标签：#ics

The outgoing executive director of the Office of Homeland Security Statistics is one of very few federal officials to speak out against the Trump administration’s immigration crackdown.

Most experiments that come back "no clear winner" were unwinnable on the day they launched. The data could not resolve an effect that size, and no amount of extra runtime was going to change that. You can find this out in about two minutes, before you spend anything, with one formula and a resampling pass over your own data. Here is the check, in three steps. Step 1. Compute the smallest lift your data can see For a two-arm test on a conversion rate, the smallest lift detectable at 95% confidence and 80% power is a one-liner: from math import sqrt Z_ALPHA = 1.96 # two-sided 95% Z_BETA = 0.84 # 80% power def mde ( baseline_cvr : float , n_per_arm : int ) -> tuple [ float , float ]: """ Minimum detectable effect: absolute (pp) and relative (%). """ se = sqrt ( 2 * baseline_cvr * ( 1 - baseline_cvr ) / n_per_arm ) abs_lift = ( Z_ALPHA + Z_BETA ) * se return abs_lift * 100 , abs_lift / baseline_cvr * 100 At a 3% conversion rate: clicks per arm smallest lift you can detect 5,000 +32% relative 20,000 +16% relative 100,000 +7% relative Read the middle row twice. Twenty thousand clicks per arm is a serious amount of traffic for a mid-market account, and a real 15% improvement still lands inside the confidence interval. The report will say "inconclusive," and the team will read that as a verdict on the idea. It is a verdict on the instrument. Invert the same formula and the planning question gets easier: at 3% baseline, detecting a 10% lift needs about 51,000 clicks per arm, and detecting a 5% lift needs about 203,000. If your account produces 8,000 clicks a month, you now know the honest answer to "how long should we run this." Step 2. Stop assuming your conversions are independent The formula above treats every click as an independent coin flip with the same probability. Account data does not behave that way, and the gap is not small. In a corpus of 31 advertiser accounts I maintain for diagnostic work (9.46 million search term rows, roughly $133M of spend, September 2024

Today, I’m talking with Evan Smith, who is cofounder and CEO of Altana, a company that develops software tools to manage big, messy supply chain networks around the world. We last had Evan on in early 2025 to talk about how Trump’s first few waves of tariffs were starting to affect global trade and what […]

The massive seed round was led by Index Ventures and Ribbit Capital, with participation from Sarah Guo's Conviction Partners.

“It’s not an argument with two sides, it’s an argument with 10 sides,” one senior administration official tells WIRED about how US AI policy is being shaped.

Clear a corner of your workbench and gather a handful of parts, because this palm-sized television is an afternoon build, not a semester project. Here is the shopping list for a Scanwheel of your own: A Raspberry Pi Pico to run the show An A4988 stepper driver A 21-02485 stepper motor (or a similar small NEMA-style unit) Five LEDs, plus current-limiting resistors A 3D-printed case and spinning disk The Scanwheel, built by a maker who goes by [Ancient], is a mechanical TV that fits in your hand. Instead of a glowing panel, it leans on persistence of vision: your eye holds each flash of light for a fraction of a second, so a row of blinking LEDs seen through a moving slit reads as a solid picture. Spin the disk fast enough and the flicker melts into an image. How the picture actually forms The disk sitting on top of the case carries 20 small holes spaced evenly around its edge, each drilled at a slightly different height. As the motor turns, only one hole passes in front of the LEDs at a time, so light escapes in a scanning line rather than a wash. The Pico drives the stepper up to roughly 900 RPM through the A4988, then fires the LEDs in a precise order timed to the disk position. Get that timing right and the holes trace out a grid. The payoff is a 20x20 pixel color display in the center, flanked by two more 20x20 black-and-white panels that can each show a different image. Five LEDs feed all three. The whole coordination job lives on the Pico's GPIO pins, which is why the wiring stays simple enough to manage on a breadboard before you commit anything to a soldered protoboard. Small light baffles in the base keep the LEDs from bleeding into each other, a detail worth copying if your first image looks smeared. Give it a spin The full build guide, firmware, and disk files are on the project's GitHub repository , so you can match the hole spacing and LED timing exactly. If your image drifts or tears, start by trimming the RPM and re-checking when each LED switches rela

Black holes eject powerful energy jets that blow away the surrounding gas to great distances. So how can they continue to grow?

Sequential Testing and the SPRT: How to Stop a Test Early Without Cheating Meta description: Peeking at a fixed-sample A/B test inflates false positives. Sequential testing lets you check results repeatedly and stop early without cheating. TL;DR Fixed-sample testing assumes you'll wait for a pre-calculated sample size before looking at results. Checking early and stopping the moment you see significance — "peeking" — quietly inflates your real false-positive rate, often far above the 5% you think you're getting. Abraham Wald's Sequential Probability Ratio Test (SPRT), developed for wartime quality control, is the mathematically rigorous alternative: a procedure built to be checked repeatedly, with pre-calculated boundaries that keep the false-positive rate honest by construction. The difference between the SPRT and peeking isn't willpower — it's that the SPRT's stopping rule is part of the math from the start, so stopping early doesn't cost you anything in error-rate control. Sequential design is the right call when traffic is limited, the cost of running a test too long is high, or the business genuinely can't commit to waiting for a fixed horizon — not a substitute for rigor, but a different kind of rigor suited to a different constraint. This is a methodology choice, not a shortcut — and it's one input into the broader question of how much certainty a given bet needs, covered in the Confidence Tier Model . Every experimentation program eventually hits the same moment: a test has been live for four days, the dashboard shows a lift, and someone — a stakeholder, a PM, sometimes you — asks "can we call it?" The honest answer depends entirely on what kind of test you designed, and most teams don't have a clean answer, because most teams designed a fixed-sample test and are now trying to read it like a sequential one. Those are not interchangeable. Knowing the difference, and choosing deliberately between them before the test starts, is the actual skill — not "wait lon

The Confidence Tier Model: How to Decide When Your Data Isn't Enough Meta description: Most testing programs are built for traffic they don't have. Three confidence tiers — proven, directional, speculative — each with its own bet-sizing rule. TL;DR Fixed-sample A/B testing assumes you can wait for statistical significance. Most teams can't — traffic is too thin, or the market is moving too fast to wait. The fix isn't lowering your standards. It's replacing the binary "significant / not significant" gate with three explicit confidence tiers — Proven, Directional, Speculative — each with its own evidence bar and its own bet-sizing rule. Underpowered tests systematically overestimate effect size (the "winner's curse" ). A confidence tier that accounts for this is more honest than a p-value that pretends otherwise. The way to move a learning up a tier isn't more of the same test — it's triangulation: stacking correlated, individually-weak signals until they converge. This is a methodology choice, not a compromise. Teams that name their confidence tier explicitly make faster, more defensible decisions than teams that either wait for certainty they'll never reach, or ship everything with false confidence. A product manager says: "Users want better deals." A brand marketer says: "TV is driving more direct demand." A performance marketer says: "This channel has a strong ROAS." Finance says: "But is this incremental?" Product says: "Will this hurt user trust?" Leadership says: "Should we scale this?" Six people, six kinds of evidence, and a decision that needs to get made this quarter — not whenever a test finally clears p<0.05. This is the actual job: not running tests, but converting six competing claims into one evidence base leadership can act on. Most experimentation methodology is written for a world where you have the traffic to wait for a clean answer. Most companies don't live in that world. The problem classic A/B testing doesn't solve Fixed-sample significance tes

Forget YouTube videos—frontier physical AI models need multiple camera angles, dense annotation, and soon, brain wave readings.

Herodotus wrote of a bird that lived five hundred years in Arabia, and when its life came to an end, it did not wait to be surprised by death. It built its own nest of cinnamon and myrrh, set the nest and itself alight, and let a new bird rise from what the fire left behind. The Hestia argued for tending a fire that must never go out. That’s true, and it isn’t the whole truth. Teams end. People leave. Companies get acquired, reorganized, shut down, and five years from now some part of this whole model will probably look as dated as the practices it was written to replace. No amount of tending prevents that. Pretending otherwise is its own kind of Alexandria , a slow decline dressed up as continuity, right up until the fire goes out anyway and nobody chose the moment. The bird in Herodotus doesn’t get caught by surprise. It builds the pyre itself. Chooses the moment, gathers what matters, and burns deliberately, trusting that what rises afterward carries the shape of what came before, not because the fire preserved the old bird whole, but because starting over was never the same thing as starting from nothing. That’s the part tending alone can’t promise. A team that’s about to be split up can hand its shared model to whoever inherits the work on purpose, the way a rep in the Boule carries a decision back instead of leaving it to travel however it happens to travel. A team about to lose its most experienced person can spend the weeks before that departure making sure the framing, not just the conclusions, made it into someone else’s head, the way the Mimesis argued a junior actually learns. None of that stops the ending. It decides what the ending leaves behind. This series doesn’t get to end with a fire that never goes out. Nothing does. It gets to end with the only thing actually inside anyone’s control. Build the pyre on purpose. Choose what goes into the fire. References The Myth of the Phoenix: Rebirth and Renewal : Greek Mythology, on Herodotus’s original accoun

Mike Flanagan's latest Stephen King adaptation, Carrie (this will be his fourth), is slated to make its debut on Amazon Prime on October 7th. The new trailer dropped at Comic-Con 2026 and doesn't contain many surprises. If you're familiar with Carrie at all, it hits all the expected notes. Though it's clearly been updated for […]

While perhaps less dramatic than in years past, Marvel nonetheless had some big reveals lined up for Comic-Con. The highest profile was certainly the announcement that Ryan Gosling would be joining the MCU as Ghost Rider, with the Shawn Levy-directed (Deadpool & Wolverine) standalone feature set to drop sometime in 2028. The studio also brought […]

The dangerous part of AI analytics is not that a model may write a bad chart title. It is that one friendly question can turn into a warehouse query your user was never supposed to run. That risk is growing because builders are adding natural language analytics to products, dashboards, internal tools, support consoles, and agent workflows. Users want to ask, “Which accounts are slipping this month?” and get an answer. That is useful, and it is a permissions trap. If your AI analyst connects through one powerful service account, every customer question may inherit the same access. Your app may have perfect tenant checks in the UI, while the AI path quietly bypasses them. This guide shows how to design AI analytics row-level security so customers can ask useful questions without leaking rows, metrics, or private business context. Why this topic matters now Recent AI platform activity points in the same direction: builders are moving from “chat with documents” to “ask questions about live business data.” Developer pain points are consistent: safe natural language questions, tenant-scoped queries, auditable user identity, consistent metric definitions, and charts that do not expose raw tables. The search gap is clear. Many articles compare embedded analytics tools. Others explain database row-level security in isolation. Fewer walk through the product architecture for a customer-facing AI analyst that must handle tenant scope, natural language, semantic metrics, safe SQL, and audit evidence together. The core failure: one AI user, many real users Traditional analytics has a simple identity chain: Human user → app session → analytics permission → database query The database or BI layer knows who is asking. The app can apply tenant filters, role checks, and column restrictions. AI analytics often breaks that chain: Human user → app session → AI service → service account → database query Now the warehouse sees one identity: the AI service account. That account usually need

The AI “startups” worth billions of dollars are raising alarm bells about Chinese AI. The smaller players have a totally different take.

In Pasadena, California, there's a cute red brick courtyard where one storefront isn't like the rest. The glass doors open onto a sparse industrial hallway, which leads to a sunlit foyer with a large spiral staircase. Go up, and you'll see a typical coworking space: open tables, healthy snacks, a collection of meeting rooms with […]

TikTok has attracted the ire of the European Union over its protection of children who use the video sharing platform. The European Commission announced preliminary findings today under the Digital Services Act (DSA) that offer specific issues to address. The Commission suggests that TikTok should adjust the default settings of minors' "public" accounts so the […]

US robotics researchers flew to Venezuela with snakebots after getting a call.

Never mind the “heat death of the universe.” Entropy is just a bridge between two levels of reality.

After Trump publicly backed pillow salesman turned election denier Mike Lindell for governor of Minnesota, the local GOP pushed back strongly.