The Next Frontier of AI in Production Is Chaos Engineering
Blast-radius control tells you how much to break. Intent tells you what breaking it will teach. Only one of these has mature tooling.
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PyTorch NaNs Are Silent Killers — So I Built a 3ms Hook to Catch Them at the Exact Layer
NaNs don’t crash your training — they quietly destroy it.
After losing hours to a silent failure in a ResNet training run, I built a lightweight detector that pinpoints the exact layer and batch where things break. Using forward hooks and gradient checks, it catches issues early with minimal overhea...
How Spreadsheets Quietly Cost Supply Chains Millions
A simulation of how a single forecast change moves through five planning teams, and why most retailers lose money in the gap between Sales and Stores.
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Comparing Explicit Measures to Calculation Groups in Tabular Models
With the advent of UDFs and their combination with calculation groups, I see a lot of discussion about not creating explicit measures but instead offering calculation groups to report creators.
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I Reduced My Pandas Runtime by 95% — Here’s What I Was Doing Wrong
Most slow Pandas code "works", until it doesn't. Learn how to spot hidden bottlenecks, avoid costly row-wise operations, and know when Pandas is no longer enough.
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A local, zero-cost project that cleans, structures, and summarizes your reading automatically
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A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted LLM, no labeled training data required.
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Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London
Turning free-to-use data into a hypothesis-ready dataset
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From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills
How I turned LLM persona interviews into a repeatable customer research workflow
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DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling
How you can build your own Thompson Sampling Algorithm object in Python and apply it to a hypothetical yet real-life example
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Your RAG Gets Confidently Wrong as Memory Grows – I Built the Memory Layer That Stops It
As memory grows in RAG systems, accuracy quietly drops while confidence rises — creating a failure that most monitoring systems never detect. This article walks through a reproducible experiment showing why this happens and how a simple memory architecture fix restores reliability.
The post Your RAG...
Context Payload Optimization for ICL-Based Tabular Foundation Models
Conceptual overview and practical guidance
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Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval
Open source. 5-minute setup. Vector RAG done right—try it yourself.
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KV Cache Is Eating Your VRAM. Here’s How Google Fixed It With TurboQuant.
Explore the end-to-end pipeline of TurboQuant, a novel KV cache quantization framework. This overview breaks down how multi-stage compression achieves near-lossless storage through PolarQuant and QJL residuals, enabling massive context windows with minimal memory overhead
The post KV Cache Is Eating...
AI Agents Need Their Own Desk, and Git Worktrees Give Them One
Git worktrees, parallel agentic coding sessions, and the setup tax you should be aware of
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Beyond Prompting: Using Agent Skills in Data Science
How I turned my eight-year weekly visualization habit into a reusable AI workflow
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What if an unsupervised model could become a strong classifier with only a handful of labels?
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The upstream decision no model, or LLM can fix once you get it wrong
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Building My Own Personal AI Assistant: A Chronicle, Part 2
Building a personal AI assistant is rarely a single, monolithic effort. In this piece, I walk through my latest addition: a task breaker module that decomposes complex goals into structured, actionable steps — and why that single component changed how I think about AI-driven productivity.
The post B...
memweave: Zero-Infra AI Agent Memory with Markdown and SQLite — No Vector Database Required
The problem with agent memory today
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Introduction to Deep Evidential Regression for Uncertainty Quantification
Machine learning models can be confident even when they shouldn't be. This article introduces Deep Evidential Regression (DER), a method that lets neural networks rapidly express what they don't know.
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5 Practical Tips for Transforming Your Batch Data Pipeline into Real-Time: Upcoming Webinar
Bringing your batch pipeline to real-time requires careful consideration. This post brings you five practical tips to make the most of your modernization efforts. Join us for an upcoming webinar to learn even more.
The post 5 Practical Tips for Transforming Your Batch Data Pipeline into Real-Time: U...
From OpenStreetMap to Power BI: Visualizing Wild Swimming Locations
How to turn OpenStreetMap data into an interactive map of wild swimming spots using Overpass API and Power BI.
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