Nice opportunity for researchers and applied folks working with text data: Text as Data 2026 is accepting presentation proposals. The focus is on computational analysis of documents across politics, society, culture, social science and the humanities. One-page proposals are due Aug 1. tada2026.org

"You need a vector database" is the default advice but complexity means there's new infrastructure, new formats, and data coordination overhead. What if tuning Parquet page sizes and adding lightweight footer metadata gave you vector search without any of that? blog.xiangpeng.systems/posts/vector...

Vector search using only Parquet and DataFusion – Xiangpeng’s blog

Just tune your existing stack

blog.xiangpeng.systems

Most data bugs aren't algorithm failures, they're assumption violations upstream. Pointblank makes validation a first-class citizen in R/Python workflows instead of an afterthought scattered in assert statements.

Fifty Releases of Pointblank: a Year of Building Data Quality Tooling - Posit

The Pointblank Python library's journey has resulted in a premier, data-agnostic toolkit for tabular data validation.

posit.co

Plot twist: You can have Normal residuals even when Y isn't Normal. And non-Normal residuals when Y actually is Normal. Model misspecification shows up everywhere except where you expect it.

All Your Data R Fixed – Homepage

In all conventional regression models, the data are fixed–they don’t have a probability distribution. This means transforming your data with logs or other functions won’t change the probability…

robertkubinec.com

Here's what they don't tell you about topic modeling: preprocessing determines everything. Corpus homogeneity, document length, and context windows matter more than algorithm choice. BERTopic won't fix bad inputs. No model will.

The General Inquirer in the time of LLMs: a BERTopic tutorial – CSS@IPP — Tutorials and resources

A. Morin

css-polytechnique.github.io

Indexes speed up reads but slow writes. The real insight: indexes only help when you're returning <15-20% of rows. Beyond that, sequential scans win. Space and memory costs matter too. btree indexes often exceed table size.

Introduction to PostgreSQL Indexes

Who’s this for Basics How data is stored in disk How indexes speedup access to data Costs associated with indexes Disk Space Write operations Query planner Memory usage Types of Indexes Btree Hash…

dlt.github.io

What if a model’s prediction was literally an eigenvalue? This post kicks off a thoughtful series exploring spectrum-based models as a middle ground between linear models and neural nets, with interpretability and robustness baked in. A fun, slightly off-the-beaten-path ML read.

Behold the power of the spectrum!

Eigenvalues as neurons: represent nonlinear models as the k-th eigenvalue of a learned symmetric matrix pencil. Explore monotonicity/convexity properties and train simple spectral models.

alexshtf.github.io

Data To Art is a new curated gallery transforming datasets into visual storytelling. Latest addition: Alisa Singer's Environmental Graphiti turns climate science into vibrant data-driven art. The line between science and abstraction is thinner than you think.

Data to Art

A curated gallery celebrating data visualization as art. Discover innovative artworks from international artists who transform data into emotionally compelling visual experiences.

data-to-art.com

ClickHouse solved Advent of Code 2025 puzzles using single ClickHouse queries. No UDFs, no temp tables, no preprocessing. Just pure SQL doing things SQL probably shouldn't do. Impressive and slightly cursed in the best way.

Solving the "Impossible" in ClickHouse: Advent of Code 2025

At ClickHouse, we don't like the word "impossible." We believe that with the right tools, everything is a data problem. To prove it, we decided to complete the 2025 Advent of Code unconventionally:…

clickhouse.com

If you're wondering where database tech is really headed in 2026, Andy Pavlo's annual review cuts through the noise. Sharding, Postgres evolution, and hot takes you won't find in vendor blogs.

Databases in 2025: A Year in Review

The world tried to kill Andy off but he had to stay alive to to talk about what happened with databases in 2025.

cs.cmu.edu

MarkItDown (Microsoft) converts a variety file types to Markdown optimized for LLMs. Handles structure, not just text. Pairs well with Kreuzberg for extraction (supports 50+ file formats!). If you're building RAG systems, these belong in your stack.

Top Python libraries of 2025

Explore our 11th annual Top Python Libraries roundup, featuring two curated Top 10 lists for General Use and AI / ML / Data tools that matter today.

tryolabs.com

jax-js compiles NumPy-style array code into WebAssembly and WebGPU kernels that run entirely client-side. No server, no dependencies, just JAX's programming model in your browser. This changes what's possible for interactive ML demos.

jax-js: an ML library for the web

JAX in pure JavaScript, as a flexible machine learning library and compiler.

ss.ekzhang.com

Jan Van Haaren's 2025 soccer analytics review is a solid reference if you're doing applied ML in sports or want to see how domain experts handle sequential data. Covers spatio-temporal models, graph methods, Bayesian forecasting, and tracking data metrics. janvanhaaren.be/posts/soccer...

ML progress isn't driven by elegant theory. It's benchmarks, leaderboards, and engineering culture. In this post, Ben Recht explores why empirical testing beats clean math in practice and why that tension defines the field.

Benchmark Studies

It is impossible to disentangle technical innovation from technical debt

argmin.net

The paradox: show a complex graph and execs check their phones. Show a simple one and they demand endless breakdowns. The solution isn't more or less data. It's recentering discussions on the actual decision at hand. methodmatters.github.io/true-stories...

True Stories from the (Data) Battlefield – Part 1: Communicating About Data

A blog about data science, statistics, and data analysis with open-source software.

methodmatters.github.io

Your browser can run Python (Pyodide), execute OCR on PDFs, crop videos, and call LLM APIs—all without uploading anything to a server. The localStorage + CORS pattern makes surprisingly powerful tools possible with zero backend infrastructure.

Useful patterns for building HTML tools

I’ve started using the term HTML tools to refer to HTML applications that I’ve been building which combine HTML, JavaScript, and CSS in a single file and use them to …

simonwillison.net

Tired of charts that hide the story? Density plots + percentile intervals reveal what averages can't: full variability, extremes, historical context. Nine examples comparing Spain's temperature data show how geometry choice changes what readers understand.

Broken Chart: discover 9 visualization alternatives

Researcher in climate science at MBG-CSIC

dominicroye.github.io

Fisher arbitrarily chose p<0.05 a century ago and we've just... kept it. The problem: calling it "arbitrary" only works if you can suggest something less arbitrary. No one has, so here we are circling p-values close to 0.05 like it means something. vilgot-huhn.github.io/mywebsite/po...

Haskell for data science? dataHaskell adds dataframes, NSE-style column operations, and compiler optimizations that turn chained operations into single-pass computations. Immutability + strong types + functional composition might be the combo we've been missing. jcarroll.com.au/2025/12/05/h...

Haskell IS a Great Language for Data Science

I’ve been learning Haskell for a few years now and I am really liking a lot of the features, not least the strong typing and functional approach. I thought it was lacking some of the things I missed…

jcarroll.com.au

Learning SQL is like learning a foreign language: you need to read more variations than you'll actually write. Learn disciplined canonical syntax for your own queries, but understand the messy dialects others use.

A modern guide to SQL JOINs

There are many SQL JOINs guides and tutorials, but this one takes a different approach. We try to avoid misleading wording and imagery, and we structure the material in a different way. The goal of…

kb.databasedesignbook.com

Side projects still open more doors than traditional applications in data roles. The trick: build small, interesting things that signal skills and make your work discoverable. Especially relevant given the current job market.

Make Things, Tell People

On side projects and finding work

presentofcoding.substack.com

Most time-to-event metrics are broken. Amazon thought customer support wait times were under 1 min. Bezos called in: 10+ minutes. Why? They only measured customers who stayed on hold long enough to be served. The ones who hung up? Never counted. www.counting-stuff.com/why-you-are-...

Why You Are (Probably) Measuring Time Wrong: Why do we need to use Survival Analysis more

Author: Michał Chorowski [Hey everyone! A guest post from Michał this week! This newsletter is always willing to host/share data-related content, so if you've created anything you'd like to share,…

counting-stuff.com

Hot take: Your analysts doing "some basic data engineering" is killing your analytics function. The MTA hired 5 dedicated data engineers and it unlocked everything else. Stop asking data scientists to maintain pipelines. www.mta.info/article/less...

Lessons learned in starting a central data team

Learn how the MTA succeeded in setting up a central data team and a general purpose, cloud-based platform for data analytics.

mta.info

Sometimes the best polars pattern is knowing when to exit the DataFrame. partitionby() splits data into a dict of frames, letting you process with list comprehensions. Cleaner than forcing everything through mapgroups() when further wrangling isn't needed.

Python Rgonomics: User-defined functions in polars | Emily Riederer

Polars provides a consistent API for conducting transformations against a DataFrame. But what do you do when you need to apply a user-defined function beyond the native API? This post surveys the…

emilyriederer.com

Most devs treat AI coding agents like infinite context machines. Reality: a 200k token window fills fast. The /compact feature is a trap. Better approach: /clear + document state in markdown, then resume. Treat context like disk space. You need a cleanup strategy.

How I Use Every Claude Code Feature

A brain dump of all the ways I've been using Claude Code.

blog.sshh.io

Most modern dimensionality reduction (t-SNE, UMAP, Isomap) shares a pattern: represent data as a graph capturing local similarity, then embed to preserve that structure. It's graphs all the way down.

I curated some readings for class on "data tensions" and the list felt worth sharing. Come on a tour of datasets, books, the web, and AI with me... We'll start with this piece on the Google Books project: the hopes, dreams, disasters, and aftermath of building a public library on the internet. 1/n

Torching the Modern-Day Library of Alexandria

“Somewhere at Google there is a database containing 25 million books and nobody is allowed to read them.”

theatlantic.com

Everyone's rushing to pgvector for "simple" vector search in Postgres. This reality check shows what actually happens at scale: indexing nightmares and performance walls. Simple isn't always sustainable in production.

The Case Against pgvector | Alex Jacobs

What happens when you try to run pgvector in production and discover all the things the blog posts conveniently forgot to mention

alex-jacobs.com

When healthcare becomes algorithmic, what gets optimized out? This Guardian essay asks the hard question about AI spreading through diagnostics and therapy: are we trading care quality for efficiency without realizing the cost?

What we lose when we surrender care to algorithms | Eric Reinhart

A dangerous faith in AI is sweeping American healthcare – with consequences for the basis of society itself

theguardian.com

Thinking Machines Lab solved a problem everyone accepted as unsolvable: LLM nondeterminism at temperature 0. Same prompt, same model, 1000 runs → 80 different outputs. With batch-invariant kernels? Bitwise identical every time. Open sourced. www.distributedthoughts.org/will-i-make-...

Will I Make It To The Restaurant Before The Soup Dumplings Get Cold? (And Other Problems In Machine Learning)

I'm chronically late. Not because I want to be rude - I feel terrible about it every single time - but because I'm catastrophically bad at predicting how long it takes to get anywhere. Turns out…

distributedthoughts.org