One Capital Letter Was Silently Breaking My AI Support Bot, and It Wasn't in the New Model
A real Weave project that regression-tests three OpenAI models against the exact reply format your app depends on.
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Coding Agents Don't Need Longer History — They Need Intent Continuity
I built a system that automatically discovers, verifies, and applies relevant requirements from earlier interactions without asking the user where they came from.
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Why autonomous agents expose a new explainability problem in fraud detection
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Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing
Moving from static model assignment to intelligent, task-level LLM selection.
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Who Questions What Works: When Should We Retest Our Assumptions?
A model is only as reliable as the assumptions behind it
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Understanding permutation symmetry in deep learning, and what it means for weight averaging and model merging
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The Model Validation Playbook for GenAI: Lessons from Banking
How model validation standards are changing for LLM-based systems: what breaks, what carries over, and how to test output quality
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Context Windows Don’t Know What’s Still True — I Built a Validity Layer That Does
A context window can be technically complete and still describe a world that no longer exists. I built a deterministic benchmark to measure the cost of acting on stale context.
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Text Watermarking in Python: Catch Whoever Copies Your Writing
AI companies quietly watermark billions of words a day. Here’s how to apply the same three families of techniques to your own writing—and what real experiments reveal about which watermarks survive copy-paste, editing, and paraphrasing.
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Dynamical System Transfer Learning with Reduced Order Models
Improving reinforcement learning for complex physics
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My Model Worked Perfectly. Then I Tried to Make It Useful.
Turning a trained churn classifier into a FastAPI service that other software can actually call.
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Changing One Prompt Can Affect 50 Others — I Built a Prompt Dependency Graph to Find What Needs Retesting
I built a prompt dependency graph that separates everything a component can reach from the smaller set that actually needs targeted evaluation.
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Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working
I built a matcher meant to finish the cleanup that normalization left behind. Testing it against real data showed that no version of it could be made safe. What follows is the architecture that was left once the matcher was set aside.
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Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply
A visual guide to how graph neural networks work under the hood
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Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch
Five failure modes that survive constrained decoding, and why your schema validator will never catch them.
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Your LLM Can Return Perfect JSON and Still Be Wrong
What I learned after thinking more carefully about Structured Outputs on messy, incomplete data
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AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production
The five MLOps monitoring assumptions agents break, and which inherited signals now pass failed runs as healthy.
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Quick and simple tips to help you write better agent instructions
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Context Engineering Is Changing. Here’s What It Means for Data Scientists
How to apply the latest context engineering guidelines to your day-to-day data science work
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RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need
Enterprise Document Intelligence [Vol.1 #B00] - Retrieval answers one kind of question. Classifying a request, matching free text to a reference list, reading a table, cleaning OCR noise: each has a cheaper method that works, and the engineering is knowing which one to reach for
The post RAG Is Not ...
How we stopped reviewing every agent action and started routing human attention where it actually mattered
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From One Agent to a Team: Understanding Codex Subagents
A hands-on guide to defining specialist agents and coordinating their work in the Codex CLI
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