A Harness for Every Task: Putting a Team of Claudes on One Job
Claude can now write its own harness on the fly, custom-built for the task at hand.
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Take the next step to building real workflows with Spark on your laptop
The post PySpark for Beginners: Beyond the Basics appeared first on Towards Data Science.
A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins
The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science.
Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines
Stop re-computing the same context. Learn how to build a C++ runtime with copy-on-fork KV snapshots to eliminate redundant LLM prefills in multi-agent pipelines.
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The Exact ML Project I’d Build to Get Hired in 2026
Follow this framework to build a project that will impress hiring managers
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Increase Recommendation Systems’ Precision with LLMs, Using Python
This is how LLMs are used today to increase precision in recommendation systems
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The Polynomial That Fixed 30 Years of Cloth Simulation
The clipping bug has lived in every 3D simulation pipeline for three decades. Here is exactly why it happens, how the math breaks, and how swapping one equation fixes it; as well as the python code to see it for yourself!
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Picking an Experimentation Platform: A Retrospective
My approach to guiding the choice between Eppo and Statsig, and the lessons learned
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My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server
I got tired of copying files into an AI chat just to get feedback. So I built a pure Python MCP server that gives AI tools direct access to my local project—no frameworks, no dependencies. It runs over stdio for local use and switches to HTTP/SSE for concurrent clients with a single flag. The result...
Using DSPy to automatically create, evaluate, and optimize your prompts
The post Automate Writing Your LLM Prompts appeared first on Towards Data Science.
Five Ways to Fine-Tune Chronos-2, the Time Series Foundation Model
In Part 1 of this series, we introduced Chronos-2, a time-series foundation model. We got our hands dirty by walking through a real case study and saw what Chronos-2 can do straight out of the box, with no training. But as we noted at the end of Part 1, zero-shot isn’t always enough. In cases […]
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Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce
When images, mosaics, and data cubes exist in abundance, but field labels are expensive, rare, and imperfect.
The post Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce appeared first on Towards Data Science.
FPN Paper Walkthrough: Leveraging the Internal Pyramid
Understanding how FPN allows deep learning models detecting small objects and how to implement it from scratch
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I Built a C++ Backend So My GPU Would Stop Eating Air
A comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing.
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How to set the rules that keep agents effective and out of trouble
The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science.
Code Is Cheap. Engineering Judgement Is Now the Scarce Resource
The barriers to building have collapsed. That shifts the bottleneck to ownership, validation, taste, and deciding what should actually exist
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Three free ways to quickly deploy a static web app that anyone can access
The post From Local App to Public Website in Minutes appeared first on Towards Data Science.
From Regex to Vision Models: Which RAG Technique Fits Which Problem
Enterprise Document Intelligence [Vol.1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover
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RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem
Enterprise Document Intelligence [Vol.1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead
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