Soon, James Gunn and Peter Safran will no longer head up Skydance's DC Studios. After taking the helm of DC Studios as co-CEOs in 2022, Gunn and Safran are being replaced by former Marvel SVP of Production and Development Jeremy Latcham, Deadline reports.

However, Gunn and Safran will "continue to produce on various DC projects" and are still working on the Superman followup Man of Tomorrow, Deadline says. Gunn also posted on Instagram that he and Safran have "just been engaged on another DC project which I'm writing." In his Instagram post, Gunn said that his time at DC Studios has been "trying, to say the least. I dislike the at-times cor …

Read the full story at The Verge.

Tomorrow, developers and founders will gather at The Pearl in San Francisco for Run by Apify, a one-day conference about building with AI agents. Among the speakers giving an opening lecture is Apify CEO Jan Čurn, who has been thinking about how to make tools those agents will actually use.

Last week I had the chance to sit down with him and talk about AI agents. The conversation showed me how solving one problem in the AI space often creates another, more complex but just as interesting. Software, for example, is easier to build than ever, yet finding the right tool is harder because there are so many to choose from.

Apify knows this tension well: the company started out in 2016 offering web tools for all sorts of jobs, such as web scrapers and other automations. Since then, it has watched software development change, from tools like Zapier to AI agents. It’s those AI agents that are some of Apify’s customers today, Jan explained:

People are no longer selecting the tools themselves, but they have their agents do it on their behalf. The agents are basically like a surogate of the customer.

Here’s the uncomfortable part: if your number one customer is an AI agent, your beautiful UI might not exactly be what’s needed. Agents read your API and your docs, and they look for tools in specific places. Jan says people have stopped clicking around interfaces too and rather hand the job to an agent.

“If there are 50 tools, or maybe 500, or 5,000, which one are you going to pick?”

Jan and I talked about how cheap software has become to build. Good software still costs money, but the cost of creating it has dropped across every part of development. When building something is almost free, we end up with so many tools and products that finding the right one is like walking through fog.

With so much choice, it’s hard to figure out which is good, so we end up doing the same thing, and using the same tools.

Jan said that a good example of this is the Apple App Store, where the number of new apps is growing very fast, but the usage is the same: the supply is massive, but the demand is pretty much the same as it ever was. For those building software, getting seen was never exactly an easy thing to do, but now it has become exponentially harder:

If there are 50 tools, or maybe 500, or 5,000, which one are you going to pick? Who can you trust? Trust is becoming really important.

Apify aims to solve this challenge through their marketplace, where they host thousands of “Actors”, or tools for different tasks their clients and other customers require help with. These tools are created by the community, and Apify pays their creators:

If those Actors are used by our users and customers, we pay 80% of the revenue to the creators. These creators are making about $2m per month. I think this month will go over $2m, and this is growing maybe four to five times year over year.

Jan added that having this type of spotlight could be a beneficial thing for developers, noting that someone could earn several thousand dollars per month by having their tool on the platform.

You don’t need to be a traditional developer to build for this marketplace

The marketplace’s growth is organic in a sense that more users can attract more creators, who will create more tools and Actors for more use cases, and that again attracts more users. Since Actors can be created relatively easily, Jan says that the barrier of entry is also reasonable, but there’s plenty of plans for the future:

Actors are self-contained, well-defined tools that do one job. In a way, these are tools that are pretty good to be completely generated by AI. So over time, we want to bring capabilities that make it easy for creators to create self-healing Actors that will automatically repair themselves. Our goal is for anyone who’s entrepreneurial to be able to do it, not just traditional software developers.

Right now, Apify is also working on expanding what Actors can do. In the past, Jan explained, Actors were batch-job oriented, meaning that one Actor would do its task with some inputs and return a result. Now, a lot of customers are looking for use cases similar to APIs, which means quick request and quick response. Apify is thus working on Server Actors, which will bring more features to Apify:

Server Actors will allow us and our creators to bring many new kinds of services to Apify, including APIs to serve data or perform transformations. We also have MCP connectors, so a user can connect external systems like Notion or Slack, and Actors can get access to them. We’re moving away from web data extraction into data as a service, general automation, and agentic tools.

The future looks bright but challenging, and not just for Apify. The same goes for any company building tools for people and AI agents. For developers, getting a tool to work is the easy part. To get an agent to use it, you also need clear documentation and a good API, so the agent can understand what the tool does and call it without trouble.

Still, building for agents instead of people is something the whole industry is trying to figure out. AI is built on predictions and training, yet it still seems to have a mind of its own. Whether that mind picks your tool out of thousands of others is an open question.

The post Developers, Your Next Customer Is an AI Agent appeared first on ShiftMag.

Developers can now build applications powered by Google Maps Platform directly within Google AI Studio by prompting Gemini with real-world geospatial concepts. The integration eliminates traditional configuration hurdles by providing one-click demo keys that generate instantly without requiring Cloud Console redirects or credit cards. This setup grants developers immediate access to core APIs—including Maps, Places, and Routes—supported by daily sandbox quotas for seamless prototyping.

Two of the biggest players in the AI coding space are betting that developers want to talk to their agents while they work, with both announcing a two-way voice feature that lets users ask questions and give follow-up instructions without returning to the keyboard.

On Friday, Cursor announced Voice Chat with Agents, a Grok Voice-powered feature that lets developers call an agent and talk through a change. On the very same day, Google announced Voice mode in Antigravity, which uses Gemini Live to bring spoken conversations to its desktop app and command-line tool.

In a blog post announcing the feature, Nick Van der Auwermeulen, developer relations engineer at Google, contrasts the back-and-forth dynamic of human colleagues with the “rhythm” of working with coding agents.

“Some of the best engineering happens out loud,” Van der Auwermeulen writes. “You talk a bug through with a teammate, think out loud in front of a whiteboard, or say ‘wait, try it the other way’ halfway through a pairing session. Working with an agent has mostly had a different rhythm: you type a prompt, the agent works, you read what it did, and you type again.”

“Some of the best engineering happens out loud.”

The longer a job runs, he adds, the more often developers need to check in. A command might be waiting for approval, or a developer might spot a requirement the original instructions missed.

“Each of those moments pulls you back to the keyboard and away from whatever you were looking at,” he continues.

And so Antigravity’s voice mode keeps a live call open while the coding agent works, so the developer can hear an update or approve a command without reaching for the keyboard.

Cursor, for its part, makes a similar pitch for keeping the conversation going.

“Describe a change out loud, ask what an agent is working on, and hear updates as the work moves forward,” the company writes.

“Describe a change out loud, ask what an agent is working on, and hear updates as the work moves forward.”

Look who’s talking

Antigravity, for the uninitiated, is Google’s agentic software development platform. It debuted alongside Gemini 3 last November, with version 2.0 landing in May replete with a separate desktop app. There’s also a terminal client, Antigravity CLI, and extensions for developers who want to work from editors such as VS Code, JetBrains IDEs and Xcode.

Antigravity is now where Google is consolidating its various coding tools, having moved individual Gemini CLI users to Antigravity in June, while it’s also in the midst of sunsetting Jules, a previously launched background coding agent.

It’s worth noting that Antigravity’s existing dictation feature already turns speech into a written prompt. Now, to start a two-way call in the desktop app, the user clicks the waveform button beside it and tells the agent what they want it to do.

Antigravity replies aloud and keeps the call open while the coding agent works, with the user able to ask how it’s going or give it further instructions. You can also interrupt the agent mid-sentence to ask a question or give it another instruction.

When the agent finishes, its written summary and a button to review the code changes appear in the same chat.

Additionally, voice mode also works in the Antigravity CLI. Users run the /voice live command to start a call, and hear the agent reply while it works away in the terminal.

Coding agents talk back

Cursor, which was acquired by Elon Musk’s SpaceX in August for $60 billion, has followed a similar path toward embracing voice. It added dictation in October 2025, and in release notes last month, the company said it had updated existing voice calls to let agents give short spoken progress updates.

One difference with Cursor’s rollout is where you can call from. Cursor supports voice chat on the web and iOS as well as on desktop, while Google offers it in the Antigravity desktop app and CLI. Cursor’s calls run on Grok Voice, which xAI launched through its API back in April.

“The push now, it seems, is to keep the conversation going after the agent starts work.”

However, Google and Cursor aren’t the first AI companies to bring two-way voice to coding agents. OpenAI launched a Codex desktop app in February with a built-in dictation feature. And as The New Stack reported in July, the company added two-way voice to Codex in the ChatGPT desktop app, letting developers give instructions and check on tasks by speaking. The Codex CLI has offered live voice conversations since the spring, with OpenAI enabling them by default in September.

Anthropic, for what it’s worth, has a voice mode which offers two-way voice functionality inside the Claude app only. In Claude Code, specifically, voice still only goes one way via dictation — the /voice command turns developers’ vocal instructions into prompt text, and the agent replies in writing.

Dictation itself is useful for describing a bug or sketching out a feature aloud, and it’s showing up across the AI coding landscape, from the major AI labs to well-funded startups such as Replit.

The push now, it seems, is to keep the conversation going after the agent starts work, so developers can follow up about a decision, clarify a requirement, or change direction before the work is finished.

The post Cursor and Google join OpenAI in giving coding agents a voice. Claude Code stays silent. appeared first on The New Stack.

Meta on Monday debuted Muse Ember, a smaller, more efficient multimodal member of its Muse model family.

The company is clearly taking a similar approach to the other frontier labs, with a family of models that cover everything from focused, high-volume tasks (think OpenAI’s GPT-6 Luna and Anthropic’s Claude Haiku 5.5) to frontier intelligence like OpenAI’s GPT-6 Astra, Anthropic’s Claude Fable 5.1, and, most recently, Meta’s Muse Wonder Full. And just like its competitors, Meta is pitching the smaller model for agentic subtasks like reading files, running tests, and making targeted fixes, with a larger model like Muse Wonder doing the planning.

Muse Ember is available in Muse Code and through the Meta Model API under the model ID muse-ember-1.0. It will likely soon be available on services like OpenRouter and on the neoclouds, too, given that Meta has done the same for its other Muse models in the past.

Like with previous Muse model releases, Meta is keeping Ember’s model weights private.

Ember is Meta’s take on a small, price-efficient model, and at least when it comes to the benchmarks, it’s a strong entry.

At max reasoning, Meta puts Ember at about $0.14 per task on the Artificial Analysis Intelligence Index, compared with $0.41 for Haiku and $0.15 for Luna. Ember matches Haiku’s rounded score at about a third of the cost and scores higher than Luna for about a cent less per task.

At xhigh, the other Ember setting shown in Meta’s chart, the model scores about 34 at roughly $0.12 per task.

Muse Ember pricing

Ember, which has a 1M-token context window and supports up to 512K output tokens, is priced at $0.10 per million input tokens, $0.004 per million cached input tokens, and $0.50 per million output tokens. Those input and output rates match GPT-6 Luna and Claude Haiku 5.5 at their standard short-context prices. Both charge $0.01 per million cached input tokens at those rates, with higher prices for longer prompts. Ember is available in Muse Code and through the Meta Model API as muse-ember-1.0, with support for the Responses API.

How Chinese rivals compare

And while Meta doesn’t directly compare the new model to Chinese competitors like DeepSeek V4.1 Flash 1017, Z.ai’s GLM-5.3 Flash, and MiniMax M3.1 Flash in the chart, Ember undercuts the listed uncached input rates for all three. It also undercuts DeepSeek’s direct off-peak and MiniMax’s listed output prices and matches GLM’s. DeepSeek has a slightly lower off-peak cache-hit rate of $0.003 per million tokens.

On Artificial Analysis’ Intelligence Index, for example, Ember scores 36 at max reasoning, while DeepSeek V4.1 Flash 1017 is at 33, MiniMax at 29, and GLM at 28. OpenAI’s GPT-6 Luna, meanwhile, scores 28 on this same benchmark, while Haiku gets to 36, both at max reasoning.

Muse Ember vs. Muse Spark

Meta’s own Muse Spark 1.3 scores 37 at max reasoning and costs $2.39 per task at the standard API rates used in the comparison. Meta also offers a Spark Contributor tier, where it uses requests to improve its products. That tier matches Ember’s uncached input price and charges $0.20 per million output tokens.

Meta reports a lead over Haiku and Luna on its own Personal Superintelligence Index, a measure of personal and knowledge work. One benchmark where Ember doesn’t fare as well is FrontierCode 2.0, which evaluates whether a repository maintainer would merge an agent’s changes. Ember scores 27.2 percent, compared with 34.9 percent for Haiku and 28.5 percent for DeepSeek. Spark 1.3 scores 23.1 percent.

Computer use on OSWorld

Ember can take text, images, audio, and video as inputs, and Meta says it can operate desktop apps from screenshots.

On OSWorld 2.1, it got nearly the same partial-credit score as Haiku, 69.5 percent versus 68.9 percent, but finished more tasks outright, 33.8 percent versus 27.8 percent for Haiku and 17 percent for Luna. In a separate OSWorld 2.1 run, Spark 1.3 earned nearly identical partial credit to Ember and completed 39.25 percent of tasks.

Meta says the new model is the first it trained using the larger Muse Wonder Full model. That’s similar to what the company did with Muse Wonder to train the specialized models it began rolling out to its Muse personal agent and Meta AI.

The post Meta launches Muse Ember to take on Claude Haiku and GPT-6 Luna appeared first on The New Stack.

Most of the cost of adopting something new isn’t the new thing, it’s the move. Moving a Hive table to Apache Iceberg has typically meant weeks of engineering work. Last week, Amazon S3 Tables turned that into three steps: start, sync as often as you need, complete. The source stays writable except for a few minutes at cutover. The same week, Amazon DynamoDB started loading data from S3 into tables that already exist.

The pattern I like is that neither one asks you to stop what’s running. That’s also the way to read the agent news this week. The new Amazon Bedrock AgentCore Runtime is a single field on the runtime you already have, and Web Answer is a tool you attach to a gateway you already run. Now, let’s get into this week’s AWS news…

Headlines
The new AgentCore Runtime: elastic, optimized, and consistently fast starts – If you run agents on AgentCore and haven’t tried the new runtime since it became generally available, it’s worth a look. It changes two things. First, memory: sessions start small, page in more on demand, and give memory back when it goes cold instead of holding the peak, so you pay for what your agent uses. Second, cold starts: AgentCore prepares your environment once, snapshots it, and restores the snapshot for every new instance. In the team’s tests, that kept P75 cold starts at about 2 seconds for images from 200 MB up to 2 GB, while the original runtime rose from roughly 5.4 seconds to nearly 30 seconds as images grew. The per-unit rate is higher, but you use far fewer GB-hours, so the team expects most agents to see a lower bill, though that isn’t guaranteed.

To opt in, you set platformVersion to V2 when you create or update a runtime. Create and update take several minutes while the snapshot is prepared, and your container must report healthy on /ping within 120 seconds, so report health only after initialization finishes. The docs cover how to optimize your agent for V2. V2 is available in 10 Regions, and committed baseline pricing is coming soon.

Amazon S3 Tables now offer managed migration of Apache Hive tables to Apache Iceberg – You can now migrate Parquet tables registered in the AWS Glue Data Catalog into S3 Tables without building a pipeline or provisioning compute. S3 Tables validates the source and produces a compatibility report covering every object in scope. Each sync copies only what changed, so production keeps writing until cutover. Afterward you can query the table with Amazon Athena, Amazon SageMaker Unified Studio, or any engine that speaks the Iceberg REST API. You pay only for the data copied, with no charge for the conversion, and objects in S3 Intelligent-Tiering keep their tier. At launch it works within the same account and Region, in 10 Regions.

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

Amazon DynamoDB now supports bulk put items – You can now load line-delimited DynamoDB JSON or Amazon Ion files from Amazon S3 into existing tables as a managed, asynchronous job. Each record can carry its own condition expression, so one file can mix plain, insert-only, and version-guarded writes. Writes use dedicated system capacity and cost less than the equivalent per-request writes. An Accelerated tier handles deadline-bound jobs. The Database blog post gives illustrative Accelerated times of less than 1 hour for 100 million 1 KB items. One thing to know before you start: canceling doesn’t roll back items already written.

Announcing Web Answer on Amazon Bedrock AgentCore – You can now give an agent a single tool call that returns a synthesized, cited answer to a natural-language question, instead of building your own search, retrieval, and citation pipeline. Web Answer plans its own searches, runs several in parallel, and decides when it has enough evidence. You attach it as a target on AgentCore Gateway and call it over MCP, and you can limit it with domain include and deny lists and publish-date filters. It’s available in US East (N. Virginia). The getting started post explains when to use it instead of Web Search, which returns ranked results for your own model to synthesize.

AWS Lambda now supports queues for Apache Kafka in self-managed Kafka event sources – You can now process records from a single Kafka partition across many parallel AWS Lambda invocations. Classic consumer groups cap parallelism at the partition count. Queue mode uses Kafka share groups and needs Kafka 4.2 or later with share groups enabled. It works with self-managed clusters and managed offerings such as Confluent Cloud. You give up ordering and get at-least-once delivery, so your functions need to be idempotent. The Compute blog walkthrough covers poller scaling and when to keep stream mode.

AWS announces AWS Interconnect – last mile preview with Equinix – You can now set up a private, managed connection from Equinix’s network of 282 data centers to AWS without ordering cross-connects or configuring routers. Capacity is quad-redundant across two locations and four links, with BGP and VLANs configured for you, a 99.99% availability SLA, and MACsec by default. You pick a Region, bandwidth, and AWS Direct Connect gateway, then paste the activation key into Equinix Fabric. The preview covers the San Jose metro and, as of last Thursday, Northern Virginia, with 1 Gbps connections at no charge.

AWS Security Hub now provides an SBOM inventory for your compute resources – You can now see every software package installed across your organization’s Amazon EC2 instances, Amazon ECR images, and Lambda functions in AWS Security Hub, with each package’s version and how many resources run it. That’s the list you want the next time a widely used library has a vulnerability. The inventory builds on the SBOMs that Amazon Inspector already generates, so it fills in automatically wherever Inspector is enabled. It’s included in Security Hub Essentials at no additional cost.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts and resources that you might find interesting:

Serverless ICYMI Q3 2026 – Julian Wood’s quarterly recap is a quick serverless catch-up before re:Invent: Lambda SnapStart for container images, a 90-minute timeout on Lambda Managed Instances, Lambda MicroVMs in 10 Regions, 1 MiB payloads in Amazon SNS, and the new AgentCore Runtime.

AWS contributes a large-transaction binary log optimization to MySQL – With binlog on, a huge transaction holds a global commit lock while its events are copied, blocking every other commit. The optimization, which Amazon Aurora MySQL has run in production since 2019, promotes the spill file into the binlog instead of copying it. In AWS’s tests, commit latency for a 50 GiB transaction dropped from about 7 minutes to about 2 to 2.5 seconds. It has now shipped to community MySQL.

How OrderServ achieved up to 64% cost savings with multi-tenant ECS – Cognizant’s food-ordering platform went from one Amazon ECS cluster per brand on AWS Fargate to a shared cluster on EC2 Auto Scaling groups with warm pools. The 64% applies to a 3-year Compute Savings Plan (47% with a 1-year plan). Scaling time also fell from 5–7 minutes to 30–60 seconds.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

AWS re:Invent – re:Invent returns to Las Vegas from November 30 to December 4. The event catalog is live and reserved seating is open, so register and claim your spots before they fill up.

AWS Community Days – Community-led conferences planned and delivered by community leaders, with a few more before the year ends: Belém, Brazil (November 14), JAWS FESTA 2026 in Akita, Japan (November 17), and Port Harcourt, Nigeria (December 12).

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Daniel Abib

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!