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If approved, the combined company would become one of the largest food delivery platforms in the world.

As part of a broader DHS surge to investigate alleged voter fraud, ICE has announced it plans to pay up to $5 million to create what amounts to a federal voter database.

Trump’s secret reviews of frontier AI models may hide corruption, lawsuit says.

I think automakers are finally getting the memo. Rather than try to reinvent the wheel with its new electric vehicle, Range Rover just made one that looks nearly identical to its lineup of gas-powered SUVs. They didn't try to pass off some sleek, spaceship-looking pod as a Range Rover. Sometimes familiar designs are the way […]

The DOJ proved to the court that Google acted illegally, but it won't get the big win it wanted.

Jio is betting it can turn an aging computer into an AI-ready PC for as little as about $11 for two months.

US District Court Judge Leonie Brinkema declined the Justice Department's request to make Google sell off parts of its ad tech business, accepting milder remedies to restore competition to markets it illegally monopolized for years. Brinkema said she would adopt most of the behavioral changes proposed by the parties, with some modifications. But those won't […]

We serve customers primarily in Australia, but we are now expanding to the USA. The timeline for launch is less than 2 months. This is now a race against time to design a multi-region routing system that fits all of our needs. Here is the story. Background Almost all of our customers were based in Oceania. We run our Kubernetes Cluster on GCP in Australia. Go microservices, federated GraphQL, gRPC services. 2 products - Tutoring and Schools. All designed for Australia. Then we expanded to the USA, which meant a new Kubernetes Cluster in US Central. The latency for serving US customers from Australia is an extra 200ms-300ms depending on network conditions - unacceptable. This would mean sharding the data by region, or does it? There are definitely ways to keep a unified dataset even across regions - though we did not need to do so. More on this later. What are the requirements If the only requirements were "Americans get served from America", we wouldn't be here discussing this, would we? Logged in users are served from their own region, wherever they happen to be in the world. Logged out users are routed geographically, as we have no other information to infer their actual region. Account Managers and Admins should be able to access both regions from one button, with a single account. Teaching materials opened via links from the Schools product must be shareable across both regions. Geography takes care of the logged out user, but nothing else. Using geography for a logged in user can be actively wrong. They might be travelling or simply using a VPN. Then comes the Admin; we have a lot of admin operations regarding curricula, which will be entirely separate for both clusters. Account Managers need to be able to see and modify information on both clusters. One admin should be able to access both clusters with a single account. We considered showing data of both clusters on one screen, but ruled it out as it may become too ambiguous or confusing, not worth the technic

He reported from space that only "fat" Japanese tree frogs enjoyed weightlessness.

This is Adobe's second acquisition out of India after Rephrase.ai in 2023

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can compute quartiles by hand, build the standard outlier fence from them, and run that fence over any column to get back a short list of rows worth looking at. On the sixteen orders below, one mistyped quantity gets flagged automatically while every honest large order stays inside the fence. Here is what to actually do today. On the column you care about most, get four numbers: the 25th percentile, the 75th, their difference, and 1.5 times that difference added to the 75th. Anything above that last number is a row to open and read. It is one query, and it turns "is this data clean" into a list of specific rows. The short version: a percentile is a value with a known share of the data below it. The interquartile range is the width of the middle half. Values more than one and a half of those widths beyond the middle half get flagged. The fence is easier to see than to read, so it gets the picture. The original carries a diagram here. In words: A horizontal line with a row of small filled dots along it, spaced unevenly and thinning out towards the right. A tall rectangle is drawn around the dots in the middle of the row, covering the central half of them, with a thick vertical bar inside it. The rectangle's left edge is labelled Q1, its right edge Q3, and the bar inside it median. From each edge of the rectangle a horizontal whisker line runs outward to a short vertical cap, reaching the furthest dot on that side that still lies within range. To the right of the right-hand cap stands a tall dashed vertical line labelled fence, drawn one and a half rectangle-widths beyond the rectangle's right edge, with a small double-headed measuring arrow underneath showing that distance against the rectangle's own width. One lone dot sits well to the right of that dashed line, drawn as a hollow ring instead of a filled dot, so it reads as picked out rather than belonging with the rest. Every oth

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can say what a p-value measures in one sentence, compute one by hand with no distribution theory at all, and name the four things people routinely claim a p-value says that it does not. The worked example is nine real orders where two regions differ by 168.50, and the answer comes out of counting rather than out of a table. Here is what to actually do today. Any time you are about to report that two groups differ, write down the two group sizes first. If either is under about twenty, a p-value will almost certainly come back large no matter how real the difference is, and the honest report is the difference, the sizes, and an interval, not a verdict. The short version: a p-value is the share of results at least as extreme as yours that you would get if the thing you are testing had no effect at all. Small means your result would be unusual under nothing-happening. It does not mean the effect is large, and it does not mean the effect is real. That definition is doing a lot of work in one sentence, so it gets the picture. The original carries a diagram here. In words: A histogram built from small dots, one dot per outcome, arranged in ten vertical columns of different heights standing on a horizontal baseline. The columns rise from one dot at the far left to a peak of twenty-seven dots just right of centre, then fall away to a single dot at the far right, giving the whole shape a rounded hump centred slightly left of the middle of the picture. Two vertical dashed lines cut down through the shape, one on the left of the hump and one on the right, placed symmetrically about the hump's centre. The dots lying in the two tails beyond those lines are drawn in a darker, warmer shade, and the dots in the bulk between them are drawn in a lighter blue, so the tails stand out from the middle. In the two columns the dashed lines pass through, the darker dots are stacked at the bottom of the co

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can build a moving average by hand, choose a window length on purpose rather than by habit, explain the lag it introduces, and spot the one data problem that silently corrupts every moving average you will ever compute. On the sixteen weeks below, the raw series wobbles with a standard deviation of 237. A three-week average brings that to 101, and a five-week to 62. Here is what to actually do today. Add a moving average to your busiest weekly or daily chart, using a window that matches your cycle: 7 for daily data with a weekday pattern, 4 or 13 for weekly, 12 for monthly. Then plot it on top of the raw series rather than instead of it, so the reader can see both the noise and the trend. The short version: a moving average replaces each point with the average of it and the points around it. Wobble cancels out, trend survives, and the price is that the smoothed line reacts late. The effect is easier to see than to describe, so it gets the picture. The original carries a diagram here. In words: A line chart with two lines drawn on the same axes over sixteen evenly spaced points. The first line is thin and violently jagged, swinging from near the top of the chart down to near the bottom and back again several times, with one especially deep trough about a quarter of the way along and one especially tall spike just past the middle. The second line is thicker and starts two points later than the first. It stays in a narrow band through the middle of the chart, rising and falling only gently, and never comes close to either the peaks or the troughs of the jagged line. Where the jagged line spikes to its highest point, the thick line rises only slightly and does so one point later than the spike. The word actual labels the jagged line near its left end, and the phrase three-week average labels the thick line. Every number on this page is real. The same sixteen orders used across these

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can compute both averages by hand, say in one sentence which one your question needs, and show what each does when one number in your data is wrong. On the sixteen orders below, a single mistyped quantity moves the mean by 337.50 and the median by 30. That gap is the whole reason both words exist. Here is what to actually do today. Next to every average you report, put the median beside it and look at the two numbers together. If they are close, report the mean and move on. If they are far apart, you have found something worth saying out loud, and this page is about what. The short version: the mean shares the total out equally, so every value pulls on it. The median is the middle value once you sort, so only the position of a value matters, not its size. That difference is easiest to see rather than read, so it gets the picture. The original carries a diagram here. In words: Two horizontal number lines, one above the other, drawn on the same scale. On the top line, sixteen small filled dots sit in a loose cluster across the left and middle of the line. Two markers sit almost on top of each other just past the centre of that cluster: a solid triangle pointing up, labelled mean, and a short vertical bar, labelled median. On the bottom line the same sixteen dots appear, except one dot from the middle of the cluster has moved far to the right and off the end of the line, shown by an arrow leaving the line through a small break mark at the right edge. The median bar on the bottom line has barely shifted from where it was on the top line, moving only a hair to the right. The mean triangle on the bottom line has slid a long way to the right, roughly ten times further than the median moved, and a horizontal dotted guide connects its old position on the top line to its new position on the bottom line to show the size of the slide. Every number on this page is real. One sixteen-row order

The first number I saw on Qwen3.6-35B-A3B was 12 tok/s . I almost hit publish on "Qwen regressed at generation speed" and moved on. The 3.5 baseline on the same RTX 4070 was 34.6 tok/s. A new generation running at a third of the old one would have been a hell of a headline. It was also completely wrong. The culprit was not the model. Another process on the box was sitting on 9-11 GB of VRAM, so the layers that were supposed to live on the GPU were spilling to system RAM. The tell was that my sanity-check run of Qwen3.5 slowed down too. When two independent models degrade together, the model is not the variable. I killed the offending process, re-measured, and got numbers that told a completely different story. Model Generation speed tg128 (tok/s) Runs Qwen3.6-35B-A3B 38.76 ± 0.82 avg of 3 Qwen3.5-35B-A3B 36.7 ± 1.4 avg of 3 (range 34.9-38.6) Both models sit inside the ±1.5 tok/s band on the same RTX 4070. On the tokens-per-second axis, "the new generation" is not a story. Same architecture, same activated-parameter count (3B active out of 35B), same MoE routing pattern. The half-speed regression was a measurement bug, and it lived for about half a day before its own inconsistency killed it. The lesson I keep re-learning: when the number you got is dramatically convenient for your narrative, measure it again before you write anything. The moment I could sell 12 tok/s as a regression, I should have been suspicious. The version of me that ran the second test earned the version of me that got to keep his self-respect. So where did the generation move to? If speed did not change, does the 3.5-to-3.6 bump mean anything? It does. The move lives on a different axis. The official Qwen3.6-35B-A3B model card publishes benchmarks with a very lopsided shape: Benchmark Qwen3.5 Qwen3.6 Lift Terminal-Bench 2.0 40.5 51.5 +27% QwenWebBench (frontend generation) 978 1,397 +43% SWE-bench Pro 44.6 49.5 +11% LiveCodeBench v6 74.6 80.4 +8% SWE-bench Verified 70.0 73.4 +5% AIME26 91.0 92.7

Uber is laying off about 3,300 people, or about 10% of its global headcount, in a bid to reduce management layers and invest more in its ride-sharing, delivery, and robotaxi divisions.

Edelson PC is filing 30 new lawsuits against OpenAI over the Tumbler Ridge shooting, escalating claims to aiding and abetting and naming Chris Lehane, though evidence remains unconfirmed.

The electric-auto maker will officially debut its steering-wheel-free robotaxi on Thursday. But building the vehicle is the easy part.

At least three of the first 32 Rassvet satellites likely suffered outright failures, and more may be in trouble.

Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints. By Ricardo Ferreira