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The Example Store Is Model State

Consider a classifier that retrieves labeled examples for an incoming query and gives them to an LLM to choose a label. This is a form of k-nearest-neighbor classification with an LLM decision rule: the store supplies the examples and the LLM decides how they imply a label. More

Streaming Calibration

Modern applications—from ad platforms calibrating click-through predictions to polling systems incorporating responses to ML algorithms adapting fairness thresholds—share a common challenge: maintaining calibrated weights on live data streams. To address streaming data, we recast raking as a streaming convex optimization problem: minimize the squared error between current

npm fund

In November 2019, npm introduced the npm fund command. If you've run npm install recently, you've seen the gentle reminder: "4 packages are looking for funding. Run npm fund for details." As npm’s former CEO, Isaac Schlueter, noted, maintainers have historically had “very

Beam-GD

Gradient descent commits to a single direction at each step based on the local gradient. This myopic approach can be suboptimal when gradients are noisy, local geometry is misleading, or the loss landscape has multiple competing descent directions. The algorithm makes irrevocable decisions based on local information, potentially missing better

Good Enough: Satisficing in Production Machine Learning

Herbert Simon observed that managers rarely chase the global optimum. Instead, they set an aspiration level, a “good enough” performance, and quit searching once they found an option that met it. Simon called this satisficing. That habit makes sense because every decision is a trade‑off. In modeling, the benefit

Advertising Without Signal: The Rise of the Grifter Equilibrium

Economists credit ads with two welfare‑enhancing roles: 1. Informative – trimming search costs (Stigler 1961 (pdf)). 2. Signaling – In classic models, high-quality sellers are more willing to incur large, sunk ad costs because they expect to recoup them through future sales, especially in experience-good markets where quality is

Boosting Stability: Reducing Instability Under Row Permutation

Shuffle your training data, and XGBoost might give you a different model. Even when you keep features, hyperparameters, and random_state fixed. This behavior violates what most practitioners reasonably expect: that models should be invariant to row permutation. And can lead to silent drift, flaky tests, and spurious alerts. This,

From Vibe Coding Assistants to Coding Assistants

Modern coding assistants often firehose code: a single prompt yields pages of plausible implementation that the developer must reverse‑engineer, reconcile with project conventions, and defend in review. That behavior is optimized for vibe coding—outputs that look complete and fluent—rather than for actual coding, where teams must understand,

A Pink Revolution in Indian Policymaking

The 2025 Union budget earmarks 8.86%* of the total expenditure for gender-related programs (see MWCD). Pair that with the fact that over the last five years, more than 14 states have introduced women-centric unconditional cash transfer programs, reaching over 110 million women—nearly a fifth of India’

(Better) Split Decisions: Toward A More Structurally Stable CART

Classification and Regression Trees (CART) condense data into one transparent decision tree. Their weakness is structural instability: a small change to the training data can replace an early split, such as “age > 65” with “income > $50 k,” propagating a completely new tree. This instability makes trees unsuitable for