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X now has a built-in video editor on iOS.

Male dragonflies' dramatic aerial combat maneuvers emerge from relatively simple vision-based rules.

Transaction lifecycle trong Postgres: BEGIN mở state machine, COMMIT đóng — quên đóng là dò mìn Một transaction trong Postgres không phải chỉ là cặp BEGIN ... COMMIT cú pháp; nó là một state machine sống cùng connection. BEGIN đẩy connection từ idle sang active , mỗi statement kết thúc đẩy nó về idle in transaction đợi statement kế tiếp, một statement lỗi đẩy sang idle in transaction (aborted) , và chỉ COMMIT / ROLLBACK mới trả connection về idle . Dev gặp lifecycle này trong việc thật không phải vì cú pháp khó mà vì một BEGIN quên COMMIT trong một code path lỗi: connection nằm trong pool ở idle in transaction vô thời hạn, giữ snapshot và lock, chặn autovacuum , kéo lock chain, làm bảng update-nóng bloat dần rồi cả service chậm chết. Cơ chế hoạt động Mặc định mỗi connection ở autocommit mode : mỗi statement là một transaction tự đóng. BEGIN (hoặc START TRANSACTION ) tắt autocommit cho tới khi gặp COMMIT / ROLLBACK . Trong khoảng đó connection có một xid (cấp khi cần ghi) và một snapshot, và lifecycle của nó đi qua các trạng thái mà Postgres phơi ra trong pg_stat_activity.state : idle — connection mở, không có transaction nào đang chạy. active — đang thực thi một statement (kể cả ngoài transaction block). idle in transaction — đang trong transaction block, vừa chạy xong một statement, đợi statement kế tiếp hoặc COMMIT / ROLLBACK . idle in transaction (aborted) — đang trong transaction, một statement đã ném lỗi, mọi statement tiếp theo trả ERROR: current transaction is aborted, commands ignored until end of transaction block cho tới khi ROLLBACK . fastpath function call / disabled — ít gặp, không phải mục tiêu của bài này. -- t0: state = 'idle' BEGIN ; -- t1: state = 'idle in transaction' (vừa thực thi xong BEGIN, đợi statement kế) INSERT INTO orders ( user_id , total ) VALUES ( 42 , 100 ); -- trong lúc chạy: state = 'active' -- sau khi statement xong: state = 'idle in transaction' lại INSERT INTO orders ( user_id , total ) VALUES ( NULL , 100 ); -- ERROR: null value

X is rolling out a new video editor and recorder for iOS with multilingual captions, green-screen effects, and other editing tools.

Discord says a bug affecting its safety system caused it to mistakenly ban more than 8,000 accounts since May. The platform's statement follows a wave of reports from users over the past week, who say they've been banned for posting images containing grids, such as chessboards, game textures, and even Minecraft inventories. Stanislav Vishnevskiy, Discord […]

Amazon Web Services (AWS) has announced a major expansion of its AWS DevOps Agent, introducing new release management capabilities designed to assess code changes and autonomously test software before it reaches production. By Craig Risi

With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture—the…

Norwegian striker Erling Haaland isn’t just a footballer anymore. He’s become an internet character perpetuated by fans and AI.

The lab will beam back data to train AI models to predict how proteins behind age-related diseases like Alzheimer’s and certain cancers behave.

Meta is facing a massive lawsuit from four US states over the addictive designs of its platforms.

Aaron Erickson explains how NVIDIA designs and tests purpose-built AI agent hierarchies. For senior developers and architects, he outlines why balancing deterministic tools with agentic discovery is crucial. Discover how to leverage rare context, implement LLM-as-a-judge test pyramids, and avoid the paradox of choice to build highly reliable, production-grade AI systems at scale. By Aaron Erickson

Test Isolation: A Lesson I Learned While Migrating Playwright Tests During my software engineering internship, I helped optimize our CI pipeline by identifying which E2E tests could safely run in parallel. That work quickly taught me that the biggest obstacle wasn't Playwright or Python, it was test isolation. This article is about that lesson. What is test isolation? A simple rule I now use is this: if a test can't run by itself with the same outcome, it probably isn't truly isolated. A well-isolated test should produce the same result whether it: runs by itself runs first or last runs after another test runs in parallel with hundreds of other tests To understand test isolation, it also helps to understand what state means. State isn't limited to database rows. During the migration, I found tests interacting with many different kinds of state. database records global configuration filesystem resources application caches If any of these are shared between tests, they become potential sources of hidden dependencies. How tests lose isolation As I started reading the existing test suite, I noticed a recurring pattern. Many tests assumed something about the environment instead of creating it themselves. Some expected specific data to already exist. Others modified global settings without restoring them afterward. Some searched for rows based on their position in a table instead of using a stable identifier like a name or ID. None of these looked particularly problematic when reading a single test. The problems only appeared once the entire suite started running together. One test would leave behind data another test didn't expect. A shared configuration would silently affect unrelated tests. A UI assertion would suddenly fail because another test inserted an extra row into the same table. Individually, the tests appeared independent. Together, they formed hidden dependencies. Not all shared state is equally difficult to isolate One realization that helped me reason abou

Tilt a CD or DVD under a desk lamp and a band of color sweeps across its surface. The disc is not painted; it is a spiral of microscopic pits, packed so tightly that they act on light the way a finely ruled scientific instrument does. Each wavelength of white light leaves the surface at its own angle, and your eye sees the result fanned out as a rainbow. That is a diffraction grating at work. The same principle that decorates a CD is the engine inside spectrometers that identify chemical elements, tune lasers, and read the composition of distant stars. This article explains how a grating spreads light, how to compute the angles, and where the analysis goes wrong. Why this calculation matters A prism also splits white light, but a grating does it with far more control and far more precision. Because the spreading depends on a countable number — the spacing between lines — a grating can be designed to send a chosen wavelength to a chosen angle. That predictability is what makes it the heart of the spectrometer. Spectroscopy underpins a remarkable range of work. Astronomers read a star's chemistry and velocity from the dark lines in its spectrum. Chemists identify unknown compounds by the wavelengths they absorb. Telecommunications engineers use gratings to combine and separate the many wavelengths sharing a single optical fiber. In every case the first task is the same: given the grating and the light, predict the angle at which each wavelength emerges. Get that wrong and a spectral line lands on the wrong detector pixel, and the measurement is meaningless. The core formula A diffraction grating is a surface ruled with a large number of equally spaced, parallel lines. When light passes through or reflects off it, each line acts as a source of secondary waves. Those waves interfere, and they reinforce each other only in specific directions — the directions where waves from neighboring lines arrive exactly in step. The condition for that reinforcement is the grating equ

The industry is described as a "dual-track" race. On one side are incumbents (Big Tech) with massive infrastructure and deep pockets. On the other is a wave of nimble startups specializing in specific engineering, error-correction, and simulation challenges. The sector is currently transitioning beyond the Noisy Intermediate-Scale Quantum (NISQ) era toward fault-tolerant systems and commercial quantum advantage—the point where quantum machines reliably outperform classical supercomputers for useful tasks. These companies are building the foundational cloud-accessible platforms and hardware: Amazon Braket (AWS) IBM Google Quantum AI Microsoft NVIDIA These players are driving innovation in specific qubit modalities or niches: Superconducting Qubits: Rigetti Computing, IQM, and Atlantic Quantum. Trapped Ion: IonQ, Quantinuum, and Alpine Quantum Technologies. Neutral Atom: QuEra, PASQAL, and Atom Computing. Photonic: Xanadu, PsiQuantum, and Quandela. Silicon/CMOS: Diraq and Silicon Quantum Computing. Error Correction: Riverlane and Q-CTRL are focused on the "noise" problem, helping make unstable qubits behave predictably. Software & Algorithms: Classiq (design automation) and Multiverse Computing (finance/optimization applications). Quantum-Safe Cybersecurity: PQShield and evolutionQ are developing cryptographic solutions to protect data against future quantum threats.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. OpenAI CEO Sam Altman’s oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the Financial Times reported that Altman is in…

Its issues with current nuclear safety standards are termed semantic, not physical.

Bullshit is cheap but truth is expensive. Anyone with half a brain cell can post wild misinformation that goes mega viral, which wastes the time and expertise of highly trained people who feel an obligation to inform others of the truth. Today I want you to meet one of those highly-trained people, Dr. Zachary Rubin, […]

It will take several weeks for the Link spacecraft to rendezvous with NASA's Swift observatory.

It will take several weeks for the Link spacecraft to rendezvous with NASA's Swift observatory.

Anthropic accused of spying on users; engineer says “experiment” is over.