Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings
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Most AI Agents Fail in Production Because They’re Built Backwards
Good models don't save bad architecture, and most teams learn that the hard way.
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How I turned 100 messy pdfs into structured insights by building a deterministic loop around agents
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The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment
How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment.
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I Built My First ETL Pipeline as a Complete Beginner. Here’s How.
A beginner's honest walkthrough of Extract, Transform, Load using the GitHub API
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From TF-IDF to Transformers: Implementing Four Generations of Semantic Search
How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search systems step by step using Python.
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Introducing the Agent Toolkit for Amazon Web Services
It’s like having your own personal expert AWS solutions architect and data engineer rolled into one.
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From Prototype to Profit: Solving the Agentic Token-Burn Problem
Engineer token-efficient, self-adapting workflows for production
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Hybrid AI: Combining Deterministic Analytics with LLM Reasoning
How AI architecture prevents plausible but wrong analytics
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Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale
For AI engineers who want to understand every step, not just call the library
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The Hidden Bottleneck in Quantum Machine Learning: Getting Data into a Quantum Computer
Quantum Machine Learning promises access to exponentially large representational spaces, but before any computation can happen, classical data must first be embedded into quantum systems. This article explores one of the most overlooked bottlenecks in QML: getting data into a quantum computer effici...
Prompt Engineering Isn’t Enough — I Built a Control Layer That Works in Production
Most LLM failures in production aren’t random — they’re predictable.
I kept hitting broken JSON, silent failures, and outages that froze my entire app. Prompt engineering didn’t fix it.
So I built a control layer above the model — and took structured output reliability from 0% to 100% without changi...
Optimizing AI Agent Planning with Operations Research and Data Science
AI agents can quickly become expensive without a clear strategy for planning, skill coverage, and budgets. This article shows how to use operations research and data science to optimize AI agent cost and resource allocation. You will learn how to frame common agent problems—skill coverage, project a...
Deploying a Multistage Multimodal Recommender System on Amazon Elastic Kubernetes Service
A practical walkthrough of building and deploying a multistage, multimodal recommender system on Amazon EKS, covering data pipelines, model training, Bloom filters, feature caching, and real-time ranking.
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Grounding LLMs with Fresh Web Data to Reduce Hallucinations
Why production LLM systems need live web search to overcome knowledge cutoffs and stale training data
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Six Choices Every AI Engineer Has to Make (and Nobody Teaches)
The production trade-offs that only appear once your model is live.
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Why MCP servers keep losing to CLIs once the agent gets a terminal
The post One Flexible Tool Beats a Hundred Dedicated Ones appeared first on Towards Data Science.
LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships
Most LLM evaluation systems rely on vague scoring and human judgment disguised as metrics. I built a lightweight evaluation layer in pure Python that turns LLM outputs into reproducible decisions by separating attribution, specificity, and relevance—so hallucinations are caught before they reach pro...
Recursive Language Models: An All-in-One Deep Dive
Exactly how does it differ from ReAct, CodeAct, Self-Loops, and Subagents?
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