# statistics Published articles for statistics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Naoki Egami's Research on Political Methodology and External Validity DevFeed: [Naoki Egami's Research on Political Methodology and External Validity]() Original publisher: [Read original article]() Author: Peter Dizikes | MIT News Published: 2026-09-16T04:00:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [AI Research]() Tags: [artificial-intelligence](), [external-validity](), [faculty](), [idss](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [naoki-egami](), [political-methodology](), [political-science](), [profile](), [research](), [school-of-humanities-arts-and-social-sciences](), [science](), [social-sciences](), [statistics](), [technology-and-society](), [voting-and-elections]() ### AI overview An MIT profile of political scientist Naoki Egami, whose research examines research methodology, external validity, and the mathematical and statistical challenges of studying civic and political phenomena. It also discusses his work on the use of AI tools in research. ### Source excerpt Naoki Egami has become a standout in political methodology, helping refine tools that give scholars durable results. ## Automating EDA With fg-data-profiling DevFeed: [Automating EDA With fg-data-profiling]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-15T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Data analysis](), [pandas](), [Python](), [Data Quality](), [Statistics](), [HTML](), [JSON]() Tags: [data-analysis](), [data-quality](), [pandas](), [python](), [statistics](), [tutorial]() ### AI overview A video course on automating exploratory data analysis with fg-data-profiling. It covers generating interactive reports from DataFrames, exporting them to HTML or JSON, analyzing time series, and comparing datasets. ### Source excerpt Automate exploratory data analysis by transforming DataFrames into interactive reports with one command from fg-data-profiling. ## Coherence, Connections, and... Spacetime Crystals DevFeed: [Coherence, Connections, and... Spacetime Crystals]() Original publisher: [Read original article]() Author: Art Anthony Published: 2026-09-09T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [API](), [observability](), [Statistics]() Tags: [2026](), [ai-agents](), [api](), [api-architecture](), [api-ecosystem](), [api-governance](), [api-management](), [api-platform](), [api-strategy](), [artificial-intelligence](), [blog](), [governance](), [observability](), [statistics](), [uptime]() ### AI overview The article discusses API coherence and how API estates can align with organizational goals and change. It covers business gardening, observability, governance, uptime, and usage statistics in the context of API performance and organizational intent. ### Source excerpt Ahead of his Nordic APIs Summit 2026 talk on API coherence, London Stock Exchange Group's Gareth Faull joins us to talk about the art of aligning APIs with organizational intent. Measuring the performance of an API is a relatively straightforward process: ensure observability is in place, follow governance best practices, measure uptime, track usage statistics, ... ## Why Spotify Is Not Using Bayesian A/B Testing DevFeed: [Why Spotify Is Not Using Bayesian A/B Testing]() Original publisher: [Read original article]() Author: Spotify Engineering Published: 2026-09-08T13:18:44Z Content type: article Language: en Sources: [Spotify Engineering](), [Spotify Engineering Blog]() Topics: [Data Science](), [Tooling]() Tags: [a-b-testing](), [data](), [data-science](), [experimentation](), [inference](), [statistics](), [testing](), [tooling]() ### AI overview Spotify argues that Bayesian A/B testing can be oversimplified and that its claimed advantages require careful configuration. The article compares Bayesian and frequentist approaches and explains why Spotify does not currently need Bayesian inference alongside its frequentist tooling. ### Source excerpt Clearing the confusion about what Bayesian A/B testing is. The post Why Spotify Is Not Using Bayesian A/B Testing appeared first on Spotify Engineering. ## Black Hat USA 2026: Safeguarding DNS with Secure Access DevFeed: [Black Hat USA 2026: Safeguarding DNS with Secure Access]() Original publisher: [Read original article]() Author: Steve Vida Published: 2026-09-07T15:00:32Z Content type: article Language: en Sources: [Security @ Cisco Blogs]() Topics: [SIEM, Security, Observability](), [telemetry](), [NOC]() Tags: [apple](), [black-hat](), [cisco-secure-access](), [cisco-security-cloud](), [cisco-talos](), [cisco-xdr](), [cloudflare](), [cybersecurity](), [dns](), [google](), [network-operations-center](), [noc](), [phishing](), [privacy](), [security](), [security-operations-center](), [soc](), [splunk-cloud](), [splunk-enterprise-security](), [statistics](), [telemetry]() ### AI overview Cisco reports on using Secure Access and DNS telemetry to protect the Black Hat USA 2026 network. The article highlights blocking unapproved encrypted DNS resolvers, DNS request statistics, and activity classified as hacking. ### Source excerpt Cisco is the Security Cloud Provider for the Black Hat conferences, over a decade providing DNS Security. Learn about protecting DNS with Secure Access. ## Quiz: Python Statistics Fundamentals: How to Describe Your Data DevFeed: [Quiz: Python Statistics Fundamentals: How to Describe Your Data]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-07T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [data]() Tags: [data](), [learn](), [python](), [statistics]() ### AI overview An interactive 13-question quiz on descriptive statistics in Python, covering central tendency, variability, correlation, calculation tools, and plots. ### Source excerpt Check your understanding of descriptive statistics in Python, from means and spread to correlation, summaries, and plots that reveal your data. ## New system views in PostgreSQL 19 DevFeed: [New system views in PostgreSQL 19]() Original publisher: [Read original article]() Author: Gülçin Yıldırım Jelínek Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [releases](), [telemetry](), [dashboards]() Tags: [memory](), [monitoring](), [observability](), [postgresql](), [release](), [statistics]() ### AI overview An overview of four new PostgreSQL 19 system views for inspecting lock contention, recovery state, autovacuum priorities, and dynamic shared memory allocations. The release is still in beta, so details may change before general availability. ### Source excerpt PostgreSQL 19 adds four system views that make lock contention, recovery state, autovacuum priorities, and dynamic shared memory allocations easier to inspect. ## How Bits Database Optimization proves a query rewrite is faster DevFeed: [How Bits Database Optimization proves a query rewrite is faster]() Original publisher: [Read original article]() Author: Alex Weisberger; Nenad Noveljić; Bowen Chen Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Optimization](), [database monitoring](), [Database](), [synthetic-data](), [datasets](), [cpu](), [IO](), [Security](), [Statistics]() Tags: [cpu](), [data](), [database](), [database-monitoring](), [infrastructure](), [optimization](), [performance](), [security](), [software](), [sql](), [statistics](), [synthetic](), [synthetic-data]() ### AI overview The article explains how Bits Database Optimization validates that a proposed query rewrite is faster. It describes controlled benchmarking with simulated production-like datasets, accounting for cache state, CPU and I/O contention, execution time, and database work. ### Source excerpt Learn how Bits generates synthetic data, measures simulation fidelity, and uses execution time and database work to determine whether an optimization is truly faster. ## Two ways to measure the cumulative impact of experiments DevFeed: [Two ways to measure the cumulative impact of experiments]() Original publisher: [Read original article]() Author: Lukas Goetz-Weiss; Eddie Cai Published: 2026-08-18T00:00:00Z Content type: opinion Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments](), [Statistics](), [Data Science]() Tags: [analysis](), [experimentation](), [experiments](), [product](), [statistics](), [testing]() ### AI overview This article explains why summing the observed lift from winning experiments overstates cumulative impact because of the winner's curse. It presents two established approaches: a randomized holdout that measures the combined effect directly, and a statistical correction that aggregates existing experiment estimates. Datadog's Cumulative Impact feature applies the correction without requiring a quarter-long holdout and can analyze an entire experimentation program or a filtered team subset. ### Source excerpt Summing individual wins overstates true impact. See two accurate methods, holdouts and Datadog's Cumulative Impact, and how to choose between them. ## From asking to doing: How the world is putting ChatGPT to work DevFeed: [From asking to doing: How the world is putting ChatGPT to work]() Original publisher: [Read original article]() Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [data](), [OpenAI](), [Artificial Intelligence](), [datasets]() Tags: [ai](), [analysis](), [chatgpt](), [coding](), [company](), [data](), [global](), [openai](), [research](), [statistics](), [work]() ### AI overview OpenAI's country-level Signals data shows ChatGPT shifting from answering questions to helping people complete tasks and create outputs. Workplace use is more than twice as likely to involve doing--such as writing, coding, editing, or analysis--than use outside work, while adoption is expanding globally, multimedia use is growing, and usage among people over 35 is rising. ### Source excerpt New OpenAI Signals data shows how people use ChatGPT worldwide, with country-level insights on adoption, usage trends, and evolving behavior. ## Alexander Rakhlin named director of the MIT Statistics and Data Science Center DevFeed: [Alexander Rakhlin named director of the MIT Statistics and Data Science Center]() Original publisher: [Read original article]() Author: Institute for Data, Systems, and Society Published: 2026-08-03T19:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [Machine learning]() Tags: [ai-in-statistics](), [alexander-sasha-rakhlin](), [alumni-ae](), [ankur-moitra](), [artificial-intelligence](), [brain-and-cognitive-sciences](), [computer-science-and-technology](), [data-science](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [fotini-christia](), [idss](), [laboratory-for-information-and-decision-systems-lids](), [leadership](), [lids](), [machine-learning](), [mit-bcs](), [mit-faculty-appointments](), [mit-idss](), [mit-leadership](), [mit-schwarzman-college-of-computing](), [mit-statistics-and-data-science-center-sdsc](), [philippe-rigollet](), [richard-dick-larson](), [school-of-engineering](), [school-of-science](), [statistics]() ### AI overview Alexander Rakhlin has been named the next director of MIT's Statistics and Data Science Center, succeeding Ankur Moitra. Rakhlin has been affiliated with the center since 2016 and has led its interdisciplinary doctoral program, overseeing more than 75 successful PhD defenses. ### Source excerpt An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra. ## NVIDIA Video Codec SDK 13.1: Zero-Copy Transcode, AV1 B-Frames, and Frame-Accurate Seek DevFeed: [NVIDIA Video Codec SDK 13.1: Zero-Copy Transcode, AV1 B-Frames, and Frame-Accurate Seek]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-31T15:13:02Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [SDKs](), [Nvidia](), [GPU](), [Hardware](), [Streaming](), [Programming](), [Docker]() Tags: [computer-graphics-visualization](), [computer-vision-video-analytics](), [featured](), [features](), [gpu](), [latest-features](), [media-entertainment](), [nvidia](), [performance](), [remote](), [statistics](), [streaming](), [tools](), [video-codec-sdk](), [video-decode-encode]() ### AI overview NVIDIA Video Codec SDK 13.1 adds GPU-accelerated video encoding, decoding, and transcoding capabilities, including AV1 hierarchical B-frame references, decode statistics, frame-specific seeking, zero-copy buffer handling, redesigned transcoder samples, and a Docker-based development environment. ### Source excerpt The demand for high-quality video continues to accelerate across industries, powering everything from immersive streaming experiences to remote collaboration,... ## Lorenz and Little: How Much Does Your Tail Cost? DevFeed: [Lorenz and Little: How Much Does Your Tail Cost?]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2026-07-29T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Latency](), [Statistics](), [Optimization](), [Concurrency]() Tags: [2026](), [concurrency](), [cost](), [latency](), [optimization](), [statistics]() ### AI overview The article explains how tail-latency percentiles contribute to mean latency, concurrency, and service cost. It introduces the empirical Lorenz Curve and uses Little's law to argue that optimizing the tail can substantially reduce concurrency, capacity demand, and lock contention. ### Source excerpt Lorenz and Little: How Much Does Your Tail Cost? Lorenz and Little sounds like hipster burger bar from 2015. It's time for Marc's Amateur Statistics Corner! Today: why I pay a lot of attention to tail latency when optimizing cost. I've written before on the importance of tail latency for customer experience (e.g. in 2026, 2021, and 2021, and 2017). Today, I want to talk about tail latency from the perspective of cost and capacity. Like many system operators, I think about tail latency using percentiles. Here's a question: how much does each of my latency percentiles contributed to the mean latency? Intuitively, the answer is "quite a lot", but can we quantify that? We can! The thing we're looking for is the empirical Lorenz Curve. It directly calculates the answer to the question: given a latency percentile $P$ (e.g. p99=100ms), how much do requests taking shorter than $P$ contribute to the mean latency? (Let's call it $L(P)$ , so the real answer to our question is $1 - L(P)$). Starting from latency samples, the calculation is pretty simple: L = sum(sorted(x)[:k]) / sum(x) (for a set of n latency samples x, and k=p*n). From a vector of quantiles, things get a little more complicated, because we have to choose how to interpolate between the samples and extrapolate out to the maximum. Here I'm interpolating using a power law, which is a little bit of a sin1, but good enough for our purposes. # Calculate 1 - L(p) for a vector of measured quantiles # q - an array of quantiles (e.g. [1, 10, 200, 10000, 20000]) # p - an array of percentiles they're measured at (e.g. [0, 0.5, 0.9, 0.99, 0.999]) # OneMinusL - One minus the empirical Lorenz curve for each of the percentiles def OneMinusL(q, p): ...Show full implementationHide implementation # Calculate 1 - L(p) for a vector of measured quantiles # q - an array of quantiles (e.g. [1, 10, 200, 10000, 20000]) # p - an array of percentiles they're measured at (e.g. [0, 0.5, 0.9, 0.99, 0.999]) # OneMinusL - One minus the empirica ## Six Out of Ten DevFeed: [Six Out of Ten]() Original publisher: [Read original article]() Author: Jon C. Phillips Published: 2026-07-27T00:58:00Z Content type: article Language: en Sources: [Jon C. Phillips]() Topics: [dashboards](), [Server](), [Benchmark](), [Statistics](), [Artificial Intelligence]() Tags: [articles](), [audience-building](), [benchmarks](), [code](), [dashboards](), [design](), [digital-products](), [enterprise](), [marketing](), [music](), [newsletter](), [photography](), [product-engineering](), [server](), [side-projects](), [six-out-of-ten](), [statistics](), [web-development]() ### AI overview The article explains why email delivery and deliverability are different metrics. It compares benchmark results, showing that messages accepted by receiving servers may still reach spam or disappear, and that outcomes vary by sender type, engagement, and measurement window. ### Source excerpt I've been sending email for over a decade. Across the newsletters I run we push roughly three million emails a quarter, every day, to lists in the tens of thousands. ## Debug your Postgres from the terminal: a tour of \`neon inspect db\` DevFeed: [Debug your Postgres from the terminal: a tour of \`neon inspect db\`]() Original publisher: [Read original article]() Author: Savannah Longoria Published: 2026-07-22T18:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Database](), [telemetry](), [SDKs]() Tags: [cli](), [debug](), [diagnostics](), [json](), [observability](), [performance](), [postgres](), [product](), [scripting](), [sql](), [statistics](), [tooling]() ### AI overview Neon introduces `neon inspect db`, a Neon CLI command suite for read-only Postgres diagnostics. Its subcommands run bounded, versioned queries against Postgres statistics and catalog views, returning tables or JSON/YAML without requiring users to assemble connection strings or write catalog queries. ### Source excerpt The Neon CLI now ships `neon inspect db`: read-only, high-signal Postgres diagnostics that answer "what is slow?" without leaving the terminal or writing a single catalog query. Each subcommand runs one known-good query against Postgres' own statistics and catalog views and prints a clean table (or JSON/YAML for scripting). ## Learning a few things about running SQLite DevFeed: [Learning a few things about running SQLite]() Original publisher: [Read original article]() Author: Julia Evans Published: 2026-07-17T00:00:00Z Content type: article Language: en Sources: [Julia Evans]() Topics: [SQLite](), [Django](), [Object-relational mapping](), [Query (disambiguation)](), [Statistics]() Tags: [blog](), [django](), [orm](), [search](), [sqlite](), [statistics]() ### AI overview A personal account of operating SQLite in production for a small Django website. The article describes how running ANALYZE dramatically improved an SQLite FTS5 query and discusses the difficulty of cleaning up large numbers of rows without blocking other database writers. ### Source excerpt Hello! I've been working on a Django site recently, and I decided to use SQLite as the database. When I was getting started with using SQLite as database for a website I read a bunch of blog posts about how it is totally fine to use SQLite in production for a small site and I think it is totally fine, but what I did not fully appreciate is that SQLite is still a database, databases are complicated, and I do not know a lot about operating databases. So here are a couple of small things I've been learning about running SQLite. This is the 4th website I've used SQLite for, and I think this one is harder because with the power of the Django ORM I've been making the database do more work than I was previously without Django. I started by turning on WAL mode like all the blog posts said to do and hoping for the best. ANALYZE is apparently important Today I was running a query (using SQLite's FTS5 for full-text search) on a table with 4000 rows and it took 5 seconds. That seemed wrong to me: computers are fast! It turned out that what I needed to do was to run ANALYZE! Immediately the problem query went from taking 5 seconds to like 0.05 seconds (or some other number small enough that I didn't care to investigate further). I still don't know exactly what went wrong in the query plan, but my best guess is that it was some sort of accidentally quadratic thing. ANALYZE generates "statistics" (I guess about the number of rows in each table? and presumably other things?) so that the query planner can make better choices. Maybe one day I'll learn to read a query plan. cleaning up the database is tricky Occasionally I've run into situations where I accidentally put a bunch of rows in my database that I don't want to be there (for example completed tasks from django-tasks-db), and I want to clean them up. What's happened to me a few times in this case is: I run some kind of command to clean up the rows The command takes more than 5 seconds, since there are a lot of rows (though I ## When the Postgres query planner goes rogue DevFeed: [When the Postgres query planner goes rogue]() Original publisher: [Read original article]() Author: Nick Van Wiggeren Published: 2026-07-13T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [cpu]() Tags: [cpu](), [database](), [engineering](), [insights](), [latency](), [postgres](), [postgresql](), [resource](), [sql](), [statistics]() ### AI overview PlanetScale describes containing a runaway Postgres query whose planner choice caused severe latency and CPU saturation. A strict Database Traffic Control budget stopped further executions while the customer investigated the root cause. ### Source excerpt How PlanetScale Database Traffic Control contained a runaway Postgres query after the planner abandoned an index. ## Waiting for PostgreSQL 20 - Add backend-level lock statistics DevFeed: [Waiting for PostgreSQL 20 - Add backend-level lock statistics]() Original publisher: [Read original article]() Author: depesz Published: 2026-07-06T17:30:55Z Content type: article Language: en Sources: [select \* from depesz;]() Topics: [PostgreSQL](), [Back end](), [Statistics]() Tags: [backend](), [pg-stat-get-backend-lock](), [pg-stat-lock](), [pg20](), [postgresql](), [statistics](), [uncategorized](), [waiting]() ### AI overview A walkthrough of a PostgreSQL patch that adds per-backend lock statistics, including lock wait counts, wait times, and fast-path exceeded counts. The author demonstrates the feature with concurrent psql sessions and discusses its usefulness for diagnosing lock behavior. ### Source excerpt On 30th of June 2026, Michael Paquier committed patch: Add backend-level lock statistics This commit adds per-backend lock statistics, providing the same information as pg_stat_lock. It is now possible to retrieve those stats (lock wait counts, wait times, and fast-path exceeded count) on a per-backend basis. This data can be retrieved with a ... Continue reading "Waiting for PostgreSQL 20 - Add backend-level lock statistics" ## CVE Mid-Year 2026 Check-In: Volume Vertical, Exploitation Rare DevFeed: [CVE Mid-Year 2026 Check-In: Volume Vertical, Exploitation Rare]() Original publisher: [Read original article]() Author: jgamblin Published: 2026-07-01T15:47:10Z Content type: article Language: en Sources: [Jerry Gamblin]() Topics: [Vulnerabilities](), [Security](), [Statistics](), [cisa]() Tags: [2026](), [comparison](), [cve](), [cves](), [report](), [security](), [statistics](), [uncategorized](), [vulnerabilities]() ### AI overview This mid-year review finds that 35,364 CVEs were published in the first half of 2026, up 49.5% from the same period in 2025, while only 85 had entered CISA's KEV list. The article argues that the main challenge is distinguishing exploitable vulnerabilities from the rapidly growing volume of disclosures. ### Source excerpt We are halfway through 2026, so it is time for the mid-year CVE check-in. The short version: the volume curve has gone vertical while exploitation has not. This review covers everything published in the first half of 2026 (Jan 1 - Jun 30, 2026), the volume, the severity, what is actually being exploited, and who ... Read more ## Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart DevFeed: [Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart]() Original publisher: [Read original article]() Author: Benjamin Knight Published: 2026-06-29T16:06:24Z Content type: article Language: en Sources: [Instacart]() Topics: [benchmarking](), [math](), [Algorithms, Complexity](), [Software](), [Programming](), [Routing (disambiguation)]() Tags: [benchmarking](), [bias](), [cardinality](), [data-science](), [delivery](), [efficiency](), [estimator](), [fixed-effects-model](), [instacart](), [linear-regression](), [measurement](), [memory](), [precision](), [pyfixest](), [regression](), [routing](), [speed](), [statistics](), [techniques]() ### AI overview This Instacart article explains why ordinary least squares regression becomes computationally impractical for marketplace experiments with high-cardinality categories. It presents the mathematical basis for using Fixest and Pyfixest, discusses switchback experiment designs for addressing treatment spillover, and describes benchmarks comparing processing speed, memory efficiency, and estimator precision. ### Source excerpt Benjamin S. Knight Scaling Marketplace experiments requires specialized statistical techniques. We examine why standard ordinary least squares regression (OLS) becomes computationally intractable when controlling for high-cardinality categories. We then dive into the underlying math and demonstrate how modern packages -- specifically Fixest and Pyfixest -- bypass these limitations. We conclude by benchmarking these methods to show their real-world impact on processing speed, memory efficiency, and estimator precision. At Instacart we strive to give our customers access to all the fresh foods and ingredients that they would normally get from a trip to the grocery store, but without the hassle of driving, finding parking, waiting in line, etc. Instacart's Marketplace team is responsible for surfacing customers' orders to shoppers, aligning Instacart's delivery windows with shoppers' projected availabilities as efficiently as possible. This entails a careful balancing act. If we offer delivery windows that are sooner / more popular, then we risk overextending shoppers' ability to fulfill those orders on time. If we are too conservative in our delivery option offerings, then we risk losing potential orders. Accurately measuring the impact of changes in our batching and routing algorithms requires thoughtful experiment design and software. Better predictions of future demand / time-to-fulfill allow Instacart to offer more convenient delivery windows.Experimentation on Marketplace One of our primary concerns in Marketplace is treatment spillage. For example, if we adjust our batching algorithm and increase the rate at which multiple orders are combined into batches in Brooklyn and Queens, then we face a real risk of also influencing the rate of batch creation / completion in Staten Island, the Bronx, and Manhattan. In this case the treatment impacts the control group -- a classic source of measurement bias as a consequence of violating the Stable Unit Treatment Value Assumpt ## The 2026 DBIR says the quiet part loud: fundamentals still win DevFeed: [The 2026 DBIR says the quiet part loud: fundamentals still win]() Original publisher: [Read original article]() Author: info@1password.com (Dave Lewis) Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Security](), [Vulnerabilities](), [ransomware](), [Cybercrime](), [Artificial Intelligence]() Tags: [ai](), [breach](), [exploits](), [ransomware](), [security](), [statistics](), [tips-advice](), [vulnerabilities]() ### AI overview The article reviews the 2026 Verizon Data Breach Investigations Report, arguing that basic security practices remain essential. It highlights rising vulnerability exploitation, slower remediation, ransomware prevalence, and the potential impact of AI on future vulnerabilities. ### Source excerpt Every year, the Verizon Data Breach Investigations Report (DBIR) is one of the most hotly-anticipated and widely-read documents in security. And every year includes some surprising stats and reshuffles the top few threat vectors. But longtime readers will notice that the 2026 DBIR features some advice that ought to be familiar to everyone by now: get the basics right. The report's authors even say that the overarching theme this year is "keeping a strong foundation in the face of change." So what does a strong foundation look like? It looks like patching faster, reducing credential reuse, tightening third-party access, and making it harder for attackers to turn one weak login into a company-wide mess. Glamorous? No. Effective? Yes. Exploits, credentials, and AI: The stories that stood out in the 2026 DBIR This year's DBIR analyzes more than 31,000 incidents, including more than 22,000 confirmed breaches across 145 countries. It's not light reading, unless your idea of a beach read includes ransomware economics, exploit chains, and the occasional donut chart. But diving deep into these topics is worthwhile, because the numbers show both change and stubborn repetition. Vulnerability exploitation is surging In terms of eye-popping statistics, the big story this year is the explosion of vulnerability exploitation, which is now the leading initial access vector for breaches-far exceeding phishing and credential abuse. Only 26% of critical vulnerabilities in the CISA Known Exploited Vulnerabilities catalog were fully remediated in 2025, down from 38% the prior year. Median time to full remediation rose to 43 days, a huge jump from last year's 32 days. Maybe the scariest part of this whole scenario is that these are pre-Mythos numbers, and security experts are still bracing for an AI-powered hurricane of vulnerabilities. The report's authors attribute this escalation to the sheer volume of vulnerabilities organizations had to face, finding that there were roughly 50% more ## How AI Changes the Role of Applied Scientists DevFeed: [How AI Changes the Role of Applied Scientists]() Original publisher: [Read original article]() Author: Tilman Drerup Published: 2026-05-22T17:40:32Z Content type: article Language: en Sources: [Instacart]() Topics: [Artificial Intelligence](), [Data Science](), [AI, ML & Data Engineering]() Tags: [ai](), [analysis](), [applied-science](), [artificial-intelligence](), [changes](), [coding](), [economics](), [machine-learning](), [math](), [statistics]() ### AI overview Instacart's Economics Team examines how artificial intelligence is changing the work of applied scientists. The article frames the role as a bundle of tasks and proposes analyzing changes in the team's project portfolio from 2023 onward, with potentially larger effects on coding than on causal inference. ### Source excerpt Levi Boxell, Tilman Drerup, Alexandr Lenk The Economics Team at Instacart is an applied science team that operates at the intersection of machine learning engineering and economics. Similar to other applied science teams, our work involves a good chunk of engineering, steeped in statistics, math, theory, and strategy. And while that is still at the heart of what we do today, the surprisingly rapid emergence of artificial intelligence has also fundamentally altered our work in ways that we did not see coming. With this post, we want to provide a brief check-in and share an analysis of the patterns we are seeing from a distinctly economic perspective. To do so, we analyze the empirical dynamics of our project portfolio between 2023 and today, looking at the evolution of both the nature and quantity of our work over time. To start, let's have a quick refresher of what economists at Instacart do and provide a theoretical framework to think about the impact of technological change through AI. Background & Theoretical Framework At Instacart, economists spend their day-to-day on a diverse portfolio of tasks and activities. Similar to other applied science teams within the company, our work relies on a blend of skills, including economics, statistics, math, machine learning, data manipulation, coding, and AI. Due to this versatility in tasks, the team's work provides a particularly rich testing ground for predictions derived from economic theories concerning the impact of technological change. But what does economic theory actually tell us? A useful theoretical abstraction for an applied scientist's role is to frame it as a bundle of tasks (Autor, Levy, and Murnane, 2003), with each task characterized by its own production function (Acemoglu and Autor, 2011). Slightly simplified, a production function tells us how much output we can produce for a given level of input in a specific task. Comparisons of production functions across tasks in turn determine how we allocate our t ## How CockroachDB v26.1 Uses Elastic Admission Control and Go Scheduler Changes for Background Work DevFeed: [How CockroachDB v26.1 Uses Elastic Admission Control and Go Scheduler Changes for Background Work]() Original publisher: [Read original article]() Author: David Taylor Published: 2026-04-14T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Go Language](), [Concurrent Programming](), [Latency](), [cpu]() Tags: [background-work](), [backups](), [cockroachdb](), [elastic](), [latency](), [processes](), [statistics](), [work]() ### AI overview This article explains how CockroachDB v26.1 uses elastic admission control and a change to the Go runtime scheduler to let background work use spare CPU capacity while limiting its impact on query latency. ### Source excerpt CockroachDB runs background work -- backups, schema changes, statistics collection, changefeeds -- in the same processes that serve user queries. Traditionally, this creates a tension... ## Predicting Rider Conversion in Sparse Data Environments with Bayesian Trees DevFeed: [Predicting Rider Conversion in Sparse Data Environments with Bayesian Trees]() Original publisher: [Read original article]() Author: Zammit Alban Published: 2026-03-30T14:43:41Z Content type: article Language: en Sources: [Lyft Engineering - Medium]() Topics: [AI, ML & Data Engineering](), [machine learning overfitting](), [Training AI Models](), [Language models]() Tags: [data](), [data-science](), [lyft](), [machine-learning](), [rider](), [ridesharing](), [statistics](), [transportation]() ### AI overview Lyft describes predicting whether a rider will request a ride after viewing a destination, price, and ETA. The article focuses on sparse, high-cardinality contextual data, where standard gradient-boosted-tree models can overfit, and introduces Bayesian trees as indicated by the title. ### Source excerpt At Lyft, understanding how riders go through our user experience is fundamental to operating a healthy marketplace. Specifically, it is important to have a robust model determining if a rider will actually request a ride after entering a destination and viewing a price and ETA. Accurately predicting this decision, that we call conversion, informs countless decisions across our platform. Whether it is to better balance supply and demand, improve user experiences, optimize recommendations and advertisement, understand long-term engagement, decide how to distribute coupons... rider conversion prediction is a central challenge for the Lyft business. However, predicting human behavior at scale is incredibly complex, and the exact same person might well open the app just to check current availability or actually to request a ride after viewing our prices. The contexts under which riders make their conversion decisions are extremely diverse and almost unique to each session. A user's intent changes based on where they are and where they want to go, what time it is, their previous interactions with the platform, current supply-demand market conditions, to cite a few. When we try to model this using standard machine learning approaches, we run into a significant challenge: data sparsity. The Challenge of High Cardinality and Sparsity To accurately predict conversion, we need to slice our data very thinly across many categorical features. Imagine trying to predict the conversion probability for a business traveler leaving the suburbs of Detroit at 4:00 AM on a Tuesday to catch their flight at the airport 30 minutes away. While Lyft has vast amounts of data overall, the amount of data available for that specific intersection of contexts often reveals to be very tiny. Maybe we only have ten examples in history. If we use standard techniques like Gradient Boosted Trees (e.g., LGBM, XGBoost), we encounter severe overfitting. A standard model looking at 10 examples in the training d [Next page]()