A new feature will indicate when advertisers have used generative AI tools to create or edit their ads, Google says.

As developers , we often face challenges keeping our workspaces perfectly synchronized across devices and collaborators. Whether it’s dealing with slow cloud sync, merge conflicts, or latency issues, these problems can disrupt our workflow and productivity. That’s why I’m excited to introduce OrBit , a local-first workspace synchronization engine designed to keep your development environments in sync with sub-millisecond latency — all while supporting offline work and peer-to-peer collaboration. What is OrBit ? OrBit is built around a multi-layered architecture that combines the power of Rust, Tauri, and VS Code to deliver a seamless synchronization experience: Rust-based local watcher daemon: Monitors file system changes with kernel-level events for ultra-low latency. Tauri-based native desktop dashboard: Provides a lightweight, secure, and cross-platform interface to manage your sync settings. VS Code extension: Integrates directly with your editor for smooth, real-time syncing of your code workspace. Unlike traditional cloud-based sync solutions, OrBit uses peer-to-peer connections and Conflict-free Replicated Data Types (CRDTs) to ensure your workspaces stay consistent even during network partitions or offline periods. Key Features Real-time sync with sub-millisecond latency: Changes propagate instantly across your devices. Offline support: Work uninterrupted without internet, with automatic merging when reconnected. Conflict resolution: CRDTs handle concurrent edits gracefully, preventing data loss. Native desktop and editor integration: Manage sync easily via the desktop app and VS Code extension. Peer-to-peer architecture: No heavy cloud servers required, enhancing privacy and speed. Why OrBit ? OrBit is designed for developers who demand speed, reliability, and seamless collaboration. It eliminates the frustration of slow syncs and merge conflicts, letting you focus on coding. Whether you’re working solo across multiple devices or collaborating with a team,

The Paris-based ElevenLabs competitor, just announced a hefty seed extension round.

There's a pattern I kept seeing. A team gives an agent real capability, like moving money, shipping a change, or resolving a ticket that touches a customer's account. For a while it's great. Then the agent does one thing nobody can explain or defend after the fact, and the entire program snaps back to a human clicking approve on everything. The blocker was almost never the model. It was that there was no clean way to do two things at once. You couldn't bound what the agent was allowed to do before it acted, and you couldn't prove what it did after, in a form that survives contact with an auditor, a regulator, or a customer dispute. You can assemble that from parts today. Use a policy engine to authorize, and an audit log to record. The problem is they're two systems, and two systems drift. Six months later, when someone is actually asking "was this action allowed, and can you prove it," the policy engine and the log disagree about what the policy even was at the time. Now you're reconstructing intent from two sources that were never the same object. That's the gap. Not authorization by itself, and not observability by itself. The thing that authorizes an action and the thing that proves it should be the same object, bound to the exact policy version in force when the decision was made. The primitive Two verbs, one primitive. Control before. You mint a capability, which is a policy scoped to one agent: a spend cap, a counterparty allowlist, an expiry, whatever the action needs. Every consequential action the agent takes gets checked against the committed policy state and returns an allow or deny in the request path. An over-budget or out-of-policy action is refused before it happens, not flagged after. Refused is the operative word. The enforcement point commits no state change for a denied action, no matter how the agent reasons, how it's prompted, or whether it's been compromised. You've turned unbounded irreversible harm into bounded irreversible harm. Prove after

iPhone Ultra or the iPhone Fold. No matter what you call it, here are all the rumors and leaks we've gathered on Apple's most anticipated iPhone in years.

I'm Safdar Ali , a frontend engineer in Bengaluru. Last quarter I audited a client dashboard that looked polished — clean Tailwind, smooth transitions, Lighthouse performance in the 90s — and failed basic keyboard navigation in under two minutes. Tab order jumped randomly, modals trapped nothing, and icon-only buttons had no labels. WCAG 2.2 is not a legal checkbox for enterprise contracts alone. It is how you ship React UI that works for everyone: screen reader users, keyboard-only users, people on slow 4G with zoom enabled, and your future self debugging at 11pm. This guide covers the wcag 2.2 react patterns I run before every merge. Why WCAG 2.2 matters for React in 2026 WCAG 2.2 added criteria that directly affect React apps: focus not obscured, dragging movements, target size minimums, and consistent help. React's component model makes accessibility both easier and easier to break — you can encapsulate good patterns in a shared Dialog component, but you can also copy-paste a div-with-onClick button across forty files. The legal landscape in India is catching up. Government portals and fintech products increasingly require accessibility audits before launch. Even when nobody asks, inclusive UI reduces support tickets — unclear error messages and broken focus management generate more "the form is broken" emails than actual backend failures. React does not ship accessible components by default. A is focusable; a is not, unless you wire it. Your job is to make the accessible path the default path in your design system. Focus traps — modals that actually work A focus trap keeps keyboard focus inside a modal until the user dismisses it. Without one, Tab sends focus to elements behind the overlay — confusing for sighted keyboard users and disorienting for screen reader users who hear content from two layers at once. Continue Reading...

Claude subscribers must soon pay usage-based fees to access Anthropic’s best consumer AI model—a sign that the golden era of AI subscriptions is ending.

Google AI recently became the official AI Model and Platform Partner of DEV Community. As someone building an AI routing platform, I paid attention. Google's Gemini Enterprise Agent Platform (formerly Vertex AI) promises enterprise-grade AI agent orchestration — and with the DEV partnership, there's never been a better time to explore it. In this article, I'll share how I integrated Google Cloud's Agent Platform with my existing AI router (built on Neon PostgreSQL), what I learned about Gemini's enterprise capabilities, and why the Google AI + Neon + Algolia trifecta is the ideal stack for AI-first applications in 2026. Why Google Cloud's Agent Platform? The Gemini Enterprise Agent Platform is Google's answer to the question: "How do I orchestrate multiple AI agents in production?" It provides: Pre-built agent templates for common workflows (customer support, code review, data analysis) Grounding with Google Search — your agents can cite real, current sources Context caching — reduce costs by reusing conversation context across turns Multimodal understanding — Gemini processes text, images, audio, and video in one call Enterprise security — VPC controls, data residency, IAM integration For QuantumFlow AI (my AI routing platform), the Agent Platform solved a critical problem: how to orchestrate 26 different AI models without building a custom orchestration layer from scratch. The Architecture: Google Cloud + Neon + Next.js Here's the stack I built: User Request → Google Cloud Agent Platform (Gemini orchestration) → QuantumFlow Router (selects optimal model) → Local models (Ollama — free, sovereign) → Cloud models (GPT-4o, Claude, DeepSeek, Gemini) → Neon PostgreSQL (logs, analytics, cost tracking) → Algolia (search across all AI responses) Why Neon (DEV's Database Partner)? Neon is dev.to's official database partner, and for good reason. It's serverless PostgreSQL with: Database branching — create a full database copy in seconds (like git for data) Bottomless storage

When Neon became the official database partner of DEV Community, I was already a user. But the partnership made me look closer at why I chose Neon — and whether those reasons apply to other AI developers. They do. Here's why Neon is the ideal database for AI applications in 2026. The Problem: AI Apps Have Unique Database Needs AI applications have database requirements that traditional web apps don't: High write volume — every AI request generates logs, metrics, and cost data Variable load — traffic spikes when a model goes viral, then drops to zero Schema evolution — you're constantly adding models, routing rules, and analytics tables Dev/prod parity — you need to test routing changes against real production data Edge compatibility — AI APIs need sub-100ms response times globally Traditional PostgreSQL (RDS, Aurora) struggles with all five. Neon was built for them. Feature 1: Database Branching (The Game-Changer) This is Neon's killer feature. It works like git branch but for your entire database: # Create a branch from production neon branches create --parent main --name test-deepseek-v31 # Get a connection string for the branch neon connection-string test-deepseek-v31 # → postgresql://...@ep-test-deepseek...neon.tech/neondb # Run migrations on the branch npx prisma db push --url $BRANCH_URL # Test your new routing algorithm against REAL data # (the branch is a copy-on-write clone of production) # When tests pass, merge neon branches merge test-deepseek-v31 Why This Matters for AI Apps When I added DeepSeek V3.1 to my model pool, I needed to test: Would the new model break existing routing rules? Would the cost calculations be correct? Would the latency meet my SLA? With traditional PostgreSQL, testing against real data meant either: Copying production to a staging DB (hours, $$) Testing with synthetic data (unreliable) With Neon branching, I branched, tested in 30 seconds, and merged. Zero downtime, zero risk. Feature 2: Scale-to-Zero (Cost Optimization) Neon's c

Here's the problem nobody talks about: the reason most AI outputs are mediocre isn't the model — it's that you asked for a final answer and got one. A model with no friction produces the path of least resistance. It pattern-matches to "good-enough" and stops. It doesn't know what your bar for quality is. It doesn't know what logic you'd push back on, what tone would make your audience tune out, or what structural flaw a sharp reader would catch in the first 30 seconds. It just fills the token space with the most statistically probable response and calls it a day. So the output hits your clipboard. You read it. You sigh. Then you spend 40 minutes editing something that should have come out right the first time. There's a better way — and it exploits the fact that AI critique is significantly sharper than AI generation. The Core Insight: Models Are Better Critics Than They Are Authors This sounds counterintuitive, so stay with me. When you ask an LLM to generate something from scratch, it operates in "produce plausible content" mode. The pressure is to fill the blank. But when you ask a model to critique an existing piece — especially if you hand it a specific evaluative persona — it switches into "find the gap between what is and what should be" mode. That's a fundamentally different cognitive task, and it's one where models consistently perform better. Research on iterative self-refinement in LLMs (Madaan et al., 2023) shows that when models are given their own output and asked to improve it with explicit feedback criteria, quality scores improve substantially across writing, code, and reasoning tasks. The key variable wasn't model size or prompt verbosity — it was the presence of a structured feedback loop. The mechanism is simple: the critique generates tokens that constrain and guide the rewrite. Those critique tokens become working context. The model rewrites against them. The output is necessarily better-fitted to the evaluation criteria than anything a single-

RQ (Redis Queue) is a delightfully simple way to run background jobs in Python. That simplicity is also why teams under-monitor it: it just works, until a downstream API gets slow or a bad deploy ships, and jobs start failing in bulk — quietly. Here's what to watch and how to get alerted before a customer tells you. RQ failures don't announce themselves When a job raises, RQ moves it to the FailedJobRegistry and moves on. The worker keeps running; nothing crashes. If you're not looking at that registry, the failure is invisible — the same trap BullMQ, Celery, and every robust queue share. So the job is to reach into the queue's state and turn it into a signal. The four signals that matter for RQ Failure count / rate — jobs landing in the FailedJobRegistry over a window. Backlog — how many jobs are queued vs. being worked; is the worker keeping up? Latency — how long jobs take, and how long they wait before a worker picks them up. Worker liveness — are your workers actually alive and heartbeating? Where to read them RQ exposes queue and registry state directly: from redis import Redis from rq import Queue from rq.registry import FailedJobRegistry , StartedJobRegistry redis = Redis () q = Queue ( " default " , connection = redis ) queued = len ( q ) # backlog failed = FailedJobRegistry ( queue = q ) # failures started = StartedJobRegistry ( queue = q ) # in-flight print ( " queued: " , queued ) print ( " failed: " , len ( failed )) print ( " started: " , len ( started )) Poll this on an interval and store the series — a single snapshot hides the trend , which is the part that matters. For failures specifically, walk the registry to get the actual exceptions: for job_id in failed . get_job_ids (): job = q . fetch_job ( job_id ) print ( job . id , job . exc_info . splitlines ()[ - 1 ] if job . exc_info else "" ) Two gotchas: Group by exception, not by job. A thousand jobs failing with the same traceback is one incident. Normalize the message (strip IDs, timestamps, host

"Exactly what that dialog looked like between the government and Anthropic and OpenAI is unclear."