For the archetypal period in years, the Internet Corporation for Assigned Names and Numbers — improved known as ICANN — is accepting applications for new top-level domains. These are the suffixes at the end of all URLs, and you may cognize them as things akin .com, .org, and .pizza.

ICANN fair announced the 1,615 applications that have been submitted so far, from 481 distinct applicants. The trend volition not astonishment you: AI is everywhere. Ten distinct companies, including the two Meta and OpenAI, applied for the .agent domain. Seven, including OpenAI, applied for .agi. Six, including OpenAI, applied for .asi, plainly attempting to get in on President Trump’s petition to toggle from “artificial intelligence” to “super intelligence.” Four companies applied for .intelligence, and Meta is one of two curious in the preposterously lengthy .superintelligence. (OpenAI applied for a total of 15 TLDs; Meta wants a whopping 21.)

There are, broadly, two types of domains in these applications. One is additional generic, the benevolent of item many of group or companies power use. There are hundreds of those in this current batch, from .geek to .haha to .anime to .jackpot. There’s additionally my individual favorite new one, .itiswhatitis.

The another is purely branded, giving a business its own TLD to use for its own purposes. There are 345 brand applications in this batch, including everything from .applebees and .bankofamerica to .zillow and .zoom. Meta applied for .facebook, .instagram, .messenger, .meta, .frommeta, and .threads, which it could use for many purposes; Bluesky applied for .bsky alongside the apparent aim of weaving the domain into its usernames; OpenAI applied for .chatgpt, .codex, .openai, .gpt, and others; Anthropic wants .anthropic and .claude. These strings could ultimately create for memorable merchandise pages, social media usernames, and much additional besides.

It has mostly been a durable highway for these substitute TLDs complete the final few years. Some, akin .ai or .app, have rotate into somewhat popular gratitude to assorted tech trends, but most group fair never got used to the idea of typing .restaurant or .babysitting, and .com and a few others have really rotate into additional crucial than ever. It’s engaging to see so many brands go for their own domain, which lone Google and a few others have accomplished before.

This is but one stage in the procedure of creating a new top-level domain, and nothing volition be made authoritative before sometime next year. But it appears that equal as the earth becomes additional filled alongside and mediated by AI, awesome domain names continue to matter. Even the AI companies think so.

Microsoft fair wrapped up a big Windows and Surface-focused keynote in San Francisco.

The biggest notice was arguably the publish particulars concerning the Surface Laptop Ultra, its new laptop that’s powered by Nvidia’s RTX Spark Arm-based chip. The device volition commencement at $2,599 for a configuration alongside an 8-core CPU, 24GB of RAM, and 512GB of storage, and it volition be released on October 16th. The laptop additionally has built-in magnetic USB-C charging alternatively of the proprietary Surface Connect magnetic charging port. The Verge’s Tom Warren published a profound dive all concerning the new laptop. Microsoft also announced that the Surface RTX Spark Dev Box, a mini PC for developers, is accessible for preorder for $5,999 onward of shipping in November.

In addition, Microsoft shared particulars concerning some updates coming to Windows, including “Hybrid Intelligence” features that lets Copilot use records on your Windows PC to obtain actions and a new hunt cognition that lets you do quick actions from the hunt bar.

You can capture up alongside all of our safety from the event correct here.

Anker has really made a name for itself exterior of their batteries, expanding to headphones, speakers, projectors, and another tech products. Their power stations are motionless among the most recognized in the industry, but their another products are climbing up their corresponding ladders as well. There are many Anker products on transaction correct now during the October Prime Day sale, and I've rounded up the best power banks, headphones, and power stations I could find. The transaction ends tonight, so don't young female out.

The finest Anker power position deals during the October Prime Day sale

The finest agreement on a power position is the Anker SOLIX S2000, which is 46% off correct now, landing at $649.99 (originally $1,199). I own an Anker power position that is slightly bigger than this one, and I can vouch for their norm and practicality. The S200 is awesome for anyone who lives in a home that is prone to losing power during a storm or anyone looking for a compact but mighty portable power position they can obtain to a backyard or camping. It can power an average fridge for 35 hours, according to Anker, which gives you an idea of how mighty the S200 is. The finest period to buy a power financial institution is whenever they're massively discounted, not whenever the food in your fridge is spoiling from a power outage.

Anker SOLIX S2000 1500W Solar Portable Power Station

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Other Anker power position deals

• Anker Solix C300 DC Portable Power Station $199.99 (originally $249.99)

• Anker Solix C200 Power Station alongside 60W Solar Panel $229.99 (originally $259.99)

• Anker SOLIX C1000 Gen 2 Portable Power Station $469.99 (originally $599.99)

• Anker SOLIX C2000 Gen 2 Portable Power Station $849.99 (originally $1,499)

The finest power financial institution deals during the October Prime Day sale

There is item concerning having a charging position for all of your devices that gives me a awareness of peace. Maybe it's practicality or organization, but I affection it. The Anker Qi2.2 Foldable MagSafe Charger can do that for your phone, smartwatch, and earbuds all at once. It additionally props your phone up as a stand. Right now, it's $94.74 (originally $149.99), following a 37% discount.

Anker Prime 3-in-1 Wireless Charging Station, MagSafe Charger Stand Compatible, Qi2.2 25W Certified Palm-Sized Foldable Charger for iPhone 17/16, Apple Watch, AirPods (Non-Battery, Adapter Included)

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Other power financial institution deals

• Anker 167.5W Max 3-Port Ultra-Compact USB C Fast Charging Car Charger $32.29 (originally $53.99)

• Anker 100W Max USB C Charger, 3-Port GaN Phone Charger alongside Smart Display $45.99 (originally $69.99)

• Anker 313 Qi-Certified 10W Max Wireless Charger Stand $15.99 (originally $25.49)

• Anker Prime Charging Station, 200W 6-Port GaN Desktop Charger $69.99 (originally $79.99)

The finest Anker headphone deals during the October Prime Day sale

The earth power have you convinced that you need to expend upwards of $100 to get fine headphones, but the Anker Soundcore Q20i headphones interrupt that notion alongside a $37.97 (originally $69.99) pair. These headphones have most of the features you'd anticipate from a flagship headphone, akin ANC, High-Res audio, lengthy power division existence of up to 40 hours alongside ANC on, and an amazing app. You really don't need to expend big to get fine headphones in 2026.

Soundcore by Anker Q20i Hybrid Active Noise Cancelling Headphones, Wireless Over-Ear Bluetooth, 40H Long ANC Playtime, Hi-Res Audio, Big Bass, Customize via an App, Transparency Mode (White)

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Other Anker headphone deals

• Soundcore by Anker P20i True Wireless Earbuds $18.99 (originally $39.99)

• Soundcore P40i by Anker Noise Cancelling Earbuds $41.99 (originally $59.99)

• Anker Soundcore Liberty 4 Noise Cancelling Earbuds $66.49 (originally $99.99)

• Soundcore AeroClip by Anker Open Ear Earbuds $94.99 (originally $169.99)

• Anker Soundcore Sleep A30 Special Earbuds $159.99 (originally $229.99)

Amazon October Prime Day FAQs

How lengthy is Prime Day?

October Prime Day is a two-day event that starts Oct. 6 and ends on Oct. 7.

What period does Prime Day start?

October Prime Day starts Tuesday, October 6, 2026, at 12:00 a.m. PT / 3:00 a.m. ET.

Is Prime Day formerly a year?

Prime Day happens all summer. Prime Big Deal Days (or October Prime Day) happens all October. Big Spring Sale happens all spring.

Is Anker a dependable brand?

Yes, Anker is among the finest brands whenever it comes to batteries and power banks. They've additionally been making awesome projectors and headphones complete the final few years.

Is Anker a fine brand for chargers?

Anker makes several of the finest chargers in the industry. Its archetypal merchandise that put them on the map was their batteries and power banks, so its chargers are additionally dependable and recommendation awesome value.

Do Anker's Prime Day discounts use to all their merchandise lines, or fair choose items?

Unfortunately, not all Anker products are on sale. However, there are many Anker products on sale, and you can discover them all on the Amazon landing page.

Read next

Chrome stools, bottomless coffee, and a list filled alongside leaf upon leaf of traditional comfort foods — there's no argument: Diners are quintessentially American.

Diners nationwide have been recognized for their historic pasts, award-winning list items, and elevated quantity of affirmative reviews, making them must-stop destinations on any cross-country highway trip.

While diners are disappearing throughout America — because of rising rents and labor costs, declining gain margins, and changing client habits — several have remained fixtures gratitude to loyal locals, cherished list items, and distinctive atmospheres.

From decades-old landmarks akin Tops Diner in New Jersey to no-frills lunch counters that have quietly rotate into traditional icons in their corresponding towns, these are the most iconic diners in all state.

October 7, 2026 · Mainbrella Engineering

You ask a server to create a Linux machine. The device boots. The answer disappears. From the client’s chair, this looks much akin a petition that never reached the server. Retrying is reasonable. Starting a second device would create the improvement additional costly than the failure.

This is a helpful way into Mainbrella’s backend. Follow one device far adequate and the identical issue keeps returning: a command can run without its visitant seeing the result; a personal petition can outlive a network membership; a disk grasp can win without leaving us the grip needed to reconstruct it. Each border needs a decision concerning what a retry is allowed to do.

Let’s prosecute an explanatory job that runs analysis.py, says data from another container, and writes metrics.csv. Its device occupies slot c17. The filenames and slot are examples; the mechanisms below arrive from the open-source backend. We’ll commencement before Linux boots, since that is anywhere ownership and prosperity have to be decided.

One location decides who gets the final slot

Two create requests can attain during an document has one slot left. Reading a counter, booting a machine, and updating the oppose afterward gives the two requests a chance to win. By the period we notice, the costly part has already happened.

We provision all document a Cloudflare Durable Object: a persistent coordinator alongside its own storage. The community API resolves the authenticated person and paid entitlement, afterward addresses that coordinator as account:<userId>. Each device slot has a distinct Durable Object. For c17, its name is user:<userId>:slot:17. The archetypal slot retains the older name user:<userId> and the community ID small; that ID does not choose the machine’s size.

The document coordinator serializes admission. A petition must reserve its slot, monthly start, and compute allowance before it can ask the runtime to boot. The next petition hence sees occupied capability equal during the archetypal device is motionless starting. The account controller implements the queue explicitly alongside a commitment tail; an await inner an admission decision doesn’t let the next decision stumble former it.

Holding this fastener until all device was prepared would additionally serialize all their footwear times. We publish it following the durable booking and provision exterior it. One document makes the decisions in order; its runtimes can footwear together.

There’s a particularly helpful test for this distinction. It sends six concurrent requests on a Builder account, which has five slots, and holds all runtime’s preparedness inspect rearward a gate. Five machines attain footwear before that entrance opens. The sixth petition receives a conflict, and the document records five starts. It checks the two halves of the design: exclusive admission and parallel provisioning.

Ownership is established before this coordination begins. The authentication adapter accepts an API key or a login session. An explicitly invalid Bearer credential fails authentication equal if a valid browser biscuit accompanies it. The internal petition builder supplies the document character and entitlement itself. Letting the visitant choose an x-mainbrella-user header would undo the document border we fair built.

A receipt exists before the device does

For our analysis.py job, the client supplies an Idempotency-Key alongside POST /containers. That key method “this particular attempt to create a machine.” The document records which slot and booking pertain to it, together alongside a fingerprint of the requested configuration. Changing the image, size, or another fingerprinted choice during reusing the key produces a conflict.

The crucial compose is small. Here is the applicable part of container-account-core.js, alongside the surrounding validation omitted:

const reservationId = ++state.nextReservationId; state.reservations[slot] = reservationId; const innovation = { id: crypto.randomUUID(), slot, reservationId, fingerprint, expiresAt: this.now() + CREATION_RETENTION_MS, }; await this.ctx.storage.put({ [KEY]: state, [CREATION_PREFIX + idempotencyKey]: creation, });

That multi-key compose commits the charged booking and innovation receipt atomically. We don’t desire a receipt for a slot we never reserved, or a charged slot that a keyed retry cannot find. Only afterward does the controller dispatch the boot.

Now endure the reply. A matching retry finds the receipt before attempting caller admission. While the booking is pending, it can come back starting. Pending reservations have a ninety-second reconciliation window; following that, the coordinator asks the runtime what really exists. If it finds the operating machine, it returns that device without charging another start. The retry never dispatches the ambiguous booking a second time.

The receipt lasts twenty-four hours. If the device has stopped or its slot has been reused, that identical retained key returns creation_no_longer_running. It doesn’t quietly initiate a replacement. A client that wants a replacement makes a new innovation attempt alongside a new key. This is additionally why the client should preserve its key and petition before sending them: server-side deduplication is small assistance if the client forgets the character it needs to ask about.

What establishes readiness? The runtime controller starts the selected depiction alongside sleep infinity as its entrypoint, afterward executes uname -a. The command must exit successfully inside sixty seconds. This establishes that the visitant can execute a command. Our Python inspection motionless needs its own requirements and checks; an answering kernel cannot certify a financial calculation.

Yesterday’s communication can attain at tomorrow’s machine

Suppose the dispatch for our archetypal footwear is delayed. Meanwhile, the document cancels the reservation, releases c17, and assigns that slot to a replacement. The old dispatch eventually arrives. Its mark is motionless the identical runtime object. Looking up the slot by name cannot inform us whether this communication is entitled to commencement anything.

Each admitted commencement receives an expanding booking number. At the runtime, we persist two high-water marks: the newest accepted commencement and the newest cancellation. A footwear at or below either grade is rejected. Cancellation writes its barrier equal whenever there is no operating visitant to destroy, so a afterward arrival cannot resurrect canceled work.

The reverse competition matters too. A delayed cleanup for booking 41 must not halt the device from booking 42. The runtime’s DELETE path advances the cancellation grade but leaves the visitant solitary whenever the cancellation is older than the accepted start. Back at the account, the completion of a footwear must motionless equivalent the current slot booking before it can apparent pending state. An old prosperous answer has no authority complete a replacement either.

Reservation numbers defend the inner lifecycle messages. Public commands and document requests acknowledge a operating generation alongside { id, createdAt }. The slot ID is reusable; the generation is not. Despite its timestamp-shaped representation, createdAt is computed as max(now, previousCreatedAt + 1). Recreating a device in the identical millisecond, or moving the partition clock backward, motionless gives the new visitant a distinct identity.

Keep the two values from the returned operating container. A cleanup petition for fair c17 cannot province which life you mean. The lifecycle tests deliberately halt and reuse a slot before releasing an old footwear dispatch, including without advancing the clock. This is a additional revealing inspect than another prosperous hello-world launch.

The difficult deadline is part of admission

Our device additionally needs approval to keep spending compute. A monthly oppose checked lone at initiate would let many simultaneous machines consume the identical remaining allowance. We reserve the runtime they may use before any of them starts.

Machine sizes have weights in plan-policy.js: Lite uses one compute unit, Medium uses ten, and XL uses twenty-eight. The document reserves unit-milliseconds. Its lease ends at the earliest of four boundaries:

hard deadline = min( commencement period + scheme meeting limit, paid admission expiration, next UTC duration boundary, commencement period + remaining unit-ms / device weight )

For an arithmetic example, reserving a Medium device for one hr commits ten compute-unit-hours. Confirm a halt following five minutes and the consumed amount is 10 × 5 / 60, concerning 0.833 compute-unit-hours; the unused booking is released. A unsuccessful halt or unreadable runtime keeps its reservation. Treating “couldn’t communication it” as “it must be free” would authorize the document to expend that allowance twice. A unsuccessful admitted initiate motionless consumes its monthly start; runtime colony is a distinct calculation.

The runtime persists this difficult deadline and an idle deadline. Real action can move the idle deadline, capped by the difficult deadline. Status polling doesn’t. A Durable Object alert enforces expiration equal whenever the client has gone away. Plan changes can shorten an existing lifetime, but cannot broaden its first difficult deadline.

Stopping activity should remain imaginable whenever the billing lookup is unavailable. The community DELETE way authenticates ownership without requiring a caller billing resolution; the coordinator uses its saved entitlement for cleanup whenever it remains valid. Likewise, a newer voluntary study strikes an older paid one. Otherwise a delayed inspect could reauthorize a device we had already revoked.

A detached spectator shouldn’t own a process

With a operating generation in hand, we can commencement analysis.py. A abbreviated foreground command has a sixty-second maximum. For activity whose outcome we desire to discover following a disconnect, we use a managed execution. Its identifier belongs to a particular receptacle generation, and its innovation requires an idempotency key of its own.

// device is the exact { id, createdAt } returned for a operating guest. // Persist executionKey and this petition before sending them. const query = new URLSearchParams(machine); const reply = await fetch(`${apiOrigin}/containers/executions?${query}`, { method: 'POST', headers: { Authorization: `Bearer ${apiKey}`, 'Content-Type': 'application/json', 'Idempotency-Key': executionKey, }, body: JSON.stringify({ argv: ['python3', '/workspace/analysis.py'], timeoutMs: 120_000, }), }); if (!response.ok) throw new Error(`Execution HTTP ${response.status}`); const implementation = await response.json(); // Save execution.id.

The argv form supplies literal arguments alternatively of assembling casing text. In executions.js, the implementation document is saved before the procedure starts. A matching retry finds that retained identity. An aborted innovation petition or a detached event stream doesn’t get the correct to slay the managed job; definitive cancellation does.

Output events have expanding sequence numbers, committed alongside the record’s updated cursor. A client reconnects to the events endpoint alongside its final processed sequence as cursor. It says the stored suffix fairly than depending on a particular socket having seen all byte. The stream itself is bounded to thirty seconds, so reconnecting is average operation. The procedure can run for up to fifteen minutes, additional constricted by its requested timeout and the container’s remaining difficult lease.

These records are finite resources. Each runtime retains up to thirty-two implementation records, alongside a one-hour preservation deadline measured from job admission. Managed jobs portion a pond of four concurrent operations alongside foreground commands and document transfers. Output is bounded to one MiB and a finite event count. Hitting the output bounds terminates the job and marks it truncated; finding a few plausible lines in stdout is hence insufficient. We inspect terminal status, exit code, timeout, and truncation before trusting the result.

Here is the uncomfortable boundary: durable records don’t create procedure handles durable. When a runtime entity restarts, improvement marks unfinished executions interrupted. If they pertain to its current visitant generation, it destroys that visitant fairly than depart untracked activity running. It never silently reruns the command. For our inspection this method an break can discard an unsaved CSV; for a command that sends invoices, automatic replay could do item worse. Restart improvement is deliberately additional disruptive than reconnecting a viewer.

The data petition arrives alongside an character the visitant can’t choose

Suppose analysis.py says a shard from http://data.internal/shards/west. We enroll its generation as a associate of a personal assistance network. In that identical network, another generation owns the assistance name data and harbor 8080. The origin may be a caller-only member, alongside no listening port.

The guest’s petition names neither an document nor a network. private-services-runtime.js installs an outbound HTTP interceptor for *.internal. Its relay removes reserved character headers and supplies trusted account, slot, and generation values from the runtime entrypoint’s configured properties. Sending an invented ownership header from Python doesn’t choose a distinct customer.

The account’s private assistance registry finds the network containing that exact origin generation, afterward resolves data inside it. Another account—or another network in this account—can reuse the name. The destination rechecks its operating generation and registration under its lifecycle fastener before beginning the registered use port. Resolving the name is lone the archetypal approval check.

Why inspect again? Our data assistance could be detached or replaced during it is answering. The destination buffers the bounded reply and checks its registration again. The document rechecks origin liveness and the two memberships before releasing the response. A petition admitted under yesterday’s affiliation must not provision bytes under today’s arrangement.

Those checks don’t undo an use flank consequence that already happened. If the petition changed the data assistance before its reply was denied, the use motionless needs a way to reconcile that change. Immutable shard says are uncomplicated here; a project queue or fee assistance would need its own procedure identities.

This characteristic is bounded personal HTTP routing: one-MiB petition and reply bodies, a ten-second timeout, and no WebSocket upgrade or arbitrary TCP connection. A PostgreSQL client won’t rotate into private-service-aware since its hostname ends in .internal. Deployment enablement and the capability advertised by GET /capabilities must additionally be current before we build a workflow about it.

The CSV needs a existence exterior the command’s output

Our job writes /workspace/metrics.csv. We recover it alongside a generation-bound document petition during the visitant is motionless running. The document endpoint transfers raw bytes, alongside a one-MiB limit, fairly than decoding binary data as content or hiding an export inner a truncated stdout stream.

The compose way in files.js demonstrates another helpful ordering choice. It writes a impermanent document in the destination’s existing genitor directory, afterward renames it complete the target. Readers shouldn’t detect a half-uploaded regular file. The way is passed as a positional argument, not interpolated into casing code. Writes refuse symlink targets; says may prosecute links inner the owned guest.

These guarantees are narrower than a transaction complete the analysis. A prosperous document transfer doesn’t demonstrate the job used the correct input or completed all its rows. The use should inspect the artifact’s schema and provenance, and copy helpful output exterior the ephemeral device before teardown. If we desire to come back to the surroundings itself, we need a saved workspace.

A snapshot can be without being recoverable

Saving appears akin one operation: grasp the disk, recall the result, halt the machine. It really crosses three owners of state—the account, the runtime, and the provider—and none of them can atomically commit the another two.

In workspaces.js, the document reserves preserve capability and persists the procedure before requesting capture. At the runtime, a receipt containing the preserve character is persisted before calling snapshotContainer(). After grasp returns, the runtime saves the provider grip in that receipt. The document afterward commits the grip to its workspace record. Only following that commit may stop: true demolish the source.

Lose the answer between the runtime and document following the runtime has saved the handle, and a retry can peruse the receipt. We regain the identical grasp without taking another one. But interrupt the runtime following the provider accepts grasp and before its grip is saved, and the receipt contains lone an intent. We cognize we tried; we don’t cognize which provider entity to restore.

The runtime refuses to recapture that unresolved procedure and returns workspace_save_unavailable. The preserve way leaves the origin intact, topic to its average lease. Choosing a new key fair to create the error go distant would be a new grasp attempt, not improvement of the old one. That difference is the bounds of the idempotency promise.

Save quotas document for the disbursal of doubt too. Admission reserves the origin size’s complete disk capability before capture. Deleting a workspace frees its live saved-workspace quota, but doesn’t refund the historic grasp budget: the provider may already have done the work. A unsuccessful or ambiguous call cannot rotate into a cheap way to reiterate captures indefinitely.

Restoring a prepared workspace goes through average receptacle admission and consumes a new start. The restored visitant receives a caller generation and must use the saved size and net policy. The depiction digest must motionless match; an incompatible depiction or unsuccessful provider reconstruct produces an error fairly than silently substituting an bare machine.

The saved province is a filesystem. RAM, operating processes, previews, and private-service memberships do not come back alongside it. Our Python job needs advancement in records if it is to resume, and a restored assistance must commencement its procedure and enroll its new generation. Quiescing writers before grasp is an use duty too. We can maintain a disk complete of mutually inconsistent records perfectly.

Make the delayed communication arrive

The quickest way to inspect these choices is to run the lifecycle tests from a backend checkout. They postpone dispatch, autumn replies following flank effects, rebuild controllers from saved storage, and reuse slots before delivering the old messages:

node --test containers/container-account.test.mjs \ containers/user-container.test.mjs \ containers/workspaces.test.mjs \ containers/private-services.test.mjs

Start alongside stopping and reusing a slot fences old delayed dispatches equal in the identical millisecond. Then peruse misplaced snapshot reply reconciles receipt without recapture beside uncertain grasp cannot be repeated in the workspace tests. The archetypal recovers a saved answer. The second preserves the fact that an answer is missing. That is the decision an delegate needs from its compute backend before it can safely decide what to do next.

Source review: backend commit 6e5bef3, October 7, 2026. Examples and diagrams explain authority flow; they are not manufacturing traces or latency measurements. The lifecycle tests use simulated provider state. Public petition shapes and deployment capabilities are documented in API.md and the agent workflow.

Introducing Claude Haiku 5.5: the cheapest, fastest, and most capable small example we’ve always released.

Claude Haiku 5.5 is designed for high-volume, cost-sensitive tasks. It reliably handles quick and repetitive workloads (like summaries, compactions, repository queries, and classification requests). It pairs fine alongside Opus 5.5 and Sonnet 5.5 as a subagent on coding work. And, since it’s additionally our fastest example to date, it plant particularly fine for speed-sensitive tasks akin live client assistance and browser use.¹

Haiku 5.5 is accessible at a much lesser cost than Haiku 4.5. On average, it now expenses about 75% small to run.²

Along alongside this launch, we’re making improvements to the value of our example range. We’re halving the cost of Claude Sonnet 5.5’s cache reads, which method Sonnet 5.5 now runs about 20% cheaper on most agentic work. And we’re introducing a new monthly API credit for our Claude Max and Team subscribers, designed to assistance our users in construction new agents and applications that run on the Claude Platform.

Performance

Here’s how Claude Haiku 5.5 performs throughout a range of benchmarks:

Haiku 5.5 is our archetypal Haiku-class example to arrive alongside an adjustable attempt setting. This method that, as alongside our another models, users can decide whether to optimize for disbursal or intelligence. The charts below display how Haiku 5.5 performs on three benchmarks at all attempt setting:

In first testing, our customers reported results accordant alongside the achievement and disbursal improvements shown above. Here’s what they told us concerning the new model:

Pricing

The array below shows how Claude Haiku 5.5’s pricing compares to our another models. Haiku 5.5 is particularly fine value whenever used for tasks alongside prompts up to 100,000 tokens, which create up about 90% of requests to our former Haiku model.

Safety

Alignment. Claude Haiku 5.5 shows important improvements throughout nearly all of our alignment evaluations related to Haiku 4.5. In particular, we established far small instances of misaligned behavior, and a lesser willingness to cooperate alongside misuse. The model’s system card describes our evaluation procedure and results in additional detail.

Safeguards. Consistent alongside its capabilities, Haiku 5.5’s cybersecurity safeguards are additional restrictive than Haiku 4.5’s, but slightly small restrictive than those we’ve applied to another latest models. In cybersecurity, they authorize a wider range of protective tasks than our safeguards for Sonnet 5.5, but they motionless obstacle intrusion evaluation and another techniques additional apt to be used by attackers.

Haiku 5.5’s existence discipline safeguards are the identical as for Sonnet 5, Sonnet 5.5, and Opus 5. They authorize investigation existence discipline questions but restrict admission to requests that we fairness as apt to logic harm. Organizations operating on wider-ranging existence discipline and cyber activities can use to our Life Sciences Verification Program and Cyber Verification Program.

Availability

Claude Haiku 5.5 is accessible now on all platforms, including Amazon Web Services, Google Cloud, and Microsoft Azure. On the Claude Platform, developers can get started alongside claude-haiku-5-5.

See our migration guide for details.

Further updates

Alongside our new pricing for Claude Haiku 5.5, we’re making additional improvements to the value of our models and products.

First, starting today, we’re lowering the cost of cache says on Claude Sonnet 5.5. Cache says now disbursal 50% less: $0.10 per myriad tokens fairly than $0.20. Because cache says create up a ample portion of models’ token consumption, this reduces the disbursal of Sonnet 5.5 on most agentic tasks by about 20%.

For instance, here’s what the cost cut method for Sonnet 5.5’s achievement related to disbursal on Terminal-Bench 4.0:

This diagram illustrates an crucial difference between Haiku 5.5 and our larger models. Sonnet 5.5 and Opus 5.5 remain improved choices for complex agentic coding tasks akin those measured by Terminal-Bench 4.0. By contrast, Haiku 5.5 is finest suited to additional narrowly scoped tasks that power alternatively have been cost-prohibitive alongside former versions of Claude—like compaction, summarization, or subagent work.

Second, this week, we’ll rotate out a new monthly API credit to all Max and Team subscribers for use on the Claude Platform. Max 5x users volition get $100 in credits per month, Max 20x users volition get $200, and Team subscribers volition obtain up to $500, pooled throughout their users. These credits are designed to authorize our users to test alongside construction tools, apps, and agents that call our API. They can be used on any of our models. For additional information, see our Help Center article.

For developers, we’re additionally updating our Claude Python and TypeScript SDKs to add assistance for device use and browser use in beta. Haiku 5.5 is particularly well-suited to these tasks, stated its blend of speed, capability, and price. You can peruse additional concerning this in our Claude Platform docs.

It shouldn't arrive as a astonishment that Amazon collects data concerning you according to what you buy. And now, you can get a straightforward appearance at what the business has concluded from all that activity.

Amazon's "About You" page, currently in beta following launching this May, uses data from customers' action to create particulars concerning them in categories akin Product Preferences, Interests & Hobbies, and Home & Family. Amazon uses those particulars to personalize the buying experience. Here's how to navigate and personalize your "About You" leaf in your Amazon account.

What Amazon thinks it knows concerning you

The results can be oddly specific. My editor-in-chief, Jordan Calhoun, shared his page, which includes these entries:

You May Also Like

• "Likely has 2 adults"

• "Owns a PlayStation 5 console"

• "Prioritizes plant-based flesh alternatives complete traditional options"

• "Subscribes to multiple streaming services continually"

• "Owns a ample dog"

• "Likely has a cat"

Full disclosure: I try to never use Amazon personally. I beautiful much exclusively use it to circular up deals for you, my beloved reader. That being said, here's what Amazon thinks it knows concerning me: "Values durable journey gear," "prefers sporty and informal garments styles," "practices yoga regularly," and "listens to music frequently."

What counts as data

In its Privacy Notice, Amazon lists all action that provides person data to the company. That includes the obvious, akin searching for items and placing orders. It additionally includes actions you may not acknowledge are being tracked, akin interacting alongside Alexa Voice Service, participating in conversation boards, or linking an Amazon document to a third-party business.

How to perspective your own "About you" page

On desktop:

1. Hover complete anywhere Amazon says "Hello, [your name]" in the high correct corner.

2. Under the "Your Account" section, click "Account."

3. Under the "Ordering and buying preferences" section, click "Your Shopping preferences."

From here, you can add data of your own, akin your footwear size, interests, and dietary preferences. Scroll to the base of the page, and you'll see a blue hyperlinked choice that says, "Manage your information." Click it, and you'll see what dirt Amazon has on you.

On the mobile app:

Tap the hamburger icon in the base list bar, afterward go to Account > Shopping preferences > About you.

You can edit it, but not opt out

Shoppers can't immediately opt out of the "About you" feature. They can, however, customize the data by adding particulars they desire Amazon to cognize and deleting assumptions they regard inaccurate or too personal.

Importing data from another AI chatbots

On the "About you" page, customers additionally have the choice to "make Amazon equal additional individual to you" by importing individual data they've shared alongside another AI agents. Amazon provides a immediate for you to nourish to your chatbot to create a buying profile, afterward asks you to copy the bot's reply and paste it rear into Amazon.

Whether that's value doing is up to you. Given how much Amazon already infers from your browsing and buying, handing complete a overview from another AI assistance is a choice value thinking through. Either way, taking a few minutes to assessment your leaf and delete item inaccurate or too individual is a uncomplicated way to see and form what Amazon knows concerning you.

Today, whenever a buyer asks ChatGPT, Perplexity, or Google’s AI Mode a question, they rarely see a catalog of suggested links. They see one synthesized answer that cites a fistful of sources. If your brand is one of those citations, you win attention, traffic, and trust. If it isn’t, you’re invisible, equal whenever you position on leaf one.

That change matters for income teams who need to attain those buyers. Referral traffic from AI tools akin ChatGPT and Gemini has tripled complete the former year, and 44% of marketers say they’ve made a endeavor acquisition according to a brand they archetypal discovered in an AI answer. Nearly 1/3 have done it additional than once. The spectators didn’t disappear; it moved into the answer.

In this guide, we explain how AI hunt optimization really works: How AI answer engines retrieve, ground, cite, and measure content, and what brands need to do to create their satisfied easier for AI systems to retrieve, understand, and cite. Also included: A side-by-side difference of AI hunt optimization versus traditional SEO, quick wins you can execute this week, and expert tips from practitioners on the Found in AI podcast.

Table of Contents

• TL;DR: AI Search Optimization
• How does AI hunt optimization activity under the hood?
• Where You Show Up in AI Search (and How to Qualify)
• How AI Search Optimization Differs From Traditional SEO
• How to Improve AI Search Optimization for Content Structure and Q&A Content
• How AI Search Optimization Works for Entities and Internal Linking
• How does AI website optimization activity on the specialized side?
• AI Search Myths to Skip Right Now
• Quick Wins for AI Search Optimization You Can Ship This Week
• How to Measure AI Search Visibility and Impact
• Frequently Asked Questions About AI Search Optimization
• Where to Start With AI Search Optimization

TL;DR: AI Search Optimization

AI hunt optimization makes satisfied easier for AI systems to retrieve, understand, and cite. While traditional SEO focuses on keywords to position higher in hunt results, AI hunt optimization prioritizes context, frequently using retrieval-augmented generation (RAG) to recover and citation live satisfied according to citations, entities, summaries, and Q&A structure.

How does AI hunt optimization activity under the hood?

To optimize for AI search, it helps to cognize what happens between the immediate and the answer. As Pat Reinhart, vice chairman of expert services at Conductor, explained on Found in AI, “An LLM does not run the identical way that a traditional hunt motor akin Google or Bing does. They’re reaching into an indicator and trying to semantically nexus a query to a part of content. An LLM is operating off of its own model. If it doesn’t cognize the answer, or doesn’t awareness it’s complete enough, it’s going to go out and Google things to discover it in genuine time.”

For this procedure to work, two mechanisms do most of the dense lifting: Retrieval-augmented generation and query fan-out.

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation enhances the accuracy and norm of an LLM’s output. LLMs are trained on an first dataset and create answers from their inner cognition base. So it can quickly rotate into out of date or hallucinate answers. RAG fixes that by adding external search, combining live sources alongside generated answers.

In practice, the scheme fetches relevant, current web pages, afterward writes its reply restricted in what it fair retrieved. Grounding is the connective tissue. It ties the answer to origin matter that supports the response, which is why you see clickable citations next to a generated answer.

Google describes its own AI features this way in its guide to optimizing for AI features: Systems recover relevant, up-to-date web pages from the Search indicator to dirt responses, and a leaf must be indexed and eligible to appear alongside a snippet to be used at all.

In another words, satisfied must be crawlable, indexable, and snippet-eligible to be included in an AI-generated answer.

Query Fan-Out

Where RAG focuses on the anywhere (sources), query fan-out focuses on the why (user intent). Query fan-out enhances LLM answer output by breaking a person query into connected subqueries, analyzing them for contextual criteria specified as pricing or timeline, and synthesizing the results into a sole answer according to those refinements.

According to Search Engine Land, a sole inquiry can trigger dozens of sub-queries throughout predictable patterns, including equal phrasings, follow-ups, broader and narrower versions, and logically implied questions. Google confirmed its systems may matter multiple connected searches throughout subtopics and data sources during a reply is generated.

This is why environment strikes keywords. Prompts are longer and additional conversational than searches, so a leaf that answers a entire collection of connected questions has additional surfaces to be retrieved against. As Reinhart put it, the average Google query is three to four words, during the average ChatGPT immediate runs about 23, which is a complete conversation’s value of environment for the example to activity with.

Where You Show Up in AI Search (and How to Qualify)

In AI-powered search, you’re competing to be cited throughout multiple AI engines, and all rewards slightly distinct content.

• AI Overviews: The summarized answer boxes at the top of many Google results. They drag from indexed pages and citation a few sources inline.
• AI Mode: Google’s conversational, fan-out-driven hunt experience, anywhere a sole immediate spawns many sub-queries and a synthesized, citation-rich answer.
• AI conversation and answer engines: ChatGPT, Perplexity, Gemini, and Copilot answer conversationally and exterior citations or brand mentions depending on the tool and whether live retrieval is on.

The satisfied types most apt to appear portion a few things in common:

• They answer a particular inquiry early.
• They’re cleanly structured.
• They’re uncomplicated to extract.

Definitions, step-by-step how-tos, difference tables, FAQs, first data, and plainly attributed expert commentary all meet requirements well. As Reinhart noted on the Found in AI podcast, the bots desire to arrive in, get the answer as quickly as possible, chunk it out, and put it rear to the user.

How AI Search Optimization Differs From Traditional SEO

Traditional SEO focuses on ranking pages and earning clicks, during AI hunt optimization emphasizes citations, entities, summaries, and Q&A structure.

AI hunt is an additional tier on top of SEO, not a replacement. The specialized fundamentals motionless matter, and a contemporary generative motor optimization scheme is mostly SEO alongside a new structural site bolted on.

As Reinhart stated on Found in AI, “It’s not concerning ranking for a particular keyword. It’s concerning how frequently am I showing up and being mentioned and cited for this topic that group are talking about.”

Side-by-Side: Classic SEO Tasks vs. AI Search Optimization Tasks

How to Improve AI Search Optimization for Content Structure and Q&A Content

Answer engines lift passages fairly than regurgitating entire pages. So the component of optimization shifts from the leaf to the extractable block. When a obstacle plainly states a assertion and immediately supports it, an AI example can catch it, rely it, and citation it.

Here’s how to build satisfied that is effortlessly extracted.

(For a deeper penning tutorial, see HubSpot’s guide on how to compose for AI search.)

1. Write assertion statements alongside contiguous citations.

Lead a division alongside a direct, self-contained claim, afterward assistance it correct distant alongside data, a source, or a named example. This claim-then-evidence form mirrors how a restricted answer is assembled, so it’s uncomplicated for a example to reuse a passage and nexus a citation.

Pro tip: Avoid burying the answer three paragraphs down. Put the extractable declaration first, afterward add the nuance a individual audience wants.

2. Use question-based optimization in subheads.

Write subheads as the questions group really ask, afterward answer them in the archetypal two or three sentences underneath. Query fan-out generates follow-up and clarification queries, so a leaf organized about genuine questions gives the example additional matchable surfaces.

Think of your H2s and H3s as a map of the intent cluster, not fair keyword slots.

What the experts say: Romana Kuts, originator of SaaStorm, stated on the Found in AI podcast that she asks writers to provision extremely straightforward answers in their content. She said, “Most of the traffic that is coming from GPT is coming from a extremely abbreviated and saccharine FAQ.”

3. Add FAQ schema to fortify Q&A blocks.

Marking up Q&A satisfied alongside FAQPage schema labels the question-and-answer association for machines, assisting systems parse and reuse it.

One crucial accuracy note: Google has deprecated FAQ affluent results, so schema volition no longer acquire you the expandable FAQ snippet in the SERP. But you should keep the Q&A content, since it motionless serves readers and gives answer engines clean, tagged pairs to extract. Just don’t add it expecting a rich-result characteristic that no longer exists.

What the experts say: As Kuts describes it, Google sees satisfied through the lens of code, snippets, and schema: internal-link signals, FAQ markup, and author schema that says a genuine individual wrote this. Schema won’t rescue lean content, but it removes ambiguity from fine content.

How AI Search Optimization Works for Entities and Internal Linking

In AI search, authority is small concerning domain-authority scores and additional concerning entities, or the people, brands, and organizations a example recognizes and trusts.

Grounding connects an answer to sources the scheme trusts, and confidence is built through consistency. That consistency gets built in two places: how you depict yourself on your own property, and how consistently you’re described that way everyplace else.

Internal links do the identical activity inner your own site. When you nexus an author to a genuine bio page, or a definition to the deeper guide that expands it, you’re telling a example which pages pertain to the identical being and how they relate. Consistency is the signal, and inner links are how you display your work.

1. Standardize author and institution entities.

Pick one canonical clarification of your brand and your experts, and reiterate it everywhere. That includes:

• Your site.
• Author bios.
• LinkedIn.
• Podcast bios.
• Third-party profiles.

Pro tip: Add author schema and an institution entity, nexus authors to a genuine bio page, and keep titles and business names identical throughout surfaces. Consistent repeat strengthens the being a example associates alongside your topic.

2. Build assessment and citation signals off-site.

On-page activity makes you eligible. Off-site mentions create you credible. Getting cited, quoted, and discussed on third-party sites, podcasts, and communities teaches AI systems that your brand exists beyond its own website.

Reddit deserves particular notice since of how heavily it feeds these models. As of August 2026, Google and OpenAI motionless have partnerships alongside Reddit, definition those platforms heavily exterior user-generated content.

The capture to making Reddit, or any off-site mention, activity is authenticity. Communities, and the models trained on them, punish satisfied that says as manufactured. Show up as a person, not a media release. As Beth Chernes, J.D., an SEO strategist, stated on Found in AI, “If you audio akin AI on Reddit, no one volition akin you. They’ll say things concerning you being AI, and afterward you get banned. You should display up and be a person.”

Pro tip: Run your brand name through ChatGPT and Perplexity and appearance at what gets cited — not whether you’re mentioned, but whose leaf the example pulled from. Roundups, assessment sites, Reddit threads, and industry publications keep popping up. Those third-party sources are your placement targets, and that catalog is normally additional helpful than a keyword report.

How does AI website optimization activity on the specialized side?

Technical AI website optimization is mostly fine specialized SEO, applied alongside extraction in mind. HubSpot’s guide to AI specialized SEO and its overview of AI website optimization go into additional depth, but current are the two levers that matter most.

1. Use organized data anywhere it matters.

Structured data is helpful, but it isn’t a magic switch. Google is definitive that it doesn’t necessitate particular machine-readable records for AI features, and that organized data supports understanding fairly than guaranteeing inclusion.

Prioritize the schema types that explain meaning:

• Organization and Person for entities
• Article or BlogPosting alongside a genuine author
• Product and Review anywhere relevant
• Breadcrumb for structure

Use it to eliminate ambiguity, not as a substitute for substance.

2. Optimize JavaScript and rendering for crawlability.

AI crawlers desire to get in, extract, and depart quickly. If your chief satisfied lone appears following dense client-side JavaScript, many crawlers may never see it.

Here’s how to fix it:

• Serve meaningful satisfied in the first HTML (server-side render or pre-render key text)
• Keep crucial copy out of images and script-dependent widgets
• Make certain your robots rules aren’t accidentally blocking the answer engines you desire to reach

Pro tip: Turn off JavaScript in your browser and reload your top page. Whatever’s motionless on the display is approximately what an AI crawler sees. If your key explanations, comparisons, or FAQ answers disappear, that’s the satisfied you’re not getting cited for.

AI Search Myths to Skip Right Now

There is a lot of AI-search direction that is folklore. Here are five myths to drop, all checked against current chief guidance.

Myth: You need an llms.txt document for Google.

There’s a lot of conversation about whether brands need the llms.txt file. As of August 2026, Google has stated plainly that you don’t need to create new machine-readable records for AI features, and that Google Search ignores records akin llms.txt.

Pro tip: Spend the period on satisfied and crawlability alternatively of a document important engines don’t currently read.

Side note: In first May 2026, Google released llms.txt direction for developers. That’s Chrome tooling aimed at AI agents navigating your site, not Search direction — which is why it doesn’t refuse the above. Even there, the document is optional. Lighthouse marks the audit “Not Applicable” if you don’t have one. If agentic browsing is on your roadmap, it may be value adding. However, it is not part of an AI hunt optimization strategy.

Myth: Chop everything into small chunks.

Extractability is concerning apparent structure, not shredding your satisfied into fragments. Google explicitly says there’s no necessity to interrupt satisfied into small pieces for AI to comprehend it.

Pro tip: Use spotless headings, abbreviated guide paragraphs, and tagged sections, not a partition of one-sentence blocks.

Myth: Rewrite your satisfied fair for AI.

Rewriting solely for machines is wasted attempt since models comprehend synonyms and natural phrasing. The winning move is penning that serves group archetypal and is uncomplicated to extract.

Pro tip: Humanize your content, add genuine perspective, afterward construction it well.

Myth: Manufacture mentions and you’ll get cited.

Inauthentic mentions and spammy nexus schemes don’t build durable being authority, and communities emblem them fast. Earned, genuine mentions throughout dependable channels are what power inclusion in AI-generated answers. Quality and consistency attack volume.

Myth: Schema solitary is a shortcut.

Schema clarifies fine content, but it can’t industry authority for lean content. Treat it as labeling, not leverage. If the underlying passage doesn’t answer the inquiry well, no markup volition create a example citation it.

Pro tip: Run marked-up pages through Google’s Rich Results Test. A valid schema on a leaf that isn’t getting cited rules out markup as the logic and points you rear to whether the passage really answers the question. And for schema types that aren’t rich-result eligible, use the Schema Markup Validator. (Important: Google’s Rich Results Test isn’t a visibility tool. It’s fair a helpful check.)

Quick Wins for AI Search Optimization You Can Ship This Week

Here are three high-leverage moves that can enhance AI hunt optimization inside days.

1. Add abbreviated Q&A blocks.

Source

On your top five business pages, add a two-to-four-question FAQ that answers all inquiry in three sentences or less. Short, straightforward answers are exactly what gets pulled into AI responses.

2. Standardize being names.

Audit how your brand and authors are described throughout the site, social profiles, and third-party bios. Pick one canonical descriptor and create all overview equivalent it this week.

What the experts say: David Kirkdorffer, a partial marketer, gave a apparent alert on Found in AI: “We can say the identical item alongside distinct words and motionless have the identical meaning. But if we alter the words enough, we jump out of one benevolent of definition into another.” Reuse the identical descriptor everywhere.

3. Add author bios.

Source

Give all key part a real, credentialed author byline connected to a bio page, alongside author schema attached. It’s a fast, durable indication of cognition and ability for the two readers and models.

Want the data rearward all of this? HubSpot’s State of AEO study breaks downward how AI hunt is changing buyer behavior, including citation data from 4,000+ marketers and six answer engines so that you can pressure-test your own scheme against the benchmarks.

How to Measure AI Search Visibility and Impact

You can’t oversee what you don’t measure, and AI visibility needs its own scoreboard since rankings no longer inform the story. Set a monthly benchmark and track the trend, not the regular noise. Here are the KPIs that matter for your AI hunt optimization efforts.

• Share of sound and citation share: How frequently your brand is cited for mark topics versus competitors.
• Mentions and sentiment: How frequently you’re named throughout ChatGPT, Gemini, and Perplexity, and whether the framing is positive.
• AI-referred traffic: Sessions arriving from AI answer engines, tracked separately from traditional organic.
• Presence quality: Are you cited as a chief origin or a passing mention? Are the facts concerning you accurate?
• Downstream impact: MQLs, pipeline, and deals influenced by AI-search sessions, which are the metrics that nexus visibility to revenue.

For a quick baseline, run a one-time audit using a tool akin HubSpot’s AI Search Grader, which scores brand visibility throughout answer engines. Then track regular movement alongside HubSpot AEO. The item is to established a benchmark you can enhance against all month.

Pro tip: Tying AI visibility to pipeline in supplement to citations is what earns AEO a prosperity line. Within two years of investing in first AEO tactics, HubSpot reported a 1600% lift in qualified leads from AI and 2x improved conversion from those leads, alongside a 411% betterment in brand citations. That’s the benevolent of figure that moves a CFO.

Frequently Asked Questions About AI Search Optimization

Should I authorize GPTBot and another AI crawlers?

To be cited, AI engines have to be capable to peruse your content. Allow OAI-SearchBot, ChatGPT-User, and Google’s norm crawler in your robots.txt. GPTBot is OpenAI’s training crawler, and Google-Extended governs Gemini training. Some brands authorize those too for broader presence, during others restrict training use as a matter of policy. Whatever you choose, do it deliberately. Accidentally blocking search-and-citation bots is the most average mistake.

How frequently should I refresh satisfied for AI search?

For AI search, freshness method demonstrating that a leaf is actively maintained. AI engines favor current, well-maintained pages, so refresh precedence satisfied on a rolling monthly-to-quarterly cadence, including:

• Update stats.
• Add “as of [date]” context.
• Incorporate new examples.
• Prune item stale.

What’s the finest way to construction Q&A for AI extraction?

To optimize FAQs for AI extraction, use the genuine person inquiry as the subhead, answer it in the archetypal one to three sentences, afterward expand. Keep all answer self-contained so it makes awareness out of context, and add FAQPage schema to tag the pairs. Remember that affluent results are deprecated, so the value current lies in device clarity versus a SERP feature.

Can I optimize existing posts, or should I create new ones?

Start alongside what you have. Optimizing existing posts that already have traction is faster and compounds freshness signals:

• Add Q&A blocks.
• Tighten claim-and-citation passages.
• Standardize author entities.
• Confirm crawlability.

How do I nexus AI hunt visibility to revenue?

Segment AI-referred sessions, afterward prosecute them through your chimney to MQLs, pipeline, and closed deals, the identical way you’d trait any channel. The form to appearance for is conversion quality, not fair quantity — Similarweb established AI referrals converting at 11.4% versus 5.3% for integrated hunt in earth ecommerce as of September 2025.

Where to Start With AI Search Optimization

AI hunt optimization makes your satisfied uncomplicated for machines to retrieve, understand, and cite. Answer engines use retrieval-augmented generation to dirt answers in live sources, and query fan-out to fulfill a collection of connected questions at once. AI hunt engines favor pages that create apparent claims, assistance them immediately, construction Q&A cleanly, standardize their entities, and remain crawlable and up to date.

AI hunt optimization doesn’t substitute SEO. It adds citations, entities, summaries, and Q&A construction on top. You can use HubSpot’s AEO tools to track brand visibility throughout ChatGPT, Gemini, and Perplexity and rotate that data into prioritized recommendations.

One final note from the field: When I spent weeks in delayed 2025 operating the identical prompts throughout ChatGPT, Perplexity, and Google’s AI features, the pages that got cited weren’t the highest-authority domains. They were the ones that were fresh, cleanly structured, and ridiculously uncomplicated to extract. Optimize for the answer, and you optimize for the forthcoming of search.

Enterprise email promotion application is built for high-volume programs that extend teams, brands, and regions. With so much application available, the difficult part comes before and following the dispatch — whether governance keeps all squad inside the rules, CRM-native data surfaces the correct consented audience, and attribution connects email involvement to pipeline and revenue.

A feeble email promotion phase pushes the activity of governance, data surfacing, and attribution rear onto people. A powerful endeavor email phase absorbs this complexity instead. With a powerful phase handling these tasks, marketers can initiate faster, rely their data, and demonstrate their impact. This guide compares endeavor email platforms, specified as HubSpot’s email promotion tools, that provision on that promise.

Table of Contents

• What Enterprise Email Marketing Software Must Do
• The 5 Best Enterprise Email Marketing Software at a Glance
• The Top Enterprise Email Marketing Software
• How to Choose Enterprise Email Marketing Software
• Enterprise Email Marketing Software Migration and Change Management
• Frequently Asked Questions About Enterprise Email Marketing Software
• Which endeavor email promotion application should you choose?

What Enterprise Email Marketing Software Must Do

Enterprise email marketing application must assistance complex, multi-team programs without weakening control. The phase should create campaigns easier to measure during keeping client data, consent, brand standards, and reporting intact. These six capabilities distinct enterprise-ready platforms from email tools alongside generous communication limits.

Enforce governance and safety controls.

Consent records must display when, where, and how all communication subscribed. Consent records that display when, where, and how all communication subscribed defend the business whenever data moves throughout brands, regions, or integrations. Enterprise promotion teams cannot oversee to underestimate safety controls. For example, in the UK, two companies were fined £150,000 for sending 7.5 myriad unwanted messages. Third-party suppliers were unable to demonstrate valid consent.

Enterprise email promotion teams have to justify their application has these features:

• Role-based permissions
• SSO and two-factor authentication
• Campaign approvals
• Audit logs
• Asset partitioning
• Sandbox environments

Global businesses should additionally inspect local data hosting, encryption, data retention, and assistance for deletion requests.

Connect email action to CRM income data.

Email metrics endure value whenever run data sits distinct from the CRM, making it difficult to trace results to revenue. CRM-native email promotion platforms forestall this by keeping engagement, lifecycle stage, business data, deals, and income on connected records, enabling apparent income attribution.

Marketing Hub, functioning on HubSpot Smart CRM data, lets marketers create attribution reports and nexus email promotion interactions to agreement innovation and won revenue, giving promotion and revenue one dataset to activity from.

Support ABM and multi-brand operations.

Account-based marketing (ABM) targets companies, but email reaches idiosyncratic people. Enterprise email application must nexus those two levels. It should collection consented contacts under an account, acknowledge their roles, and modify nurture emails as the agreement progresses.

Enterprise email application enables this coordination. For example, it can vigilant revenue whenever multiple stakeholders engage, toggle satisfied following an chance opens, or intermission promotion emails during energetic revenue conversations.

The identical coordination matters throughout multi-brand companies. Each brand may need its own domains, consent preferences, templates, and sender reputation, during guidance motionless needs shared client data and reporting. HubSpot’s multi-account management, accessible at the Enterprise level, solves this by keeping endeavor accounts distinct during allowing data, assets, workflows, and reports to move throughout them according to configured connections.

Apply AI to high-friction workflows.

Personalized email at endeavor measure requires additional than a fine topic line. AI in endeavor email marketing handles the infrastructure rearward it: spectators creation, CRM data classification, workflow design, run QA, and send-time optimization.

HubSpot’s AI tools can assistance marketers build section filters, afterward map and create workflows, summarize CRM data, and update or categorize records. This automation frees teams from the workflow setup that slows endeavor campaigns down.

Protect deliverability throughout domains and IPs.

Enterprise deliverability starts alongside SPF, DKIM, DMARC, spotless lists, accordant volume, and accelerated unsubscribe processing. For example, Yahoo now requires bulk senders to use SPF and DKIM, publish a valid DMARC policy, and assistance uncomplicated unsubscribes.

A dedicated IP gives a business authority complete sender reputation, but it additionally transfers complete duty for maintaining that reputation. An endeavor email phase should assistance gradual warm-up, distinct promotion and transactional streams, and brand-level infrastructure. HubSpot, for example, applies a 40-day automated warm-up to new dedicated promotion IPs.

Fit the endeavor data architecture.

Enterprise email application may need data from the CRM, warehouse, ecommerce platform, and customer-success stack. A lengthy integration directory method small if those connections endure fields, create duplicates, or neglect without warning. When choosing an email promotion platform, inspect for:

• Two-way sync
• Custom objects
• Field mapping
• APIs
• Webhooks
• Test environments

HubSpot Data Sync gives teams visibility into which records are syncing, flags issues before they power your data, and lets teams configure site mappings and sync direction without coding. That turns integrations into manageable infrastructure alternatively of imperishable cleanup work.

The 5 Best Enterprise Email Marketing Software at a Glance

The Top Enterprise Email Marketing Software

No email promotion phase wins all endeavor use case. The following five solutions all recommendation distinctive capabilities suited to distinct endeavor needs. HubSpot leads for CRM-connected B2B marketing. Adobe and Braze go deeper into real-time person journeys. Salesforce makes the most awareness for teams already set up inside the Salesforce ecosystem, during Klaviyo remains the finest for commerce.

1. Marketing Hub Enterprise

Marketing Hub Enterprise connects email promotion immediately to Smart CRM. All the crucial sectors — communication activity, business data, deals, lifecycle stages, and run involvement — live on connected records. Marketers can build exact audiences and trace email power through contact, deal, and income attribution without rebuilding the client journey in a distinct reporting tool.

Marketing Hub additionally covers send-time optimization, run approvals, and multi-account management. Meanwhile, HubSpot’s AI tools can create segments and workflows from plain-language instructions.

HubSpot provides dedicated IP and transactional email options to assistance teams alongside additional advanced sender requirements. Those emails can run as one-off campaigns or act automated nurture workflows according to live CRM changes.

Key Features

• Multi-touch income attribution and omni-channel endeavor promotion automation
• Automated insights for high- and low-performing promotion messaging
• Marketing email sole dispatch API and email approvals
• Customer journey analytics
• Behavioral event triggers and reporting
• Email health reporting
• CRM-powered segments
• A/B tests
• Enterprise email deliverability tools

Pricing: Starts at $3,600 per month, billed annually. The scheme includes five Core Seats and 10,000 promotion contacts. Required Enterprise onboarding expenses $7,000.

Best for: B2B enterprises that desire marketing, sales, and income data in one system.

2. Salesforce Marketing Cloud Engagement

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Salesforce Marketing Cloud Engagement uses CRM and Data 360 data to build audiences, personalize promotion emails, and trigger follow-ups according to client behavior. Marketers can use Agentforce to rotate a run concise into email and landing-page content, afterward dispatch the email as a standalone run or location it inner an automated journey. Opens, clicks, and another responses remain connected to Salesforce client records, giving revenue and assistance teams the identical involvement context.

Key Features

• Personalized email campaigns
• Agentforce satisfied creation
• Automated email journeys
• Send-time optimization
• Engagement tracking
• Salesforce CRM connections

Pricing: Corporate starts at $4,200 per institution per month, billed annually. Enterprise-level plans necessitate a tradition quote.

Best for: Large organizations alongside established Salesforce architecture and inner Salesforce expertise.

3. Adobe Journey Optimizer

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Adobe Journey Optimizer supports scheduled campaigns and event-triggered emails from Adobe Experience Platform profiles. Marketers can build emails alongside reusable content, energetic personalization, and recommendation decisioning. Preview, proofing, satisfied checks, frequence rules, and subscription administration assistance earth teams capture problems before a ample send.

Adobe additionally provides the sender controls expected from endeavor email software. Teams can oversee subdomains, suppression lists, IP pools, and phased IP warm-up plans.

Key Features

• Email Designer
• Real-time audiences
• Dynamic satisfied and recommendation decisioning
• Email proofs or automated satisfied checks
• Frequency caps and send-time optimization
• Data use labeling and implementation (DULE) governance
• Suppression lists and IP warm-up management

Pricing: No community liberated plan; Adobe provides pricing lone by quote.

Best for: Global B2C enterprises that already use Adobe Experience Platform and need strict authority complete high-volume email programs.

4. Braze

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Braze is built for ample person brands and turns live merchandise and client behavior into triggered email campaigns. Marketers can create emails through drag-and-drop tools, reusable blocks, templates, or native Figma and Canva integrations. Dynamic satisfied can drag in products, promotions, recommendations, and another customer-specific data at dispatch time. Braze additionally supports interactive AMP emails.

Key Features

• Real-time segmentation
• Behavioral triggers
• Drag-and-drop editing and reusable satisfied blocks
• Dynamic recommendations and AMP email
• BrazeAI assistance
• IP warm-up and deliverability monitoring

Pricing: Custom quote; A complete 14-day liberated test is available.

Best for: High-volume person and mobile-first brands that need email to react to live merchandise behavior.

5. Klaviyo

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Klaviyo turns commerce action into ready-to-use email segments, flows, and income reports. It can trigger messages from browsing, cart, purchase, product, and predicted customer-value data. Marketers can afterward test campaigns, propose products, difference achievement through earth holdouts, and nexus email action to attributed revenue.

For endeavor senders, Klaviyo supports tagged sending domains and distinct domains for marketing, transactional, and assistance email. Eligible managed accounts can use dedicated IPs alongside automated warm-up. Deliverability and RFM tools assistance teams place position or involvement problems before they drag downward a important campaign.

Key Features

• Commerce-focused profiles
• Segment AI
• Automated flows
• Predictive analytics
• Product recommendations
• Attribution
• 350+ integrations

Pricing

• Standard Klaviyo plans renew monthly and measure alongside energetic profiles and email volume. Larger customers may move to tradition contracts formerly their plans total $2,000 or additional per month. Contract billing can run monthly, quarterly, semi-annually, or annually
• Free scheme for up to 250 energetic profiles and 500 monthly email sends.

Best for: Ecommerce and retailing enterprises that desire accelerated activation of transaction and merchandise data.

How to Choose Enterprise Email Marketing Software

A polished demo can create any phase appearance easy. The correct choice lone becomes apparent whenever teams test it against their data, endorsement process, integrations, and run workload.

• Define requirements and governance. Map the center seven features — brands, regions, dispatch volumes, person roles, consent rules, endorsement paths, and transactional email needs. Separate must-haves from nice-to-haves so flashy features aren’t distracting.
• Validate integrations and data quality. Trace how client data moves from its origin into segments, campaigns, CRM records, and income reports. Confirm sync speed, site mapping, copy handling, API limits, and which phase wins whenever records conflict.
• Run a evidence of concept. Build a genuine segment, initiate an endorsement workflow, automate a nurture path, test personalization, and inspect the resulting attribution. Use delegate data and period all project — the item is to disclose any manual work.
• Score vendors and decide. Weight all phase against the requirements that power regular email execution. Then add migration, implementation, add-ons, and inner staffing to the cost. Speak alongside vendors that resemble the business in size, regions, and tech stack before making the final call.

Free resource: How to Launch a Successful Email Marketing Campaign

Enterprise Email Marketing Software Migration and Change Management

Teams migrating from a phase akin Salesforce or Adobe to HubSpot should evade treating the project as one ample data transfer, as this creates risk. Instead, move the program in five controlled stages and keep the old phase accessible until the new phase can run a complete run correctly.

1. Audit what must move.

Decide what HubSpot needs for regular activity and what can realistically be archived. Importing years of unused data creates clutter and can addition the figure of billable promotion contacts. Export the following:

• Contacts
• Companies
• Consent records
• Suppressions
• Custom fields
• Templates
• Workflows
• Reports
• Campaign history

2. Build the CRM construction first.

Create the required properties, lifecycle stages, objects, and contact-to-company associations before importing data. Test a small lot alongside email addresses or another distinctive identifier, since HubSpot uses these identifiers to prevent duplicates and nexus connected records.

3. Move consent and suppressions before energetic audiences.

Import the existing opt-out list, afterward map subscription types and keep consent sources and timestamps. Moving consent and suppressions before energetic audiences prevents the new phase from emailing group the former phase had already suppressed.

4. Prepare the sending infrastructure.

Authenticate all sending domain alongside MX, DKIM, SPF, and DMARC records before launching campaigns. If the business uses HubSpot’s dedicated IP, authorize for its 40-day automated warm-up alternatively of moving the complete dispatch quantity on day one.

5. Rebuild, test, and cut complete in waves.

Start alongside the templates, segments, workflows, and reports that assistance energetic campaigns. Send archetypal to an busy audience, verify recipient counts and CRM updates, afterward gradually addition volume. Keep the former phase in read-only manner until attribution and deliverability checks continue on the new platform. Assign an owner to all live workflow before shutting downward the bequest platform.

Frequently Asked Questions About Enterprise Email Marketing Software

What is the difference between endeavor email promotion application and SMB tools?

The difference between endeavor email promotion application and SMB tools is operational complexity. Enterprise platforms add granular permissions, endorsement controls, and assistance for multiple brands or endeavor units. They have advanced automation, dedicated sending infrastructure, and attribution tied to CRM revenue.

How do we grip data residency and consent throughout regions?

To grip data residency and consent throughout regions, choose the hosting area first, afterward document anywhere integrations, subprocessors, and distant users may move or admission the data. HubSpot offers hosting in the U.S., Canada, Australia, and the EU, but regional hosting does not justify that all handling action stays in the chosen region. Track consent source, date, region, channel, and subscription category on all record. Then use local suppression rules and have lawful advise validate the setup.

How lengthy does a immigration typically obtain and how do we decrease risk?

HubSpot says customers typically complete its Marketing Hub onboarding inside three months, which is a sensible starting point. Multi-brand programs alongside tradition integrations, dedicated IPs, or years of bequest data may need longer.

Reduce immigration hazard by locking the data example early, evaluation one complete campaign, warming the new sending infrastructure, and keeping the old phase accessible through a rollback window.

Can endeavor email promotion application assistance the two content and email channels?

Yes, but SMS safety and consent rules differ by country, so “multichannel” needs a nearer appearance during procurement. HubSpot can coordinate email and SMS through shared CRM segments and workflows, but its current SMS add-ons use U.S.-based numbers.

Long-code messages can attain the U.S. and Canada, during short-code sending is constricted to the U.S. High-volume short-code admission additionally requires Marketing Hub Enterprise and additional add-ons, alongside endorsement taking eight to dozen weeks.

Which endeavor email promotion application should you choose?

Enterprise email promotion looks uncomplicated from the exterior — compose a fine email, dispatch it to the correct people, measure what happens. The difficult part is everything underneath: keeping consent records spotless throughout regions, connecting involvement data to income without rebuilding the client journey in a distinct tool, maintaining sender position throughout brands, and making certain all squad stays inside the rules.

The five platforms in this guide all grip high-volume programs. Where these platforms differ is in how much of that underlying complexity they assimilate and how fine they nexus email to the remainder of the business.

Adobe and Salesforce create the most awareness whenever client data already lives in those ecosystems; switching expenses are real, and the attribution degree they recommendation is difficult to replicate elsewhere. Braze is the correct call for fast-moving B2C brands anywhere email has to react to live merchandise behavior in genuine time. Klaviyo remains the category chief for commerce — if your segments run on acquisition former and predicted life value, it’s difficult to beat.

For complex B2B, I keep coming rear to Marketing Hub since it operates on CRM-native data. It turns that unified client environment into deeply personalized email experiences and clear, defensible income attribution. If email has to demonstrate income as fine as create it, Marketing Hub Enterprise belongs at the top of your shortlist.

Before Microsoft releases a laptop to the public, it needs to put the equipment through its paces. That all happens inner a windowless, warehouse-like lab on the company’s Redmond, Washington, campus, anywhere the squad evolving Surface devices programs robots to media their buttons thousands of times, pelts devices alongside broadcast waves from elephantine antennas, and builds contraptions that autumn objects from increasingly daring heights to see whether the products can clasp up.

Its latest laptop, the merchandise I’m current to see, really needs to clasp up. The Surface Laptop Ultra isn’t fair another high-end config of the Surface Laptop, it’s the debut phase for Nvidia’s RTX Spark — Nvidia’s entrance into the earth of Arm-based PC chips, and a chance to attack expectations out of the h2o whenever it comes to what a Windows laptop is capable of.

Microsoft’s final big laptop shove — the Copilot Plus PC undertaking from 2024, focused initially on Qualcomm chips — “was really aimed at MacBook Air,” says Brett Ostrum, company vice chairman of Surface at Microsoft. Microsoft additionally wanted to go head-to-head alongside the MacBook Pro, but existing chips weren’t capable enough. “We knew that was not going to attain MacBook Pro-level compute.”

Around that time, Nvidia began privately showing off what its forthcoming chips could grip whenever it came to AI, and Microsoft jumped at the possibilities. “It exactly made a ton of awareness for us in conditions of how to go and vie alongside the MacBook Pro,” Ostrum says. “Nvidia was a logical choice.” The two companies have spent the years since operating hand in hand, resulting in today’s launch.

Much akin the MacBook Pro, the Surface Laptop Ultra is a high-end device. It has a CNC-machined aluminum body, a huge trackpad, chicklet keys, and fan vents tucked underneath its slightly elevated base. There’s an gathering of ports, including HDMI and an SD cardstock slot, and a high-res touchscreen that looks awesome up close. A example alongside 24GB of RAM starts at $2,599.99, and if you desire the extremely top of the row alongside 128GB of memory, you’re looking at $5,899.99.

Some of that cost comes from the Ultra being among the most polished laptops you can buy in the Windows world. But several of it comes from the all-new part inside: the archetypal of Nvidia’s RTX Spark line, which is expected to bring essential components of Nvidia’s workstation-level power downward to a portable device. The RTX Spark chips inner the Surface Laptop Ultra are geared toward local AI workloads and imaginative tasks. Microsoft says it’s mighty adequate to grip compressed 280 billion-parameter AI models operating locally — models that needed to run in the haze lone a twelvemonth ago. The RTX Spark is the two CPU and GPU, and the GPU have to be capable to grip Blender 3D objects alongside comfort and cruise through another creator workloads in Premiere or Photoshop gratitude to the system’s use of unified recollection — the identical method Apple takes on its M-series chips.

Microsoft is mostly positioning the Ultra at developers who desire a mighty GPU for local AI compute, but it’s additionally targeting the creatives and professionals who would buy a MacBook Pro. “It is built for us,” says Jit Hirani, guide decorator for Surface devices. “We arrive to activity to build a merchandise for ourselves.”

Because Nvidia blended the CPU and GPU on the RTX Spark, Microsoft was capable to create its Surface Laptop Ultra motherboard smaller and addition the size of the fans on the device. The area inner the device is maximized for battery, alongside components neatly arranged in a dreary grid. Microsoft has additionally focused on repairability, alongside no components glued downward and plainly marked instructions and QR codes throughout for disassembly.

As this is an Nvidia laptop, you can additionally use it for gaming, but I get the awareness that Microsoft is treading carefully concerning positioning this as a gaming laptop. The RTX Spark is an Arm chip, which method most games aren’t native and volition have to be emulated. Still, I got a chance to briefly perform an emulated type of Gears of War: E-Day on the Surface Laptop Ultra, and it ran buttery smooth, equal transitioning between the laptop being plugged in and operating off power division without a achievement hit.

Microsoft put a lot of attention into making certain the Ultra’s scheme was as premium as a MacBook’s, too. One item in particular stood out to me: the way Microsoft has cleverly reworked its magnetic Surface Connect charging port.

“I think we gave our engineering squad likely concerning 300 iterations on this journey since it’s the world’s archetypal built-in magnetic nexus for USB-C,” says Hirani. “The sole constraint from day one whenever we designed the merchandise was that we could not alter the worldwide USB-C [port].”

Instead of losing a USB-C harbor to several tradition solution, Microsoft has built a magnetic nexus about the USB-C harbor instead. It feels extremely akin to the first Surface Connect, complete alongside a charging ray and no need to exactly guide it into place. You can equal disconnect the cable from the 140-watt charging block, reverse it, plug it rear in, and use the regular USB-C harbor on the another end to accusation all of your another devices.

Ostrum tells me that Microsoft spent years “grinding on this” to create a magnetic USB-C charging solution, so my inquiry to him is: Will we see this on additional Surface devices? “We’ll delay and see,” he says. “We have been excellent passionate concerning that experience. This is the location anywhere we wanted to current it to the world.”

The amount of evaluation and merchandise repeat that went into getting this charging harbor correct is mirrored in the remainder of the Surface Laptop Ultra. Microsoft has an Automated Experience Lab (AXL) tool that helps the teams measure the responsiveness of a device, beyond fair regular benchmarks. That’s item from measuring how lengthy it takes to initiate Microsoft Teams to how accelerated it is to category alphabet on a keyboard.

This is all automated through an Arduino commission that can inject code onto a scheme and measure its reply times. Microsoft has created what it affectionately calls its own BB8, a robot that helps the thousands of engineers operating on getting the stability fair correct for the RTX Spark platform.

“We have engineers at Nvidia that are operating on power, performance, graphics, stability, and Wi-Fi,” explains Ostrum. “When they activity on all of those things, they have to complete their code, inspect it into Nvidia. Once Nvidia is satisfied alongside the stability and the achievement of that chunk of code, it flows into the next branch.”

That procedure creates a lag, sometimes enduring weeks, between Nvidia, Surface, and Windows all getting code prepared for a new phase akin RTX Spark. To speed things up, Microsoft has got Nvidia engineers uploading code remotely to this small robot that automates tasks that regular consumers do alongside a laptop. The miniature robot can unplug USB-C devices, use the touchscreen, pen, and trackpad, and open and near the device.

“By allowing our silicon partners to download onto this setup, we greatly accelerate [testing],” explains Ostrum. “It power have been weeks from the period they were done alongside their code to the period they were prepared to autumn it to us. They can have that data rear the next day.”

Microsoft’s lessons of construction Surface hardware for nearly 15 years have additionally been applied to the Surface Laptop Ultra in ways I’ve never heard concerning before. I’ve seen several of Microsoft’s hardware labs complete the former 15 years, but the one I visited this week was eye-opening. It’s so secretive that I wasn’t capable to obtain photos of the exterior of the construction or the lobby, and it lacks windows so nobody can see inside.

It houses the unsung heroes of Microsoft’s Surface squad who expend hours creating new ways to emphasis test new Surface devices. That includes uncomplicated tests akin automated autumn tests or rows of robots beginning and decision laptop lids thousands of times, but it additionally includes electromagnetic compatibility and RF compliance testing, and X-ray machines and expert speck microscopy equipment that are used to discover failures at nanometer scale.

Microsoft additionally has what it calls a “CSI lab” to execute inspection on hardware failures and justify the results are fed direct rear into the manufacturing process. This can acknowledge faults alongside component suppliers. In one case a few years ago Microsoft established contact failures on several of the displays it was using. The Surface squad traced it rear to sulfur and another halogens established on the display, following the provider had changed the gloves that the manufacturing operators were using.

The evaluation has helped Microsoft get to the item anywhere it now has the confidence to obtain on the MacBook Pro. It additionally gets at the bigger logic Surface continues to be inner Microsoft. Surface isn’t fair a regular hardware endeavor for Microsoft, it’s a way to display its OEM partners what Windows hardware have to be capable of. Surface has helped shove the industry onward on trackpads, displays, pen support, performance, and design. Without Microsoft’s own efforts, Windows laptops and tablets would have fallen equal additional rearward Apple’s MacBooks.

The Surface Laptop Ultra is the clearest example yet of that role. Microsoft is using its own hardware to demonstrate that Windows can vie at the extremely highest end of the laptop market, during giving its OEM partners a mark to beat. That makes Surface far additional than a showcase for Microsoft’s application and hardware. It’s among the ways Microsoft makes certain Windows itself keeps moving forward.

Aaron Sorkin’s The Social Reckoning has its fair portion of cinematic flair, but many particulars in the narrative hew closely to genuine life. The movie tells the narrative of how Frances Haugen, a Facebook civic integrity merchandise manager, came to leak a ample trove of inner documents to Wall Street Journal investigative newsman Jeff Horwitz, in what would afterward rotate into a 2021 sequence called “The Facebook Files.” In the two genuine existence and in the film, Haugen’s leaks guide her to attest as a legislative whistleblower. The final frames of the movie depict the effect she had equal following that decision scene, speaking before governments about the earth and bolstering lawsuits against Meta nationwide alongside the inner communications she disclosed.

The film’s Haugen, played by Mikey Madison, says that she really believes in Facebook, echoing a communication the genuine Haugen remaining for her colleagues fair before she signed off for her final day in May 2021. “I don’t dislike Facebook,” she wrote on the company’s inner communication board, according to The Journal. “I affection Facebook. I desire to preserve it.”

Sorkin, the two directing and screenwriting the semi-follow up to 2010’s The Social Network, which he lone wrote, took several liberties alongside the conversation and the chronology of the movie. The most apparent is that he condensed the timeline and shifted important events onward by multiple months. Some key moments in the movie — Zuckerberg (Jeremy Strong) preparing for a legislative hearing concerning misinformation and extremism and Horwitz (Jeremy Allen White) getting prepared to publish the archetypal Facebook Files study — happen on the identical day, which happens to be January 6th, 2021, amid the insurrection at the US Capitol. In reality, Zuckerberg testified in the House alongside another tech CEOs in March 2021, and the first installment of the Facebook Files wasn’t published until that September.

But the movie pulls immediately from Horwitz’ inquiry and the documents that Haugen collected, which showed how the business applied its satisfied moderation policies in inconsistent ways, drove users toward dislike groups, and had meager responses to apparent individual trafficking on the platform. Meta declined to provision a comment on the movie, but in former statements, the business has referred to the documents collected by Haugen and their recounting in the Facebook Files as cherry-picked to assist a particular narrative. Zuckerberg called the reporting Facebook Files “a coordinated attempt to selectively use leaked documents to coat a false image of our company” during a 2021 earnings call following the first reports were released, and stated that whenever faced alongside “impossible tradeoffs … these questions are not chiefly concerning our business, but concerning balancing distinct difficult social values.” Here’s what you should cognize concerning the genuine existence events underpinning the film.

The Social Reckoning opens alongside a young blonde female position next to her overheating car on the flank of a highway. When cops drag up to ask if she’s fine and odor liquor on her breath, the toggle quickly grows tense. The female appears unnerved by the officer, a individual of color, and whips out her phone to document him; soon, she’s threatening aggression and is arrested. Then, we see Madison’s Haugen in her flat observing the video the female posted to her Facebook page, “Voice of Reason,” alongside the caption “Watch me own DEI cops.” Haugen reveals afterward that the female was a companion she met in rotation class, radicalized by conspiracy theories on Facebook following the advancing applicant she campaigned for lost.

The environment and particulars concerning the companion appear to be fictionalized, but are according to a true part of Haugen’s story. Haugen told The Journal thatshe took the function on Facebook’s civic integrity squad following seeing a family companion radicalized online. “It’s one item to study misinformation, it’s another to endure person to it,” she said.

In the movie, among the archetypal important revelations Haugen shares alongside White’s Horwitz is that 64 percent of group who joined extreme groups on Facebook were sent there by its algorithm. That figure — in the movie and genuine existence — became the basis of a May 2020 article in The Journal. In the genuine article, Horwitz and co-author Deepa Seetharaman wrote that a 2016 inner Facebook display established “64% of all extreme collection joins are because of our advice tools.” It added, “Our advice systems develop the problem.” At the period of the 2020 article’s publication, a Facebook spokesman told The Journal it had since strengthened protections and “are not the identical business today.”

As they hunt through pages of documents in the movie, Haugen and Horwitz keep trying to fig out why Facebook tuned its advice algorithm to reward “Meaningful Social Interactions” (MSI), fairly than likes, in 2018. Haugen senses favoring MSI could be driving users akin her companion toward radicalization, but she and Horwitz initially aren’t certain how — particularly since Facebook publically stated the change was expected to undercut engagement. They arrive to comprehend that the new ranking scheme valued emoji reactions and reshares much additional extremely than likes; Zuckerberg and another executives knew this alter would advance rage-sharing.

That’s a fairly exact translation of what we cognize concerning MSI. Zuckerberg promoted the shift as a way to accurate for a trend of “public content” from brands and media “crowding out the individual moments that guide us to nexus additional alongside all other.” Though he anticipated the alter would be fine for users, he stated it would really hurt Facebook’s engagement. But, he said, “if we do the correct thing, I accept that volition be fine for our community and our endeavor complete the lengthy term too.”

In an installment of The Journal’s “Facebook Files” series in 2021, however, Horwitz and co-author Keach Hagey established that Facebook workforce had warned that prioritizing MSI was promoting angrier engagement. Documents obtained by The Journal showed that foremost up to the change, Facebook had been concerned concerning declining involvement from users through actions akin resharing or commenting. To combat this, the documents showed, Facebook tried to advance additional first posts and resharing. While Facebook established several affirmative person benefits of the change, researchers found, “Misinformation, toxicity, and aggressive satisfied are inordinately prevalent among reshares.” Zuckerberg, in the movie and apparently genuine life, resisted several mitigation proposals that would damage engagement.

Meta has downplayed the importance of MSI. In a declaration to The Journal at the time, Meta spokesman Andy Stone said, “Is a ranking alter the origin of the world’s divisions? No.”

In the movie, Haugen’s coworker shares a thumb run alongside documents including a study concerning how Instagram harms adolescent girls’ build image. She tells Haugen to get the term out before the business starts promotion Instagram Kids. It’s not apparent if this coworker really existed, but the data she shares appears to be based on an inner study that established 32 percent of adolescent girls surveyed who stated they felt bad concerning their bodies felt worse following using Instagram. Instagram had certainly planned to initiate a assistance specifically for kids under 13, but announced shortly amid the uproar from the “Facebook Files” publish that it was pausing the project to activity through concerns.

As Strong’s Zuckerberg preps for the 2021 legislative hearing in the film, sitting on a phase rearward a array and rehearsing answers alongside his team, a project capitalist named Charlie emerges from the shadows. Charlie, played by clown Bill Burr, gives Zuckerberg feedback and urges him to average governmental ads. Movie-Zuckerberg says he’s disturb alongside Charlie following he wrote a scathing op-ed, and reminds him he 100x’d his investment, which would have been value complete $1 milliard if he’d held his position.

Charlie is inspired by real-life Facebook investor-turned-critic Roger McNamee, an administrator manufacturer of the film. McNamee published an op-ed in The Guardian in 2017 charging that Facebook and Google “threaten community health – and democracy,” and in 2019 published the publish Zucked: Waking Up to the Facebook Catastrophe. Sorkin told Vanity Fair that although McNamee was equivalent to be depicted in the movie, “it was made clear” that “there power be a issue alongside Facebook” in depicting the conversation between the VC character and Zuckerberg in the movie. So the character became a fictionalized type inspired by McNamee, instead.

In the film, Haugen seems dejected whenever she realizes that the civic integrity squad she plant on — which worked on worldwide election-related risks including misinformation — volition apt be dissolved, and she’ll be reassigned. A Facebook administrator assures the squad that equal if that were to happen, the activity would be “absorbed” into another teams, and it would lone be beneficial to their work.

This reflects how the business communicated the alter in construction following the 2020 US election. In her “60 Minutes” interview in 2021, Haugen said, “when they got rid of Civic Integrity, it was the instant anywhere I was like, ‘I don’t rely that they’re consenting to really allocate what needs to be invested to keep Facebook from being dangerous.’” (That’s really whenever Haugen archetypal responded to Horwitz’ first outreach on LinkedIn.) Meta administrator Guy Rosen responded at the time that the squad was not disbanded, but fairly “integrated” into “a larger Central Integrity squad so that the amazing activity pioneered for elections could be applied equal further, for example throughout health connected issues. Their activity continues to this day.”

Haugen’s movie character mentions that Zuckerberg has bullet-proof features in his office, which appears to be based in reality. A 2019 study from Business Insider established that a “glass-walled gathering room” he would sometimes activity from had “bullet-resistant windows and a panic button.”

The movie depicts a push-and-pull association between Haugen and Horwitz, as the newsman tries to get Haugen to portion additional data without crossing a susceptible ethical row of soliciting her to pilfer confidential documents. As Haugen decides whether or not to go the additional mile, she additionally becomes disappointed alongside Horwitz for not making additional advancement on his own.

The two encounter regularly on a hiking path, anywhere Horwitz eventually gives Haugen a burner phone to communicate alongside him and on which she afterward takes pictures of inner documents. At one point, Horwitz equal travels to Puerto Rico to collect additional data from Haugen, following she relocated there during the pandemic.

These particulars all appear to mostly prosecute the true way of their origin construction relationship. Haugen told The New York Times in 2021 that she had “auditioned” Horwitz for several time, and decided to provision him the scoop since she established him to be “less sensationalistic than another choices I could have made.” Horwitz acknowledged in an on-stage interview alongside Haugen in 2022 that “things got strained between us” at times, in part since Haugen had concerns concerning The Journal’s organizational approach, and in part complete several mess concerning whenever certain data was released by another publications or agencies Haugen worked with.

Haugen really did relocate to Puerto Rico during the pandemic, and Horwitz additionally visited her to collect information, although the timeline of those travels is distinct than in the movie. Haugen welcomed him to Puerto Rico formerly she realized Facebook’s policies prevented her from operating from a US territory, prompting a deadline to discontinue or move. She chose to quit, definition the brace had one final duration of period they could admission inner documents at Facebook. Speaking to Horwitz on phase in 2022, Haugen stated she was “so grateful that you put in a huge amount of attempt to fair be there and create certain that we asked as many questions as we could so former got to have several benevolent of document for it.”

Build, run, and portion AI agents alongside a declarative YAML config, affluent tool ecosystem, and multi-agent orchestration.

docker-agent lets you create and run intelligent AI agents that collaborate to resolve complex problems — no code required.

docker-agent is a docker CLI plugin and can be run alongside docker agent.

Define agents in YAML, provision them tools, and let them work.

agents: root: model: openai/gpt-5-mini description: A helpful AI assistant instruction: |  You are a informed aide that helps users alongside assorted tasks.  Be helpful, accurate, and concise in your responses.  toolsets: - type: mcp ref: docker:duckduckgo

docker delegate run agent.yaml

• Multi-agent architecture — Create teams of specialized agents that assign tasks automatically
• Rich tool ecosystem — Built-in tools + any MCP server (local, remote, or Docker-based)
• AI provider agnostic — OpenAI, Anthropic, Gemini, AWS Bedrock, Mistral, xAI, Docker Model Runner, and more
• YAML configuration — Declarative, versionable, shareable
• Advanced reasoning — Built-in think, todo, and recollection tools
• RAG — Pluggable retrieval alongside BM25, embeddings, hybrid search, and reranking
• Package & share — Push agents to any OCI registry, drag and run them anywhere

Docker Desktop (4.63+) — docker-agent CLI plugin is pre-installed. Just run docker agent.

Homebrew — beer instal docker-agent. Run docker-agent immediately or symlink the binary to ~/.docker/cli-plugins/docker-agent and run docker agent.

Binary releases — Download from GitHub Releases. Symlink the docker-agent binary to ~/.docker/cli-plugins/docker-agent to be capable to use docker agent, or use docker-agent directly.

Set at smallest one API key (or use Docker Model Runner for local models):

export OPENAI_API_KEY=sk-... # or ANTHROPIC_API_KEY, GOOGLE_API_KEY, etc.

See Set Up a Model for the complete walkthrough of the two paths (cloud API key or local model).

# Run the default agent docker delegate run # Run from an OCI registry docker delegate run myorg/agent:tag # Generate a new delegate interactively docker delegate new # Run your own config docker delegate run agent.yaml

More examples in the examples/ directory.

📖 Full documentation

• Installation · Set Up a Model · Quick Start
• Agents · Models · Tools · Multi-Agent
• Configuration Reference
• TUI · CLI · MCP Mode · RAG
• Model Providers · Docker Model Runner

Read the Contributing guide to get started. We use docker-agent to build docker-agent:

docker delegate run ./golang_developer.yaml

We collect anonymous use data to enhance the tool. See Telemetry.

Docker Community Slack · #docker-agent channel

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