NVIDIA used its SIGGRAPH announcements to line up a wave of creative software behind Model Context Protocol (MCP), the open standard that lets an AI agent read an application's state and operate it directly. Tools from Adobe, Affinity, Blender, Boris FX, Foundry, SideFX, and Epic Games now expose MCP connections, putting agent control inside the apps artists already run.

• The shift is from chat to control. Instead of generating an asset in a separate window, an agent can inspect a project, change settings, and render inside the host application.

• Repetitive production work is the first target. NVIDIA lists texture inspection, color-management validation, export-variant prep, playblasts, and shot validation as jobs an agent can take on.

• NVIDIA supplies the plumbing, not the apps. The company is positioning its Agent Toolkit, RTX PRO workstations, and DGX systems as the layer underneath.

MCP lets an agent read a project and call its functions, not guess from a screenshot

MCP gives an AI client a structured way to see an application's project state and call its functions, rather than working from pasted text or a captured image. We covered the pattern when Unreal Engine embedded an MCP server in its editor. NVIDIA's SIGGRAPH slate extends that same approach across a wider set of creative tools at once, standardizing on one protocol rather than a per-vendor plugin.

Seven creative apps now expose MCP connections

• Adobe is expanding its AI Assistant across Firefly, Express, and Creative Cloud, and shipping an Adobe Express Developer MCP Server so coding assistants can build Express add-ons against official documentation.

• Affinity by Canva added an AI Connector for Claude that uses MCP for natural-language automation, covering layer renaming, asset resizing, bulk edits, vector optimization, and file preparation.

• Blender offers a lightweight MCP server through Blender Lab, exposing a natural-language interface to Blender's Python API and documentation.

• Boris FX Silhouette ships an MCP server that lets an assistant inspect projects, build node trees, edit shapes and keyframes, and render frames.

• Foundry Griptape natively supports MCP, providing AI orchestration built for professional VFX pipelines; we covered its Griptape agents landing in Nuke and Blender.

• SideFX Houdini brings MCP support to Houdini 22 through its new APEX Script workflow, aimed at procedural character-rig generation.

• Unreal Engine connects AI clients to the Unreal Editor through MCP for workflows that interact with editor capabilities.

NVIDIA supplies the agent framework and the hardware, not the creative apps

NVIDIA's role sits underneath the software. Its Agent Toolkit adds MCP client and server support so developers can wire agents to these tools, and the company points creators toward RTX PRO workstations plus DGX Spark and DGX Station systems for running models locally. Its Omniverse libraries are also exposed as agent-accessible tools. Virtual-production experiments already pointed at where this goes: Aximmetry's MCP server lets an agent build and adjust scenes from a plain-language request.

A shared protocol means one agent can move from Houdini to Nuke to Unreal

The connective thread across these announcements is standardization. When Adobe, Foundry, SideFX, and Epic all expose the same protocol, an agent built once can move between applications instead of being locked to a single vendor's integration. For artists, the near-term payoff is offloading setup and cleanup work; the larger one is a toolchain where the same assistant can follow a project from Houdini to Nuke to Unreal. Availability varies by vendor and much of it is early, but the direction is now shared across the biggest names in production software.

NVIDIA is positioning DGX Station as the local hardware base for an open agent stack that can run large models, govern their behavior, and call simulation tools inside Blender. Announced at SIGGRAPH, the stack combines NVIDIA’s Agent Toolkit with NemoClaw blueprints, Nemotron 3 Ultra, OpenShell, and Omniverse libraries.

For technical teams that need to keep assets and workloads on-premises, the point is not simply a larger local model. NVIDIA is packaging the model, runtime, and tool connections so an agent can work against controlled, callable software functions rather than a cloud API.

• Local agent stack. DGX Station is designed to run Nemotron 3 Ultra, a 550-billion-parameter open model, on a single desktop system.

• Governed execution. OpenShell provides a sandboxed runtime with defined policies for agent behavior.

• Callable simulation tools. A NemoClaw blueprint connects agents in Blender to Omniverse libraries for RTX sensor simulation and physics work.

DGX Station supplies the local capacity for NVIDIA’s agent stack

DGX Station runs on NVIDIA’s GB300 Grace Blackwell Ultra Desktop Superchip. NVIDIA says the system delivers up to 20 petaflops of FP4 compute and 748GB of coherent memory, capacity intended to make a 550-billion-parameter model practical on one desktop machine.

That matters because the Agent Toolkit stack is not a single application. It combines the Nemotron 3 Ultra model with OpenShell, NemoClaw blueprints, and Omniverse libraries. NVIDIA describes each piece as open:

• Nemotron 3 Ultra is the 550-billion-parameter model tuned for DGX Station hardware.

• OpenShell is an open source runtime intended to sandbox agents and apply defined policies.

• NemoClaw provides open blueprints for assembling custom autonomous agents from the model, harness, and runtime.

• Omniverse libraries expose physics simulation and 3D asset workflow functions as tools an agent can call.

The practical proposition is a stack that technical teams can inspect, adapt, and keep within their own infrastructure.

NVIDIA says a ConnectX-8 SuperNIC provides up to 800GB/s of bandwidth and can link two DGX Stations. The company has published playbooks for using two systems to run larger models or support more concurrent users. It also says local operation can be set up in three steps in about 30 minutes, without an internet connection.

DGX Station is available to order from ASUS, Dell Technologies, Exxact, GIGABYTE, HP, MSI, and Supermicro. NVIDIA did not announce pricing.

NemoClaw makes Blender’s simulation tools callable by an agent

The creative-software angle arrives through a NemoClaw blueprint that integrates Omniverse libraries into Blender. It gives an agent callable RTX sensor-simulation and physics tools within an artist’s existing scene, allowing the agent to initiate simulation work rather than merely suggest steps in a chat window.

The setup follows a broader shift toward AI agents operating applications through explicit tool connections. We covered Foundry’s Griptape integration, which brought agents into Nuke, Blender, and Maya through the Model Context Protocol.

Aximmetry’s MCP server offered another version of the idea, letting agents build and adjust virtual-production scenes from a plain-language request. NVIDIA’s approach ties that application-level access to its own local model, runtime, and DGX Station hardware.

The studio question is governance, not only speed

For studios working with unreleased assets, scripts, or client material, local execution addresses a basic operational constraint: whether creative data has to leave the building for an agent to be useful. A DGX Station running an offline model, with OpenShell enforcing policies and Blender exposing limited callable tools, gives teams more control over where that work happens and what the agent is allowed to do.

The substantive test is whether the tools remain reliable on production scenes. NVIDIA’s published specifications establish the hardware capacity and the software components, but pricing and performance with real studio workloads remain open questions.

This episode of Denoised starts with a simple test that lands somewhere bigger: feeding a handful of cell-phone photos to OpenAI's Codex and asking it to reconstruct a real building in Blender. From there we get into the AI Odyssey trailer everyone argued about, Foundry's SmartRoto for Nuke, Seedance 2.5's pricey teaser, and America's first genuinely competitive open-weights model. It also serves as our SIGGRAPH primer, since the show is landing in LA and we will both be there.

Quick Take

One quick test set the theme for the whole episode: a coding agent took cell-phone snapshots of a theater and rebuilt it as editable Blender geometry in roughly 10 minutes. That reframes a bigger argument we keep having on the show about where AI fits into a 3D pipeline. Is the future fully synthetic generation, or is it 3D doing the precise structural work while AI handles the final look? The AI Odyssey trailer, Seedance 2.5, and a new wave of open-weights models all push on the same question from different angles.

What We're Watching: SIGGRAPH Lands in LA

SIGGRAPH runs in LA, which makes it easy to reach for people in media and entertainment. Addy's framing: it is NAB for the research crowd, where university labs and companies show work that can be five or ten years out. Netflix already teased some of the papers it plans to present, and the density of AI researchers makes the show a recruiting event as much as a technical one.

Two satellite events are worth flagging. The ASWF Open Source Days sessions run on Sunday and dig into the open standards, like USD and OpenTimelineIO, that this episode keeps circling back to.

There is also a free AI Workflows Summit on Tuesday evening that does not require a SIGGRAPH pass, with talks on static Gaussian splats from NVIDIA, real-time volumetric workflows, and a ComfyUI agentic tool called ComfyCode.ai that runs locally and hooks into production-grade pipeline standards. We are moderating the closing panel there.

What We Tested: Codex Rebuilt the Culver Theater in Blender

Here is the test that opened the episode. We pointed OpenAI's Codex, its coding agent and answer to Claude Code, at Blender and asked it to install the tooling itself.

The agent found the third-party Blender MCP server, set it up, and connected to Blender without hand-holding.

Then came the real ask: rebuild the Culver Theater and its surrounding block using a few phone photos shot at AI on the Lot. These were not ideal reference images, mostly one-sided, with no clean angle on the corner. The Art Deco detailing (curved forms, the sphere held up on top, intricate trim) is exactly the geometry a human modeler would dread.

The result, after one revision pass asking for a more rounded corner door, was a strong starting point rather than a finished asset.

• What it got: the overall massing, the lamps, the rounded corners, and a believable interpretation of the Art Deco floor stamping and side-wall movie posters that were barely visible in the source photos.

• What it missed: the theater marquee sign was the most obvious error, and it skipped several of the door windows.

• Why it still matters: the geometry came in cleanly separated in the outliner, ready to modify. It even generated a camera move and, on request, relit the scene as a moody night exterior, though some emissive values landed on trim that should not glow.

The whole build took around 10 minutes once the server spun up, and the agent felt notably more responsive driving the computer than screen-reading control loops we have used before.

Where 3D Becomes the Pilot and AI Becomes the Renderer

The test crystallized a prediction we have been building toward. For our own work, photoreal accuracy is not the point; a gray-box model that gets you 70% of the way there gives you real geometry to run camera moves and feed into video-to-video passes, with generative cleanup handling the rest.

Addy's larger read: a year or two ago the assumption was that generative AI would pull production from 3D back to flat 2D, because why build in 3D when you can reach final pixel directly. That was wrong. You still need 3D for control, for the specific camera move, for the sphere held up by four supports. The workflow taking shape is 3D as the pilot doing the precise structural work, and AI as the renderer filling the photorealism gap. As GPUs push from billions of polygons toward trillions and inference keeps getting faster, the pitch is an AI 3D operator that handles modeling, shading, and rigging under the hood while a director just talks to the scene.

What We Explored: A New Video Format Built From a Prompt

If an agent can install its own tools and script complex software, it can also invent new plumbing. Alex Barashkov used Codex to build Aval, an open-source format for state-driven interactive video on the web, with small file sizes, low CPU overhead, alpha transparency, and a web-native runtime.

That is the part worth sitting with. The barrier to inventing a new file format or delivery standard used to be enormous; USD took Pixar the better part of a decade. The risk is a Wild West where every studio vibe-codes its own incompatible pipeline, which is why open standards matter more, not less, as building custom tooling gets cheap.

What We Debated: SmartRoto Brings AI Into a Pro Roto Pipeline

Foundry announced SmartRoto for Nuke, an AI-assisted rotoscoping add-on the company says can be up to 4x faster while keeping artists in control. Similar masking and tracking already lives in Resolve and After Effects, but Foundry has historically been careful about how it frames AI for high-end professionals.

That care is notable given Foundry now owns Griptape and is leaning into agentic pipelines. Griptape's studio push, which we covered when Foundry brought its agents into Nuke, Blender, and Maya, emphasizes OCIO color support and local GPU inference. That local angle addresses a real studio anxiety about pushing unreleased IP to the cloud.

We landed on a live question worth testing: AI generations carry inherent noise, and the open debate is whether that hurts rotoscoping or, more likely, camera tracking, which has to solve a 2D image back into a 3D camera move.

What We Questioned: Does an AI-Generated Odyssey Prove Anything?

An AI-generated feature trailer built for a few thousand dollars got framed as going head-to-head with Christopher Nolan's $250 million adaptation. The reality check: the clip runs under two minutes, so it is a proof of concept, not a competing feature.

Some individual shots look genuinely good, well color-graded and striking on their own. The breakdowns show up in the connective tissue. AI still cannot nail real lensing and glass, so perspective and distance shots fall apart. Camera geography and placement drift, and fully synthetic performances stay in the uncanny valley.

Addy's recurring argument is the useful takeaway: the real unlock is hybrid, combining AI with physical performance and a real cinema camera and lens for the reference plate, then letting AI fill in the world around it. Solve for the expensive parts of production and cut costs in half, and that is the win. Betting on fully synthetic everything trades away quality that the technology cannot yet recover.

What We Watched: Seedance 2.5's Teaser and the $5-a-Shot Problem

On the generation side, ByteDance released a teaser made with Seedance 2.5, a soccer-themed spot following a kid dribbling a ball through London. The ball-to-foot contact, normally a hard problem for AI, holds up well enough that we may end up eating our earlier skepticism, which is the whole point of tracking this stuff in the open.

Two caveats stand out. The model reportedly supports up to 30 reference images, which raises an open directing problem: whether it can place all those references correctly in space from a wide shot plus close-ups. And the cost is steep. A maxed-out 4K shot of a few seconds runs roughly $4 to $5, which makes real iteration expensive. At those prices, shooting something practically can be cheaper than blasting a model at it, a tradeoff that already pushed some software firms to rehire humans over pricey tokens.

What We Explored: America Finally Has a Competitive Open-Weights Model

In the open-source lane, Mira Murati's Thinking Machines released its first model, Inkling, an open-weights, multimodal model built to be fine-tuned. It is not at the level of the top closed frontier models, but it is a meaningful leap as an American open model in a category that has been dominated by Chinese labs like DeepSeek.

A few standout traits from the discussion:

• Built to be fine-tuned. Addy flagged a feature he had not seen elsewhere: the model can fine-tune itself on your data, so an agent running a specialized use case can adapt without the current markdown-and-context-window workaround.

• A large context window and a free playground to test it, with third-party providers likely to add hosting support.

• A momentum play. Open-weighting the model is a way for a newcomer to build a user flywheel rather than trying to out-scale ChatGPT or Claude head-on, much like early Stable Diffusion did with image models.

That ties into a bigger watch list: Ilya Sutskever's and Yann LeCun's new ventures. LeCun's bet, per Addy, is that LLM scaling is hitting diminishing returns and the JEPA architecture points toward a more fluid model where text, image, and video inputs blend rather than getting stitched together. Meanwhile, Kimi 3 is teased at a reported 2 to 3 trillion parameters as an open-weights release, echoing the scale of Alibaba's 2.4-trillion-parameter Qwen preview. The uncomfortable note we ended on: the biggest open model may again ship from China rather than the US.

Bottom Line: 3D Structure, AI Finish

The through-line across every story is the same split between structure and finish.

• Codex plus Blender MCP shows AI can now handle the tedious software work of building and lighting a 3D scene, turning phone photos into editable geometry that feeds a hybrid pipeline.

• The AI Odyssey and Seedance 2.5 show pure generation nails individual shots but still breaks on lensing, continuity, and cost, which is why real cameras and physical performance stay in the loop.

• Inkling and the open-weights wave show the models underneath are getting cheaper to adapt and self-host, with fine-tuning and local inference mattering more as API costs climb.

The pattern to watch is not whether AI replaces 3D or live action, but how quickly the agent layer makes both easier to control.

Links from This Episode

Tools & Platforms:

• OpenAI Codex, the coding agent used to run the Blender test

• Blender MCP, the server that let Codex drive Blender

• Seedance 2.5, ByteDance's teased video model

Tools & Releases:

• Foundry SmartRoto for Nuke, VP Land coverage

• Foundry Griptape agents in Nuke, Blender, and Maya, VP Land coverage

• Thinking Machines Inkling, VP Land coverage

• Aval interactive video format, VP Land coverage

News & Analysis:

• AI-generated Odyssey trailer, The Hollywood Reporter

• Alibaba's 2.4-trillion-parameter Qwen preview, VP Land coverage

Events:

• SIGGRAPH, official site

• ASWF Open Source Days, VP Land coverage

• AI Workflows Summit, free SIGGRAPH satellite event

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NVIDIA introduced the Synthetic Video Detector NIM microservice at SIGGRAPH, adding an AI-assisted signal that flags whether a video clip contains synthetic content. It is part of the NVIDIA AI for Media platform and is aimed at editorial and media teams making fast calls on questionable footage.

• The tool produces a classifier score, not a verdict. It analyzes video frame by frame and outputs a probability that the clip is synthetic, which teams can use to prioritize, flag, quarantine, or escalate.

• Accuracy reached up to 92% on uncompressed video in NVIDIA testing, holding at 82% even after heavy compression.

• Wowza is already embedding it for real-time detection across more than 35,000 livestreaming deployments.

A frame-by-frame score built to survive newsroom compression

The microservice analyzes video frame by frame to generate a classifier score for synthetic content. Editorial teams can use that score to prioritize clips for review, flag or quarantine questionable footage, or escalate it for deeper analysis. NVIDIA positions the tool as an additional signal for time-sensitive decisions rather than a replacement for established verification practices.

Detection quality usually degrades once a clip is compressed, resized, cropped, or re-encoded, which is exactly what happens to video moving through newsroom and social pipelines. NVIDIA says the model stays effective through those steps. In its testing, accuracy reached up to 92% on uncompressed video, 87% at 15% compression, and 82% at 50% compression.

The latest model revision reported an AUC of 0.9614 and accuracy of 0.9453 on NVIDIA's internal test set. AUC, or Area Under the Curve, measures how well a classifier ranks positive samples above negative ones independent of thresholds. Those thresholds can be tuned, including more conservative settings that reduce the chance a synthetic clip slips through unflagged.

Built for real-time review at newsroom speed

Speed matters when a clip needs a decision before it airs or posts. The microservice can process 1080p video in as little as 22 milliseconds on NVIDIA RTX systems and roughly 30 milliseconds on NVIDIA L40 GPUs.

The detector ships in NVIDIA's NIM microservice format, the packaged deployment model we covered when NVIDIA first brought it to production media pipelines. That packaging is what lets editorial teams drop the model into an existing workflow instead of standing up a separate detection stack.

On-prem and air-gapped deployment for sensitive footage

Organizations can run the microservice closer to where sensitive video is captured, stored, or distributed, including on-premises, edge, hybrid, and approved air-gapped environments. NVIDIA frames that flexibility as a way for teams to keep control over video data, access, and operations.

That deployment model targets a specific set of buyers. NVIDIA names broadcasters, government agencies, financial institutions, and critical infrastructure operators as the groups most exposed to synthetic media risk, and also the ones facing strict requirements around data residency, security, and operational control.

Partner adoption is where the detector moves from a model to deployable infrastructure. Wowza is embedding the microservice through its Video Intelligence Framework, which we saw the company introduce at NAB, bringing real-time synthetic video detection into livestreaming workflows that span more than 35,000 deployments across over 170 countries. Pairing the detector with a video layer customers already run puts AI-assisted verification closer to ingest and streaming operations while keeping footage inside their own environments.

Detection tooling catches up to the generation curve

The release lands as synthetic video quality keeps climbing and detection shifts from a research topic toward a working newsroom requirement. We covered how quickly a single AI clip can trigger a public trust crisis, the kind of scenario a frame-level score wired into ingest is meant to catch earlier. NVIDIA is positioning the Synthetic Video Detector as one signal inside a broader verification process, which sets a realistic bar for what automated detection can do as generation tools continue to improve.

The American Society of Cinematographers is releasing StEM3-VP, a set of open reference assets built to evaluate and calibrate in-camera visual effects stages, and the material is joining the Academy Software Foundation's Digital Production Example Library.

• StEM3-VP targets LED volumes. The assets cover virtual production, in-camera visual effects, and large LED walls, the parts of a modern set that are hardest to prove out before a shoot.

• It is openly available. The project lands in DPEL, the ASWF library that hosts production-grade sample content for testing hardware and software.

• It continues a two-decade lineage. StEM3-VP follows the ASC's original StEM from 2004 and StEM2 in 2022.

The work comes from the ASC's Motion Imaging Technology Council, the same technical body behind the society's earlier evaluation films.

What StEM3-VP is built to do on an ICVFX stage

StEM3-VP is Standard Evaluation Material aimed squarely at LED-based in-camera visual effects. According to the ASC Motion Imaging Technology Council, the goal is to give productions a suite of tools to evaluate and verify LED ICVFX stages before principal photography begins.

The material is meant to aid the processing, evaluation, and calibration of the image path between the LED wall and the camera. That is the exact seam where color, moiré, and brightness problems surface on a volume, and where a bad match between wall output and camera capture can quietly cost a production time on the day.

The working group frames the assets as a way to standardize the setup and proving of LED walls for a given production, using openly available media and tools rather than proprietary test patterns that vary from vendor to vendor.

Why an open, shared reference matters for volumes

LED volumes are expensive to book and unforgiving to misconfigure. A common, openly licensed reference gives cinematographers, DITs, and stage engineers a fixed target to test against, so a stage can be characterized and signed off before the crew arrives.

Because the assets live in DPEL, they sit alongside other production-grade test content the Academy Software Foundation maintains for hardware and software development. We covered the Open Source Days program where StEM3-VP was slated to be presented, part of a schedule spanning color fidelity standards, OpenPBR, and studio-safe AI workflows.

The lineage gives working professionals a known quantity. The original StEM helped calibrate digital cinema projection in 2004. StEM2, released in 2022 under the ASWF Digital Assets License, shipped as a short film in QuickTime, DCP, IMF, and EXR formats, with SDR Rec709 and HDR Rec2020 PQ versions running from 48 to 1000 nits, specifically to stress a color pipeline. StEM3-VP extends that same evaluation approach to the LED stage.

The cinematographers and technologists behind it

StEM3-VP is led by a working group of ASC members, color scientists, technologists, and manufacturers. Named contributors include David Morin, who co-chairs the effort, alongside Michael Goi, ASC, Jay Holben, Curtis Clark, ASC, Wendy Aylsworth, Joachim Zell, Rod Bogart, Gary Mandle, and Tim Kang.

The DPEL integration was presented at the Academy Software Foundation's Open Source Days by Jim Geduldick of Spaceboy Labs and Jay Holben, an independent director and producer.

Curtis Clark, ASC, a named contributor to StEM3-VP, has long led the society's motion-imaging standards work. For readers tracking how the ASC has been widening access to its technical resources, this follows the society's push into education with ASC+, its subscription learning platform.

What this means for stages and the crews that run them

For anyone commissioning or operating an LED volume, StEM3-VP offers a neutral yardstick that does not belong to a single wall vendor or processor manufacturer. That lowers the risk of walking onto a stage that looks fine in a demo and falls apart under a specific camera and color pipeline.

The assets are openly available through DPEL, so stages can adopt them without licensing friction. As volumes spread beyond the largest studios, a shared calibration reference makes in-camera visual effects more predictable for the productions that can least afford a surprise on set.

PIXERA is hosting a one-day educational summit on HDR and digital color at its Santa Monica studio on Friday, July 24. Titled HDR & Digital Color in Realtime Video Workflows, the program is built around talks, live demonstrations, and a reference system that lets attendees examine how HDR images are measured and perceived in real-time video workflows.

• PIXERA’s program runs from 9 a.m. to 5 p.m. PT, with up to 60 in-room attendees and a YouTube livestream.

• The educational sessions use an HDR reference system that includes an LED wall, cinema cameras, vectorscopes, and PIXERA software.

• The speaker program includes Paul Debevec, Tim S. Kang, Conor McGill, Camon Crocker, Angel Banchs, Damein Futch, Michael Kohler, and Kris Murray.

An educational day about what HDR means in practice

The summit’s premise is straightforward: HDR is not only a delivery label or a number on a monitor specification. Its value depends on how an image is captured, displayed, measured, and ultimately seen. PIXERA frames the day around those questions, pairing presentations with live demonstrations so the discussion can stay connected to actual signal paths and viewing conditions.

For virtual production and real-time teams, that makes the subject practical. HDR workflows can involve the LED display, camera, color pipeline, monitoring, and final delivery format at once. A mismatch in any part of that chain can change the image that a crew sees or the result that reaches an audience.

A reference system for demonstrations, not a new stage build

The Santa Monica event will use an HDR reference system comprising an LED wall, cinema cameras, vectorscopes, and PIXERA at the center. It is a teaching and demonstration environment for the summit, not an announcement of a newly built production stage.

That distinction matters. The value of the setup is that presenters can discuss HDR with common visual and measurement references in the room. Vectorscopes can help make color information visible while the LED wall and cameras provide a real-time context for the conversation.

PIXERA’s involvement also places the session near the company’s existing real-time media-server work. VP Land previously covered how PIXERA turns an LED wall into a live interactive environment, where the software is used to coordinate media playback and display systems. The July program narrows the focus to HDR and color education.

A program shaped by imaging and color specialists

The agenda includes a session titled “Luminance in HDR: Relative or Absolute?” as well as a closing discussion, “HDR: Problems & Solutions in Creative Workflows.” Those titles point to the central tension in HDR work: technical measurements matter, but so do the viewing environment and the creative decisions that make an image readable.

Paul Debevec is among the scheduled speakers. He is chief research officer at Netflix’s Eyeline Studios and is known for work in image-based lighting and computer graphics. The rest of the program brings together practitioners working across color, imaging, and video technology, including Tim S. Kang, Conor McGill, Camon Crocker, Angel Banchs, Damein Futch, Michael Kohler, and Kris Murray.

The event follows another PIXERA education initiative that VP Land covered, PIXERA Base Camp, a media-server training program. This Santa Monica session is more specific: it is an opportunity to compare perspectives on HDR and digital color in a shared real-time setup.

Who can attend

The summit takes place at PIXERA’s Santa Monica studio, 1745 Berkeley Street, Studio 3, from 9 a.m. to 5 p.m. PT. In-room attendance is limited to 60 people, while the full day will also be available on YouTube. A happy hour at Santa Monica Brew Works follows the program.

For teams working with LED displays, cameras, and color-managed pipelines, the day is less about a single prescribed HDR workflow than about building a clearer basis for decisions. The useful outcome is a better understanding of where HDR behavior is being defined, how it can be observed, and which creative and technical choices need to be discussed together.

Alibaba's Qwen team has opened preview access to Qwen3.8-Max, a 2.4-trillion-parameter model the company describes as its most capable system yet, with open weights promised at an unspecified later date. The announcement came through Qwen's official account on X, positioning the release as a multimodal flagship rather than a text-only update.

• Available now in preview through Alibaba's Token Plan, Qoder, and QoderWork.

• Multimodal input for images, video, and documents.

• Open weights "soon," with no date and no Hugging Face model card posted yet.

Twitter tweet

A trillion-parameter model built to read images, video, and documents. Qwen positions Qwen3.8-Max as the team's first multimodal model above 1 trillion parameters, able to take images, video, and documents as input, according to The Decoder. That input range is the practical detail for production teams: a model that ingests footage and reference documents directly fits review, logging, and analysis workflows a text-only model cannot serve. Alibaba also expects it to outperform the prior Qwen3.7-Max on coding and productivity tasks including full-stack development, data analysis, and office work.

The video-as-input framing tracks with the pitch behind Moonshot's Kimi K3, which we covered as a 2.8-trillion-parameter model released days before. The Decoder reports Alibaba's release is likely aimed at Kimi K3's momentum.

"Second only to Fable 5" is a vendor claim, not a benchmark. Qwen says the model matches leading frontier systems and trails only Fable 5, but the company has published no benchmark results to support the ranking. Alibaba has also not disclosed the active-parameter count or the mixture-of-experts configuration, so the 2.4-trillion figure describes total size, not the compute used per query. Without published numbers or third-party testing, the positioning stands as a company claim.

Preview access runs at a tenth of standard pricing. The Decoder reports the preview is available through Token Plan, Qoder, and QoderWork at 10 percent of the standard price, giving developers a low-cost window to test the model before general availability. Qwen called the preview "continuously evolving," which means the version teams test today may not match the eventual released weights.

Open weights would extend Alibaba's release pattern, but they aren't here yet. Alibaba has repeatedly shipped models to a paid preview first and released open weights later. The same sequence played out with Wan 2.5, which launched as a paid API with open weights promised down the line.

The company's open-source track record is real, from the freely downloadable Qwen Image Edit to a run of open model families across image and video. Qwen3.8-Max stays preview-only for now, and the open-weights claim rests on the announcement alone.

The open-weights promise is the part still missing. The usable facts are narrow: a multimodal model in paid preview, priced low, accepting image, video, and document input, with no independent benchmarks. The open weights that matter most for self-hosting and fine-tuning, the same draw behind Thinking Machines' Inkling and other open releases, remain a promise without a date. Developers can test the preview today; teams planning around open weights should wait for the model card and real numbers.

Disney is putting digital creators on a Hollywood red carpet and opening its Burbank studio lot for a new two-day gathering called Created in LA. The event, which Disney describes as its first dedicated creator-focused event, is set for September 17 and 18 and will bring together roughly 350 creators, storytellers, and executives.

The news has more weight than the partnership announcement that first circulated on LinkedIn. In its official announcement, Disney lays out a format built around two pieces of company real estate that carry real symbolic value: the El Capitan Theatre in Hollywood and the Walt Disney Studios lot in Burbank.

Created in LA is conceived and hosted with creator and journalist Jon Youshaei. The event is not a film market or a conventional studio premiere. It is Disney creating a staged point of contact between its entertainment infrastructure and an audience that increasingly produces, distributes, and finances its own work.

A creator premiere at the El Capitan

The first day takes place at the El Capitan Theatre, where Disney says creator videos will receive the venue's first red-carpet premiere. That is the clearest signal in the announcement: a theater long associated with Disney’s own tentpole releases will be used to present work made for creator audiences.

The second day moves to the Walt Disney Studios lot. Disney says the Burbank program will include talks, performances, and keynotes, bringing creators into the same physical setting that has long been reserved for the company’s film and television operation.

The basics:

• September 17-18, 2026

• Roughly 350 creators, storytellers, and executives

• A creator-video red-carpet premiere at the El Capitan on day one

• Talks, performances, and keynotes on the Disney lot on day two

Attendance is application-based through Created in LA. Disney says applications are reviewed on a first-come, first-served basis, rather than positioning the gathering as an open public festival.

Disney is making a physical bet on the creator economy

Hollywood studios have spent years trying to figure out where independent creators fit: as marketing partners, as talent pipelines, as competitors for attention, or as future production partners. Created in LA does not settle that question. It does show Disney treating creators as an audience worth convening inside its own venues, with executives and established storytellers in the room.

That distinction matters. Creator-economy announcements often arrive as software launches, ad-sales initiatives, or content deals. Disney’s choice here is experiential and highly visible. The company is lending the El Capitan's premiere ritual and the studio lot's institutional cachet to work that usually premieres on social platforms or creator-owned channels.

It also comes as Disney is exploring new ways to engage audiences around its intellectual property and digital creation tools. VP Land previously covered Disney’s plan to explore generative-AI creation tools for Disney+ subscribers. Created in LA is a separate initiative, but it fits the same broader question: how much of the next entertainment ecosystem gets built with, rather than simply distributed to, digital creators.

More than a one-night brand activation

The event has enough structure to be more than a red-carpet photo opportunity. A premiere, a studio-lot program, and a capped group of 350 people give Disney a setting for relationship-building across creators, storytellers, and executives. That could lead to little beyond a successful inaugural event, but it could also become a repeatable model for how a major studio identifies talent and tests new creator-facing partnerships.

For now, Disney has announced a date, venues, format, and application process. The more important story is the invitation itself: the company is bringing creator work into one of Hollywood’s most traditional premiere spaces, then bringing the people behind it onto the Disney lot the next day.

Google has open-sourced GNM Head, a parametric 3D statistical model of the human head that gives artists slider-level control over facial identity and expression. The release is the first piece of GNM (Generative aNthropomorphic Model, pronounced "genome"), which Google describes as an open ecosystem of parametric human models.

• Apache 2.0 license. Studios can ship GNM Head inside commercial pipelines and products, not only research projects.

• 636 total parameters. 253 control identity and 383 control expression, down to individual eyes, teeth, and the tongue.

• Built from scans, not generated. The model is learned from a large dataset of real 3D scans, so the heads are grounded in measured anatomy rather than AI generation.

Google Staff Technical Artist Iker J. de los Mozos, a former Lead Character TD at Walt Disney Animation Studios, announced the release. "My team at Google has just open sourced GNM, our parametric 3D statistical model of the human head, which is learned from a large dataset of 3D scans," de los Mozos wrote.

253 identity controls and 383 expression controls reach inside the face

GNM Head splits the head into two parameter sets. Identity covers the base mesh with 253 controls: 170 for the head, 80 for the teeth, and 3 for the eyeballs. Expression adds 383 more, broken into 100 each for the left and right eyes, 150 for the lower face, 32 for the tongue, and 1 for the irises.

The model generates male and female heads across a range of ethnicities, and it exposes internal anatomy that most face rigs leave out. Controllable eyeballs, teeth, and a tongue matter for close-up character work, dialogue, and any shot where the inside of the mouth reads on camera.

This is a statistical model, not a generative AI tool. It produces heads by sampling learned parameters from scan data, which keeps the output anatomically consistent and directly editable instead of hallucinated.

Apache 2.0 and four-framework support make it pipeline-ready

The license is the part that changes who can use it. GNM Head ships under Apache 2.0, which covers both non-commercial and commercial use, so a studio or a tool vendor can build it into a shipping product without a research-only restriction.

It also supports the frameworks technical artists and researchers already work in: NumPy, JAX, PyTorch, and TensorFlow, plus semantic parameter sampling. That range lets teams drop the model into existing character or perception pipelines rather than rebuilding around it.

Meta made a comparable move when it open-sourced MHR, its parametric human model, alongside SAM 3D. GNM Head narrows that idea to the head and pushes far deeper on facial control.

A free Blender importer already puts GNM Head in artists' hands

Community tooling arrived with the model. Nathan Dickson, a rigging lead whose credits include the Student Academy Award-winning short "Student Accomplice," released a free GNM Head Importer for Blender 5.1 and up. The add-on exposes sliders for ethnicity, gender, and expression with a live viewport update.

A Houdini path is in progress as well. David Eschrich, Global Head of 3D at Zoic Studios, built a GNM Head digital asset, though it was not publicly available at launch.

Open-source graphics tooling like this has become a recurring thread; we covered the ASWF Open Source Days schedule, where Blender and OpenPBR work shared the stage.

The head is the first model, with a full human suite on the roadmap

GNM Head is the starting point, not the whole plan. Google positions GNM as an open ecosystem of parametric human models and perception stacks, and the project describes a roadmap that "includes releasing a comprehensive suite of statistical models complemented by perception and analysis technology." Google Senior Research Scientist Stylianos Ploumpis confirmed further releases are coming but said the company could not yet say what or when.

Google was direct about the model's limits. The current training data uses binary gender categories and four broad demographic groups, and the company acknowledged it "does not fully represent the spectrum of gender identities or the full diversity of the global population," with a detailed report promised.

For VFX and virtual production teams, an Apache-licensed, scan-based head model with this depth of control is a usable building block for digital humans, background crowds, and avatar systems, and it is available on Google's GitHub repository.

Moonshot AI’s Kimi K3 is a 2.8-trillion-parameter mixture-of-experts model with a 1-million-token context window and native multimodal input. The important detail for production teams is not only that it can inspect images. Kimi K3 can accept video alongside text and images, letting a prompt address the footage itself instead of a transcript or a manually selected set of frames.

Kimi K3 treats video as vision content inside a multimodal prompt. A team uploads a clip, references it in the request, and pairs it with a text instruction such as identifying a visual event, checking continuity, or locating a moment that matches a brief. Moonshot says it will release K3’s weights on July 27, 2026, opening a self-hosting and fine-tuning path for teams that need more control over their material.

Video is an input to the model, not just a source for a transcript

Moonshot’s vision-model documentation describes multimodal requests as a sequence of text and visual content. For video, a team uploads the clip and references it in the request through a file ID or video_url, alongside the instruction. The model samples key frames from that video and can return text about the visual material it sees.

That distinction matters in a media workflow. A transcript can tell a model what was said. A contact sheet can show a handful of selected moments. A video request gives K3 sampled key frames from the clip, so it can assess visual material across a sequence rather than only a manually selected still-image set. It is not a frame-by-frame reading of every moment, but it can connect a visual event, an action, or a screen graphic to the prompt without first reducing the entire clip to text.

The company also pairs that capability with a 1-million-token context window. In practice, the useful question is not whether a facility can fit an entire production in one prompt. It is whether a clip, its related notes, a transcript, and a detailed review instruction can be kept together without reducing the visual material to text before analysis.

Where the comparison with Claude Opus is useful

Claude Opus can analyze image inputs, and its vision documentation describes image content blocks for that work. It does not document a native video-input block in the same way. A Claude workflow that needs to reason about footage therefore starts by converting the material into inputs the model accepts, such as extracted frames and a transcript.

Kimi K3 does not make those workflow artifacts obsolete. A transcript remains useful for dialogue search, and selected frames remain useful for precise visual review. Its difference is that the video file can be part of the model request, with the model sampling key frames rather than relying only on a preselected still-image set. That makes K3 a model to test for logging footage, reviewing a clip against a brief, or combining visual questions with production notes.

This is a capability comparison, not a blanket quality claim. The right model still depends on the task, the material, the evaluation criteria, and whether a team can run the model reliably on its own infrastructure. Moonshot's public materials identify video as an input modality, but production teams should test accuracy on their own footage before relying on it for editorial, compliance, or asset-management decisions.

A 2.8-trillion-parameter model built around long context

Kimi K3 uses a mixture-of-experts architecture, which activates only part of its total parameter count for a given request. Moonshot says the model has 2.8 trillion parameters, 1 million tokens of context, always-on reasoning, and native multimodality. It also highlights Kimi Delta Attention, which it says improves decoding speed at long context lengths, and Attention Residuals, which it says improve training efficiency.

Those are Moonshot's performance claims, and they need independent testing. The immediate operational fact is that the model is available through Kimi's products and API, while the company says the full open weights will follow on July 27. If that release arrives as described, facilities will be able to assess a very large model with text, image, and video input without being limited to a hosted API.

Open weights create a different evaluation path for post teams

For a studio or post house, self-hosting does not automatically mean simple or inexpensive deployment. A model at this scale will require substantial infrastructure, and any video-analysis workflow will need careful tests around clip preparation, latency, security, and failure cases. Open weights do, however, make it possible to evaluate, adapt, and operate the model within a team's own environment rather than sending every request to a third-party endpoint.

Before deploying K3, post teams should build a test set of representative clips, define the visual questions the model must answer, and compare its sampled-frame results with a transcript-and-contact-sheet workflow. They should also measure latency, infrastructure cost, and the rate of missed visual events before using the model for any consequential production decision.

NVIDIA's Spatial Intelligence Lab published ARDY, a framework that generates 3D human motion in real time from text prompts a user can change while the character is still moving. The lab released the code and model weights publicly rather than keeping the system behind a demo.

• Streaming, not batch. ARDY synthesizes motion frame by frame while accepting new instructions mid-sequence, so a character can shift from a stealthy walk to a victory dance without stopping to re-render.

• Two control paths. Direction comes from online text prompts and from kinematic constraints such as root trajectories, full-body keyframes, and end-effector positions.

• Open release. The code is public on GitHub, the model weights are on Hugging Face, and the work publishes in ACM Transactions on Graphics.

Real-time generation steered by live text and sparse constraints

ARDY, short for Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation, targets applications that cannot wait for an offline render: animation, simulation, and humanoid robotics. "Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics," the researchers write.

The framework accepts two kinds of direction at once. Text prompts drive behaviors the team demonstrates, including a limp, a pick-and-put action, a stealthy walk, a victory dance, a lean-and-peek, and a zombie sit. Kinematic constraints handle spatial precision: root trajectories and waypoints, full-body keyframes, end-effector positions and rotations, or arbitrary combinations of those. The system also supports long-horizon goal reaching, where a constraint is specified beyond the current generation window, and real-time locomotion control through mouse waypoints and keyboard commands.

A hybrid representation and a two-stage denoiser

The method splits the motion into two representations. It combines explicit global root motion features with latent body embeddings, which the team frames as a way to balance trajectory control against generative efficiency. On top of that sits a two-stage autoregressive transformer denoiser that predicts the root motion first, then conditions the body-motion prediction on that root output.

Two additional pieces support the streaming behavior. A variable-length history context captures longer-term semantics to improve generation quality, and masked kinematic constraints allow spatiotemporally sparse conditioning that extends past the current window. The generative core follows the diffusion approach that has moved into character work elsewhere; we covered Motorica's raise to bring generative AI to character animation.

From on-screen characters to a physical humanoid

ARDY does not stop at rendered figures. The team integrated its output with a Unitree G1 humanoid robot through the SONIC physical tracking policy, using the generated motion to drive interactive robot control. That connection to hardware fits NVIDIA's broader robotics push; we covered the company's GTC keynote and its open humanoid robot dataset for training foundation models.

For production teams, the interactive framing separates ARDY from capture-first pipelines. Where markerless systems reconstruct motion from a performance, ARDY generates it from instructions; we covered Move AI's Gen 2 markerless capture as one point on that spectrum.

Published at SIGGRAPH with code and weights available

The paper appears in ACM Transactions on Graphics, Volume 45, Issue 4, Article 86, presented at SIGGRAPH, with DOI 10.1145/3811284. The authors are Kaifeng Zhao (NVIDIA, ETH Zürich), Mathis Petrovich (NVIDIA), Haotian Zhang (NVIDIA), Tingwu Wang (NVIDIA), Siyu Tang (ETH Zürich), and Davis Rempe (NVIDIA). The model weights are posted on Hugging Face alongside the code.

Because the code and weights are public, studios and robotics teams can test real-time, promptable motion generation against their own constraints instead of waiting for a product. The open release lets teams check how the models hold up on custom rigs and live control loops directly, rather than through the paper's demo clips.

Developer Alex Barashkov has released Aval, an open source format for interactive video on the web that responds to hover, click, and application state instead of playing back as a fixed clip. He announced the technical preview on X.

The code is on GitHub under an MIT license. Barashkov is CEO of the design studio Pixel Point.

Twitter tweet

Aval targets a narrow but persistent problem: short prerendered animations, such as UI icons, that need to react to user input without a developer hand-timing every transition or seeking through a video file at each interaction.

The format packs three things into one asset:

• A deterministic state graph. Named states and authored triggers define behavior, with routing where the latest trigger wins.

• Frame-accurate transitions. Routes begin on authored content frames using portals, finishes, cuts, and reversals rather than approximate media seeks.

• Packed alpha transparency. Transparent prerendered motion composites through WebGL2, so animations sit cleanly over any interface.

The format pairs a compiler with a web renderer built on WebCodecs and WebGL2

Aval ships in two parts. A compiler produces the asset, and a web renderer plays it in the browser.

The runtime decodes with WebCodecs and renders with WebGL2, keeping the decoder timeline moving forward across loop seams instead of seeking backward. Barashkov calls these "seekless loops," and points to low CPU overhead and small file sizes as the payoff, describing the format as suited to small icons designed and animated in Blender.

For browsers that lack the required APIs, or for users with reduced-motion settings, Aval falls back to host-owned markup that the developer supplies. That progressive fallback keeps the underlying content available when the interactive layer cannot run.

Aval is an open source answer to Airbnb's Lava

Barashkov framed the project as a response to work he could see but not use. Airbnb built a similar format called Lava, a compact 3D animated icon format with transparent backgrounds designed for its app interface.

"Then, a little over a year ago, Airbnb created Lava for almost exactly the same purpose. That gave me hope," Barashkov wrote. "But Lava was never released as open source. So I decided to build my own, with AI."

For motion designers who build broadcast and interface animation, that positions Aval alongside the tooling we covered in Maxon One's Cinema 4D and Redshift updates, but aimed at the web runtime rather than the render.

Built solo with an AI coding agent

"This is probably the craziest thing I've ever built with Codex," Barashkov wrote, referring to OpenAI's coding agent. He said the format had been a years-long goal that never penciled out before AI-assisted development.

"Before AI, building it would have taken months of work. I could never justify that investment for a noncommercial open source project," he wrote.

A single designer shipped a compiler, a web runtime, and a new asset format, matching a capability that a company the size of Airbnb kept internal. AI-assisted coding is what made that solo effort feasible, according to Barashkov.

An early preview, not a production dependency

Aval is a technical preview, and Barashkov said he will keep polishing it in the coming days. Its runtime depends on WebCodecs and WebGL2, so behavior varies by browser, and the fallback path matters for anyone shipping to a broad audience.

For teams already exporting icon and micro-animation sequences from Blender, the release offers a way to keep those animations interactive and lightweight on the web without waiting for a proprietary format to open up. Whether Aval attracts contributors past the preview will determine if it becomes a dependable option or a well-documented experiment.

Netflix disclosed that generative AI workflows were used in roughly 300 of its titles in 2026, the clearest number the company has put on its production-side AI use so far. The figure appeared in Netflix's second-quarter shareholder letter.

Netflix said the largest concentration of that work sits in post-production. The letter described GenAI as scaling "across the production lifecycle, from concept and pre-visualization through post and delivery," and named three productions that used the tools for shots the company says would have been cut otherwise. Variety first flagged the disclosure inside the earnings report.

GenAI now touches every production stage, with post carrying the most work

Netflix told shareholders that "GenAI workflows have been used in roughly 300 of our titles" in 2026, and described adoption among its creative partners as scaling quickly rather than a set of one-off tests. The heaviest use is downstream, in post-production.

The pitch to investors was about cost and speed. "We are increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost than traditional methods," the company wrote. Netflix has made this efficiency case before, telling investors that AI can lift the finished product, not only trim the budget.

Three named productions used GenAI for crowds, battles, and worldbuilding

Netflix singled out Glory (India), Brasil 70: A Saga do Tri (Brazil), and The American Experiment (US) as titles that used GenAI to build "highly complex sequences." The company listed the specific applications as enhanced crowds, historical battle sequences, and worldbuilding establishing shots.

Netflix tied those shots to access, not just savings. "In some cases, productions would have had to leave out key shots and sequences in the absence of GenAI technology," the letter said, positioning the tools as a way for productions to reach for scenes that would otherwise sit outside their budgets.

The 300 figure sits on top of Netflix's build-in-house AI strategy

Netflix has spent 2026 building its production AI internally rather than licensing general-purpose tools. Co-CEO Ted Sarandos restated the approach on the earnings call, saying the company is "primarily builders, not buyers, and that remains the case today."

That strategy is now visible across the pipeline. We covered Netflix's first public AI model, VOID, which is built specifically for post-production object removal, the same stage where the company says most of its GenAI work is concentrated.

The company has also been buying and building teams to feed that pipeline. We covered its acquisition of Ben Affleck's AI startup InterPositive, which pulled post-production tooling in-house.

On the content side, Netflix stood up INKubator, an internal studio building GenAI-native animation pipelines for short-form content.

AI also spread into discovery and the ads business

Beyond production, Netflix said it is using large language models to improve title discovery and rolling out voice search and AI-powered natural language search for members. On the advertising side, the company said it expanded AI tools "across the full advertising lifecycle, from planning and creative production to campaign management, optimization, and reporting."

That ads push runs alongside a target Netflix reaffirmed of roughly $3 billion in ad revenue for 2026.

GenAI shifts from pilot to standard line item at the largest streamer

Netflix placed the 300-title figure in a shareholder letter, next to Q2 revenue of $12.6 billion, up 13% year over year, and a 33% operating margin. Reporting production AI beside its financials, rather than as an experiment, signals how routine the tools have become at the company.

For post houses and international productions, GenAI is already inside hundreds of shipped Netflix titles, weighted toward post, and Netflix is now tracking that work as a standard part of how its shows get made.

Thinking Machines Lab released Inkling, its first in-house AI model, and put the full weights up for download. It is an open-weights, multimodal Mixture-of-Experts model that takes text, images, audio, and video as input, and the company is positioning it less as a finished product than as a base other teams retrain for their own work.

• Open weights, downloadable now. The full model weights are on Hugging Face in both standard and NVFP4 formats, the latter tuned for NVIDIA Blackwell hardware.

• Two sizes. Inkling runs 975 billion total parameters with 41 billion active per token. A preview variant, Inkling-Small, runs 276 billion total and 12 billion active.

• Built to be customized. Thinking Machines is steering users toward fine-tuning Inkling through Tinker, its model-customization platform, rather than shipping it as a one-size-fits-all system.

Twitter tweet

An open-weights MoE trained on text, images, audio, and video

Inkling is a Mixture-of-Experts transformer with 256 routed experts and two shared experts per layer, activating six routed experts per token. It was pretrained on 45 trillion tokens spanning text, images, audio, and video, then post-trained with large-scale reinforcement learning across more than 30 million rollouts, according to the model announcement. Training ran on NVIDIA GB300 NVL72 systems.

The model handles a context window of up to 1 million tokens and accepts multimodal input: images as 40x40 pixel patches, audio as spectrograms, and video. It also exposes a controllable "thinking effort" setting that lets users trade off how many tokens the model spends reasoning before it answers.

Inkling joins a run of open-weight releases aimed at teams that want to run and modify models on their own hardware. We covered Google's Gemma 4, which shipped under an Apache 2.0 license for local use without per-token fees.

Strong reasoning and coding scores, from a company that says it isn't the best

On benchmarks run at a high thinking-effort setting, Inkling reports 97.1% on AIME 2026, 87.2% on GPQA Diamond, and 77.6% on SWEBench Verified for agentic coding. With tool use, it scores 46.0% on Humanity's Last Exam. On multimodal tests it reports 73.5% on MMMU Pro for vision and 91.4% on VoiceBench for audio.

Thinking Machines is candid about where the model sits, stating that Inkling is "not the strongest overall model available today, open or closed." The pitch behind that framing, as TechCrunch reported, is a bet that "AI that organizations can adapt for themselves will outperform the one-size-fits-all models the biggest labs currently sell."

Access runs through Tinker, hosted APIs, and a free Playground

Teams can fine-tune Inkling on Tinker, which Thinking Machines is offering at a 50% limited-time discount with 64K and 256K context-length options. TechCrunch noted that Tinker, not the model itself, is where the company's revenue has to come from, through training, fine-tuning, and a cut of the hosting ecosystem around the weights.

For inference, the weights are hosted on TogetherAI, Fireworks, Modal, Databricks, and Baseten, and run on open tooling including SGLang, vLLM, and llama.cpp. A free Inkling Playground offers a chat interface with web search for a limited time.

Thinking Machines Lab, led by former OpenAI CTO Mira Murati, built Tinker as its first product before releasing any model of its own. Inkling is its first public model.

What Inkling gives creative-tech teams

For studios and product teams weighing where to run AI, an open-weights multimodal model with downloadable weights changes the calculation. Teams can fine-tune Inkling on their own data, run it on their own hardware without per-token API fees, and point one base at text, image, audio, and video tasks.

The constraint is scale. A 975-billion-parameter model needs serious compute to serve, and the smaller Inkling-Small preview points to where more accessible versions may land. For teams already working with downloadable model weights, Inkling adds a multimodal reasoning option with a permissive release and a customization platform built around it.

Foundry launched Griptape Enterprise, a new license tier of its Griptape AI workflow orchestration platform built to run generative pipelines entirely inside a studio's own infrastructure. The tier is available now.

• On-premises or private-cloud deployment keeps plates, models, and metadata behind the studio firewall.

• Modular diffusion pipelines run FLUX, Stable Diffusion XL, LTX, and WAN models locally on studio GPUs.

• Kyle Roche, Griptape co-founder, steps into a new role as Foundry's Chief AI Officer.

Foundry positions it, in its launch announcement, as a governed alternative to the improvised setups studios have been building to bolt AI models onto existing pipelines.

A studio-controlled license tier aimed at security and governance

Griptape Enterprise sits on top of the base Griptape platform and adds the controls high-end facilities need before they let generative tools touch client footage. It offers studio-controlled licensing and permissions, plus enterprise governance with centralized, unlimited licensing across a facility.

Deployment runs on-premises or in a private cloud, so proprietary plates and trained models never leave studio-owned hardware. Color handling uses OpenColorIO for studio-grade plate and color management, and show-ready projects ship with templates for workspaces and versioning.

We covered Foundry's acquisition of Griptape, the deal that brought the orchestration technology in-house and set up this enterprise tier.

Nuke integration publishes workflows as versioned gizmos

Griptape Enterprise connects directly to Nuke. Artists can publish workflows as versioned gizmos and run scripts headlessly, which pushes generative steps into the same versioned, repeatable structure compositors already use for the rest of a shot.

The tier also adds agentic automation with a choice of local or cloud LLMs, and it ships an MCP server that lets AI agents pilot Griptape at scale. We previously detailed how Foundry brought Griptape agents into Nuke, Blender, and Maya through MCP, the same protocol underneath this release.

Modular diffusion pipelines run open models on studio GPUs

The generative core is a set of modular diffusion pipelines that support FLUX, Stable Diffusion XL, LTX, and WAN models running locally on studio GPUs. Keeping inference on local hardware matters for facilities that cannot send frames to an outside API for legal, security, or client-confidentiality reasons.

That local-first design lines up with Foundry's broader push to fold machine learning into its compositor, which we saw in Nuke 17.0 and its expanded machine-learning toolset.

Kyle Roche moves from Griptape CEO to Foundry Chief AI Officer

Kyle Roche, Griptape co-founder and its former CEO, has been named Foundry's Chief AI Officer. He joined the company in February 2026 when the acquisition completed.

Roche framed the enterprise tier as a way to move studios into AI production without the one-off workarounds many have assembled on their own:

What governed, off-the-shelf AI means for studio pipelines

For facilities weighing generative tools, the constraint has rarely been model quality. It has been control: where the data lives, who holds the license, and whether an AI step can slot into a versioned pipeline without breaking review and audit trails. Griptape Enterprise answers those questions by keeping deployment, models, and color management inside the studio's own walls, and by wiring the whole thing into Nuke's existing gizmo and scripting workflow.

Whether high-end studios adopt a packaged orchestration layer over their own internal tooling is the open question. With FLUX, Stable Diffusion XL, LTX, and WAN all running locally and Nuke as the front end, Foundry is betting that the facilities most cautious about AI are the ones most likely to pay for a governed way in.

Foundry has released SmartRoto, an AI rotoscoping add-on for Nuke that fills in the frames between an artist's keyframes. According to Foundry, the tool moves and deforms splines across a shot from a few input frames, while leaving the artist in control of the final shape.

• Up to 4x faster. Foundry says SmartRoto can cut rotoscoping time to roughly a quarter of a manual pass.

• Runs locally, trained on licensed data. The model processes shots on the artist's own machine, and Foundry says it was trained on licensed footage.

• $499 to start. The introductory licence is priced at $499 and rises to $599, with a 90-day trial that closes August 16, 2026.

How SmartRoto handles the in-between frames

Rotoscoping is the manual work of drawing and animating mattes around objects so compositors can isolate them, and it remains one of the most time-intensive tasks in a VFX pipeline. SmartRoto targets the slowest part of that job: the tweening between an artist's keyframes.

The artist still sets up the initial splines and a small number of keyframes. SmartRoto then predicts the intermediate shapes across the sequence, tracking and deforming the splines to follow motion in the plate. Foundry says the artist keeps control throughout and can correct any frame, so the tool assists the roto pass rather than replacing the artist's judgment on tricky edges.

From a DNEG research project to a shipping product

SmartRoto did not start as a product. It began as a Foundry research collaboration with visual effects studio DNEG and the University of Bath, documented in Foundry's own research write-up. The model was trained on DNEG's production rotoscopy artwork, a dataset the team describes as more than 650,000 artist-animated shapes and 125 million keyframes drawn from real shot work.

The research goal was more modest than the launch claim. Ben Kent, a research engineering manager at Foundry, framed the original target as saving "even 25% of an artist's time." The commercial release now advertises up to a 4x speedup, a larger figure that VFX teams will want to test against their own footage before budgeting around it.

Part of Foundry's longer AI build-out in Nuke

SmartRoto lands on top of years of machine learning work inside Nuke. Foundry's AI lineage in the compositor traces back to CopyCat, and the company has kept expanding those capabilities. We covered Nuke 17.0, which added native Gaussian Splat support and broadened its machine learning toolset.

The company has also been buying and building around AI infrastructure. We reported on Foundry's Griptape acquisition, which brought Python-based agent orchestration into its pipeline tools. SmartRoto fits the same pattern: targeted AI applied to a specific, repetitive artist task rather than a general-purpose generator.

AI roto joins a crowded problem space

SmartRoto enters a category where other teams are also attacking hard matte work. We previously covered CorridorKey, the open-source neural keyer that tackles green screen edges many traditional keyers struggle with. Where CorridorKey focuses on keying, SmartRoto focuses on the spline-based roto that artists fall back on when keying is not an option.

For studios weighing it, the practical questions are cost and fit. At an introductory $499 per licence and a local install that keeps footage off external servers, SmartRoto is priced for individual artists and small teams as much as large facilities. The 90-day trial gives roto artists a window to run it against their own shots before the price steps up to $599, and before the August 16, 2026 deadline closes.

This week runs from the practical to the existential: a new image model that edits from annotations, Meta walking back an opt-out AI feature after public backlash, MCP arriving inside Unreal Engine and ComfyUI, and a research claim about a "hidden brain" that has AGI watchers talking.

We test Seedream 5.0 Pro's precise editing live, break down Meta Muse and the Instagram remix reversal, walk through what MCP actually changes for Unreal and ComfyUI workflows, and dig into the J-Space claim about a reasoning structure that appeared inside a neural network on its own.

Quick Take

Four stories, one throughline: the tools keep closing the gap on precision while the questions around consent, cost, and control get harder. We run a new image model against a period-accuracy test, weigh an opt-out remix feature that pulled in a SAG statement, separate where agentic tool access beats a plain API, and sit with a research claim that is either a real step toward machine reasoning or an investor talking point. The capabilities are arriving faster than the frameworks for using them.

What We Tested: Seedream 5.0 Pro's Annotation Edits

ByteDance shipped Seedream 5.0 Pro, and the headline for Addy is precision. You can target a specific change in an image and the model applies it without disturbing the rest of the frame.

The part worth noting is how it takes direction. It does not need a mask. Mark up an image with a red box around a region, then write a prompt like "change the red box to something else," and the model reads the annotation and follows it.

The capability jump. Joey put that annotation control in the same league as GPT and Nano Banana Pro, and Addy argued it lands even more precise, with better text rendering and stronger photorealism on text-to-image than earlier versions. When we covered Seedream 4, Nano Banana beat it in a head-to-head; Addy's read is that the gap has since closed on targeted edits.

The 1970s New York test. Joey ran his standard litmus prompt live: a busy 1970s New York City street with taxi cabs and pedestrians. The result captured the period vibe, put a recognizable skyline down the middle, and kept modern buildings out of frame. It also showed the model's weak spots.

• Background softness. Fine detail stayed fuzzy, a trait Addy has flagged since Seedream 4.5, and small signage text came out blurred.

• Physical logic. The cars pointed in directions that did not make sense, with a near-collision in the middle of the frame.

• A cultural gap. Both of us landed on the same observation: American models render the gritty, high-contrast Taxi Driver version of 1970s New York, while the output here read cleaner and more idyllic. Joey's theory is a training-data nuance, since a Chinese model trains on less American reference material.

Resolution ceiling. Seedream 5.0 Pro tops out around 2K and needs an upscale to reach 4K, where Nano Banana Pro renders 4K natively. Addy expects native high resolution to follow, since ByteDance's video side already does 4K.

The workflow pairing. The natural use is to generate keyframes in Seedream and move them into Seedance as start and end frames. Looking ahead, Addy pointed to rumors he called "pretty confirmed" that a coming ByteDance video model will accept up to 50 reference images and generate clips as long as three minutes. Treat that as unconfirmed for now. His caveat is real either way: the model still tries to use every reference you feed it rather than choosing the right ones per shot, and temporal control over which image lands at which second is still missing.

What We Debated: Meta Muse and the Instagram Remix Reversal

Meta is back on the board with Muse, a new image and video model out of its Superintelligence Labs. Meta describes it as agentic image generation, which means the model calls tools based on the prompt rather than generating in one pass. The showcased examples include sharp text rendering and generating a working QR code inside an image. We covered the Muse Image launch when it dropped.

On raw output, Addy's early read is that Muse looks on par with what is already available. The significance is positioning: after a stretch of falling behind, Meta has a competitive model again.

Where the business logic points. Joey connected Muse to reports that Meta is monetizing spare data center capacity, an AWS-style move that suggests its own internal AI demand has slowed. The counterweight is Meta's advertising machine. A native model wired into its ad tools fits the company's MarTech strength, and both of us landed on the target user being small advertisers.

• Not the enterprise buyer. Coca-Cola-scale brands run in-house AI teams and will not need this.

• The micro and nano brands. Mom-and-pop shops and small Etsy-style sellers already pay for Facebook and Instagram ads and lack creative capacity. Muse is a way for them to scale content, mirroring what Google offers advertisers.

The remix controversy. The friction point is a feature that lets people remix anyone else's Instagram photos with AI, launched as opt-out, meaning every account was included by default. After we recorded, Meta walked it back following online backlash, so accounts are no longer auto-enrolled. SAG issued a statement, since the default exposed any actor or public figure on the platform.

Joey's concern: the harm does not require an extreme edit. A teenager's photos could be remixed into something merely embarrassing, which current safeguards built for clearly harmful content would not catch.

Addy's counter: anyone could already screenshot an Instagram photo and run it through another model. The opt-out design mostly removed friction rather than creating a new capability.

That tension pulled us to a broader number Joey raised: a reported figure that roughly 20% of YouTube is already AI content. Addy suspects that undercounts, and both of us noted the open question is not how much AI gets uploaded but how much gets watched, with a likely swing back toward authentic, personality-driven content.

What We Explored: MCP Lands in Unreal Engine and ComfyUI

Model Context Protocol, the standard Anthropic introduced in early 2025, moved from concept to production tooling this week, with beta MCP support arriving in Unreal Engine and MCP access now in ComfyUI.

Addy's working definition of why it matters: a traditional API needs rigid, structured scripting to call anything, while MCP lets an agent ask a tool what it can do and act more fluidly, which fits how agents operate. In Unreal's beta, you can tell Claude to build a blueprint that does a specific thing and it builds it. In ComfyUI, you can describe a workflow and it assembles the nodes.

The ComfyUI caveat. The integration currently works only with ComfyUI Cloud, not the desktop build. You can still build a workflow in the cloud and download it locally, which pairs with the direction we noted around Comfy Cloud earlier.

Where it helps most. Joey's take is that MCP flattens the learning curve. It took him roughly three years to reach a generalist level in Unreal; he thinks the same competence is now reachable in closer to three months with the hours still required. For ComfyUI, the value is exploratory: most users understand a fraction of the available nodes, and an agent that knows every node can suggest which ones solve a given problem.

APIs are not going away. Addy's caution: MCP calls burn tokens, so for predictable, repetitive work against structured data, a script or API is faster, more reliable, and cheaper. Anyone building for scale and customers still needs that reliability.

The bonus use case. Addy described pointing Claude Code at a DaVinci Resolve project that kept crashing on open. Instead of the effect everyone suspected, the logs showed a clip that had gone offline while Resolve still treated it as online, and the fix was to mark the clip offline. A half-day support headache turned into a few minutes of log reading.

What We Questioned: The "J-Space" AGI Claim

The episode's most speculative thread is J-Space. Joey described a claimed Anthropic research finding: researchers examining a neural network reportedly located a hidden layer, a reasoning structure that developed inside the model on its own and was not part of the original design. Delete it and the model reportedly loses the ability to reason; restore it and reasoning returns.

Treat this as unverified. Joey flagged it directly as something that could be an investor talking point, and neither of us had fully read the paper on air. Addy also recalled a related claim that the model's internal reasoning ran in a compressed shorthand of English that used fewer tokens.

The question: if a reasoning structure really can emerge on its own, it is a signal AGI watchers point to. If it cannot be reproduced, it is a headline. Either way, the image and video tools filmmakers actually use keep advancing regardless of where the AGI debate lands.

We closed on a lighter forward-looker: The Verge reported that Character AI is moving into interactive microdramas, a game-like format you can steer rather than just watch, which lines up with the commercial microdrama use cases we keep circling back to.

Bottom Line: Capability Is Outrunning the Guardrails

Every story this week is a version of the same gap between what these tools can do and the frameworks for doing it responsibly.

• Seedream 5.0 Pro proves annotation-based editing is now precise enough to compete, while the resolution ceiling and cultural blind spots show where a model's training still shows through.

• Meta Muse put a competitive model back in Meta's hands, but the opt-out remix rollout showed the company reaching for engagement ahead of consent, and the reversal came only after the backlash.

• MCP makes complex software approachable inside Unreal and ComfyUI, without retiring the APIs that still win on scale and cost.

• J-Space is either an early marker of machine reasoning or an investment narrative, and the claim is unverified until the research holds up.

The tools are getting easier and sharper. The decisions about consent, cost, and trust are getting harder.

Links from This Episode

Tools & Platforms:

• Seedream 4.5. VP Land coverage of the prior ByteDance image release.

• Meta Muse Image and Muse Video. VP Land coverage of the Superintelligence Labs launch.

• Seedance 2.0. VP Land coverage of ByteDance's video model.

Models & Comparisons:

• Nano Banana Pro. VP Land coverage of Google's native-4K image model.

• Is Seedream the Next Nano Banana? Plus Comfy Cloud. Our earlier Seedream 4 test and Comfy Cloud context.

Mirelo AI and Kyutai open-sourced MuScriptor, an audio-to-MIDI model that takes a finished music mix and transcribes every instrument at once into separate MIDI tracks. Most audio-to-MIDI tools handle one sound at a time. MuScriptor reads the whole mix.

The model outputs a separate MIDI track per instrument on a piano roll, with automatic instrument detection and labeling. It also detects chords, key, and tempo, and supports one-click export. The weights are on Hugging Face, the inference code is on GitHub, and a companion tool ships free inside Mirelo Studio.

Transcription treated as a language modeling problem

MuScriptor uses a decoder-only transformer that accepts mel-spectrograms and autoregressively generates token sequences representing pitch, timing, and instrument type. Mirelo frames the approach as "music transcription as language modeling," borrowing the same architecture pattern that powers text generation and applying it to notes instead of words.

The design targets a specific failure point. Dense, multi-instrument audio has historically tripped up transcription models built to isolate a single voice or instrument. According to Mirelo, MuScriptor "takes the full mix and transcribes every instrument at once" rather than requiring source separation as a first step.

A three-stage training pipeline mixing synthetic and real audio

The model was trained across three datasets, each doing a different job:

• DSynth. Roughly 1.45 million MIDI files rendered to audio with on-the-fly synthesis, giving the model broad coverage of note and instrument combinations.

• DReal. About 170,000 real recordings, more than 11,000 hours, paired with aligned annotations so the model learns how studio audio actually sounds.

• DRL. A smaller set of 300 high-quality tracks used for a GRPO-like reinforcement learning stage to sharpen output quality.

Fine-tuning on real recordings mattered. Mirelo reports that training on real data improved transcription metrics by roughly 20 percentage points over a version pre-trained on synthetic audio alone, per the team's ArXiv paper, which credits IRCAM in its citation.

Why per-instrument MIDI matters for scoring and remixing

For composers, editors, and music supervisors, MIDI is editable in a way that audio is not. A transcription that separates a mix into per-instrument tracks turns a reference recording into a starting point: swap a synth patch, re-voice a chord, quantize a drum pattern, or lift a bassline into a new arrangement. Getting there previously meant either manual transcription or stems that still had to be transcribed one instrument at a time.

MuScriptor lands amid a run of open and accessible music AI. We covered Google's Lyria 3, which generates full tracks with vocals from text prompts.

We also covered ElevenLabs Music Finetunes, which trains a custom music model on a user's own catalog. MuScriptor works the opposite direction: instead of generating audio, it reads existing audio back into a symbolic, editable format.

Open weights lower the barrier for building on top

Because MuScriptor ships with open weights and inference code, developers can run it locally or wire it into their own tools rather than depending on a hosted API. The decoder-only, language-modeling framing also means the architecture will feel familiar to teams already working with transformer models.

For anyone who wants to try it without touching code, the audio-to-MIDI tool in Mirelo Studio is free and runs the improved model. The combination of a free hosted tool and open weights gives both casual users and builders a way in.