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Earlier this year, a Reddit user had asked members of a skincare-focused subreddit if anyone had tried a specific hypochlorous acid spray, a product often used for acne. There were dozens of responses; one from a user named Primary-Taro4254 seemed innocuous enough, at least at first. "I haven't personally tried [that brand] so I can't […]

An algorithmic trading platform doesn't operate in isolation it responds to the changing conditions of the financial markets. One of the biggest factors affecting automated trading performance is the market session. Liquidity, volatility, trading volume, and price movements can vary significantly throughout the trading day, influencing how an algorithmic trading platform executes trades. Understanding how different market sessions impact automated trading can help traders choose the right strategies, manage risk more effectively, and improve overall trading performance. What Are Market Sessions? A market session refers to a specific period during which a stock exchange is open for trading. In India, the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) follow a structured trading schedule that includes the pre-open session, regular trading hours, and post-closing session. Each session has unique market characteristics, making it important for traders to understand how their automated strategies may behave during these periods. Why Market Sessions Matter in Algorithmic Trading An algorithmic trading platform follows predefined rules, but the market environment changes throughout the day. A strategy that performs well during high-volume periods may struggle when trading activity is low. Market sessions influence several key factors, including: Trading volume Market liquidity Price volatility Bid-ask spreads Order execution quality Recognizing these differences allows traders to build strategies that are better suited to specific market conditions. Pre-Open Session The pre-open session is used to determine the opening price of securities before regular trading begins. During this period: Orders are collected but not executed immediately. Prices may fluctuate as the market discovers the opening level. Liquidity can be limited. Large overnight news events may influence price movements. Most intraday automated strategies are designed to become active only afte

In 2018, more than 20,000 employees walked out to protest how Google handled allegations of sexual harassment. Here’s how they organized it from inside the company.

The European Union's voluntary General-Purpose AI Code of Practice gives providers of general-purpose AI models a practical framework for supporting compliance with the EU AI Act. Finalised in July 2025, the code addresses transparency, copyright, and safety and security. Its public signatory list includes major AI and technology companies, but official EU material does not support claims that roughly 190 organisations have signed the GPAI code. The distinction matters for companies assessing AI suppliers. Signing the code can signal engagement with the EU's emerging governance expectations, but it is not a substitute for examining a provider's specific commitments, documentation, and product-level controls. The European Commission describes the code as a voluntary instrument, and its official GPAI Code of Practice page states that the signatory process and public information continue to be updated. What the GPAI Code of Practice covers The code is designed for providers of general-purpose AI models, a category that can include models used across multiple downstream applications. Rather than creating a separate legal regime, it is intended to help providers demonstrate how they can meet relevant AI Act obligations . Its three chapters cover different aspects of provider responsibility: Transparency: commitments related to information and documentation that can help downstream providers understand and use general-purpose AI models appropriately. Copyright: measures intended to address copyright-related obligations for providers of general-purpose AI models. Safety and security: commitments focused on managing risks associated with the most capable models, including systemic-risk considerations where applicable. Code chapter Primary focus Why it matters to AI buyers Transparency Provider information and documentation Helps buyers assess whether a model provider can supply information needed for downstream use. Copyright Copyright-related provider commitments Relevant

The first time we deployed Celery to production on a client project, we thought we had done everything right. We had workers running, tasks queuing, and Redis as the broker. Six weeks later, the task queue was backed up with 40,000 unprocessed jobs, the workers had silently died, nobody knew, and a batch of client invoices had not been generated for two weeks. That was four years ago. Since then we have deployed Celery on dozens of projects and we have learned what actually goes wrong — not in development, where everything works, but in production, where things fail in ways you do not anticipate. This post covers the configuration and operational patterns we now use on every Celery deployment. Why tasks fail silently (and how to stop it) The most dangerous thing about Celery is how quietly it can fail. A worker process dies, the task queue fills up, and your application keeps accepting work and sending it to a queue that nobody is processing. No exception is raised. No alert fires. Users notice eventually, or you notice when a daily report does not arrive. The fix has two parts: monitoring and task acknowledgement configuration. Task acknowledgement By default, Celery acknowledges a task (removes it from the queue) as soon as a worker picks it up, before the task runs. If the worker dies mid-task, the task is lost. # celery.py app = Celery ( ' myproject ' ) app . conf . update ( # Only acknowledge after the task completes successfully task_acks_late = True , # If a worker dies, reject the task back to the queue task_reject_on_worker_lost = True , # Limit memory — workers that leak memory will restart cleanly worker_max_memory_per_child = 200_000 , # 200MB in KB # Limit tasks per child process to prevent long-running workers # from accumulating state worker_max_tasks_per_child = 1000 , ) With task_acks_late=True , a task that is picked up by a dying worker will be requeued and picked up by another worker. The task might run twice (more on that shortly), but it will n

Beyond the consumer surfaces most people know, the Gemini app and Gemini Notebook (formerly NotebookLM), Google offers a whole set of AI tools and interfaces built for developers and enterprise teams. At first glance, it's a lot to wrap your head around. AI Studio, Gemini Enterprise Agent Platform, the Gemini Enterprise app, Antigravity in its various forms. The names overlap, the marketing language overlaps, and it's not obvious where one tool's job ends and another's begins. AI Studio is for prototyping and building web and Android apps AI Studio is where you test a prompt, try a multimodal input, or compare models before committing to anything. It also builds full applications straight from a prompt, complete with backend infrastructure. When your app needs to store data or handle sign-ins, AI Studio provisions a database and authentication for you, either Cloud Firestore or Cloud SQL for a relational setup, and Firebase Authentication, without you configuring any of that by hand. It also connects to Google Workspace APIs, so an app you build there can work with a user's real Gmail, Docs, Sheets, or Calendar data. It supports mobile app development as well, for native Android apps. Use AI Studio when you want to validate an idea or ship a lightweight web or Android app fast, with real backend support, and without provisioning any Google Cloud infrastructure yourself. Gemini Enterprise Agent Platform is where agents actually get built This is the next generation of Vertex AI. Google folded Vertex AI's entire service catalog, model access, custom training, evaluation, pipelines, all of it, into what's now called the Agent Platform. If you go looking for Vertex AI in the console today, it redirects you here. This is the one-stop shop for the full agent development lifecycle. You build with Agent Studio, the low-code visual builder, or the Agent Development Kit for code-first development in Python, Go, Java, or TypeScript. Agent Engine handles the managed runtime, Mo

Google is rolling out an update that will finally allow you to connect your Fitbit workouts, steps, vitals, and other data to Apple Health, as reported earlier by 9to5Mac. With Google Health's 5.05 update, you can now tap your profile icon, select "Partner apps," and choose Apple Health to link your data directly. Previously, you […]

The orbiting observatory in minifigure scale.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Humanoid robots usually elicit more cringe than awe: They stumble, kick children, and despite advances are still worse at using their hands than my toddler. It’s a nascent industry, and such robots…

Google's AI assistant can "use your logged-in accounts and saved passwords to handle tedious web errands."

Hugging Face has published a detailed timeline of the attack. From the summary: The agent was running an internal OpenAI cyber-capability evaluation based on the ExploitGym benchmark, which tasks an AI agent with finding and exploiting software vulnerabilities. OpenAI ran this on its own infrastructure, and the ExploitGym maintainers and their infrastructure had no involvement in the deployment or operation of that evaluation environment. As far as we were able to infer, across the course of being evaluated on this benchmark, the agent inferred that Hugging Face may host that benchmark’s models, datasets, and reference solutions. We believe the entire intrusion was, from the agent’s point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own...

House spending records show OpenAI's ChatGPT dominates paid AI use on Capitol Hill, with congressional offices relying on the chatbot to draft memos, summarize legislation, and assist constituent communications.

Series: Platform engineering with Go | Topics: Go, Kubernetes, Cobra, client-go, metrics-server, Platform Engineering This is part of the Platform Engineering with Go series. This post builds on the Cobra CLI patterns from post 4 and client-go from post 3. Read post 4 first if you haven't yet. kubectl top tells you what's happening. It doesn't tell you how close to the edge you are. In post 3 and post 4 , we built a health reporter and learned how to structure a Go CLI with Cobra. Now we put both together into something with real operational value. kubectl top pods -n production NAME CPU ( cores ) MEMORY ( bytes ) go-api-7d6b9f8c4-xk2pq 240m 490Mi go-api-7d6b9f8c4-mn9rt 180m 210Mi go-api-7d6b9f8c4-p8wvz 200m 198Mi That first pod is using 490Mi of memory. Is that fine or is that a problem? Without knowing the limit, you can't tell. You'd have to run kubectl describe pod go-api-7d6b9f8c4-xk2pq , find the resources section, do the mental arithmetic, and repeat for every pod you care about. ferctl top does all of that in one command: ferctl top -n production NAMESPACE NAME CPU USE CPU REQ CPU LIM CPU% MEM USE MEM REQ MEM LIM MEM% STATUS production go-api-7d6b9f8c4-xk2pq 240m 250m 500m 48% 490Mi 256Mi 512Mi 95% !! CRITICAL production go-api-7d6b9f8c4-mn9rt 180m 250m 500m 36% 210Mi 256Mi 512Mi 41% OK production go-api-7d6b9f8c4-p8wvz 200m 250m 500m 40% 198Mi 256Mi 512Mi 38% OK One pod is at 95% of its memory limit. In production, that's a page waiting to happen. ferctl top catches it before it becomes an incident. What you'll learn How to extend the Cobra CLI structure from post 4 with a real subcommand How to query the metrics-server API using k8s.io/metrics How to correlate live metrics with pod specs to show usage vs limits How to implement configurable near-limit warnings How to format clean aligned output with tabwriter How to verify the tool against your real minikube cluster Prerequisites Posts 1–4 read; client-go patterns from post 3 , Cobra CLI structure from pos

Lenovo is expected to release some of the first Googlebook models later this year, and leaked images have now given us a good idea of what they might look like. Leaked press images shared by Digital Citizen and Android Headlines include a laptop and a 2-in-1 tablet, all of which feature Googlebook branding on the […]

GitHub热门项目 | DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running. | Stars: 29,612 | 877 stars today | 语言: Go

If you just want the recommendation: build the uptime poller yourself, put exponential backoff with jitter in front of every health check, and treat a 429 as a scheduling signal instead of an error you swallow. Query-style observability APIs hand you metrics and logs, not threshold rules or notification channels, so the polling worker is the thing that has to decide what "down" means and who gets woken up. That decision is the whole job. I got burned by exactly this. What follows is the pattern that survived the postmortem, the alternatives I weighed before writing a line of it, and the conditions where you should not do any of this yourself. The 429 my retry loop ate for six hours Last spring I was running a homegrown health checker for 40 internal services. One goroutine per service, all driven off the same 15-second ticker, which meant every check landed inside the same 200ms window. The status API we polled had a per-minute quota I'd never bothered to read, and for months it didn't matter, because 40 checks a minute sat comfortably under the ceiling. Then a colleague onboarded 12 more services, we crossed the quota, and the API started answering with HTTP 429. My retry wrapper caught it, retried three times in a tight loop, and on the last attempt returned the previous cached result — which said healthy . It logged the rate limit at debug level. Nobody reads debug. Six hours. Green dashboard. Dead queue consumer. We found out when a customer asked where their export was. The consumer had died on an unrelated deploy, the checker never noticed, and when I finally restarted it the backlog got re-processed on top of a manual replay I'd already run — two customers got the same notification twice. Duplicate deliveries are the specific thing I lose sleep over, and I had caused a batch of them with a retry loop that was trying to be helpful. The postmortem produced one line I now paste into every runbook: a check that can't reach the API reports unknown, never healthy.

TL;DR: Google’s Gemini AI agents identified and helped remediate 1,072 Chrome security flaws in 60 days, dramatically shrinking the window for attackers. The race to protect 3.5 billion Chrome users has taken a high‑tech shortcut. Instead of relying solely on human researchers, Google deployed its Gemini‑powered AI agents to hunt for bugs, triage findings, and even suggest patches. The result? Over a thousand vulnerabilities squashed in just two months—a pace that would have taken years using traditional methods. How Gemini’s AI Agents Accelerated Chrome’s Bug Hunt Google’s internal security team integrated Gemini, the company’s latest large‑language‑model platform, into its vulnerability‑scanning pipeline. The AI agents performed three core tasks: Automated code analysis – By ingesting Chrome’s massive codebase, the models flagged risky patterns, unsafe API calls, and legacy modules that often hide bugs. Prioritization and risk scoring – Gemini assigned a severity score to each finding, allowing engineers to focus on exploits with the highest potential impact. Patch drafting assistance – For many low‑complexity issues, the AI generated candidate code changes, which senior engineers then reviewed and merged. The system worked in a loop: the AI scanned, reported, received feedback, and refined its heuristics. This iterative approach cut the average time‑to‑detect from weeks to hours and reduced manual triage effort by an estimated 40 %. The Scale and Impact of Fixing 1,072 Vulnerabilities During the 60‑day sprint, the AI‑augmented process uncovered 1,072 distinct security bugs across Chrome’s rendering engine, JavaScript runtime, and networking stack. Roughly half were classified as “high‑severity,” meaning they could have enabled remote code execution or data exfiltration. Key outcomes include: Reduced exposure window – The median time between bug discovery and patch release dropped from 45 days (historical average) to under 7 days. Broad coverage – The AI identifie

This essay originally appeared in Foreign Policy . Earlier this month, two of OpenAI’s models broke out of their containment sandbox and attacked another AI company. The story is kind of wild . OpenAI was running security tests on two of its models: GPT-5.6 Sol and an unreleased model that is almost certainly GPT-6. In particular, it was running the ExploitGym benchmark, which measures how good a model is at turning security vulnerabilities into working exploits: basically, offensive cyberattacks. Since these were internal tests, OpenAI locked those models in a secure sandbox that denied them access to the internet. But it was running the models without any safety filters that would prevent them from offensive cyber-actions. That meant that there was nothing to prevent the models from trying to ...

I Built a Language Where AI Calls Are Sandboxed by Default The 30-line Python problem Last month I needed a script that reads server logs, classifies errors with an LLM, summarizes them, and writes a report. In Python, it looked like this: Import the SDK Initialize the client Handle the API response Parse JSON Add asyncio.gather() because sequential calls took 8 seconds Write a custom sandbox because I don't trust LLMs with exec and file writes Package it in Docker because requirements.txt always breaks on the server 80 lines later , it worked. But it felt wrong. I wasn't building logic — I was plumbing. So I asked myself: What if AI operations were language primitives, not library calls? Meet Pipe Pipe is a small runtime (~10 MB, single binary, zero dependencies) that treats summarize , translate , classify , and ask as first-class citizens — on the same level as + , sort , or len . Try it Browser Playground (WASM, no install): pipe-lang.com Source: github.com/MachuraHarry/pipe Docs: pipe-lang.com/docs

Microsoft has released TypeScript 7.0, featuring a native compiler that improves build speeds by 8x to 12x. Notable performance enhancements were evidenced in real codebases. The version lacks a stable programmatic API, anticipated in 7.1. Transitioning includes a compatibility package for existing tooling, and TypeScript remains an open-source project. By Daniel Curtis