daily brief: 2026-05-31
a look at ai subscription fatigue, browser-based python apps, and the growing pushback against always-on ai.
topic
data science
daily brief: 2026-05-30
anthropic revenue surges, github copilot billing sparks anger, and meta plans an ai pendant test.
momentum dynamics under sparse updates in high dimensions
new theory reveals when momentum helps or hurts in sparse training settings, based on two key timescales.
daily brief: 2026-05-29
a roundup of ai news covering new research, industry moves, and practical tools for data scientists.
deep learning for bivariate survival treatment rules
a new method uses deep neural networks and adaptive prediction-powered learning to optimize treatment rules for bivariate survival outcomes in randomized trials.
auditing job descriptions with textstat
use python's textstat library to automatically detect overly complex language in entry-level job postings.
run nlp in the browser with transformers.js
a tutorial on using transformers.js for text classification, zero-shot labeling, and question answering directly in the browser with no server needed.
multi-task ppi boosts inference with few labels
a new framework uses labeled data from related tasks to improve statistical power in prediction-powered inference when only a handful of labels are available per task.
federated probe-logit distillation rate limits under heterogeneous bandwidth
new lower bounds show that the bandwidth term in federated probe-logit distillation is tight, and the method extends to nodes with different upload budgets.
anytime-valid federated conformal rag for llm swarms
a new method extends federated conformal rag to provide valid uncertainty estimates at any stopping time, enabling safer adaptive control in distributed language model systems.
seven ai projects to automate real workflows in 2026
build practical ai assistants for job search, research, invoice processing, and more with step-by-step guides.
tuning local llm settings with ollama
learn to fine-tune local language model parameters, optimize hardware, and format prompts using ollama's modelfile, environment variables, and go templates.
triangular-reference bridges improve time series generation
a new method extends schrödinger bridge models for time series by using a frozen, triangular reference process across latent volatility levels, preserving the h-transform structure even with degenerate covariance.
calibrated uncertainty for treatment effects with small control groups
bayesian x-learner intervals under-cover in few-placebo settings due to nuisance model bias, but a gaussian process approach can fix calibration.
causal discovery with per-edge impossibility certificates
a new protocol for observational causal discovery attaches impossibility certificates to each edge, distinguishing data-driven orientations from those needing expert input.
five scipy.stats tricks for what-if simulations
learn five scipy.stats techniques to build rigorous simulations for scenario analysis, uncertainty quantification, and tail risk modeling using only numpy and scipy.
pandas groupby explained with examples
learn how to use pandas groupby to summarize, compare, and analyze grouped data with simple, practical examples.
cart forests as sequential allocation over random opportunity sets
a new stochastic-control theory explains how cart random forests work by viewing feature subsampling as random opportunity sets and split rules as allocation policies.