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Demis Hassabis thinks the world needs an AI watchdog with the power to hit the brakes if frontier models become too dangerous. Writing in a blog post, the Google DeepMind CEO and cofounder said the US should lead the initiative, arguing that the country is the best place to set global standards "given its economic […]

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

RGB TVs are the latest hotness in the world of televisions, and I’ve tested many of the latest models to see which you should buy.

AI video infrastructure to explain, train, and sell faster. Discussion | Link

Reflect Orbital has been given the green light to launch its first space mirror that aims to redirect sunlight down to Earth at night. The US Federal Communications Commission (FCC) has authorized the California-based startup to build and operate a single prototype satellite in low-Earth orbit later this year, despite concerns over how the technology […]

It’s not the upgrade Ring 4 owners have been waiting for, but it’s easily the best smart ring Oura has ever made.

Hello there (General Kenobi's favorite way to start the conversation), I’m Rashid, a sophomore at UT Austin. I built Rejourney.co ( https://rejourney.co/ ) to predict issues with your apps and websites before they happen, based on real user session recordings. Here is a silly video (with cats) on how it works: https://www.youtube.com/watch?v=Z95MDxBXMjk It’s open source, and the post link is the github repo, but here it is again: https://github.com/rejourneyco/rejourney I originally built this b

75 ready-to-use email templates for work & job search Discussion | Link

The Architecture Shift: SPA vs. Framework Internationalization (i18n) is one of those features that feels straightforward in a Single Page Application (SPA). You install react-i18next , wrap your app in a provider, and you're good to go. However, when you decide to migrate that Vite-based React app to Next.js for better SEO and performance, the strategy for i18n changes fundamentally. In a Vite SPA, i18n is typically client-side. In Next.js, i18n happens at the routing and server level. If you don't plan the migration carefully, you'll end up with hydration mismatches, flashing text, or broken search engine indexing. Here is how to navigate the transition. 1. Defining the Routing Strategy In Vite, your translations often live in the same bundle, and you swap them out using a state hook. Next.js, particularly with the App Router, prefers sub-path routing (e.g., /en/about or /es/about ). This is crucial for SEO because it allows search engines to crawl localized versions of your pages individually. Instead of relying on localStorage to remember a user's language, you should now rely on the URL. Most teams moving from Vite use a middleware approach to detect the user's preferred locale and redirect them to the correct sub-path. 2. Choosing the Right Library If you were using react-i18next in your Vite project, you have two main paths in Next.js: next-i18next (Pages Router): The traditional choice for the Pages Router. next-intl or i18next + i18next-resources-to-backend (App Router): These are modern solutions that leverage Server Components. When handling complex migrations involving many components, using a specialized tool like ViteToNext.AI can help automate the transformation of your Vite project structure into a Next.js-ready architecture, saving you hours of manual refactoring. 3. Handling Server Components vs. Client Components one of the biggest hurdles is that useTranslation() hooks from standard i18n libraries are "Client hooks." In the App Router, you'll wan

A scraper fails, someone swaps the User-Agent, someone else adds a proxy, then the job starts passing locally but fails again in CI. That usually happens because Cloudflare did not block “scraping” as one thing. It evaluated several signals, and each failure needs a different fix. This is about authorized automation: your own sites, customer-approved workflows, testing, monitoring, data access you are allowed to perform. If you do not have permission to automate against a site, changing fingerprints or rotating IPs does not make it okay. Start with the failure you actually see Cloudflare failures often get collapsed into “403”, but the page body matters. Common cases: Error 1020 : usually an access denied page from a Cloudflare rule or bot score decision. The HTTP status may still be 403, so inspect the HTML. 403 without a 1020 page : often IP reputation, firewall rules, geo restrictions, or an auth problem. 429 : rate limit exhaustion. Slowing down can help here, but it will not fix a fingerprint problem. Endless Just a moment... page : your client did not complete the browser-side challenge. CAPTCHA or Turnstile loop : Cloudflare still considers the session borderline after earlier checks. Add classification before you add workarounds. Even a basic classifier saves time: import time import requests CLOUDFLARE_MARKERS = { " 1020 " : " cloudflare_access_denied " , " Just a moment " : " cloudflare_js_challenge " , " cf-turnstile " : " cloudflare_turnstile " , " cf-error-code " : " cloudflare_error_page " , } def classify_response ( resp : requests . Response ) -> str : body = resp . text [: 5000 ] if resp . status_code == 429 : return " rate_limited " for marker , label in CLOUDFLARE_MARKERS . items (): if marker in body : return label if resp . status_code == 403 : return " forbidden_unknown " return " ok " if resp . ok else f " http_ { resp . status_code } " def get_with_backoff ( url : str , max_attempts = 4 ): for attempt in range ( max_attempts ): resp = request

In the 1960s an MIT professor named Joseph Weizenbaum created a chatbot called ELIZA. The conversations people had with it set precedents for the chatbots to come.

Learn how enterprises can manage AI investments in the agentic era by measuring useful work per dollar, improving efficiency, and scaling high-value workflows.

Why this matters I’ve worked on a few frontend projects where code reviews turned into style debates—tabs vs spaces, semicolons, quote styles… you name it. It slows everything down and adds zero value. At some point, I realized this shouldn’t even be a discussion. So now, whenever I start a project, I set up Prettier + ESLint + Husky on day one. No debates. No manual fixes. No messy PRs. This post is exactly how I do it. 🧰 What each tool actually does Prettier → formats your code automatically ESLint → catches bad patterns & enforces rules Husky → runs checks before commits (so no one skips them) Together → clean, consistent code without thinking ⚙️ Step 1 — Install dependencies npm install -D prettier eslint husky lint-staged 🎯 Step 2 — Setup Prettier Create: prettier.config.js module . exports = { semi : true , singleQuote : true , trailingComma : ' all ' , tabWidth : 2 , }; Create: .prettierignore node_modules dist build 🔍 Step 3 — Setup ESLint Initialize: npx eslint --init Then tweak your config: .eslintrc.js module . exports = { extends : [ ' eslint:recommended ' , ' plugin:react/recommended ' , ' prettier ' ], rules : { ' no-unused-vars ' : ' warn ' , ' react/react-in-jsx-scope ' : ' off ' , }, }; 👉 Important: "prettier" disables ESLint rules that conflict with Prettier. 🔗 Step 4 — Connect ESLint + Prettier Install: npm install -D eslint-config-prettier That’s it. Now ESLint won’t fight Prettier. 🐶 Step 5 — Setup Husky Initialize Husky: npx husky init Add pre-commit hook: npx husky add .husky/pre-commit "npx lint-staged" 🚀 Step 6 — Setup lint-staged Add to package.json : "lint-staged" : { "*.{js,jsx,ts,tsx}" : [ "eslint --fix" , "prettier --write" ] } 💡 What happens now? Every time you commit: ESLint checks your code Prettier formats it Only clean code gets committed No more: “fix formatting” PR comments broken lint rules in main branch inconsistent code styles 🧠 Real impact (from experience) After adding this to a team project: PR noise dropped a lot reviews

When building multi-agent systems, rigid state graphs quickly fall apart in the face of dynamic user inputs. Imagine building a smart assistant: a user hands you a checklist of three household chores today, but tomorrow it might be a list of ten software debugging tasks. Because the number of tasks, their sequence, and their execution details are entirely runtime-dependent, you cannot hardcode this path at design time. Forcing dynamic lists of work into a static graph-based workflow can lead to fragile, over-engineered code. You need a workflow that adapts dynamically at runtime. The Google Agent Development Kit (ADK) provides a flexible programming model to define dynamic workflows . With the release of ADK 2.4.0 , triggering these workflows has become even more seamless: you can register a Workflow directly in an agent's tools list, allowing the coordinator agent to execute it automatically as a first-class tool. In this article, you learn how to configure and trigger a dynamic workflow directly from a coordinator agent. This guide uses a task list coordination example, but you can adjust this pattern to other dynamic orchestration needs. The architecture of a dynamic workflow Static workflows define the execution path at design time. Dynamic workflows, however, allow agents to invoke tools, spawn other nodes, and schedule sub-agents conditionally at runtime. The system consists of three main components: Root agent ( root_agent ) : Gathers the list of tasks from the user, requests final approval, and directly calls the tasks_workflow tool. The workflow ( tasks_workflow ) : A Workflow that iterates over the approved tasks. Sub-agent ( task_explainer ) : An Agent tasked with generating a step-by-step execution plan for each task. Here is the architectural diagram of the solution: Technical implementation Let's break down how to implement this solution using the Google ADK library in Python. The complete code resides in the devrel-demos repository with core logic in

A few weeks ago I was setting up a new project and needed to do the usual dance: create a Notion doc, spin up a Linear board, invite the team to Slack, and set up a couple of Zapier automations to connect them all. It took me most of an afternoon. That's when it hit me — I wasn't actually trying to "use" any of these tools. I just wanted the outcome. I wanted the project set up. And somewhere between the fifth Zapier trigger and the third failed webhook, I found myself thinking: why am I the one gluing all this together? That question is basically the whole thesis behind this post. AI agents aren't just a new feature category bolted onto SaaS. They're starting to eat the reason SaaS exists in the first place. The old deal: software rents you a workflow Traditional SaaS sells you a workflow, not an outcome. You pay for Notion, and Notion gives you a very nice, very rigid shape to pour your thoughts into. You pay for HubSpot, and it gives you a CRM shape. You pay for Zapier so you can awkwardly stitch the shapes together. This worked great for twenty years because the alternative was building everything yourself. SaaS was the shortcut. But the shortcut came with a tax: you had to adapt your work to fit the tool, and when you needed two tools to talk to each other, you had to become a part-time integrations engineer. The new deal: software does the workflow for you An AI agent flips that relationship. Instead of "here's a tool, go operate it," it's "here's the outcome, go figure out how to get there." You tell an agent "onboard this new client" and it can read the contract, create the folders, send the welcome email, schedule the kickoff call, and post a summary in Slack — using whatever tools it has access to, without you clicking through five different dashboards. That's the part that's easy to miss if you only think of agents as "chatbots with extra steps." A chatbot answers questions. An agent does multi-step work: It breaks a goal down into subtasks It calls tools

One of the most common questions I hear is: "My API is slow. Where do I start?" The first instinct is usually: Upgrade the server Increase CPU Add more RAM But in many cases, the database query is the real bottleneck . Whenever I investigate a slow Laravel application, I follow the same checklist. It helps me identify performance issues before making unnecessary infrastructure changes. Let's go through it. 1️⃣ Find the Slow Queries First Don't start optimizing random queries. Start with the queries that are executed the most or take the most time. Useful tools: Laravel Telescope Laravel Debugbar (development) MySQL Slow Query Log Application Performance Monitoring (APM) You can't optimize what you haven't measured. 2️⃣ Stop Using SELECT * One of the easiest improvements. ❌ Instead of: SELECT * FROM users WHERE id = 10 ; Use: SELECT id , name , email FROM users WHERE id = 10 ; Why? Less data transferred Lower memory usage Faster response Easier for MySQL to use covering indexes Only fetch the columns your application actually needs. 3️⃣ Always Check the Execution Plan Before changing anything, run: EXPLAIN SELECT id , name FROM users WHERE email = 'john@example.com' ; Things I usually look for: Is MySQL scanning the whole table? Is an index being used? How many rows are examined? Is there a temporary table? Is filesort being used? EXPLAIN often tells you exactly why a query is slow. 4️⃣ Verify Your Indexes Indexes are one of the biggest performance improvements you can make—but only when they match your queries. Example: SELECT * FROM orders WHERE customer_id = 100 ; Create an index: CREATE INDEX idx_customer_id ON orders ( customer_id ); Now MySQL can jump directly to the matching rows instead of scanning the entire table. 5️⃣ Look for Composite Index Opportunities Suppose your query is: SELECT id , total FROM orders WHERE customer_id = 10 AND status = 'paid' ; Instead of two separate indexes: customer_id status A composite index is often better: CREATE INDEX idx_cu

Getting the public IP in PHP — no dependencies, no API key PHP is still one of the most widely deployed server-side languages, running a significant share of the web's backend code. If you're building a PHP application that needs the public IP address — for geolocation, DDNS, diagnostics, or country detection — this article covers the common patterns using IPPubblico.org : free, no key, HTTPS, JSON and plain text endpoints. Use case 1 — Your server's own public IP (one-liner) The simplest case: a PHP script that needs to know its own public IP. <?php $ip = trim ( file_get_contents ( 'https://ipv4.ippubblico.org/' )); echo $ip ; // 203.0.113.42 file_get_contents works if allow_url_fopen is enabled (it is by default on most servers). If not, use cURL (see below). Use case 2 — With cURL (recommended for production) file_get_contents has no timeout control and minimal error handling. For production code, cURL is better: <?php function getPublicIP (): ?string { $ch = curl_init ( 'https://ipv4.ippubblico.org/' ); curl_setopt_array ( $ch , [ CURLOPT_RETURNTRANSFER => true , CURLOPT_TIMEOUT => 5 , CURLOPT_FOLLOWLOCATION => true , CURLOPT_SSL_VERIFYPEER => true , ]); $response = curl_exec ( $ch ); $httpCode = curl_getinfo ( $ch , CURLINFO_HTTP_CODE ); curl_close ( $ch ); if ( $response === false || $httpCode !== 200 ) { return null ; } return trim ( $response ); } $ip = getPublicIP (); echo $ip ?? 'Unavailable' ; Use case 3 — Full geolocation data When you need country, city, ISP and timezone in addition to the IP: <?php function getIPInfo ( ?string $ip = null ): ?array { $url = 'https://ippubblico.org/?api=1' ; if ( $ip !== null ) { $url . = '&ip=' . urlencode ( $ip ); } $ch = curl_init ( $url ); curl_setopt_array ( $ch , [ CURLOPT_RETURNTRANSFER => true , CURLOPT_TIMEOUT => 5 , CURLOPT_SSL_VERIFYPEER => true , ]); $response = curl_exec ( $ch ); $httpCode = curl_getinfo ( $ch , CURLINFO_HTTP_CODE ); curl_close ( $ch ); if ( $response === false || $httpCode !== 200 ) { retur

You know the drill for adding an MCP server to a project: dig the exact command string out of the docs, hand-write a .mcp.json with an absolute path you'll typo once, restart the editor, and discover no tools showed up because the server expected a config file you haven't created yet. Plenty of MCP servers lose their would-be users somewhere inside that loop. Infrawise collapses the whole loop into one command. It's an open-source tool ( npm ) that statically analyzes your codebase, AWS infrastructure, and database schemas, then exposes that context to AI coding assistants over MCP — so Claude Code knows your actual partition keys, GSIs, and indexes instead of guessing from source files. This post is about the part that usually kills tools like this before they deliver any value: setup. Section 1: One command, four steps npm install -g infrawise # or skip install and use npx cd your-project infrawise start --claude start does four things, in order: 1. Probes your environment. If there's no infrawise.yaml in the project, it generates one. It reads AWS_PROFILE if set; otherwise it looks at your configured AWS profiles — one profile means zero questions, several means one prompt asking which to use. That's the entire interview. (If you want the full guided wizard instead, infrawise start --interactive runs it.) 2. Runs the analysis. It scans your AWS services, database schemas, and codebase, builds a graph of services, tables, indexes, and query patterns, and runs rule-based analyzers over it. No LLM is involved in this step — extraction and analysis are deterministic, so the same infrastructure always produces the same graph. 3. Writes .mcp.json to your project root. This is the file you'd otherwise write by hand: { "mcpServers" : { "infrawise" : { "command" : "infrawise" , "args" : [ "serve" , "--stdio" , "--config" , "/absolute/path/to/infrawise.yaml" ] } } } 4. Opens Claude Code. Claude Code reads .mcp.json automatically and starts the session with all 21 infrawise

MongoDB queries look simple. You type a field, give it a value, hit run, and you get your data back. But just because a query runs without throwing an error doesn't mean it worked right. Sometimes you get a blank screen. Sometimes you get way too many records. Other times, the data looks fine at first glance, but it doesn't actually match what you asked for. Most of these slip-ups happen for one basic reason: the query structure doesn't match the way the data actually sits in the database. To show you what we mean, we’ll use a clinic database with a collection called visits . Here is what a typical document looks like: JSON { "_id": "6871b6f9c3f1d1a4c2a10001", "status": "completed", "visitDate": "2026-07-01T09:30:00.000Z", "patient": { "name": "Anna Keller", "age": 34 }, "doctor": { "name": "Dr. James Carter", "specialty": "Cardiology" }, "symptoms": ["cough", "fever"], "prescriptions": [ { "name": "Ibuprofen", "active": false }, { "name": "Paracetamol", "active": true } ], "invoice": { "paid": true, "method": "card", "total": 250 } } You can run these examples right in the VisuaLeaf MongoDB Shell . Using visual tools makes a big difference because you can see exactly what MongoDB is returning in real time. 1. Forgetting the Curly Braces This is just a quick typo, but it breaks things right away. The Mistake: db . visits . find ( status : " completed " ) The Correct Query The find() tool always expects an object. Even if you are only looking for one specific thing, you still need to wrap that condition in curly braces {} . 2. Treating $or Like a Regular Object This one trips a lot of people up because the broken version looks like it should work. The Mistake: db.visits.find({ $or: { status: "completed", "invoice.paid": false } }) What is wrong: $or expects an array of conditions, but this query gives it one object. The error will usually be something like: MongoServerError: $or must be an array The Correct Query The first query is wrong because $or needs an array, n