Best-worst comparisons, MaxDiff-style judging, and Plackett-Luce utility scores give agent teams a cleaner way to decide which configs to ship, prune, and route toward next.
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Modern VLMs Explained: How GPT-4o, Gemini, Claude Vision, and Qwen-VL Work
Vision Language Models, or VLMs, are AI models that can understand both visual content and language. While earlier models like CLIP and BLIP connected images with text, modern VLMs can analyze images, read documents, interpret charts, answer visual questions, and support multimodal conversations. Mo...
Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs
Enterprise Document Intelligence [Vol.1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call
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PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up
Understanding how PANet shortens the path between low-level and high-level features
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Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination
Enterprise Document Intelligence [Vol.1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable
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LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler
Most "LLM wikis" use agents, embeddings, and repeated model calls to organize local notes. I built a deterministic alternative: a pure Python compiler that turns messy markdown into a linked, linted wiki using only the standard library. Along the way, I fixed two real bugs, benchmarked the pipeline ...
LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Mastra, and more. If you're building AI agents in 2026, these are the frameworks worth paying attention to before starting your next project.
t0-alpha is a decoder-style patch transformer for probabilistic time-series forecasting. Raw series are split into 32-step patches, embedded, processed through causal time-attention and group-attention layers, and decoded into future quantiles rather than a single point forecast.
The post Time-Serie...
Persistent Latent Memory for Multi-Hop LLM Agents: How a 6G Handover Paper Closes the Agent Cold-Start
Every hand-off in your multi-agent pipeline is an expensive tokenization round-trip. Discover how Inductive Latent Context Persistence (ILCP) transfers a compressed hidden state so downstream agents never have to re-create the same context.
The post Persistent Latent Memory for Multi-Hop LLM Agents:...
What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?
How Pandas chunking, Dask, and Polars help process millions of records when adding more compute isn't an option.
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5 AI Coding Platforms to Build Apps Without the Headache
Explore the best AI coding platforms, no-code app builders, and vibe coding tools that help beginners and developers build, test, and deploy full-stack apps using simple prompts.
Build and deploy an agent on AWS with Strands and AgentCore
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Anthropic has just released Claude Sonnet 5. Sonnet. Had to say it twice. It is the middle child of the Claude family, and the one most people will actually use. It is quick, capable, cheap to run, and free to start. In this article, we go over the latest iteration of the Claude’s Sonnet family […]
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Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer
Enterprise Document Intelligence [Vol.1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025. For a single document, each brick emits typed pieces that converge on one LLM call. Corpus, conversation, and tool extensions are follow-up work
The post Context Engineering for RAG : The Four...
Define a tool once as an MCP server and any MCP-compatible client, any model, any framework, can discover and call it with zero custom integration code per model.
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...