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I built this after SKHY began trading to make it easier to compare the US ADR with SK Hynix’s Korean shares. The dashboard adjusts for FX and the ADR conversion ratio. It highlights the current pricing gap, how it changed over time, and comparable trading volume. Data comes from Yahoo Finance. Feedback is welcome!

It's the latest in a barrage of efforts to win compensation from AI companies over training materials.

Meta denies using AI to terminate workers with disabilities and medical problems.

Anthropic has consistently attempted to depict itself as the ethical foil to other AI companies. This latest marketing stunt — which leans into criticism of AI as a way to make Anthropic seem aware of the responsibility it carries — would appear to be more of the same.

I like to use DuckDuckGo-style bangs and snaps, they are fast and efficient shortcuts. However, neither Kagi nor DuckDuckGo resolves them as quickly as I would like and subjectively Google has better search results than DuckDuckGo. After trying a few local alternatives eg. unduck, unduckified, I wasn't satisfied, the ones I tried briefly loaded a page before redirecting causing visible page flickering, still took time to resolve the actual redirect and lacked advanced features (address-bar autoc

SpaceXAI's Grok Build AI coding tool was spotted uploading users' entire codebases to Google Cloud before it was reported, and the company turned it off. The Register reports that Cereblab published findings on Monday showing how the Grok Build CLI was packaging and uploading entire code repositories, "including files it was told not to open […]

Microsoft Corp. today released software updates to plug at least 570 security holes in its Windows operating systems and other software, almost triple the number of vulnerabilities the software giant fixed in its record-smashing Patch Tuesday release last month. Microsoft attributed the burgeoning patch counts to vulnerability discoveries aided by artificial intelligence.

Your AI Agent Needs Tracing, Not Just Logs You've probably already called an LLM from a Node.js backend. That part's easy — every provider ships a solid SDK. The part that actually trips people up is what happens after : turning that one API call into an agent that reasons, uses tools, loops a few times, and still behaves once real users are hitting it. Here's a small, honest pattern for that — plus the one thing most tutorials skip: making the loop debuggable. Why Node.js is doing this job Node has quietly become the default home for the application layer around AI. It's become the preferred middle layer for deploying modern AI agents, wrapping heavier model inference behind fast Node APIs. Python still owns training and the heavy orchestration frameworks — Node owns the gateway, the auth, the streaming UI, and the business logic wrapped around all of it. On the SDK side, things consolidated fast: OpenAI's Node SDK holds roughly a third of weekly npm downloads across the major JS AI SDKs, and Anthropic's TypeScript SDK has grown nearly tenfold in a year. And despite all the framework noise, most production teams just use the Claude or OpenAI SDK directly — reaching for LangChain.js or Mastra only once multi-agent coordination actually earns its keep. The loop: reason, act, repeat Almost every "agent" in 2026 runs on the same loop: reason about the task, act through a tool call, look at what came back, reason again — repeat until done. That's it. The engineering is in the guardrails around it, not the loop itself. // agent.js import Anthropic from " @anthropic-ai/sdk " ; const anthropic = new Anthropic (); // reads ANTHROPIC_API_KEY from env const tools = [ { name : " get_order_status " , description : " Look up the status of a customer order by order ID. " , input_schema : { type : " object " , properties : { orderId : { type : " string " } }, required : [ " orderId " ], }, }, ]; async function getOrderStatus ({ orderId }) { // stand-in for a real DB/service call r

How to version REST endpoints, evolve GraphQL schemas, and ship mobile updates — without leaving existing users behind. Why It Matters Every deployed API is a contract. Every mobile binary installed on a user's phone is a snapshot of that contract. The moment you change a response shape, rename a field, or remove an endpoint, you risk breaking clients you cannot force-update. Backward compatibility is not about avoiding change. It is about managing change so that existing consumers continue to work while the system evolves underneath them. This article covers three layers: REST API versioning , GraphQL schema evolution , and mobile app compatibility (React Native & Flutter). Each section delivers concrete patterns and production-ready code. Part I — REST APIs The Versioning Decision REST APIs have four common versioning strategies. Each comes with tradeoffs: Strategy Example Pros Cons URI path /api/v1/users Simple, cacheable, widely understood Implies the resource itself changed; cache duplication Query parameter /api/users?version=1 Easy to implement, can default to latest Complicates routing and cache keys Custom header X-API-Version: 1 Keeps URIs clean Hard to test in browsers, invisible in logs Content negotiation Accept: application/vnd.app.v2+json Fine-grained, per-resource versioning Complex to test, requires custom media types Rule of thumb: Use URI versioning for public APIs. Use header-based versioning for internal services where you control all clients. Non-Breaking vs. Breaking Changes Not every change requires a new version: ✅ Non-breaking (no version bump needed): - Adding a new field to a response - Adding a new optional query parameter - Adding a new endpoint - Returning a new enum value (if clients handle unknowns) ❌ Breaking (requires a new version): - Removing or renaming a field - Changing a field's type (string → number) - Making an optional parameter required - Changing the response structure Pattern: Side-by-Side Versioning When a breaking cha