The Secret to Reproducible and Portable Optimization: ORPilot’s Intermediate Representation (IR)
Why production-level AI optimization modeling agent needs reproducibility and portability, and how IR helps achieve them
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Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python.
The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science.
Tired of your monthly API bill? Follow this tested guide to set up a high-performance local LLM on your Mac Mini without the headaches.
The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science.
LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer
LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity durin...
The Protocol That Cleaned Up Our Agent Architecture
A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server
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I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions.
A single model hands you a single answer and no sense of how much it hinges on the dozens of choices buried inside it.
The post I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions. appeared first on Towards Data Science.
Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG
Enterprise Document Intelligence [Vol.1 #5quater] - The other parsers read the words on a page. A vision model also reads the pictures
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GPU Time-Slicing for Concurrent LLM Agents on Kubernetes
A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads.
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Larger Context Windows Don’t Fix RAG — So I Built a System That Does
Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG en...
Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload
Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page bill, nothing leaves the building
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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.
The post Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines appeared first on Towards Data Science.
The Exact ML Project I’d Build to Get Hired in 2026
Follow this framework to build a project that will impress hiring managers
The post The Exact ML Project I’d Build to Get Hired in 2026 appeared first on Towards Data Science.
Increase Recommendation Systems’ Precision with LLMs, Using Python
This is how LLMs are used today to increase precision in recommendation systems
The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science.
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.