标签：#go

The European Commission has updated its guidelines for teachers and educators on tackling disinformation and promoting digital literacy, extending the guidance to address generative AI , influencer dynamics and prebunking . The refresh gives schools and education professionals new materials for helping young people assess online information and build resilience against misleading content. The revised guidance sits within the EU's Digital Education Action Plan (2021-2027) . According to the European Commission publication record for the updated guidelines , the Directorate-General for Education, Youth, Sport and Culture released the updated publication on 4 June 2026. The update matters because the information environment facing pupils has changed substantially since the original guidance was issued. Generative AI can now be relevant to how online content is created, altered and spread. At the same time, social-media reliance and influencer-led information dynamics have become more prominent considerations for digital literacy education. The Commission's revised material positions educators and schools as part of the response, rather than treating disinformation solely as a platform or policy problem. What the updated EU guidance adds The updated guidelines are one element of a wider package of digital education and online-safety work. European Commission press materials published on 5 March 2026 described four sets of guidelines, comprising two new sets and two updates. The digital literacy and disinformation guidance was among the updated materials, with explicit attention to generative AI and contemporary online dynamics. A Better Internet for Kids summary published on 10 March 2026 identified several practical and policy-oriented additions. These include: Lesson plans and an updated glossary to support classroom use. Consideration of generative AI's impact on disinformation . Coverage of social-media reliance and the role of influencers in shaping information exp

At Whoz , we build a SaaS platform that helps professional services companies manage their talent staffing. At the heart of our product lies a concept called a worklog — a record of time spent by a user on a given activity. Every consultant, every day, on every project, generates worklogs. It sounds simple. And for years, it was. Then the numbers caught up with us. The Problem: A Collection That Never Stops Growing Our worklog MongoDB collection had reached 530 million documents , representing just over 32 GB of data. And the growth rate was accelerating — not just because we were onboarding more clients, but because users were increasingly splitting their activity into finer-grained entries, generating more worklogs per person per day than ever before. A worklog document looks roughly like this: { "date" : "2024-03-15" , "talentId" : "abc123" , "workspaceId" : "ws456" , "duration" : 0.5 , "activityType" : "TASK" , "taskId" : "task789" } Simple enough. But at 530 million of them, even the most routine operations become painful: Backup : nearly 1 hour Restore : up to 4 hours Schema migrations : we hadn't dared run one at full scale yet — and that alone was a warning sign Every year, the collection grows faster than the year before. The backup and restore windows were becoming operationally risky. We needed to act. Exploring Our Options We identified three potential approaches before settling on a solution. Option 1 — MongoDB Sharding Sharding is MongoDB's native horizontal scaling mechanism. It distributes a collection across multiple shards, each backed by its own replica set. On paper, it looked like a match. In practice, we ran into a fundamental mismatch with our actual needs. Our core issue wasn't query throughput — worklogs from three years ago are rarely queried, and when they are, performance expectations are low. Our issue was operational overhead : backup time, restore time, and the cost of running large batch operations over the full dataset. Sharding woul

A practical guide to building reliable event-driven systems in Go using Apache Kafka. Learn how to implement tiered retry strategies with delayed reprocessing, route permanently failed messages to dead letter queues in Golang with Sarama. Prerequisites What do you need to follow along? Working knowledge of Golang. Go & Docker installed on your PC. What is an Event-Driven Architecture? An Event-Driven Architecture (EDA) is a design approach where services communicate by producing and responding to events. Each service operates independently, producing or reacting to events as they happen. What are Events? An event is a record of something that has happened in a system, typically representing a state change or a significant action. An event contains data (payload) describing what happened. An example of an event could be: A user signing up for a service. A user placing an order in your system. Components of an Event-Driven Architecture To understand how events flow through a system, we need to know three key players: Event Producers : They are the sources of events. They generate and publish events like signup events, order placed events, etc. Producers generate events and transmit them to the rest of the system. They do not know who is listening for or handling the events. Event Brokers : They sit between producers and consumers, decoupling them so neither needs a direct connection to the other. Brokers receive event messages, maintain their chronological order, make them available for consumption, and route them to the right consumers. Apache Kafka is an example of an event broker, and it's the one we'll use throughout this guide. Event Consumers : They handle the processing tasks. They listen on event channels and react when an event they are subscribed to is published, then they process the event, which can include making API calls, updating a database, triggering other events, or logging information. The Complete Flow With those three pieces in place, the flow of

If you love the multi-agent AI orchestration concepts from Python’s CrewAI, but want the performance, native concurrency, and low memory footprint of Go, check out crewai-go. The v0.4.0 release brings key capabilities to make building multi-agent systems in Go fast, type-safe, and production-ready. ✨ Key Highlights: 🛠️ Custom Tools: Easily create and bind custom tools using tools.NewTool(...). 🔄 Sequential Context Flow: Outputs from previous tasks flow directly into subsequent tasks as context. 📦 Structured Outputs: Map LLM responses straight into native Go structs using standard json:"..." tags. 🏠 Flexible Provider Support: Run fully offline with Ollama or integrate seamlessly with OpenAI. 🧠 Short-Term Memory: Agents keep context across complex task executions. 💡 Quick Example: package main import ( "context" "fmt" "log" "github.com/rhgs/crewai-go/crew" ) func main () { researcher := crew . NewAgent ( crew . AgentConfig { Role : "AI Researcher" , Goal : "Analyze tech trends" , Backstory : "An expert in discovering high-impact open-source Go tools." , }) task := crew . NewTask ( crew . TaskConfig { Description : "Summarize the main benefits of using Go for AI agent orchestration." , ExpectedOutput : "3 concise bullet points." , Agent : researcher , }) c := crew . NewCrew ( crew . CrewConfig { Agents : [] * crew . Agent { researcher }, Tasks : [] * crew . Task { task }, }) result , err := c . Kickoff ( context . Background ()) if err != nil { log . Fatal ( err ) } fmt . Println ( result . Raw ) } 🔗 Release details & GitHub repo: github.com/rhgs/crewai-go/releases/tag/v0.4.0

"We have some really ambitious plans to help you work with AI in Chrome to get things done, and I’ll have more to share soon," Jacob Bank, Relay founder and CEO, said.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Flock, the police-tech giant known for its network of some 120,000 automatic license plate readers around the US, announced some changes to its platform last Thursday. The updates are meant to prevent…

The change comes a year after YouTube applied the same approach to counting views on Shorts videos.

The Sliding Window pattern is one of the most vital algorithmic techniques for optimizing array and string problems. Instead of repeatedly processing overlapping subarrays - which leads to brute-force quadratic O(N^2) or O(N*K) complexities, the sliding window technique reuses previous computations to achieve linear time complexity $O(N)$ . In this guide, we will break down the mechanics, core variations, identification rules, real-world applications, and a curated list of 18 LeetCode problems with key solution strategies. 💡 What is the Sliding Window Pattern? A sliding window performs operations over a contiguous sub-segment (subarray or substring) of data structure. As the window "slides" across the array from left to right, elements entering and leaving the window are updated incrementally. Time Complexity Comparison Brute-Force Nested Loops: O(N^2) or O(N * K) Sliding Window Strategy: O(N) (each element is processed at most twice: once entering and once leaving) 🛠️ Recognition & Identification Rules When to Use Sliding Window Contiguous Input: The problem requires evaluating contiguous subarrays or substrings. Window Metric Criteria: You need to calculate statistics such as minimum/maximum length, sum, average, or character frequency targets. Monotonicity Property: Expanding the window strictly increases (or maintains) a target metric, while shrinking the window strictly decreases it (e.g., sum > K or at most K distinct elements over positive numbers). When NOT to Use Sliding Window Negative Numbers in Sum Constraints: If an array contains negative numbers and you are tracking a cumulative sum, expanding the window does not monotonically increase the sum. Use Prefix Sum + HashMap instead. Non-Contiguous Sequences: If the problem asks for subsequences (where elements do not need to be adjacent), sliding window fails. Non-Monotonic Metrics: If moving pointers does not give a predictable increase or decrease in your decision metric. 🔄 Fixed vs. Variable Length Slid

Criminals are hacking into public Wi-Fi devices—at hotels, conference centers, and so on—around the world and changing their DNS settings. The goal is to redirect users to fake login pages and steal their credentials.

Google has added some smart theft-detection features to Android in recent years.

You probably don't need antivirus software on your Android phone, but there are some exceptions.

The differences between the iPhone and Android show up when you connect them to your car, as well.

Dario Amodei is pushing back against the idea that he's been painting an overly pessimistic picture of AI.

This is a submission for Weekend Challenge: Dog Days Edition What I Built A game where...

1. What Is Android Developer Verification and Why Google Introduced It In August 2025, Google quietly announced a major policy change called Android Developer Verification . Starting September 2026 (first in select countries like Brazil, Indonesia, Singapore, and Thailand, then rolling out globally), every developer whose app is installed on certified Android devices must register with Google. This is not limited to apps distributed through the Google Play Store. It applies to all apps — including those sideloaded from websites, shared via APK files, distributed through F-Droid, or even internal company tools and hobby projects. Google’s official reason is “improved security and accountability” — to stop repeat malware developers. However, the implementation goes far beyond that. It creates a central registry controlled entirely by Google, where every person or organization building Android software must identify themselves. 2. How the Verification Process Actually Works (Step-by-Step Details) To get their apps installable on most Android phones, developers must complete the following: Create or use a Google Play Console developer account. Pay a registration fee (standard accounts are around $25, with possible additional costs). Agree to Google’s lengthy Terms and Conditions without negotiation. Submit government-issued identification (passport, driver’s license, or national ID). Provide proof of ownership of their app’s signing key (the private key used to sign APKs). List all current and all future application package names (com.example.myapp) they plan to use. Once registered and verified, apps from that developer can install normally. If a developer does not register or fails verification, their apps will be silently blocked by Google Play Protect on certified devices worldwide. This process turns what was once a simple “build and share APK” workflow into a permission-based system where Google acts as the gatekeeper for the entire Android ecosystem. 3. The “Adva

The woman claimed that AI tools are "taking everyday life and turning it into child sexual abuse."

Building a Restaurant Reservation System with Node.js, Express & MongoDB - A Beginner's Guide Tags: #nodejs #express #mongodb #webdevelopment #tutorial #beginner Introduction Hey everyone! 👋 This is my first Dev.to post, and I'm excited to share what I've been learning. As a 5th-semester CS student, I've been diving deep into full-stack web development, and today I want to walk you through building a Restaurant Reservation System – a real project I built that taught me so much about backend architecture and database design. If you're just starting with Node.js, Express, and MongoDB, this post is for you! What We'll Build A simple but functional restaurant reservation system where: Users can browse available time slots Users can book a table for a specific date and time Admin can manage reservations Weekly scheduling (Monday-Sunday) 2-hour time slots Tech Stack: Backend: Node.js + Express Database: MongoDB Frontend: React + Tailwind CSS (we'll focus on backend in this post) Prerequisites Before we start, make sure you have: Node.js installed MongoDB running locally or MongoDB Atlas account Basic JavaScript knowledge VS Code or any code editor Project Setup 1. Initialize the Project mkdir restaurant-reservation-system cd restaurant-reservation-system npm init -y 2. Install Dependencies npm install express mongoose cors dotenv npm install nodemon --save-dev 3. Create Project Structure restaurant-reservation-system/ ├── models/ │ └── Reservation.js ├── routes/ │ └── reservations.js ├── config/ │ └── db.js ├── .env ├── server.js └── package.json Step 1: Set Up MongoDB Connection config/db.js const mongoose = require ( ' mongoose ' ); const connectDB = async () => { try { await mongoose . connect ( process . env . MONGODB_URI ); console . log ( ' MongoDB connected successfully ' ); } catch ( error ) { console . log ( ' MongoDB connection failed: ' , error ); process . exit ( 1 ); } }; module . exports = connectDB ; Step 2: Create Reservation Model models/Reservation.js co

Introduction Your program keeps creating objects. Every function call, every loop iteration, every parsed JSON response produces new ones. You don't manually delete most of them. You've never written a line of code that says "free this memory now." And yet your application doesn't immediately exhaust all available RAM and crash. So who cleans everything up? The answer is a garbage collector, a piece of the runtime that runs quietly in the background, deciding what your program no longer needs and reclaiming that memory for future use. Most developers interact with it only when something goes wrong: an unexpected pause, a memory leak, or an out-of-memory error that shouldn't be happening. Understanding how it actually works turns those confusing moments into solvable problems. And as a bonus, the core algorithm is simple enough to build yourself. We'll do that by the end of this article. -- 1. The Memory Problem Every time your program creates an object, the runtime allocates a chunk of memory to hold it. A string, a dictionary, a class instance: they all need memory, and that memory has to come from somewhere. The somewhere is a region called the heap , a pool of memory that the program draws from as it runs. When you create an object, the runtime finds a suitable slot in the heap and reserves it. When that object is no longer needed, that slot should be freed so it can be used for something else. In languages like C, you manage this manually. You allocate memory when you need it, and you free it when you're done. This gives you control, but it creates two classic failure modes. Free memory too early and you have a dangling pointer, a reference to memory that's now being used for something else. Forget to free it at all and you have a memory leak: the program slowly consumes more and more memory until it runs out. Automatic memory management exists to eliminate these failure modes. Instead of relying on the programmer to track every allocation and release, the runti

Google has launched Gemini 3.7 Flash, providing significant updates for coding, automation, and the development of autonomous agents. The company reduced production pricing to help businesses deploy these tools more affordably. This release comes only three weeks after the previous version, signaling a faster pace for developer-focused updates. Accelerated development cycles and cost reduction strategies The introduction of Gemini 3.7 Flash highlights a shift in how technology providers manage their product lineups. Google is prioritizing rapid iteration for its Flash series, which serves as a high-speed tool for developers. This latest version arrived less than a month after its predecessor, showing the company responds quickly to user feedback. Engineers designed this model to handle software engineering tasks and complex, multi-step workflows with higher precision. Pricing for the new model sits at $0.75 per million input tokens and $3.75 per million output tokens. This represents a reduction of approximately fifty percent compared to the prior version. By lowering the financial barrier, Google aims to make large-scale production deployments more sustainable for businesses. The company describes this version as a reliable workhorse capable of following instructions with greater accuracy than previous iterations. While the Flash series moves quickly, the more advanced Pro models follow a different path. These high-end models, designed for the most difficult reasoning tasks, see less frequent updates. During recent financial discussions, leadership at the company did not provide a specific timeline for the next Pro release. This indicates a growing gap between fast, cost-effective models and the slower development of premium intelligence tiers. Industry trends in model tiering Other companies in the industry are following similar patterns by separating their offerings into distinct categories. For example, some competitors have launched high-end variants alongside

Fascinating video about searching for life undersea. The video basically makes the point that our bright white searchlights are scaring everything away, and that red light is more neutral. That, plus bait to attract sea creatures, is teaching us a lot about what’s going on down there. Lots of footage of giant squid, and speculation about the colossal squid. Worth watching. As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.