Context Window Management for Long-Running Agents: Strategies and Tradeoffs
In this article, you will learn five practical strategies for managing context windows in long-running AI agent applications, along with the key tradeoffs each approach...
I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work
The tools I use for analytics and reporting have changed more than I expected, yet my questions for any analytics project haven't moved much.
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Tail Control: The Counterintuitive Engineering of Reliable Agentic Workflows
Behind a customer's API, a high-quality answer isn't enough. It has to be usable, which means on time. Delivering that consistently is a problem about variance, not speed, and the fixes are counterintuitive.
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I Pitted XGBoost Against Logistic Regression on 358 Matches. The Boring Model Won.
A concrete bias–variance lesson: why the smallest model had the best cross-validated fit, and how to know when to reach for the big hammer.
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We Built a Routing Layer to Cut Our AI Costs. It Broke the Product.
A team cut their AI inference bill by more than half. Three months later, customer satisfaction was dropping and the cost savings were tied to the quality loss. Cost-optimization routing layers are a Pareto trap, and here's the detection methodology that catches them in days instead of months.
The p...
Using Gemma 4, Ollama, OpenAI Agents SDK, and Tavily MCP to build a lightweight research agent
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Amplify the Expert: A Philosophy for Building Enterprise RAG
Enterprise Document Intelligence [Vol.1 #M1] - The thesis behind every architectural choice in this series
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You’ve heard the warnings! Don’t tell ChatGPT your secrets. The robots are reading everything. Your data is the product. And yet here you are: using them as a subscriber. Because AI is genuinely useful! The good news: that distrust is healthy, and you don’t have to choose between using AI and protec...
Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory
I benchmarked raw chat history, vector-only RAG, and a context graph on the same multi-agent conversations. The results exposed a surprising weakness in relational retrieval.
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The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark
A reproducible benchmark on latency, cost, and reproducibility, and where agents actually earn their keep.
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In this tutorial, we will show you how to use Gemini to create Google Sheets, build a useful table, generate formulas, analyze data, and improve the spreadsheet with follow-up prompts.
3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal
Beat the 8GB VRAM limit. Learn how to run three different LLMs on a single 8GB GPU using C++ layer multiplexing and admission control.
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5 Open Source Omni AI Models That Handle Text, Images, Audio, and Video
Take a practical look at multimodal, any-to-any systems for vision-language reasoning, speech interaction, document intelligence, real-time assistants, local deployment.
Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval
Enterprise Document Intelligence [Vol.1 #7C] - One LLM call ranks the candidates with reasons. The output is one typed object your auditor can defend
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The Self-Improving Loop in AI Agents: Architecture, Benefits, and How it Outperforms Traditional Agent Workflows
Most AI agents today follow fixed instructions and never get smarter on their own. They finish a task, forget what happened, and repeat the same mistakes tomorrow. A new design called the self-improving loop changes this. It lets agents learn from every result and improve over time. This guide expla...
A Three-Phase Factual Recall Circuit in Gemma-2B and Gemma-12B-IT
Activation patching reveals how facts are stored, routed, and read out across transformer layers, and why the residual stream does most of the work
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Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End
Enterprise Document Intelligence [Vol.1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last
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Harness-1: The 20B Retrieval Subagent That Beats GPT-5.4 at Search
Most search agents try to handle too many jobs at once. They generate new queries, remember what they have already explored, collect evidence, and decide what is relevant as the search keeps expanding. That can make the whole process messy, expensive, and hard to control. Harness-1 takes a simpler a...
Explore the best local coding models for private AI coding, fast GGUF inference, agentic workflows, multimodal development, and running powerful open models on your own GPU.