The Fluid Simulator That Doesn’t Solve the Fluid Equations
I generated a Kármán vortex street without solving a single fluid equation. Here's how the Lattice Boltzmann Method gets there instead, derived from first principles, implemented in C++, and run on a supercomputer.
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Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet
Tabular foundation models predict the missing column of any spreadsheet zero-shot, the way an LLM completes text — and on the TabArena benchmark they now sit above fully tuned gradient-boosted trees. An introduction to how they work, an independent reproduction of the strongest open one, and a map o...
Build and Run an Intelligent Document Processing (IDP) System in the Cloud
Automating the classification and extraction of PII from emails using AWS
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Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship
Enterprise Document Intelligence [Vol.1 #8quater] - Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship
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Context Windows Forget What Matters — I Built a Usage-Reinforced Decay Engine for AI Agent Memory
Most AI memory systems keep the newest information—not the most important. Here's how I used the Ebbinghaus forgetting curve to build a better memory engine for LLMs.
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Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract
Enterprise Document Intelligence [Vol.1 #8ter] - Naming the RAG error correctly matters: model reads the context, so a wrong answer is an extraction error, not a hallucination. Seven typed-contract patterns keep the generation brick honest, with a decomposition rule for small models
The post Most RA...
A hands-on walkthrough of code execution with the OpenAI Agents SDK and Docker
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Detecting Vulnerabilities in Agent Skills with SkillSpector: From Green Checkmark to Real Security Judgment
Static analysis nailed the malicious skill and over-flagged the useful one. The gap between those results is where human judgement actually earns its keep.
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I Tried Fine-Tuning a Robot AI Model on Colab. Here Is What Worked
A reproducible 100-step LoRA fine-tuning run for OpenVLA, with dataset checks, Colab setup, training metrics, and W&B evidence.
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A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow.
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Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
Enterprise Document Intelligence [Vol.1 #10B] - The LLM as last line of defence, then two real escalations walked end to end: a flat table to Azure, a figure to a vision model
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Backpropagation Explained for Beginners (Part 1): Building the Intuition
Let's discover how neural networks learn, step by step
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Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Enterprise Document Intelligence [Vol.1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering. On the question side, the loop is small by design: read the doc, ask what is missing, re-parse.
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Your AI Agent Passed Every Eval. Finance Still Killed It.
An AI agent passed every metric in the eval harness I published, then the CFO killed it — its successful resolutions cost more than the humans it replaced.
The one metric that predicts whether an agent survives production, and how to measure it without a rebuild.
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Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
Everyone is talking about loop engineering, but most discussions assume an LLM sits at the center of the loop. I wanted to isolate the architecture itself. So I built a deterministic, zero-dependency Python benchmark that replaces the model with simple rules, allowing me to measure one question dire...
Prepare These 5 Assets Before Your AI Agents Take On More Work
How to define recurring work, give AI the right context, explain what high-quality work looks like, and decide where human judgment is still needed.
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Building Trustworthy Production RAG Systems Through Continuous Evaluation
A practical guide to building an evaluation workflow that catches retrieval failures, hallucinations, and performance drift before they reach users
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Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
Enterprise Document Intelligence [Vol.1 #7quinquies] - Hallucination is usually garbage-in. Fix retrieval, and the model has nothing left to make up
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How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured)
I measured the actual GPU electricity for eight local models on one RTX 3090 — and the cheapest wasn't the smallest, nor the priciest the biggest.
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Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs
How to stop parsing JSON by hand and start trusting your model's output
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A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop
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Building Models in Two Worlds: From Latent Constructs to Behavioral Signals
My PhD models tried to explain why people engage. My industry models predict who will. The statistics barely changed. Everything around them did.
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The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices
A framework for aligning agentic AI with enterprise intent to ensure consistent scenario‑wide autonomous behavior.
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