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what it costs, what it gains and the three mistakes that I make
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I got tired of copying files into an AI chat just to get feedback. So…
Latest
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How a simple choice shapes exploration, safety, and efficiency
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Using DSPy to automatically create, evaluate, and optimize your prompts
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Python tutorial for fine-tuning a Mistral Small 3.1 on an imbalanced training set to classify…
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Abacus.AI and the case for unified AI workflows
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In Part 1 of this series, we introduced Chronos-2, a time-series foundation model. We got…
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When images, mosaics, and data cubes exist in abundance, but field labels are expensive, rare,…
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Understanding how FPN allows deep learning models detecting small objects and how to implement it…
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A look at the real-world value of online graduate AI programs, combining hard data with…
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Testing fourteen engines on ninety-three human documents
Editor’s Picks
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AI does not decide who gets fired. Companies do.
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A comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing.
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How to set the rules that keep agents effective and out of trouble
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The barriers to building have collapsed. That shifts the bottleneck to ownership, validation, taste, and…
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Research projects in the age of AI
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How Knives Out teaches Bayesian thinking (without you realizing it)
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As AI gets smarter, the real differentiator may be how well humans regulate their own…
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Part 1: A practitioner’s walkthrough of univariate, multivariate, covariate-informed, and cold-start forecasting.
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A diffusion-inspired framework for stress-testing and denoising LLM-as-a-Judge pipelines, applied to safety-critical driving video.
The Variable Newsletter
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Authors can now benefit from updated earning tiers and a higher article cap
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Sorting through the good, bad, and ambiguous aspects of vibe coding
Deep Dives
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Enterprise Document Intelligence [Vol.1 #3] – Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability…
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Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and…
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Most engineers see quantization as shrinking vectors. TurboQuant asks a harder question: can you shrink…
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Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on…
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Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive…
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A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent