标签：#AR

Adding cloud regions changes latency and cost in ways simple math can't capture. This article presents a framework from multiple launches: decompose your latency budget before committing to infrastructure, choose deployment patterns by consistency and traffic profile, and optimize before expanding. A phased approach cut latency 35% through routing alone, before a new region brought it under 60ms. By Uttara Asthana

Ties van der Meer doesn’t know how many siblings he has. The 47-year-old was conceived at a private fertility clinic in the Netherlands using sperm provided by an anonymous donor. After the Netherlands banned anonymous donation in 2004, the doctor who ran the clinic destroyed records that might have identified those donors, he says. He…

Artificial intelligence has transformed software development. Instead of simply generating code snippets, modern coding assistants can understand entire codebases, refactor applications, write tests, debug issues, and even execute development workflows. Among the most capable tools available today are Claude Code and Codex. While both are designed to accelerate software development, they take different approaches to coding assistance. This article compares their strengths, weaknesses, and ideal use cases. What Is Claude Code? Claude Code is Anthropic's command-line coding assistant built around the Claude family of language models. Rather than functioning as a traditional autocomplete tool, Claude Code works as an AI development agent that can inspect projects, edit files, explain code, write tests, fix bugs, and help developers navigate large repositories. Its workflow is centered around natural language. Developers describe what they want, and Claude Code performs the necessary steps while keeping the developer involved throughout the process. Key features Deep understanding of large codebases Multi-file editing Test generation Refactoring assistance Terminal-based workflow Strong reasoning for complex programming tasks Excellent documentation generation What Is Codex? Codex is OpenAI's AI coding agent designed to help developers write, understand, and modify software. Unlike the original Codex model introduced in 2021, today's Codex operates as a software engineering agent capable of working across repositories, generating code, fixing bugs, creating pull requests, running tests, and assisting with development workflows. Codex integrates closely with OpenAI's ecosystem and focuses on turning natural language instructions into production-ready code while maintaining awareness of project context. Key features Repository-aware coding Autonomous task execution Code generation Bug fixing Test writing Pull request assistance Integration with modern development workflow

👋 Welcome to Chapter 9! Imagine a user typing in a search box. They type "i", "ip", "iph", "ipho", "iphon", "iphone" — 6 keystrokes in 2 seconds. Do you really want to make 6 API calls ? Of course not! You want to wait until they stop typing and then search once. That's what timing operators solve. They control when and how often values flow through your stream. ⏱️ debounceTime() — Wait for the Silence debounceTime(ms) waits until there's a pause of ms milliseconds, THEN lets the latest value through. Think of it like this: "Ignore everything until they stop for a moment." Like a person who waits for you to finish talking before responding. import { debounceTime } from ' rxjs/operators ' ; // User types fast: 'i' → 'ip' → 'iph' → 'ipho' → 'iphon' → 'iphone' // debounceTime(400) waits 400ms of silence, then sends 'iphone' only searchControl . valueChanges . pipe ( debounceTime ( 400 )) . subscribe ( term => { this . searchProducts ( term ); // Only called ONCE with 'iphone'! }); Timeline: Type 'i' → [400ms timer starts] Type 'ip' → [reset timer] Type 'iph' → [reset timer] Type 'iphone'→ [reset timer] ... 400ms silence ... EMIT: 'iphone' ✅ Real Angular Example — Smart Search Box import { Component , OnInit , OnDestroy } from ' @angular/core ' ; import { FormControl } from ' @angular/forms ' ; import { Observable , Subject } from ' rxjs ' ; import { debounceTime , distinctUntilChanged , switchMap , startWith , takeUntil } from ' rxjs/operators ' ; @ Component ({ selector : ' app-search-box ' , template : ` <div class="search-wrapper"> <input [formControl]="searchControl" placeholder="Search products..." (keyup.escape)="clearSearch()"> <span *ngIf="isLoading" class="spinner">🔄</span> <button *ngIf="searchControl.value" (click)="clearSearch()">✕</button> </div> <div class="results-count" *ngIf="(results$ | async) as results"> Found {{ results.length }} results </div> <div class="results"> <div *ngFor="let item of results$ | async" class="result-item"> <strong>{{ item.nam

Most organisations approach a Dynamics 365 Customer Engagement implementation with one question at the top of their agenda: How long will this take? It is a reasonable question, and one that deserves a precise, well-considered answer rather than a vague estimate designed to win the deal. The reality is that Dynamics 365 CE implementation timelines vary significantly, shaped by factors that are unique to each organisation: business complexity, data readiness, customisation depth, integration requirements, and internal stakeholder availability. This guide provides a structured, phase-by-phase breakdown of what a Dynamics 365 CE implementation actually involves, realistic timeline benchmarks by business size and industry, and the critical factors that either accelerate or delay your go-live date. Why there is no one-size-fits-all timeline for Dynamics 365 CE implementation Why There Is No Single Answer to the Timeline Question Dynamics 365 Customer Engagement is not a standalone application. It is a modular platform encompassing Sales, Customer Service, Field Service, and Marketing, each carrying its own configuration requirements, data dependencies, and user adoption considerations. A professional services firm deploying D365 Sales for a 25-person team operates in an entirely different context than a multi-national enterprise rolling out Customer Service and Field Service across three regions. Treating these as comparable projects, with comparable timelines, is where expectations first go wrong. As a reference framework, Dynamics 365 CE implementations broadly fall into three tiers: Implementation Scope Basic deployment, minimal customization :- 6 – 12 weeks Mid-market with integrations and moderate configuration :- 3 – 6 months Enterprise, multi-module or multi-region rollout :- 6 – 16 months These are informed benchmarks, not guarantees. What determines where your project lands within or beyond these ranges is examined in detail below. Core phases of a Microsoft Dyn

Building an explainable AI platform that helps district administrators allocate resources and farmers make better crop decisions using Gemini, Vertex AI, BigQuery, and Google Cloud. Climate disasters are not just weather events. They are decision problems. When forecasts predict a strong El Niño, governments do not simply need more data. They need answers to questions like: Which districts will be affected first? Where should limited water resources be sent? Which crops are likely to fail? What should farmers sow instead? Why is the AI recommending this action? Existing dashboards provide plenty of charts. Very few provide decisions. That became the motivation behind El Niño 2026 Decision Copilot , an AI-powered decision intelligence platform built during the Google Cloud Gen AI Academy APAC Hackathon . The Problem India depends heavily on the monsoon. A severe El Niño can lead to: Rainfall deficits Reservoir depletion Groundwater stress Crop failures Rising food prices Rural employment challenges The information already exists across dozens of government portals, weather services, satellite datasets, and agricultural reports. The challenge is that it is scattered. District collectors do not have time to manually combine: Weather forecasts NDVI satellite imagery Reservoir levels Mandi prices Contingency plans Drought indicators Farmers face an even bigger challenge. Most need a simple answer: Given my district, should I plant the usual crop this season? The Goal Instead of building another dashboard, I wanted to build an AI system that reasons over multiple data sources and produces explainable recommendations. The platform serves two audiences through the same intelligence engine. District Administrators They receive: District risk scores Interactive risk maps Reservoir outlook Crop stress indicators Resource allocation recommendations AI-generated explanations Instead of simply showing that a district has high risk, the system explains why . Farmers Farmers intera

Amid live coding sessions and Silicon Valley optimism, the UN’s AI for Good summit wrestled with an increasingly urgent question: Can global governance catch up before the technology races beyond its control?

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Vivo's joint venture could become a template for Chinese smartphone makers in India.

Durante o desenvolvimento do Templo Digital, meu projeto de hackathon (uma vitrine 3D de cursos...

What nobody tells you about exporting your multi-agent prototype to a local workspace. Every architect who's prototyped a multi-agent app in Google AI Studio eventually hits the same wall: the prototype works, but it lives in a browser tab. At I/O 2026, Google shipped a fix — Export to Antigravity, a one-click handoff to a local production workspace, carrying "all the context" with it. I ran a real two-agent prototype through it. Here's exactly what survived the trip, what didn't, and what I had to fix by hand — including a bug that had nothing to do with the export itself. 1. The Pilot Project + The Click The project: Research Digest — a sequential two-agent app. Agent 1 (Researcher) takes a topic, uses grounded web search to gather sources. Agent 2 (Editor) synthesizes those findings into a polished digest. Persistence via Firestore, with a history archive of past digests. Built entirely from a single prompt in AI Studio's Build mode . Along the way, provisioning Firestore surfaced my first real gotcha before I even got to the export step — more on that below. Triggering the export: Code tab → Export → Export to Antigravity. The dialog is genuinely informative — it tells you upfront what's coming: all project files, conversation history, and explicitly "1 secret will be included." 2. What Actually Survives the Trip The export dialog's claims, checked one by one: Claimed to transfer What I found All project files ✅ Confirmed — full structure landed intact: .agents, .antigravity, src, config files, README.md with setup instructions Secrets (1 secret) ✅ Confirmed — GEMINI_API_KEY arrived populated in .env, worked immediately, no manual re-entry Conversation history history❌ Did not transfer. The imported "Research Digest" project showed "No conversations yet" in Antigravity's Agent Manager, despite the dialog's explicit promise. Checked twice, on two separate screens — consistent result. 3. The Gotchas Gotcha 1 — "Conversation history will carry over" is currently no

Large language models often develop recurring symbolic patterns — archetypes, metaphors, and memetic shortcuts — that appear across unrelated contexts. One observed example is the repeated emergence of fantasy-based metaphors such as “goblins,” “gremlins,” or similar entities when describing abstract system behavior, errors, or complexity. This article presents a structured analytical trace (A11 framework passes) showing how such patterns emerge from the interaction between reinforcement learning, cultural priors in training data, and user feedback loops. It also explores how introducing explicit interpretability layers can reduce the risk of these symbolic attractors becoming dominant explanatory shortcuts in model behavior. The first A11 pass S1 — Will Understand the causal mechanism: why the “goblin / fantasy drift” emerged in LLMs S2 — Wisdom (constraints) Main pitfall: confusing correlation (goblins appearing in outputs) with causation (why those specific symbols emerge) Also: “goblins” are not a standalone phenomenon they are a case of broader archetypal language drift S3 — Knowledge (what is actually known) There are 5 established mechanisms in LLM behavior: 1. RLHF reinforces “socially engaging metaphors” Models are rewarded for: vividness humor imagery human-like explanations ➡️ fantasy imagery tends to score highly 2. Internet prior already contains strong fantasy culture Training data includes: Reddit gaming discourse D&D culture fanfiction ➡️ “goblin / elf / troll” already exist as: universal behavioral archetypes 3. Compression effect (semantic abstraction) The model seeks compact semantic units: goblin = chaotic / greedy / messy / low-level failure mode ➡️ one token replaces a complex description 4. User feedback loop If the model says: “it’s like a goblin” users: react positively repeat it reinforce it in conversation ➡️ increases probability of reuse 5. Cross-task transfer (persona leakage) Stylistic patterns from: coding assistant mode creative mode

TL;DR: In 2026, the old "cheaper hourly rate vs. more control" framing is outdated. AI-assisted delivery is compressing team size, contracts are shifting from hourly to outcome-based, and onboarding windows have shrunk from months to days. Use staff augmentation when you have strong internal PM capacity and need specific skills for 3-6 months. Use a dedicated team when you're running a 2+ year product and need a self-contained unit with its own PM/QA. Below is a breakdown of the current landscape, including how providers like Toptal-style networks, 6senseHQ , Cleveroad , ScienceSoft , BairesDev , SolveIt , and Uptech fit into each model. Why this decision looks different in 2026 than it did in 2023 Three things changed the calculus this year: AI-assisted engineers ship more per head. Teams are increasingly built around a handful of seniors paired with AI coding assistants rather than a dozen mid-level developers billed by the hour — which makes the traditional "cost per hour" comparison less meaningful than "cost per shipped outcome." Contracts are moving from time-and-materials to outcome-based. Buyers are pushing vendors to tie payment to delivery milestones, not logged hours, partly because AI tooling makes hour-counting a weaker proxy for value. Onboarding windows collapsed. Several dedicated-team providers now quote 3-7 day ramp-up instead of the 2-4 week window that was standard a few years ago, which narrows the traditional "augmentation is faster to start" advantage. None of this changes the fundamental difference between the two models. It changes how much each one costs you in practice. The core difference, restated simply Staff augmentation : you hire individual engineers who join your team, use your tools, and report to your leads. You manage the work. Dedicated team : you hire a self-contained unit (engineers + QA + a PM/lead) that runs its own delivery process. You manage the roadmap, they manage the mechanics. The break-even point most guides converge

If you’ve built AI applications in production recently, you’ve probably hit the "Agent Wall." You build a ReAct agent, give it 10 granular tools (search, extract, route, format), a massive system prompt, and tell it to go to work. It feels like magic...until you look at your latency metrics and token bills. Today’s agents act as interpreters. They re-derive the exact same routines from scratch on every single request . They embed massive tool schemas and reasoning histories into every loop. It's slow, it's incredibly token-hungry, and occasionally, they hallucinate tool calls, drop constraints, or get stuck in endless reasoning loops. In a production environment, even occasional errors can be critical failures that waste time and tokens. The problem isn't the ReAct pattern itself. The problem is that we are forcing the LLM to orchestrate low-level, predictable logic that should be deterministic code. We got tired of paying the "reasoning tax" for sub-routines that don't need it. So, we built Sparsi —a framework for shifting complex logic out of your ReAct agent's prompt and into deterministic "Macro-Tools" built as DAGs (Directed Acyclic Graphs). The Macro-Tool Pattern There are two ways to use Sparsi: as an end-to-end solution for a specific task, or to create higher-level tools that plug into your existing agents. The latter is where the magic happens. Instead of giving your ReAct agent 10 tiny, flaky tools and hoping it chains them correctly, you build one highly reliable, deterministic Sparsi DAG to handle that specific sub-routine. You then expose that DAG to your agent as a single Model Context Protocol (MCP) tool. The overall agent still drives the conversation, but it delegates the heavy lifting to a reliable macro-tool. We chose the DAG architecture for three main reasons: Deterministic & Testable: The graph is made of plain code. You only run AI where natural language understanding is strictly required. Parallel by Architecture: Independent branches run co

The move comes after Simo took significant medical leave. She will stay on as a part-time adviser.

If Homo floresiensis wasn't a fire-using hunter, its origins could be different than we thought.

Here is a question that sounds simple until you've actually shipped a UI: how many files does it take...

Lyzr, a startup that builds AI agents for enterprises, used its own AI agent to raise a $100 million round — proof, evidently, that the product actually works.

Should Anthropic trust Elon Musk to host its models? With about $40 billion in revenue at stake, Musk insists that the company can.