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Structural engineers evaluate ASCE 7 Chapter 2 load combinations on nearly every design. The factors themselves are not hard — the failure mode is a spreadsheet cell that someone “improved” six months ago, or a notebook that only checks one combination. loadcomb is a small, dependency-free Python library and CLI that evaluates the basic LRFD and ASD combination sets on scalar load effects you already have from analysis. pip install loadcomb loadcomb --D 120 --L 80 --S 40 --W 55 --method LRFD from loadcomb import LoadCase , governing g = governing ( LoadCase ( D = 120 , L = 80 , S = 40 , W = 55 ), method = " LRFD " ) print ( g . id , g . value , g . formula ) What it handles Basic LRFD and ASD combinations from ASCE 7 Chapter 2 (Lr or S or R) resolved to the roof variable with largest absolute effect Automatic ± envelope for wind and earthquake Governing combination by absolute value What it is not Not FEA, not member design, not every exception in the standard. Confirm the ASCE 7 edition and local amendments for your project. Links pip install loadcomb GitHub PyPI Blog: sybilgambleyyu.github.io/posts/loadcomb.html MIT licensed.

The rule of three for evals says zero failures in N runs is a count, not a rate. With 0 failures in N independent runs, the exact 95% upper bound on the true failure rate is 1 - 0.05^(1/N) , which 3/N approximates. After 100 clean runs you still cannot rule out a 2.95% rate, about 1 in 34. Here is the reading that bites you. Your eval harness runs the agent 100 times, prints "0 failures," and the tile goes green. Someone screenshots it into the launch thread. The unspoken translation is "the failure rate is zero." It is not what the data says. I wrote a small script to make the gap concrete, so I ran a real gate over 200 deterministic agent runs first, counted honestly, and got the dashboard everyone trusts: gate: spend<=budget over 200 deterministic agent runs observed failures: 0 (distinct scenarios: 200) naive point rate : 0.00% binomial SE: 0.00 pp naive 95% CI : [0.00%, 0.00%] <- zero width: false certainty Look at the standard error. For a zero count the binomial SE is sqrt(0*1/200) , which is exactly 0, so the naive 95% interval collapses to [0.00%, 0.00%] . A zero-width confidence interval. The math is telling you it is completely certain, from 200 samples, that the true rate is precisely zero. That is obviously wrong, and it is the exact shape of every "all green" board I have ever trusted too much. TL;DR "0 failures in N runs" is an observed count, not a rate. The naive binomial SE of a zero count is 0, which is why a green board looks like proof and isn't. The honest number is the one-sided upper bound. With 0 failures in N runs, the 95% upper confidence limit on the true failure rate is 1 - 0.05^(1/N) . The rule of three, 3/N , approximates it and rounds the risk slightly up. At N=30 the bound is 9.50% (about 1 in 11). At N=100 it is 2.95% (1 in 34). At N=1000 it is 0.30% (1 in 334). Zero failures in 30 runs is compatible with a 1-in-11 true failure rate. To rule out a 0.1% rate at 95% you need about 2995 clean runs, not 50. "We ran it fifty times" and "

The cybersecurity-focused models, including GPT-5.6 Sol, broke out of a testing sandbox, exploited a zero-day, and gained access to the open internet to pull off the attack.

The country's new law also bans the use of mobile phones in schools.

Australia was the first country to issue a ban in late 2025, aiming to reduce the pressures and risks that young users may face on social media, including cyberbullying, social media addiction, and exposure to predators.

Over the past decade, streaming platforms competed by dominating individual formats like music, video, podcasts, or audiobooks. Now, as AI makes it easier to create, organize, and recommend content, those distinctions are fading, pushing companies like Spotify, Netflix, YouTube, and TikTok to become all-purpose entertainment destinations instead.

Pat Gelsinger wants to pave the way to ever more powerful artificial intelligence using tiny beams of light.

NatGeo's Pompeii: Out of Time fuses historical fact and imagination to bring city's last 24 hours to life.

A practical framework for test planning, hardware scheduling, artifact traceability, failure classification, and evidence-based quality gates Disclaimer: The views expressed in this article are my own. The architecture, examples, terminology, and code snippets are generalized for educational purposes and do not describe or disclose any employer’s proprietary systems, confidential information, or internal implementation details. A software change can compile successfully, pass unit tests, and still introduce a serious GPU regression. The failure may appear only on one GPU generation. It may depend on a particular driver, firmware revision, operating system, graphics API, or workload. A change may preserve functional correctness while quietly reducing performance. It may also cause an intermittent failure that disappears when the test is rerun. This is why GPU validation cannot be treated as conventional CI/CD with a GPU runner attached to the end of the pipeline. A dependable GPU validation platform must coordinate: Software and firmware artifacts Hardware configurations Test coverage GPU resource scheduling Failure classification Performance baselines Engineering evidence It must do all of this while operating under an important constraint: compatible GPU capacity is limited and expensive. The objective is not simply to run more tests. It is to produce reliable evidence quickly enough to support engineering decisions. Why conventional CI/CD is not enough A conventional application pipeline often resembles: Commit ↓ Build ↓ Unit tests ↓ Integration tests ↓ Deployment A GPU validation pipeline is more multidimensional: Code or configuration change ↓ Build software and firmware artifacts ↓ Determine affected GPU configurations ↓ Reserve compatible hardware ↓ Prepare the driver and runtime environment ↓ Run functional, stability, and performance tests ↓ Collect logs, traces, metrics, and crash artifacts ↓ Classify failures and compare results with baselines ↓ Make a mer

A single chemical made by mole-rat queens enforces the social order.

Anthropic blocks authors from opting out of $1.5B settlement at last minute.

There are new 3.6 and 3.5 models today, but Google is already training Gemini 4.

With the new "Replace Audio" tool, users can update the music on their post at any time while keeping the post's existing likes, comments, shares, and reach intact.

With the new tools, parents and guardians will be able to view their teen's time spent on Threads, set daily time limits, adjust sleep mode, and manage their privacy settings.

Nvidia’s Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company’s growing ambition to power every layer of AI infrastructure.

TikTok recently settled its portion of the case with the plaintiff ahead of a jury trial that starts next week in Los Angeles. YouTube has also reached a deal, leaving Meta as the only remaining defendant.

Halliday’s G2 glasses can listen to and summarize your workplace meetings—with no video involved.

Building a Decompiler Pipeline in Rust: Why Fission Separates NIR and HIR Decompiler output often looks simple from the outside. A binary goes in. Pseudocode comes out. But between those two points, a decompiler must recover several different kinds of information: instruction semantics register and memory effects control flow stack variables calling conventions data types expressions loops and conditionals readable source-like structure Trying to represent all of this in one intermediate representation quickly becomes difficult. While building Fission , a reverse-engineering and binary decompilation workspace written primarily in Rust, I decided to separate the decompiler pipeline into two main intermediate representations: NIR , a lower-level representation intended to preserve machine semantics HIR , a higher-level representation intended to express recovered, human-readable program structure This article explains why that separation exists, what each representation owns, and why it makes decompiler development easier to reason about. Correctness and readability want different things A decompiler has at least two responsibilities. First, it must preserve the behavior of the original machine code. Second, it must produce output that a human can understand. Those goals overlap, but they are not identical. Consider a simplified fragment of machine-level behavior: tmp0 = RAX tmp1 = tmp0 + 1 RAX = tmp1 flags = update_flags(tmp0, 1, tmp1) A human reader may prefer to see: rax ++ ; The concise form is easier to read, but it omits details that may still matter elsewhere in the pipeline. The flags update could affect a later conditional branch. The operation width may matter. The source and destination could alias. The operation may have originated from an instruction with additional side effects. If the decompiler converts everything into source-like syntax too early, it becomes easy to discard evidence. If it keeps everything at machine level until the final rendering st

Michigan has been at the epicenter of the wildfire smoke and explosive diarrhea outbreaks. The progressive Democrat talks to WIRED about how he would tackle the forces behind them if he’s elected.

The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and…