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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.

ABC filed its formal opposition to the Federal Communications Commission's effort to force it to submit to an early renewal of its broadcast station licenses, calling it an effort to chill the speech of broadcasters across the country. In a new filing with the FCC, ABC charges that the agency's willingness to pursue the early […]

Polymarket and Novig have both recently brought ex-DOGE affiliates onboard as scrutiny of the industry intensifies.

Introduction It was a perfectly normal day. I opened my laptop, ready to get some work done, and then... BAM. A black screen. Not a gentle fade to black, but more like my computer shouting, "I’ve had enough of your crap!" The same operating system that had been working perfectly just five hours earlier had suddenly decided it had had enough of life. I wasn't too worried though. After all, I had ChatGPT on my side. Three hours later... Yeah... my confidence crumbled faster than my phone battery at 2%. What followed was a three-hour rabbit hole involving NVIDIA drivers, multiple Linux kernels, Secure Boot, DKMS, Xorg, GDM, journalctl , systemd , and more terminal commands than I'd like to admit. Somehow, against all odds (and probably a little divine intervention), we managed to fix it. And honestly? I enjoyed every minute of the chaos. It was like a wild adventure—except with more curse words and less danger. So I decided to document the entire debugging journey—not just because it might help someone who runs into the same issue, but also because I deserve a little sympathy after spending three hours arguing with my laptop. (And if the solution seems painfully obvious to you... please let me enjoy my victory. Don't take this away from me.😤 The Problem After rebooting my laptop, I was greeted with just a black screen. No login screen, no desktop… just nothing.** At first, I tried to enter TTY using Ctrl + Alt + F3, but that wasn’t working either. Since I wasn’t able to reach TTY directly, I had to take a different route. By editing the GRUB boot entry and booting into multi-user.target , I forced Linux to start in text-only mode, giving me access to a terminal.** For this, I edited the GRUB boot entry and appended systemd.unit=multi-user.target to the end of the kernel command line (after quiet splash ). That was the first breakthrough, though. The operating system wasn’t completely dead… only the graphical interface was failing to wake up. First Clues and Initial Ass

Despite weeks of renewed press coverage and controversy around ICE, Donald Trump’s supporters appear to be standing by the agency no matter what.

Companies are making sure we have a surplus of options for building qubits.

As schools across the country continue to implement cellphone bans, a new Pew Research Center survey shows they continue to gain support. Seventy-seven percent of US adults support banning cellphones in middle and high school classes, and 48 percent support banning them for the entire school day. That's the first time more Americans have supported, […]

The US government is targeting China with a new import ban on "advanced robotic devices" and power inverters made in foreign countries, as reported earlier by Reuters. In an announcement on Tuesday, the Federal Communications Commission says the ban will include "mobile" robots, such as humanoid and quadruped models - but it is not limited […]

Every binomial confidence interval you have ever computed on an eval pass rate, Wald, Wilson, Clopper-Pearson, all of them, rests on one assumption: each example is an independent draw. Most eval sets violate it. You have 40 questions generated from the same 8 documents, or 200 turns from the same 30 conversations, or 150 examples that are really 50 cases with 3 paraphrases each. Those are not 200 independent observations. And when you feed a correlated set into a formula that assumes independence, the interval comes out too narrow, which means you declare differences significant that aren't. I want to walk through why, put a number on how much it matters, and show the fix, because this one is invisible: the code runs, the interval prints, and it is quietly wrong. Why clustering shrinks your real sample size Independent examples each carry their own information. Correlated examples carry overlapping information. If five questions come from the same document, and the model either understands that document or doesn't, those five outcomes move together. You did not learn five independent things about the model. You learned something closer to one and a half. The survey-statistics name for this is the design effect (Kish, "Survey Sampling," 1965). For clustered data it is approximately: Deff = 1 + (m̄ - 1) · ICC where m̄ is the average cluster size and ICC is the intra-cluster correlation, the fraction of total variance that lives between clusters rather than within them. Your effective sample size is: n_eff = n / Deff That is the number of independent examples your clustered set is actually worth. The number Take a realistic eval set: n = 200 examples, drawn from 40 source documents, so average cluster size m̄ = 5. Suppose the ICC is 0.3, which is unremarkable for "questions from the same document" (I have measured higher). Deff = 1 + (5 - 1) · 0.3 = 2.2 n_eff = 200 / 2.2 ≈ 91 Your 200-example eval is worth about 91 independent examples. The correct confidence interval

A Georgia man was charged with a felony for allegedly wiping his phone while being questioned by Customs and Border Protection. Samuel Tunick had something in common with others who have had their devices searched at airports: He was an activist. Unlike others who have had their phones seized with no explanation, Tunick was told […]

Donald Trump’s base has been increasingly spilt. Laura Loomer’s sudden embrace of Ukraine has only widened that divide.

New Yorkers who want to see their Instagram or TikTok feed curated by an algorithm, or receive notification after midnight, will soon have to verify their ages to access those features. New York Attorney General Letitia James (D) released a set of final rules on how to implement the SAFE for Kids Act, signed into […]