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Title: TechScribr

Publisher:  sayan.biswas
Message frequency:  0.43 / day

Message History

Bid Landscaping, Part 2: From Win Curve to Bidding, Pacing, and Its Limits
Bid Landscaping series - Part 1: Learning the Win Curve from the Auctions We Lost · Part 2 (this post): From Win Curve to Bidding, Pacing, and Its Limits 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 losses (right-censored at our bid)...

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Inside Floor Price Optimization: Training the Bid-Threshold Classifiers
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 bidder bid above ₹X? Answer that over a grid of thresholds and we obtain a probabilistic view of demand. That post treats the classifiers as a black box. This one opens it. We will build ...

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Same Ruler, Smarter Placement: GPTQ
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 meet loud activations, fold the inverse scale into the preceding layer, and standard hardware kernels keep working unchanged. This article picks up the same problem from a different an...

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Training LLMs on a Budget: LoRA, QLoRA, and LoftQ
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 and affine formulas. In the non-linear article we reshaped the grid to fit bell-curved weights, and NF4 emerged as the practical sweet spot. But NF4 has a second act that is arguably mo...

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Bid Landscaping: Learning from the Auctions We Lost, with Survival Analysis
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 it, and how the advertiser’s bid knob actually connects to the clicks that come out the other side. My task was to design the bid optimization algorithm: given what we know about each ...

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