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SaaS teams using AI-driven experimentation platforms (also called A/B testing automation or CRO...

The new round values the physical AI startup that aims to automate heavy engineering and drug design at $41 billion.

The generative features in iOS 27’s new Photos app will add fake pixels to some of your shots, but Apple’s Jon McCormack says the company isn’t using AI “for the sake of AI.”

Provisioning a tenant-scoped email identity for your SaaS is one POST: curl --request POST \ --url "https://api.us.nylas.com/v3/connect/custom" \ --header "Authorization: Bearer <NYLAS_API_KEY>" \ --header "Content-Type: application/json" \ --data '{ "provider": "nylas", "workspace_id": "<WORKSPACE_ID>", "settings": { "email": "scheduling@customer-a.com" } }' No OAuth dance, no refresh token — just an address on a registered domain. The response comes back already valid: { "request_id" : "5967ca40-a2d8-4ee0-a0e0-6f18ace39a90" , "data" : { "id" : "b1c2d3e4-5678-4abc-9def-0123456789ab" , "provider" : "nylas" , "grant_status" : "valid" , "email" : "scheduling@customer-a.com" , "scope" : [], "created_at" : 1742932766 } } The data.id is a grant_id that works with every existing Nylas endpoint, and the account is live immediately. That's the primitive behind a multi-tenant pattern worth knowing: one Agent Account per customer, on each customer's own verified domain, all managed from a single application. (Agent Accounts are in beta, so the surface may shift before GA.) The architecture in one paragraph Your app runs scheduling@customer-a.com , scheduling@customer-b.com , and so on — same code path, different identities. Each account has its own policy, its own send quota, and its own sender reputation. A single application can manage accounts across an unlimited number of registered domains, so tenant count is a billing question, not an architectural one. Customer A's deliverability problems stay Customer A's; nothing they do contaminates Customer B's mail. Domains: register once, mint accounts forever The provisioning docs lay out two domain strategies you can mix freely in one application: Strategy Address format Setup Trial domain alias@<your-application>.nylas.email None — instant Your own domain alias@yourdomain.com MX + TXT records at the DNS provider For the per-customer pattern, each tenant brings their domain. You register it once per organization (picking the US

Last sprint, a team I talked to demoed a voice agent that handled support calls impressively — right up until a caller asked "can you email me those instructions?" and the room went quiet. The agent could talk about the docs. It had no address to send them from. The workaround on the whiteboard afterwards was grim: relay through a shared noreply@ , lose the replies, reconcile threads manually in the ticketing system. Voice agents hit this wall constantly, because phone calls generate follow-up artifacts — reset instructions, documents, meeting recaps — and email is how callers expect to receive them. The clean fix is the same one that works for text agents: the voice agent gets its own mailbox. The identity half A Nylas Agent Account is a hosted mailbox you create through the API — Agent Accounts are in beta — and the voice use case from the product docs is exactly the scenario above: a voice agent taking support calls sends documents, reset instructions, or meeting recaps from its own voice-agent@yourcompany.com address the moment the caller asks. The part that makes it more than a send pipe: when the caller replies, the reply returns through the same account, so the full conversation is one thread in one mailbox. The phone call and its written follow-ups stop living in separate systems. Each account is a real grant with a grant_id that works against the existing Messages, Threads, and Webhooks endpoints, ships with six system folders, and sends up to 200 messages per account per day on the free plan. The plumbing half The voice agents recipe covers how the runtime actually calls email tools. The flow is the same regardless of vendor: speech → STT → LLM (function-calling) → subprocess(nylas …) → JSON → LLM → TTS → speech The LLM decides on a tool, the runtime spawns a Nylas CLI subprocess with --json , the result comes back, and the model composes a spoken response. On LiveKit, a tool is just a decorated function: from livekit.agents import function_tool import sub

An AI agent that can't receive email can't finish a signup form. That one limitation quietly rules out a huge class of autonomous workflows — the research agent that needs a developer account on a data source, the QA agent that registers for a SaaS on every test run, the purchasing agent that needs a buyer profile on a marketplace. Every one of them dies at "we've sent you a verification email." The blocker was never the form. Headless browsers fill forms fine. The blocker is that verification emails traditionally route to a human inbox, which puts a human back in a loop that was supposed to have none. Agent Accounts remove that dependency. The agent gets its own hosted mailbox (the feature is in beta), signs up with that address, catches the verification email via webhook, and completes onboarding by itself. Here's the whole flow, condensed from the cookbook recipe. Provision, subscribe, sign up Three setup moves. First, create the mailbox — one CLI command, or POST /v3/connect/custom with "provider": "nylas" if you'd rather hit the API: nylas agent account create signup-agent@agents.yourdomain.com The API version is the same Bring Your Own Authentication endpoint other providers use — no OAuth refresh token involved: curl --request POST \ --url "https://api.us.nylas.com/v3/connect/custom" \ --header "Authorization: Bearer <NYLAS_API_KEY>" \ --header "Content-Type: application/json" \ --data '{ "provider": "nylas", "settings": { "email": "signup-agent@agents.yourdomain.com" } }' Save the grant ID it prints. Second, subscribe to inbound mail: nylas webhook create \ --url https://youragent.example.com/webhooks/signup \ --triggers message.created The message.created event fires within a second or two of mail arriving, carrying the message's summary fields. The webhook URL has to be publicly reachable over HTTPS; for local development, the recipe recommends VS Code port forwarding or Hookdeck to expose your dev server. Third, submit the target service's signup form wit

What does your automation do when the login flow it's driving sends a six-digit code instead of a confirmation link? For most teams the honest answer is "a human goes and checks a shared inbox," which is a strange bottleneck to leave in the middle of an otherwise fully automated pipeline. There's a cleaner shape: the agent owns the mailbox the code lands in. With a Nylas Agent Account — a hosted mailbox controlled entirely through the API, currently in beta — the OTP email arrives, a webhook fires, your handler extracts the code, and whatever orchestrates the login gets it back. No human, no inbox-checking Slack message, no screen-scraping Gmail. Step one: make sure it's the right email A message.created webhook fires on every inbound message, so the first job is filtering down to the one that actually carries the code. The recipe uses two signals together — sender domain and a subject heuristic: app . post ( " /webhooks/otp " , async ( req , res ) => { res . status ( 200 ). end (); const event = req . body ; if ( event . type !== " message.created " ) return ; const msg = event . data . object ; if ( msg . grant_id !== AGENT_GRANT_ID ) return ; const sender = msg . from ?.[ 0 ]?. email ?? "" ; const subject = msg . subject ?? "" ; const senderMatches = sender . endsWith ( " @no-reply.example.com " ); const subjectLooksRight = /code|verif|one. ? time|passcode/i . test ( subject ); if ( ! senderMatches || ! subjectLooksRight ) return ; await handleOtp ( msg . id ); }); Neither check alone is enough. Sender-only matching trips on welcome emails from the same domain; subject-only matching trips on anything that mentions "verification." Regex first, LLM second Most OTP emails follow one of a few shapes: a standalone 4–8 digit number, or a code after a label like "Your code is:". Three patterns, tried in order from most to least specific, cover the vast majority of services: const patterns = [ / (?: code|passcode|one [\s - ]? time )[^\d]{0,20}(\d{4,8}) /i , // "Your code

Two CI workers kick off at the same moment. Both sign up a test user, both poll the shared QA Gmail account for "the" verification email, and worker #7 grabs the message that belonged to worker #12. The test passes. The wrong test. You spend an afternoon staring at a green build that should've been red. Shared inboxes are the single biggest source of flakiness in email-dependent E2E tests, and every workaround — catch-all forwarding rules, label rules scoped per PR, OAuth tokens living on the runner — adds another moving part that breaks on its own schedule. The fix is structural: every test gets its own address, on infrastructure your suite provisions and destroys. One wildcard, infinite addresses The E2E email testing recipe sets this up with one CLI command: nylas inbound create e2e You get back an inbox ID and a wildcard pattern shaped like e2e-*@yourapp.nylas.email . From there, each test mints a unique address under the wildcard — e2e-<uuid>@yourapp.nylas.email — and there's nothing to provision per address. You don't pay or configure per address either; the wildcard is just a convention, so burn UUIDs freely. Mail flows through MX records hosted on the Nylas side, which means zero DNS work in your own zone (the tradeoff: addresses live under *.nylas.email ). The Playwright fixture is two pieces — an address minter and a poller: export const test = base . extend < Fixtures > ({ testEmail : async ({}, use ) => { await use ( `e2e- ${ randomUUID ()} @yourapp.nylas.email` ); }, pollInbox : async ({ testEmail }, use ) => { const poll = async ( timeoutMs = 30 _000 ) => { const deadline = Date . now () + timeoutMs ; while ( Date . now () < deadline ) { const out = execSync ( `nylas inbound messages ${ process . env . INBOX_ID } --json --limit 50` , ). toString (); const match = JSON . parse ( out ). find (( m ) => m . to . some (( t ) => t . email === testEmail ), ); if ( match ) return match ; await new Promise (( r ) => setTimeout ( r , 1500 )); } throw new Error (

A scheduling link makes the human do the work; a scheduling agent with its own calendar does the negotiating. Booking pages outsource the back-and-forth to a UI. The agent model keeps it where it already happens — in email — and answers from a real address with a real calendar behind it. The setup: meeting requests land at scheduling@agents.yourcompany.com , an LLM parses intent, the agent checks availability against its own free/busy, proposes slots, and creates events that show up as normal invitations in Google Calendar, Microsoft 365, and Apple Calendar. No human mailbox in the loop, no delegation permissions, no calendar borrowed from whoever set the bot up. This runs on a Nylas Agent Account — a hosted mailbox-plus-calendar you provision through the API. Agent Accounts are in beta, so expect some movement before GA. Provision the identity One CLI command or one API call: nylas agent account create scheduling@agents.yourcompany.com The primary calendar is provisioned automatically — no extra call before you can create events on it. The API equivalent is POST /v3/connect/custom with "provider": "nylas" and the email address in settings ; no OAuth refresh token involved. Save the grant ID, then subscribe a webhook to four triggers: message.created , event.created , event.updated , and event.deleted . When Nylas sends the challenge GET to your endpoint, respond with the challenge value within 10 seconds to activate it. The negotiation loop The full tutorial wires this end to end, but the shape is: Human emails the agent. message.created fires; the webhook only carries summary fields, so the handler fetches the full body. The LLM extracts duration, timezone, and urgency. The agent queries /calendars/free-busy against its own primary calendar and replies with 3 candidate slots. The human picks one; another message.created fires; the agent creates the event with notify_participants=true . The availability check is the part people overcomplicate. Free/busy returns bus

200 messages per account per day. That's the free-plan send ceiling on a Nylas Agent Account , and it's a surprisingly useful number to design an outreach agent around — it forces the kind of pacing that keeps cold email from becoming spam, and paid plans drop the daily cap by default when you outgrow it. The bigger idea: instead of sending campaigns through a rep's mailbox or a send-only API, the agent gets its own address. sales-agent@yourcompany.com is a real mailbox — it sends, it receives replies, it owns a calendar. Agent Accounts are in beta, but the model is straightforward: each account is just another grant, so the Messages, Threads, Events, and Webhooks endpoints you'd use for a connected Gmail account work unchanged. What the loop looks like The sales-outreach pattern from the product docs runs in three stages, all on one grant_id : Send the campaign through the standard send endpoint. Classify replies with an LLM into interested / not now / unsubscribe , threading every exchange through the Messages API. Book the meeting — when a prospect says yes, the same grant creates an event on the agent's own calendar and sends the invite. No CRM hand-offs between three tools, no rep mailbox cluttered with sequence noise. Replies arrive as webhooks Inbound mail fires message.created , and the payload looks exactly like it does for any other grant. One subscription covers your whole application: curl --request POST \ --url 'https://api.us.nylas.com/v3/webhooks/' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer <NYLAS_API_KEY>' \ --data-raw '{ "trigger_types": ["message.created", "event.created", "event.updated"], "description": "Outreach agent", "webhook_url": "https://your-app.example.com/webhooks/nylas", "notification_email_addresses": ["dev-team@your-company.com"] }' Your endpoint gets a GET with a challenge query parameter first — echo it back in a 200 and deliveries start flowing as POST s. The payload's data.object carries sender,

Every shared support inbox eventually becomes a triage problem: 80 unread messages, no agreement on what "urgent" means, and the one person who knows which customer is about to churn is on PTO. Teams keep solving this with labels and heroics. It's a better fit for an LLM — as long as the LLM has somewhere safe to live. That's the case for giving the triage agent its own mailbox. Nylas Agent Accounts (currently in beta) are hosted mailboxes you create entirely through the API. A support@yourcompany.com Agent Account receives every inbound support email, gets six system folders out of the box ( inbox , sent , drafts , trash , junk , archive ), and exposes the same grant_id -based endpoints as any connected Gmail or Outlook account. Creating one is a single request: curl --request POST \ --url "https://api.us.nylas.com/v3/connect/custom" \ --header "Authorization: Bearer $NYLAS_API_KEY " \ --header "Content-Type: application/json" \ --data '{ "provider": "nylas", "settings": { "email": "support@yourcompany.com" } }' Save the grant_id from the response — every other call hangs off it. Four buckets beat five The classification scheme from the email triage agent recipe sorts mail into exactly four categories: Bucket Meaning Action URGENT Production incident, executive ask Draft a reply within the hour ACTION Code review, meeting follow-up Draft a reply same-day FYI Status update Leave it alone NOISE Newsletter, automated alert Archive Four is deliberate. Three loses fidelity — everything collapses into "important." Five and the model starts confusing adjacent categories. The prompt runs with temperature=0 and max_tokens=10 , and the model only sees sender + subject + a 200-character snippet, not the full body. That's enough for over 90% accuracy. Here's the prompt verbatim from the recipe: You triage email into one of four categories: URGENT — production incidents, executive requests; reply within 1 hour ACTION — code reviews, meeting follow-ups; reply same day FYI — info

Most "AI agent + email" tutorials start the same way: connect the agent to a human's inbox over OAuth, hope the token doesn't expire mid-run, and pray the agent never replies to the wrong thread on someone's behalf. There's a different model: give the agent its own email address. Nylas recently shipped Agent Accounts (currently in beta) — fully functional, Nylas-hosted mailboxes you create and control entirely through the API. Each one is a real name@company.com address that sends, receives, hosts calendar events, and RSVPs to invitations. To anyone interacting with it, it's indistinguishable from a human-operated account. I work on the docs at Nylas, so I've spent a lot of time with this API. Here's a tour of what it does and how to get a mailbox running in a few minutes. Why not just connect the agent to a human inbox? You can — that's what OAuth grants are for, and they're the right tool when the agent works on behalf of a person. But a lot of agent workflows want a first-class identity instead: System mailboxes ( sales@ , support@ , scheduling@ ) that your app owns end-to-end. No OAuth consent screen, no user offboarding breaking your integration. Ephemeral inboxes for test automation — provision a fresh address per run, sign up for a service, grab the OTP from the verification email, tear it down. Per-customer identities in multi-tenant apps: scheduling@customer-a.com , scheduling@customer-b.com , each with its own send quota and sender reputation, all in one Nylas application. A scheduling bot with its own calendar that proposes slots, sends invites, and shows up as a normal participant in Google Calendar, Microsoft 365, and Apple Calendar. The key design decision: an Agent Account is just another grant . It gets a grant_id that works with every existing Nylas endpoint — Messages, Drafts, Threads, Folders, Attachments, Calendars, Events, Webhooks. If you've already built against connected accounts, nothing new to learn. Create a mailbox with one API call Every

Ever stared at a physical examination report and felt like you were reading ancient hieroglyphics? "Elevated Serum Triglycerides"? "Hypoechoic nodule"? The immediate urge is to Google it, only to be convinced by WebMD that you have three days to live. In the world of AI Agents and Healthcare Automation , we can do better. Today, we are building an AI Physician Assistant using the AutoGPT protocol. This isn't just a chatbot; it’s an autonomous agent capable of parsing complex medical data, searching verified medical encyclopedias via SerpApi , and even cross-referencing hospital schedules to suggest the right department for a follow-up. By leveraging the OpenAI API and Pydantic for structured data validation, we are moving from "chatting" to "doing." If you're looking for more production-ready patterns or advanced AI implementation strategies in healthcare, definitely check out the deep-dive articles at * WellAlly Tech Blog * . The Architecture: How the Agent "Thinks" Unlike a standard LLM call, an autonomous agent operates in a loop: Perception -> Reasoning -> Action -> Observation . Here is how our AI Assistant handles a medical report: graph TD A[User Uploads Report/Text] --> B{Pydantic Parser} B -->|Structured Data| C[AutoGPT Agent Core] C --> D[Search Tool: SerpApi] D -->|Medical Context| C C --> E[Reasoning: Match Symptoms to Dept] E --> F[Tool: Hospital Schedule API] F -->|Availability| G[Final Recommendation & Appointment Plan] G --> H[User Notification] Prerequisites To follow this advanced tutorial, you’ll need: Python 3.10+ OpenAI API Key (GPT-4o recommended for reasoning) SerpApi Key (to search Google Scholar/Medical Databases) Pydantic for data modeling Step 1: Defining the Medical Schema (Pydantic) The biggest challenge in medical automation is data integrity . We cannot allow the AI to hallucinate vital signs. We use Pydantic to ensure the agent only proceeds if the data matches our schema. from pydantic import BaseModel , Field from typing import List

Modern teams expect software to update instantly. Nobody wants to refresh a page every few seconds to see whether a task has moved from "In Progress" to "Done." Applications like Trello, Jira, and Linear have trained users to expect real-time collaboration. In this tutorial, we'll build a simplified real-time Kanban board using React, TypeScript, and WebSockets. Along the way, we'll cover project structure, state management, optimistic UI updates, and handling concurrent changes from multiple users. What We're Building Our application will support: Creating tasks Drag-and-drop task movement Real-time synchronization between users Optimistic updates Type-safe frontend architecture Tech Stack Frontend React TypeScript Vite React DnD Zustand Backend Node.js Express Socket.IO Database PostgreSQL Why WebSockets Instead of Polling? Many developers start with polling: setInterval (() => { fetch ( " /tasks " ); }, 5000 ); This works, but it's inefficient. Problems include: Unnecessary network requests Delayed updates Increased server load Poor user experience WebSockets maintain a persistent connection between client and server. Instead of asking: "Any updates yet?" the server simply says: "Here's an update." The result is lower latency and fewer network requests. Project Structure A scalable React project should avoid putting everything into a single components folder. Here's a structure that works well: src/ ├── api/ ├── components/ ├── features/ │ ├── board/ │ ├── columns/ │ └── tasks/ ├── hooks/ ├── store/ ├── services/ ├── types/ └── utils/ This feature-based organization scales much better than organizing solely by file type. Setting Up React Create the project: npm create vite@latest kanban-board cd kanban-board npm install Install dependencies: npm install zustand socket.io-client react-dnd react-dnd-html5-backend Defining Task Types Type safety becomes increasingly valuable as applications grow. export interface Task { id : string ; title : string ; description : s

On June 9, 2026 (US time), two big announcements landed on the same day. At the keynote of Datadog's annual event DASH 2026 in New York, the Bits AI family expanded significantly: Detection, Investigation, Remediation, Infrastructure, Code, Release, Testing, Data Analysis, Chat, Memories, and Evals. Counting by agent, that is more than ten, with over 100 new features announced together. The full picture is laid out in the keynote roundup. https://www.datadoghq.com/blog/dash-2026-new-feature-roundup-keynote/ The same day, AWS announced FinOps Agent as a public preview. It bundles four data sources, Cost Explorer, Cost Anomaly Detection, Cost Optimization Hub, and Compute Optimizer, and delivers automated cost-anomaly investigation, natural-language cost questions, periodic cost reports, and aggregated optimization opportunities straight into Slack and Jira. The details are in the AWS blog. https://aws.amazon.com/blogs/aws-cloud-financial-management/aws-finops-agent-is-now-public-preview/ AWS DevOps Agent had already gone GA in March, handling incident response. With FinOps Agent now added, AWS-built standard agents line up across the main operational domains. That said, DevOps Agent also covers multicloud and on-premises environments, so its scope differs from FinOps Agent, which targets AWS cost data. https://aws.amazon.com/blogs/mt/announcing-general-availability-of-aws-devops-agent/ On the surface, this looks like two separate stories: Datadog the monitoring platform, AWS the cloud provider. But read the two announcements side by side, and you see both reaching for the same territory, Ops, through different entrances. Line up their features and most of them overlap, so a surface spec comparison won't show the difference. This article sorts out the same-day releases by the two companies' positioning, asks what these very similar agent lineups are actually fighting over, and goes as far as the axes for telling them apart and the predictions that follow. This is writ

Preply uses OpenAI to launch AI-generated lesson summaries, providing personalised feedback and language learning exercises.

Today on Uncanny Valley, we take an early look at the SpaceX IPO and why you might find yourself among the investors without even realizing it.