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Texas announced new a audit on data centers that could slow approval for new facilities seeking to connect to the state energy grid. Governor Greg Abbott (R) on Monday directed the Public Utility Commission of Texas (PUCT) and the Electric Reliability Council of Texas (ERCOT) to verify and audit new data center proposals, writing that […]

A portfolio can look good on the usual scorecard and still answer the wrong question. One line says return was high. Another says risk-adjusted performance was acceptable. A third says drawdown stayed inside a tolerable range. Then the market turns, the benchmark starts recovering, and the thing I actually care about is different: how efficiently did the portfolio catch up? That is where a new metric can fool its own author. If I build a recovery measure and it moves almost exactly like an existing ratio, I have created a longer name for the same signal. The right acceptance test is geometric: a useful metric should cast a different shadow. This is the rule I used while validating Hyperlogarithmic Benchmark Catch-Up Ratio (HBCR): orthogonality to existing measures is a first-class test, not a chart for the appendix. 1. A new metric has to earn its axis HBCR was built to measure benchmark-relative recovery dynamics. The research page states the motivation plainly: traditional benchmark-relative metrics often fail to capture the true dynamics of investment performance, especially during market recoveries [ A New Metric for Private Equity Risk Adjusted Returns , Calibration of Risk and Correlation in Private Equity ]. That framing matters because the obvious validation path is tempting and weak. You compare the new number with familiar performance measures, find a comforting relationship, and declare victory. But a high correlation with a well-known score can be a warning. If HBCR strongly tracked Sharpe Ratio, it would probably be an expensive synonym for risk-adjusted return. The acceptance test I wanted was sharper. HBCR should have some relationship with performance, because recovery has economic content. It should also avoid collapsing into the same direction as Sharpe Ratio, Beta, Volatility, Alpha, Total Return, or Max Drawdown. Written as a predicate, the test has two sides. Let $\mathcal{T}$ be the set of metrics already on the scorecard, $\rho_{n,m}$ the corr

Use metrics APIs for cron-job, API-failure, and business-event charts, then add a separate heartbeat monitor for jobs that never start. That is the smallest stack I would put on call for a small SaaS. A metrics dashboard can show success and failure counts, duration, backlog size, and error-rate trends; it cannot prove that a scheduler actually invoked a job. Healthchecks-style monitoring closes that specific gap. It still isn't full monitoring coverage, and I wouldn't describe it that way in an SLO review. The distinction matters because a failed run and a missing run leave different evidence. An API error usually increments something. A business event can be counted. A cron job that never fires may produce nothing at all — no duration, no failure, no final log line. No signal. How should a backend metrics dashboard combine cron jobs, API failures, and healthchecks? Start with the questions an operator must answer, not with a vendor menu. For cron jobs, I want a success count, a failure count, duration, and any queue backlog that can delay completion. For API failures, I want error counts and an error-rate trend beside request volume, because a raw count without a denominator can make ordinary traffic growth look like a regression. For business events, I want domain verbs: invoices issued, imports completed, or messages accepted. Those widgets belong on one dashboard because they describe the same service from different angles. Heartbeat monitoring is a separate control. A job reports a start or completion ping to Healthchecks, Cronitor, or an equivalent tool; if the expected ping doesn't arrive within its schedule and grace period, that system owns the missing-run signal. Keep that alert outside the metrics query path. Otherwise the component that failed to emit data is also the component being asked to notice its own silence. Silence counts. I've learned to write the failure matrix before drawing the dashboard. In one incident, a call returned 200, but the side e

Data pipeline automation has moved from a technical aspiration to a business imperative. As organisations deploy more AI agents that need fresh, reliable data, the volume and complexity of data pipelines has grown beyond what manual management can sustain. AI-powered pipeline automation — using AI to build, monitor, and repair data pipelines — is the emerging solution. Key Insight: AI-automated data pipelines reduce pipeline development time by 60%, decrease pipeline failures by 45%, and enable data teams to manage 3x more pipelines per engineer compared to manual approaches. The Pipeline Scaling Challenge The average enterprise now maintains 1,500+ data pipelines, according to research by Barracuda Networks, and this number is growing 25% annually as organisations add new data sources, new AI use cases, and new reporting requirements. Each pipeline has an average of 4.2 transformation steps, 2.1 data quality checks, and connects an average of 2.8 systems. The total pipeline infrastructure is complex, fragile, and increasingly beyond the capacity of manual management. The scaling challenge manifests in three ways. First, pipeline development backlog — the average data team has a 3-6 month backlog of pipeline requests from business users. Second, pipeline failures — the average enterprise experiences 15-20 pipeline failures per week, each requiring manual investigation and repair that consumes 30-40% of the data engineering team's capacity. Third, pipeline maintenance — as source systems change (schema updates, API modifications, deprecated fields), pipelines break silently and produce incorrect results until someone notices. This 'silent failure' problem is particularly dangerous because it erodes trust in data without anyone being aware that anything is wrong. The root cause of these challenges is that data pipelines have been built as static, manually-maintained infrastructure. A pipeline is coded, tested, and deployed. When the source or destination changes, a hu

The European Union has ushered in some additional rules that aim to make it easier for people to identify chatbots and AI deepfakes online. The new transparency obligations under the bloc's landmark AI Act came into effect on August 2nd, requiring companies to disclose when people are interacting with AI models, and if content has […]

Why do simple features suddenly require cross-team negotiations? In this article, explore how boundary drift quietly destroys change locality and increases cognitive load across teams. Learn practical sociotechnical strategies - redistributing mechanics, exposing essential policy, and rehearsing exception paths - to restore domain boundaries and enable a truly evolutionary software architecture. By Michael Fischer, Nicholas Lawrence, Monica Karekar

The ROI Black Hole in Social Marketing Consider a mid-market B2B software company whose social team manages campaigns across X, LinkedIn, Instagram, and TikTok from a single shared workspace. Each week the managers review platform-native dashboards that display rising follower counts, solid engagement rates on short-form video, and respectable click-throughs from carousel posts. They export weekly performance reports, paste the numbers into shared spreadsheets, and celebrate the month-over-month lift in impressions. Yet when the sales operations team asks which campaigns contributed to qualified pipeline, the social group cannot produce a single account-level match. Campaign links carry UTM strings, but many prospects arrive through mobile apps or shared links that strip those parameters, leaving the CRM with only anonymous referral domains and no usable journey data. The team attempts manual reconciliation by cross-referencing campaign dates with opportunity creation timestamps, but the exercise quickly collapses under volume. One campaign on LinkedIn might drive 400 clicks while another on TikTok drives 1,200, yet both appear in the CRM as undifferentiated social traffic. Without a consistent identifier that survives across platforms and into the marketing automation system, the social team cannot isolate which creative or audience segment produced the meetings that closed. Budget conversations therefore remain anchored to vanity metrics rather than incremental revenue, and executives grow increasingly skeptical of further platform spend. Medallion Architecture and the Absent Silver Layer Modern data platforms often organize information according to a medallion architecture that progresses through successive stages of refinement. The initial bronze layer captures raw event logs exactly as they arrive from each social API, preserving original timestamps, platform-specific identifiers, and unprocessed metadata. A subsequent silver layer then standardizes those recor

From how galaxies form to the beginnings of the universe, the images from the space telescope are giving cosmologists new data to explore—and theories to build out.

This guide is a collection or a summary on how to install and use a footprint of Apache Hadoop. I tried to follow an old version 2.7.1 guide that I created few years ago and adjusted this to use the latest version. Apache Hadoop 3.5.0 is used below; check the Apache releases page before future installations. These instructions target Linux (Ubuntu/Debian) for development or testing. Production clusters need Kerberos, network controls, encryption, monitoring, backups, and an upgrade plan. Do not expose HDFS or YARN ports to the internet. Native single-node installation Prerequisites sudo apt-get update sudo apt-get install -y openjdk-17-jdk openssh-client openssh-server pdsh curl tar java -version Hadoop requires Java and SSH; pdsh is recommended by the current Apache single-node documentation. Find JAVA_HOME if needed: readlink -f "$(command -v java)" | sed 's:/bin/java::' Download and install Pin the version for repeatable installs and verify Apache's SHA-512 checksum: export HADOOP_VERSION=3.5.0 cd /tmp curl -fLO "https://archive.apache.org/dist/hadoop/common/hadoop-${HADOOP_VERSION}/hadoop-${HADOOP_VERSION}.tar.gz" curl -fLO "https://archive.apache.org/dist/hadoop/common/hadoop-${HADOOP_VERSION}/hadoop-${HADOOP_VERSION}.tar.gz.sha512" sha512sum -c "hadoop-${HADOOP_VERSION}.tar.gz.sha512" sudo tar -xzf "hadoop-${HADOOP_VERSION}.tar.gz" -C /opt sudo ln -sfn "/opt/hadoop-${HADOOP_VERSION}" /opt/hadoop sudo chown -R "$USER":"$USER" "/opt/hadoop-${HADOOP_VERSION}" Add this to ~/.bashrc, adjusting JAVA_HOME if necessary: export JAVA_HOME=/usr/lib/jvm/java-17-openjdk-amd64 export HADOOP_HOME=/opt/hadoop export HADOOP_CONF_DIR="$HADOOP_HOME/etc/hadoop" export HADOOP_HDFS_HOME="$HADOOP_HOME" export HADOOP_YARN_HOME="$HADOOP_HOME" export HADOOP_MAPRED_HOME="$HADOOP_HOME" export PATH="$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin" Then load and verify it: source ~/.bashrc sed -i "s|^# export JAVA_HOME=.*|export JAVA_HOME=${JAVA_HOME}|" "$HADOOP_HOME/etc/hadoop/hadoop-env.sh" had

Every A/B testing tutorial ends the same way: run the test, wait for significance, ship the winner. Then you run a real test and variant B converts 12% better on newsletter signups, brings in 4% less revenue per visitor, and bounce is flat. Nothing is significant except the signups. Ship it? I spent an embarrassing amount of time on this question while building an A/B engine, and most of what I read online didn't help, because most of it assumes one metric. This post is what I ended up with. It's not novel — the statistics are decades old — but I couldn't find it written down in one place with working code, so here it is. Why the p-value doesn't answer the question you're asking Two problems, and the second one is the bad one. Multiple comparisons. Three metrics at α = 0.05 means roughly a 14% chance of at least one false positive if nothing is actually different. Bonferroni fixes this, but now you need α = 0.017 per metric and your test needs to run three times as long. On a site doing 300 conversions a month that's not a fix, it's a refusal. The p-value is answering a different question. It tells you the probability of your data assuming no difference exists. What you actually want to know is: if I ship B, how much do I expect to lose if I'm wrong? Those are not the same question and no amount of Bonferroni turns one into the other. There's also the peeking problem — everyone checks the dashboard daily and stops when it goes green, which quietly inflates the false positive rate well past whatever α you wrote down. I'll come back to that, because Bayesian methods do not magically solve it, whatever you may have read. Posterior first, decision second For a conversion rate, the Beta-Binomial conjugate pair gives you the posterior in one line. With a uniform prior, after c conversions out of n visitors: p | data ~ Beta ( 1 + c , 1 + n - c ) That's it. No closed-form comparison between two Betas that's worth implementing, so sample. PHP has no Beta sampler in core, and

The FBI, the EPA, and the Cybersecurity and Infrastructure Security Agency (CISA) have stopped short of officially blaming Iran for a spate of cyberattacks on Minnesota's water systems, but consensus is that Iran is likely behind them. That, of course, hasn't stopped Donald Trump from sharing his own theory - that Governor Tim Walz is […]

Non-Gaussian Distributions Explained from First Principles (Beginner Friendly) As we all know, the real-world dataset is not normalized , but most of us thought every dataset followed the famous bell curve . After all, everyone talks about the Normal Distribution . But then I looked at real-world datasets like: Income of people Stock market returns Website traffic YouTube views Population of cities None of them looked like a bell curve. That's when I realized something important. Not every dataset in the real world is normally distributed. In this article, we'll understand Non-Gaussian (Non-Normal) Distributions from first principles using simple language, intuition, and real-world examples. First, What Does "Non-Gaussian" Mean? The Normal Distribution (also called the Gaussian Distribution) has a very specific shape. It is: Bell-shaped Symmetrical Mean = Median = Mode Most observations lie near the average But what if our data doesn't look like that? Then it is called a Non-Gaussian Distribution . In simple words, Any probability distribution that does not follow the Normal Distribution is called a Non-Gaussian Distribution. Why Should We Care? Imagine you are analyzing the salaries of employees. Most employees earn between ₹25,000 and ₹1,00,000. But a few CEOs earn ₹50 lakh or even ₹2 crore. Will this data form a perfect bell curve? No. The extremely high salaries pull the distribution toward one side. If we wrongly assume the data is normal, our analysis can become misleading. That's why understanding Non-Gaussian Distributions is extremely important in Data Science. Before Learning Other Distributions... Let's understand two important ideas. These help us decide whether our data is normally distributed or not. Kurtosis — How Heavy Are the Tails? When beginners hear the word Kurtosis , they usually think it measures how tall the peak of a graph is. That's actually a common misconception. A better way to think about Kurtosis is this: How likely is the distribution

This is obviously a bubble Jim Rickards, a former adviser to the CIA and Pentagon, warns that the United States is currently facing a tectonic economic crisis driven by an unprecedented bubble in Artificial Intelligence (AI). According to his analysis, this impending crisis has the potential to be more destructive than the dot-com crash, the 2008 financial crisis, and the pandemic-related market crashes combined. He is not alone in his dire outlook; veteran investor Jeremy Grantham has warned, "This is obviously a bubble. The probabilities it doesn't burst are slim to none. And when it does, it could be an economic catastrophe unprecedented in the last 97 years" . Furthermore, former SEC Chairman Gary Gensler has stated that "the next financial crisis will come from AI". Create God and ask him for money The Unprecedented Scale of the AI Bubble The current market relies dangerously on a single sector, with the AI bubble estimated to be 17 times larger than the dot-com bubble of the late 1990s. Many AI companies are burning through cash at an alarming rate. For instance, OpenAI is reportedly losing more than a billion dollars a month; as it is noted in the source, "for every dollar they make, they have to spend at least three". This massive cash burn led a Deutsche Bank analyst to observe, "No startup in history has operated with losses on anything approaching this scale". Despite the astronomical costs and high valuations, OpenAI’s CEO was quoted as previously saying, "I have no idea how we're going to generate revenue". Former Goldman Sachs banker and Bloomberg columnist Matt Levine summarized this extreme speculative mindset, noting, "The business model they believe they need seems to be create God and ask him for money". "Subprime AI" and Toxic Debt Just as the 2008 financial crisis was fueled by toxic subprime mortgages, the AI boom is being fueled by dangerous debt structures used to fund massive data centers. Private equity firms are financing data centers as r

I watched my team get faster after we adopted AI-assisted coding, and honestly — I felt good about it. More tickets closed. Shorter cycle times. PR volume up. I remember thinking: this is what leverage looks like. That was the mistake. Not the tool. The assumption. Two weeks after a change shipped — passed every test, got through review, deployed cleanly — we found it had been quietly degrading a retry mechanism. It only broke under specific load conditions our test environments didn't replicate. Nobody caught it because it didn't look wrong. It worked. It just wasn't safe. When I asked the engineer to walk me through it, they could. The code made sense. They understood what each part did. But when I asked what would happen if the downstream service was slow — not down, just slow — there was a pause. Not because they weren't capable. Because they'd never needed to ask that question. The AI wrote the handling code, the tests passed, and the whole thing moved forward before that question ever came up. The dashboard wasn't lying. Things were shipping faster. It just wasn't showing me the part that mattered. What I didn't see coming Here's what actually surprised me: AI can make a team look more capable before it makes the team actually more capable. I used to learn by getting stuck. The 11 PM kind of stuck. Staring at a stack trace for three hours, genuinely questioning whether I understood any of this. That friction built something real — the instinct that says this probably works, but something feels off, and I should figure out what before we ship it. I built that through a migration that silently corrupted data for six hours. Through a caching layer that sailed through staging and failed on a Friday afternoon in production. That's not nostalgia for unnecessary suffering. The suffering was the mechanism. When the first draft is free, that mechanism stops. Learning used to happen inside the act of writing the code. Now it doesn't. And I'm genuinely not sure what repl

Some group with no modern descendants contributed a lot to our genomes.

MAGA is loudly calling for the former White House chief medical adviser to end up in prison. WIRED asked legal experts to weigh in on whether that’s even possible.

Metabase is an open-source BI tool for building charts and dashboards over MySQL, PostgreSQL, MongoDB, Redshift, and more. This guide deploys Metabase on Kubernetes, loads the Sakila sample dataset into MySQL, builds a dashboard, and secures it behind Nginx Ingress with cert-manager TLS. Prerequisites: a Kubernetes cluster with kubectl / helm configured, a Linux workstation, a reachable MySQL server, and a domain name. Load the Sakila Sample Database Sakila models a DVD rental store — films, actors, inventory, rentals. $ sudo apt install zip -y $ wget https://downloads.mysql.com/docs/sakila-db.zip $ unzip sakila-db.zip Connect to your MySQL server (replace host/port/user): $ mysql -h <HOST_ENDPOINT> -P <DATABASE_PORT> -u <ADMIN_USER> -p mysql > CREATE DATABASE sakila ; mysql > SOURCE sakila - db / sakila - schema . sql ; mysql > SOURCE sakila - db / sakila - data . sql ; Deploy Metabase $ nano metabase.yaml apiVersion : apps/v1 kind : Deployment metadata : name : metabase spec : selector : matchLabels : app : metabase replicas : 1 template : metadata : labels : app : metabase spec : containers : - name : metabase image : metabase/metabase:latest ports : - containerPort : 3000 protocol : TCP --- apiVersion : v1 kind : Service metadata : name : metabase-svc spec : type : LoadBalancer selector : app : metabase ports : - name : http port : 8080 targetPort : 3000 Your cloud provider may need a provider-specific LoadBalancer annotation here (e.g. to set the listener protocol) — check its Kubernetes docs if the default doesn't work. $ kubectl apply -f metabase.yaml $ kubectl get deployments $ kubectl get services Wait for metabase-svc to get an EXTERNAL-IP (can take a few minutes), then visit http://<external-ip>:8080 to confirm the Metabase welcome page loads. Connect Metabase to the Database Let's get started → pick language. Enter your name, email, company, and a password. Select your use case. Database engine: MySQL . Set a display name, then host/port/database/user/pa

July 2026 is over. The month that opened with AUTONOMOUS 2026 and WAIC 2026 running simultaneously on opposite sides of the Pacific closed with the sector tallying what it built. The number that defines the period is $55.8 billion in robotics funding across H1 - nearly double the prior full-year record. But the more durable signal from this week is operational rather than financial: Neura Robotics has a confirmed deployment date at a Schaeffler facility in December, NVIDIA's simulation-to-real pipeline is now functional at production scale, and five simultaneous shifts are reshaping factory floors right now, not in 2027. The questions that drove the first half of 2026 - does Physical AI work, is the funding real, will the robots actually arrive - are no longer interesting. H2 starts with harder ones. Stats: Value Description $55.8B Robotics funding raised in H1 2026, nearly double the prior annual record $8.6B Humanoid startup funding in H1 2026 alone, 1.8x all of 2025 December 2026 Confirmed first deployment of Neura Robotics humanoids at Schaeffler's German facilities 5 Simultaneous operational shifts reshaping factory floors identified in the mid-2026 analysis Neura Robotics Has a Deployment Date: December 2026 in a Schaeffler Factory Most Physical AI deployment announcements are directional. "We are partnering with X to explore robotics in our facilities" is a press release. A confirmed month and a specific facility is a contract. Neura Robotics confirmed that Schaeffler - one of the key investors in its $1.4 billion Series C alongside Amazon, Nvidia, Qualcomm, and the European Investment Bank - plans to deploy Neura's humanoids in its German facilities in December 2026 . Schaeffler manufactures precision bearings and components for electric vehicles, operating in environments where dimensional tolerances are measured in micrometers. Deploying a humanoid robot in that context is a fundamentally different challenge than warehouse pick-and-place or automotive sequ

Researchers fear AI is moving too fast, while Mark Zuckerberg is worried about who owns it. Plus: Inside Black Forest Labs’ push into robotics.

Gemini Robotics 2 includes three models, but only one is publicly available right now.