Practical, deeply explained guides to machine learning, statistics, deep learning, and the systems that put them to work.
Curated sequences that build one idea at a time.
How a solo developer ships an AI-built options-market data and research system: two AI architects working in isolation, priced adversarial findings, human gates, and the bugs the process actually caught.
How prompt caching reuses identical LLM input prefixes, why causal attention makes it possible, when the economics work out, and how to diagnose cache misses.
A practical guide to attribution rules, conversion deduplication, randomized holdouts, and the gap between credited revenue and causal lift.
In the intelligence-layer post, the SSP’s floor-price section reframes “what should the minimum price be?” as a stack of binary classification problems: given this ad request, will at least one bid...
Part 1 built the object: for a given context, the win curve $W(b)=\Pr(\text{win}\mid \text{bid } b)=1-S(b)$, estimated with Kaplan-Meier from wins (which reveal the clearing price exactly) and loss...
Recently, while working for a client in the AdTech domain, I got to understand how Google’s hotel ads mechanism works end to end - how a hotel’s rate surfaces into an auction, how Ad Rank resolves ...
So far in this series, quantization has been a serving story - shrink a trained model so it fits on cheaper hardware and runs faster. In the linear quantization article we set up the uniform grid a...
In the previous article we saw how AWQ protects salient weight channels by pre-scaling them before INT4 rounding. The mechanism was direct: spend more of the grid’s resolution on the channels that ...
Most of machine learning runs on first-order information: the gradient tells us which way is downhill, and we take a step. That is enough to train enormous models. But a whole family of methods, fr...
In the previous article we saw why a uniform quantization grid wastes precision on bell-curved LLM weights. We also saw how non-linear methods (quantile quantization, NF4, k-means) fix the problem....