral tweet says LLMs pick 7 because of gradient descent. The math says it’s more interesting than that. The post Pick a Number Between 1 and 10 appeared first on Towards Data Science .
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Binning or Feature Selection: Which One Won Us an Insurance Hackathon?
For a year, we believed that supervised binning carried our winning entry to an insurance competition. After four seeds per arm and one shared feature set, the credit belongs elsewhere. The post Binning or Feature Selection: Which One Won Us an Insurance Hackathon? appeared…
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Your Agent’s Tools Can Make Decisions, Too
Decision models as tools: a hands-on guide The post Your Agent’s Tools Can Make Decisions, Too appeared first on Towards Data Science .
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Claude Code Mods: Custom Hooks to Fix My Biggest AI Coding Frustrations
How I built four Claude Code mods to protect API keys, track plan progress, catch untested code, and monitor context usage — with 135 passing tests and up to 100× less overhead than traditional hooks — and why they can outperform traditional hooks. The post Claude Code Mods:…
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When Labels Say No, But Embeddings Say Yes
Why traditional retrieval metrics miss meaningful similarities The post When Labels Say No, But Embeddings Say Yes appeared first on Towards Data Science .
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Difference-in-Differences vs Lagged-Outcome Regression
A simulation study and a pre-period check for choosing the right design for self-selected treatments in product analytics. The post Difference-in-Differences vs Lagged-Outcome Regression appeared first on Towards Data Science .
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Is the #1 AI Model Really #1?
On a 2,000-question test, my top two models couldn't be told apart at the usual 5% level. On a 200-question version, a different model would have taken first place about three times in ten. The post Is the #1 AI Model Really #1? appeared first on Towards Data Science .
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What Is Harness Engineering? A Hands-On Introduction
Learn three essential primitives of harness engineering to future-proof your career The post What Is Harness Engineering? A Hands-On Introduction appeared first on Towards Data Science .
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How to Make AI Work for End-to-End Business Processes
From simplifying workflows to connecting knowledge and rules, coordinating AI agents, and delivering measurable business results. The post How to Make AI Work for End-to-End Business Processes appeared first on Towards Data Science .
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Where a Cell Sits Matters More Than How Many There Are
Using spatstat raster layers and point process models to ask how neurons, astrocytes and microglia sit relative to each other The post Where a Cell Sits Matters More Than How Many There Are appeared first on Towards Data Science .
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Building and Visualising Kolmogorov–Arnold Networks
Exploring what happens when neural networks learn functions instead of weights. The post Building and Visualising Kolmogorov–Arnold Networks appeared first on Towards Data Science .