标签：#go

Preservation "goodies" also includes downloadable 3D models for desktop perusal.

Many production problems are aggregation problems in disguise: merge partial results, combine filters, reduce collections, or compute values in parallel. Abstract algebra gives those operations precise names and laws, which makes it easier to design reusable code and reason about whether regrouping or parallel execution is safe. This article introduces semigroups and monoids through C# examples. The point is not to decorate ordinary code with mathematical vocabulary; it is to make composition rules explicit enough that the compiler and your teammates can see them. Many of us studied at technical universities and took a long list of mathematics courses while wondering why we needed them. Algebra may have been one of those courses. I do not mean a school textbook packed with tedious exercises about polynomials and equations. Abstract algebra is a difficult, expansive subject with a steep learning curve, but it is also fascinating. Its central objects of study are algebraic structures, which you can build almost anywhere and from almost anything. They can also inspire new approaches to writing code. Before we get there, let us take a short algebraic detour. Semigroups, Monoids, and Their Laws We will barely scratch the surface of abstract algebra. We will cover only the basic axioms and definitions needed to understand what is going on. There will not even be any theorems or proofs. Let us begin with an arbitrary set X , assuming familiarity with sets and mappings. If we take a mapping ∘ : X × X → X together with our set, we get an algebraic structure. In other words, an algebraic structure is a pair ( X , ∘ ) consisting of a set and a closed binary operation. The integers under addition, ( Z , + ) , are one example. This is where the operations become important: they determine the structure and behavior of the elements. Arbitrary functions are not useful enough, so we require them to have certain properties. Put differently, the operation must satisfy a set of axioms.

EU users are welcome to look up the definition of "malicious compliance" on, say, DuckDuckGo.

Director Takashi Yamazaki's hotly anticipated sequel takes place two years after 2023 film's events.

When you are aiming to publish an app on the Google Play Store with a personal developer account created on or after November 13, 2023, you must meet closed testing criteria before applying for production access. In December 2024, Google adjusted this requirement from 20 testers down to 12 testers opted in for 14 consecutive days. But what actually happens if you fail google play 14 day testing requirement? Many developers worry that failing will permanently damage their developer standing, trigger account suspensions, or permanently lock their app in testing mode. The reality is more straightforward, though still frustrating if you are eager to launch. Failing to satisfy the requirement manifests in two main ways: an interrupted streak timer that resets before you can submit your request, or an outright rejection from Google when you request production access after completing the 14 days. The Difference Between a Counter Reset and Production Rejection It is essential to understand that failing the testing process can happen at two distinct stages. The first stage is during the active testing window itself. To satisfy Google's criteria, at least 12 testers must remain opted into your closed test every single day for 14 consecutive days. If your opted-in tester count drops below 12 because users opt out or uninstall the app, the 14-day counter pauses or resets. You cannot click the button to apply for production access until that continuous 14-day mark is reached with at least 12 enrolled testers. The second stage occurs after you complete the 14 days and submit your application for production access. At this point, reviewers or automated evaluation scripts at Google analyze the telemetry gathered during those two weeks. If Google determines that your testers were inactive, fake, or unengaged, your application for production will be denied. Why Google Rejects Production Access Applications Reaching 14 days with 12 opted-in accounts in your Play Console dashboard is o

Like traditional weather models, it benefits from an expanded set of inputs.

This essay was written with Barath Raghavan, and originally appeared in Lawfare . In April, an artificial intelligence (AI) agent conducting a routine task at a company hit a snag, tried to solve it, and soon ended up deleting the company’s database along with all of its backups. In July, OpenAI asked an unreleased AI model to attempt a hacking test. Instead of staying in the isolated box the developers had put it in, the model hacked onto the open internet and into another company to steal the answers. And as reported in August, an AI agent booked someone into a full gym class by ...

The EU continues to go after Google search practices it sees as anticompetitive.

Google said it would bring an Iowa nuclear power plant back from the dead. Now, the plant's owner is getting a $1.9B loan from the U.S. Energy Department.

If you're interested in trying out Claude's Gmail capabilities but are worried it will make mistakes, just tell it to ask for approval before doing anything.

Google DeepMind has unveiled an AI tool that its scientists claim could help unravel the mysteries of the human genome and transform our understanding of biology, accelerating scientific research and ultimately paving the way for new treatments for diseases. The platform, called AlphaGenome Atlas, contains a "predictive map of every possible DNA letter change in […]

Google is a step closer to getting access to nuclear power for its AI data centers.

Interesting research: “ Stealing Reasoning Traces from Proprietary LLM APIs “: Abstract: Leading large language model providers now conceal their models’ step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different sessions, users, and models within a provider’s ecosystem. We exploit this compatibility to develop a scalable decryption jailbreak. By injecting an encrypted reasoning trace from a given model into a weaker, and less safeguarded model from the same provider, we force it to decode and output the trace verbatim in plaintext, without ever jailbreaking the more capable model directly. This vulnerability enables four distinct attack vectors. First, it circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model’s reasoning, as we demonstrate across Anthropic, OpenAI, and Google. Second, it allows for large-scale private data extraction. Developers frequently share session logs publicly, unaware of contents of the encrypted blocks. By decoding 315,320 reasoning blocks scraped from public repositories, we recovered 367 Personally Identifiable Information (PII) artifacts and 182 credentials. Third, it inadvertently reveals hazardous information hidden within the reasoning process, even in cases where the model’s final, visible output safely rejects a malicious request. Fourth, attackers can leverage this flaw to execute invisible prompt injections, embedding malicious payloads entirely within encrypted blocks to poison public agentic rollouts. Following responsible disclosure, we propose concrete cryptographic and system-level mitigations to secure client-side reaso

Turn Google Sheets into a Fully Interactive, Differential ACID Mutation Console for Apache Iceberg without Reverse ETL SaaS or Cloud Servers. Hero Infographic: Interactive Bidirectional Lakehouse Writeback via Google Sheets & Apache Iceberg. Enables business operators to query filtered records from an open Apache Iceberg table on Google Cloud Storage, visually edit values, add new rows, or purge obsolete records directly within a Google Sheets grid with an embedded dark-themed console, and commit atomic, microsecond-tolerant ACID mutations back to Parquet storage via BigQuery without Reverse ETL SaaS or persistent servers. Structural Analysis of the Hero Infographic: The hero infographic illustrates the complete, self-contained operational loop connecting frontline spreadsheet agility with immutable open lakehouse storage across three interconnected stages: 1. Predicate Query (Apache Iceberg Open Lakehouse on GCS) : The left section shows the enterprise analytical foundation hosted on Google Cloud Storage, where Apache Iceberg manages immutable Parquet data files, hierarchical Avro metadata, and commit snapshots. When a user requests high-value records, BigQuery acts as an on-demand distributed compute accelerator, executing SQL queries with predicate pushdown (e.g., SELECT * WHERE price > 1000 ORDER BY id ASC ) to fetch precise subsets in sub-seconds. 2. Frontline Editing in Google Sheets (Intuitive Operational Experience) : The central section features a modern, user-friendly Google Sheets grid docked with the sleek dark-themed Iceberg Lakehouse Console sidebar. A business user effortlessly modifies data on the grid with immediate visual feedback: modifying existing values (e.g., updating price from 1500 to 123 ), appending new rows with unique primary keys ( + ADD (New Row id:121) ), and deleting obsolete rows ( 🗑️ DELETE (Removed id:104) ). Native cell validation guarantees data cleanliness, while a Privacy Mode toggle ( [🔒 Privacy: ON] ) automatically masks sen

Chromebooks are great devices, but they might not be able to do everything a Windows or Mac device can. Consider carefully before using one daily.

GM's lineup is dropping Apple CarPlay and Android Auto in favor of its own infotainment systems. But one model is keeping the phone-mirroring capabilities.

There are many reasons why you'd want to download a YouTube video, but doing so comes with a bevy of questions about the legality of the available methods.

⚡ TL;DR Four things about controller mechanics are widely half-understood: what Reconcile receives, where its work comes from, what a periodic resync is, and what a predicate turns off. I built an operator, broke it five ways, and measured each mechanism directly. The reconcile function runs in 2.71ms mean, 77/77 under 25ms , a short resync period costs zero additional API requests , and GenerationChangedPredicate cut steady-state reconciles by 48.5% without touching live repair at all. That last combination is the one that matters at scale. Repo, raw data, and harness: kirPoNik/k8s-drift-operator . 🧩 The four barriers Everyone who runs Kubernetes knows the platform repairs itself. Delete a pod, it comes back. Scale a Deployment by accident, something puts it back. Almost nobody who relies on that property can say how it works, and the gaps are specific and consequential. I keep meeting the same four: People think a controller is told what changed. It is not, and the reason it is not is the single most important design decision in Kubernetes. People think a controller polls the API server. It does not, and knowing what it does instead tells you where your API load actually comes from. People think a resync is a re-check against the cluster. It is not, which is why a short resync period is nearly free — and why the number that is expensive sits somewhere else entirely. People treat a predicate as a pure optimisation. It is a filter with a silent cost, and the cost is not the one the documentation warns you about first. So I built the smallest system that has the self-healing property, broke it on purpose ten times per failure mode, and instrumented each of those four mechanisms until I could state what it does rather than what it is said to do. What I built. One CRD called Echo , holding an image, a replica count, and a greeting. A controller keeps three child objects in sync with it — a Deployment, a Service, and a ConfigMap holding the greeting — with owner referen

Millions rely on Android Auto every day for navigation, music and messaging, but one of the common issues is apps vanishing from the platform without warning.

The Google Store has some of the best trade-in values for a new Pixel, whether you're turning in a Pixel, iPhone, or Galaxy device.