标签：#Go

The neon flying squid can fly in formation. The shoal of about 100 squid rose unexpectedly from a patch of the Pacific Ocean around 370 miles from Tokyo and glided near the boat for about 30 metres. The astonished researchers were the first to capture photographs of such a thing, which looked like the early stages of an alien invasion. They were probably neon flying squid ( Ommastrephes bartramii ), the subsequent study states , a species that is part of a 20-strong flying squid family that was known to leap from the water but, until then, was only rumoured to also be able to glide above it...

Two years after the U.S. Department of Justice alleged that TikTok violated the Children’s Online Privacy Protection Act, it has reached a $400 million settlement.

The private Android-based OS will expand beyond Pixels next year.

Walmart will soon allow you to pay for your items with Google Pay or Apple Pay. In an announcement on Friday, Walmart says it's going to bring tap-to-pay capabilities to "select" Walmart and Sam's Club locations starting August 24th, before rolling out support to all US stores by the end of 2026 and gas stations […]

This sort of research is both exciting and terrifying: The two models in question were told to generate complete genomes for a viable bacteriophage—a type of virus able to infect and replicate itself inside bacteria, destroying them from the inside. Using an existing bacteriophage as an example—ΦX174 (pronounced “fie-ex-1-7-4”), known for its ability to infect and destroy E. coli bacteria—the models generated about 700,000 potential designs, of which the researchers picked 285 that looked most promising. The researchers then synthesised new DNA molecules using those designs and inserted them into E. coli bacteria, before waiting to see if viable bacteriophages would emerge...

The security review is being performed by the Idaho National Laboratory, and the research is being funded by a company -- or a group of companies -- in the electric and autonomous vehicle industries.

Are pigs flying? Walmart has finally caved on its refusal to support Apple Pay and Google Pay.

I recently built WordScrambler, a free tool for unscrambling letters and solving anagrams, mostly out of frustration with existing tools being cluttered with ads or requiring sign-up just to see a result. Here's a quick look at the core technique behind how it works. The problem Given a jumbled set of letters (say, ucim), find every valid dictionary word that can be formed from some or all of those letters. The naive approach, generating every permutation and checking each against a dictionary, gets slow fast. A 7-letter input has 5,040 permutations; a 12-letter input has nearly 480 million. That's not viable for instant results. The signature trick The key insight: two words are anagrams of each other if and only if their letters, sorted alphabetically, produce the same string. For example: "listen" -> sorted -> "eilnst" "silent" -> sorted -> "eilnst" Both hash to the same signature. So instead of generating permutations, you can: Precompute a signature for every word in your dictionary and group words by signature. For a given input, generate the signature of the input (and its relevant sub-combinations, for partial-length matches). Look up matching signatures in a hash map, an O(1) lookup instead of a brute-force search. This turns "find every valid word from these letters" into a fast lookup problem rather than a combinatorial one, which is what makes results feel instant even against a large dictionary (WordScrambler checks against roughly 246,000 words). Handling partial-length matches Most real unscrambling needs go beyond "use every letter", people want every valid word of any length using a subset of the given letters. That means generating signatures for all relevant letter subsets (not full permutations, just subsets, which is a much smaller set) and checking each against the dictionary map. Try it You can play with the live version here: wordscrambler.online — it also shows word definitions and Scrabble/Words With Friends point values alongside each resu

Cloudflare has recently detailed how it is using AI to transform internal engineering standards from passive documentation into an actively enforced control system across the software development lifecycle. By Craig Risi

Full walkthrough: https://www.youtube.com/watch?v=vFuFA3ByCs0 LeetCode 3116 — Kth Smallest Amount With Single Denomination Combination. Here’s the trick everyone misses: Brute force (generate all multiples, pick k-th) fails because k can reach 2×10⁹. The real approach: Binary search the answer X Count valid amounts ≤ X using inclusion-exclusion Odd subsets add, even subtract (bitmask over coins) LCM via GCD, break when LCM > X O(n · 2ⁿ · log(k·M)) — passes cleanly. The 26% acceptance rate makes this look harder than it is. Once you see the count(X) monotonic trick, it clicks.

The AI Security Institute has a new report of AI systems engaging in “unsanctioned behavior”—what I have been calling “ genie behavior —while being tested on their cybersecurity capabilities. The incident stemmed from a single evaluation where agents were given a task of solving a cyber security challenge. We ran this challenge 122 times across several models. Our investigation found that in 10 of those runs, an AI agent took autonomous, unsanctioned action on the live internet, targeting real people and organisations. In total, we catalogued 19 such actions. Almost all of this behaviour (17 actions) came from a single model, Anthropic’s Mythos 5, with 2 actions involving OpenAI’s GPT-5.6-Sol with cyber classifiers (mechanisms to prevent misuse) disabled. In the most serious case, an agent tried to insert malicious code into an open-source project. In an attempt to get the code approved, the agent engaged in social engineering—creating fake online identities and using them to pressure the project’s maintainer to approve the code. A human maintainer caught and refused to approve the malicious code...

VRP Is Ready for External Validation — Who Will Be the First to Pilot It? My name is Vitalijus Riabovas. I am the independent architect and creator of VRP — Veil Routing Protocol . VRP is a continuity-first networking architecture built around a simple principle: A logical session should not have to die simply because the network underneath it changed. Wi-Fi → LTE/5G. IP mutation. NAT / CGNAT churn. Temporary blackout. Path failure. Recovery. Replay attempts. Stale authority. Duplicate execution. For a long time, VRP was primarily architecture, runtime engineering and internal validation. That stage has changed. The public validation boundary exists now. And I am inviting serious engineers and organisations to test it. DON'T TRUST MY CLAIMS. TEST THEM. I am not asking the networking industry to believe a presentation. I built the measurement boundary. The public VRP Validation Kit provides engineers with an environment for evaluating observable behaviour independently. You can: clone the repository; run the Docker scenarios; inspect generated evidence; verify manifests and hashes; attack the evidence; delete events; duplicate events; reorder events; attempt replay; introduce stale conditions; corrupt artifacts; run the verifier; reproduce PASS / REJECT / INCOMPLETE outcomes. If you believe something is wrong, try to produce a reproducible contradiction. Give me: environment → scenario → commands → evidence → result That is useful engineering. WHAT HAS BEEN BUILT? VRP has moved far beyond an architectural diagram. The project now includes multiple engineering layers. Continuity architecture Logical session identity is designed to survive changes in the underlying network path. The architecture is being developed around continuity rather than assuming that transport identity and logical session identity must always be the same thing. Runtime The protected runtime implements the private VRP mechanisms. That implementation is not public . State and transition handling T

“Daddy?” Theo curled against my side in bed. “Where do words go when they die?” I’d orchestrated the bedtime routine flawlessly: bath (taken), teeth (brushed), potty (tinkled), books (two), song (one, poorly sung), and snuggle (his chin on my second rib). Now was the moment when our son’s eyelids were supposed to flutter gently closed,…

I am a full-stack developer in the Netherlands, a bit over ten years in. For the last year my evenings have gone into one side project: I Like Movies, an Android app for tracking what you watch and deciding what to watch next. It went live on Google Play this summer. This is the honest version of how it got built, what the stack looks like, and the three or four decisions that mattered more than the rest. The problem was never finding a film Every movie app I tried was built for one person keeping one list. My actual problem was two people on one sofa, each with a watchlist, neither remembering which of us had saved the film worth watching. Picking something to watch with someone else is genuinely harder than picking alone, and no amount of better search fixes it, because search is not the bottleneck. Deciding is. So the app is organised around that. A household shares one library: one watchlist, one watched history, visible to everyone who lives with you. Add a film on your phone in the supermarket and it is on your partner's phone before you are home. That one feature is why the app exists, and it shaped almost every backend decision that followed. The stack, and why it is boring on purpose The backend is Go, GraphQL via gqlgen, and Postgres. The app is React Native with Expo. Film and TV metadata comes from TMDB. That is close to the most conservative stack you could pick in 2026, and that is the point. A solo project dies when the maintenance load exceeds one person's evenings, so every technology had to be something I could debug at 11pm without a second opinion. Go earned its place. The whole backend is one binary with no framework magic, and the type system plus gqlgen's generated resolvers mean a schema change breaks loudly at compile time instead of quietly in production. Postgres does everything: data, full-text search support, import staging. No microservices, no queue, no Redis. A single process and a single database will carry a consumer app much furthe

In our inaugural issue , we established that the future of enterprise AI lies not merely in raw model parameters, but in the architectural paradigms—specifically Graph Neural Networks (GNNs)—that capture relational intelligence. However, the most sophisticated architectural decision is rendered obsolete if the deployment infrastructure introduces prohibitive latency. At Informatiqs, we emphasize that model deployment is fundamentally an operations research problem. As we transition from batch-processed predictions to real-time Generative AI and dynamic Machine Learning on Google Cloud Platform (GCP), we confront the inherent friction between compute elasticity and system responsiveness: the notorious "Cold Start" problem. In this issue, we dissect the mathematics of serverless inference, the orchestration of Cloud Run and Eventarc, and how minimizing initialization latency is the ultimate enabler for high-frequency, event-driven enterprise intelligence. 1. The Mathematical Anatomy of the Cold Start To engineer a solution, we must first formalize the problem. In a serverless architecture (scale-to-zero), infrastructure scales dynamically with demand. The total response time for an inference request can be understood as a composite of three phases. First, the baseline network latency. Second, the actual inference time—the computational effort of the model itself. The critical variable, however, is the conditional penalty phase. If a serverless container has scaled to zero, the system must endure the time required to provision new compute resources and the heavily taxing process of loading massive neural network weights into memory. If the container is already 'warm', this penalty is completely bypassed. We can model the probability of encountering this cold start using queueing theory. Assuming incoming inference requests arrive as a stochastic process, the likelihood of a cold start is determined by the mathematical relationship between the frequency of incoming requ

Google will soon allow you to customize your Discover feed by describing what you want to see. The new feature, rolling out to the Google app in the "coming days," will use AI to automatically tweak your feed and "remember" your preferences for future visits. You'll find the option within the three-dot menu on your […]

In the current epoch of Artificial Intelligence, the industry remains singularly preoccupied with the "Model" — obsessing over the raw parameter scales of the latest LLMs or the specific benchmark performance of a new transformer variant. However, at Informatiqs, we shift the lens. We recognize that sustainable enterprise value is rarely derived from the model in isolation; instead, it emerges from the high-stakes architectural decisions and systemic orchestration that define its environment. As we launch our inaugural edition, we dissect a critical technological nexus: the convergence of Graph Neural Networks (GNNs), Generative AI, and the industrial-grade infrastructure of Google Cloud Platform (GCP). We argue that for complex enterprise datasets, the transition from flat vector embeddings in latent space toward non-Euclidean, graph-based relational intelligence is the primary differentiator for the next generation of resilient AI applications. 1. The Scientific Foundation: Exploiting Relational Inductive Bias Traditional Deep Learning architectures, such as Convolutional Neural Networks (CNNs) for images or Transformers for text, primarily operate on data structured as sequences (Euclidean space). While exceptionally powerful, these structures often fail to capture the topological nuances of real-world systems like supply chains, molecular structures, or fraudulent transaction webs where data is inherently non-Euclidean. Graph Neural Networks (GNNs) provide a framework for learning from data represented as nodes and edges. Unlike standard neural networks that process inputs in isolation, GNNs utilize a Message Passing paradigm. In this process, a node's internal representation is iteratively updated by aggregating information from its immediate neighbors. Instead of looking at a data point as a single row in a database, the GNN looks at who that data point "talks to" and how those connections define its identity. By utilizing Graph Attention mechanisms, we can fu

When 150,000 tickets went on sale for The Odyssey in 70mm IMAX, they sold out almost instantly. But plans change, cancellations happen, and good seats randomly open up at odd hours. To solve this, Andrew Baker from Temporal built IMAXXING : a service that monitors every 70mm IMAX showing across the US and alerts subscribers the moment great seats become available. What started as a fun weekend project quickly scaled, now over 9,000 users. I sat down with Andrew to break down the architecture: how durable execution keeps long-running workflows alive, how to debounce alerts so you don't spam users, and how serverless workers on Google Cloud Run handle sudden spikes in demand without overprovisioning. What's in the video Durable execution 101: How Temporal allows you to rewind history to the point of failure. The Entity Workflow pattern: Why there is one persistent workflow per user subscription and separate monitoring workflows per showing across the country. Signals & smart debouncing: How showing workflows send signals to wake up subscription workflows, and how a 60-second in-workflow timer batches multiple theater alerts into a single digest—without consuming active CPU while sleeping. Serverless workers on Cloud Run : How running Temporal workers as serverless containers lets compute autoscale directly with task queue depth rather than generic CPU metrics. AI agents for ops: How modern coding agents paired with Terraform and the gcloud CLI accelerated the deployment and operational dashboard setup. The point that stuck with me is how durable execution fundamentally changes how you think about long-lived state and retries. Instead of building complex cron jobs, custom retry databases, and alert queues, the workflow state itself is the queue and the timer. Have you experimented with entity workflows or running workflow workers on serverless infrastructure? How do you handle debouncing and noisy downstream APIs in your own apps?

A hacker pretending to work for a leading cryptocurrency news website targeted several cybersecurity professionals using Google Docs as a way to deliver malware.

Google is giving publishers a new button that lets readers make them a preferred source across Search, Discover, and Google News, potentially boosting their traffic as AI search sends fewer clicks to the web.