docs: add competitive landscape and deep dive findings

Version evolution (SL 1.0→2.0→3.0), team background, no
patents, NVIDIA DiffusionRenderer as open-source competitor,
dataset landscape (POLAR, SynthLight, etc.), botocore/AWS SDK
in privacy app, MetaHuman EULA fix, user data controversy,
and DiffusionRenderer ComfyUI integration across all docs.

README.md

@@ -117,10 +117,31 @@ documented here are narrower:
    not appear to match the deployed application
 4. Missing license attribution required by MIT and Apache 2.0
 
+5. The CVPR 2024 paper describes SwitchLight 1.0; the shipped product
+   is SwitchLight 3.0, which went through at least two "complete
+   architecture rebuilds." The physics-based architecture (Cook-
+   Torrance, Normal Net, Specular Net) described in the paper may not
+   reflect the deployed product.
+
 The first and fourth are correctable. The second is a question for
 investors. The third is a question for the research community.
 
 
+### Competitive landscape
+
+The competitive moat described in Beeble's investor materials is
+eroding rapidly. NVIDIA's DiffusionRenderer (CVPR 2025 Oral, open
+source) performs video-to-PBR decomposition and relighting using video
+diffusion models. Multiple research groups have demonstrated that
+synthetic training data (Blender renders of 3D characters with known
+PBR properties) produces results comparable to lightstage-trained
+methods, without requiring proprietary lightstage captures.
+
+No patent applications were found for Beeble or its founders related
+to SwitchLight, relighting, or inverse rendering. The CVPR 2024 paper
+has no associated code release.
+
+
 ## License
 
 This repository is licensed under

docs/COMFYUI_GUIDE.md

@@ -223,21 +223,67 @@ roughness, metallic. Physical lightstage captures are one way to
 obtain this data, but modern synthetic rendering provides the same
 thing more cheaply and at greater scale:
 
-- **Unreal Engine MetaHumans**: photorealistic digital humans with
-  full PBR material definitions. Render them under varied lighting
-  and you have ground-truth PBR for each frame.
-- **Blender character generators** (Human Generator, MB-Lab):
+- **Blender character generators** (Human Generator, MB-Lab, MPFB2):
   produce characters with known material properties that can be
-  rendered procedurally.
+  rendered procedurally. Blender's Cycles renderer outputs physically
+  accurate PBR passes natively. Fully open source, no licensing
+  restrictions for AI training.
 - **Houdini procedural pipelines**: can generate hundreds of
   thousands of unique character/lighting/pose combinations
   programmatically.
+- ~~**Unreal Engine MetaHumans**~~: photorealistic digital humans
+  with full PBR material definitions. However, **the MetaHuman EULA
+  explicitly prohibits using MetaHumans as AI training data**: "You
+  must ensure that your activities with the Licensed Technology do
+  not result in using the Licensed Technology as a training input or
+  prompt-based input into any Generative AI Program." MetaHumans can
+  be used within AI-enhanced workflows but not to train AI models.
 
 The ground truth is inherent in synthetic rendering: you created the
 scene, so you already have the PBR maps. A VFX studio with a
 standard character pipeline could generate a training dataset in a
 week.
 
+### Existing datasets and published results
+
+The lightstage data advantage that the CVPR paper frames as a
+competitive moat was real in 2023-2024. It is no longer.
+
+**Public OLAT datasets now rival Beeble's scale:**
+
+- **POLAR** (Dec 2025, public) -- 220 subjects, 156 light directions,
+  32 views, 4K, 28.8 million images total. Beeble's CVPR paper reports
+  287 subjects. POLAR is at 77% of that count, freely available.
+  https://rex0191.github.io/POLAR/
+
+- **HumanOLAT** (ICCV 2025, public gated) -- 21 subjects, full body,
+  40 cameras at 6K, 331 LEDs. The first public full-body OLAT dataset.
+  https://vcai.mpi-inf.mpg.de/projects/HumanOLAT/
+
+**Synthetic approaches already match lightstage quality:**
+
+- **SynthLight** (Adobe/Yale, CVPR 2025) -- trained purely on ~350
+  synthetic 3D heads rendered in Blender with PBR materials. Achieves
+  results comparable to lightstage-trained methods on lightstage test
+  data. No lightstage data used at all.
+  https://vrroom.github.io/synthlight/
+
+- **NVIDIA Lumos** (SIGGRAPH Asia 2022) -- rendered 300k synthetic
+  samples in a virtual lightstage. Matched state-of-the-art
+  lightstage methods three years ago.
+
+- **OpenHumanBRDF** (July 2025) -- 147 human models with full PBR
+  decomposition including SSS, built in Blender. Exactly the kind
+  of dataset needed for training PBR decomposition models.
+  https://arxiv.org/abs/2507.18385
+
+**Cost to replicate:** Generating a competitive synthetic dataset
+costs approximately $4,500-$18,000 total (Blender + MPFB2 for
+character generation, Cycles for rendering, cloud GPUs for compute).
+Raw GPU compute for 100k PBR renders is approximately $55 on an A100.
+CHORD (Ubisoft) trained its PBR decomposition model in 5.2 days on
+a single H100, costing approximately $260-500 in compute.
+
 With model sizes under 2 GB (based on the encrypted model files in
 Beeble's distribution) and standard encoder-decoder architectures,
 the compute cost to train equivalent models from synthetic data is
@@ -246,7 +292,8 @@ modest--well within reach of independent researchers or small studios.
 This does not mean Beeble's trained weights are worthless. But the
 barrier to replication is lower than the marketing suggests,
 especially given that the model architectures are standard
-open-source frameworks.
+open-source frameworks and equivalent training data is now publicly
+available.
 
 
 ## 5. Relighting
@@ -256,13 +303,49 @@ relighting. This is the least well-characterized stage in our
 analysis--the relighting model's architecture could not be determined
 from the available evidence.
 
-### AI-based relighting
+### NVIDIA DiffusionRenderer (replaces both PBR decomposition AND relighting)
+
+This is the most significant recent development. NVIDIA's
+DiffusionRenderer does the same thing as Beeble's entire core
+pipeline--video to PBR passes plus relighting--in a single open-source
+system.
+
+- **DiffusionRenderer** (NVIDIA, CVPR 2025 Oral--the highest honor)
+  -- a general-purpose method for both neural inverse and forward
+  rendering. Two modes:
+  - **Inverse**: input image/video → geometry and material buffers
+    (albedo, normals, roughness, metallic)
+  - **Forward**: G-buffers + environment map → photorealistic relit
+    output
+
+  The upgraded **Cosmos DiffusionRenderer** (June 2025) brings
+  improved quality powered by NVIDIA Cosmos video foundation models.
+
+  GitHub: https://github.com/nv-tlabs/cosmos-transfer1-diffusion-renderer
+  Academic version: https://github.com/nv-tlabs/diffusion-renderer
+  Weights: https://huggingface.co/collections/zianw/cosmos-diffusionrenderer-6849f2a4da267e55409b8125
+  **License: Apache 2.0 (code), NVIDIA Open Model License (weights)**
+
+  Hardware: approximately 16GB VRAM recommended.
+
+  **ComfyUI integration**: A community wrapper exists at
+  https://github.com/eggsbenedicto/DiffusionRenderer-ComfyUI
+  (experimental, Linux tested). Requires downloading the Cosmos
+  DiffusionRenderer checkpoints and NVIDIA Video Tokenizer
+  (Cosmos-1.0-Tokenizer-CV8x8x8).
+
+  This is a direct, open-source replacement for Beeble's core value
+  proposition, backed by NVIDIA's resources and published as the
+  highest-rated paper at CVPR 2025.
+
+### IC-Light (image relighting)
 
 - **IC-Light** (ICLR 2025, by lllyasviel / ControlNet creator) --
-  the leading open-source relighting model. Two modes: text-conditioned
-  (describe the target lighting) and background-conditioned (provide a
-  background image whose lighting should be matched). Based on Stable
-  Diffusion.
+  the leading open-source image relighting model. Two modes:
+  text-conditioned (describe the target lighting) and
+  background-conditioned (provide a background image whose lighting
+  should be matched). Based on Stable Diffusion. V2 available with
+  16-channel VAE.
   GitHub: https://github.com/lllyasviel/IC-Light
 
   IC-Light uses diffusion-based lighting transfer rather than
@@ -330,7 +413,8 @@ model = timm.create_model('vit_large_patch14_dinov2.lvd142m',
 | Metallic | SMP + timm backbone (proprietary weights) | CHORD / RGB-X | Weaker for portraits |
 | Specular | SMP + timm backbone (proprietary weights) | CHORD / RGB-X | Weaker for portraits |
 | Super resolution | RRDB-Net (open source) | ESRGAN / Real-ESRGAN | Identical (same model) |
-| Relighting | Proprietary (not fully characterized) | IC-Light / manual | Different approach |
+| Relighting | Proprietary (not fully characterized) | DiffusionRenderer / IC-Light / manual | Comparable (DiffusionRenderer) |
+| Full inverse+forward rendering | Entire pipeline | DiffusionRenderer (NVIDIA, CVPR 2025) | Direct open-source competitor |
 
 The "Beeble model" column reflects what was found in the application
 binary, not what the CVPR paper describes. See
@@ -350,6 +434,15 @@ models were trained on material textures and interior scenes. However,
 as discussed above, the barrier to creating equivalent training data
 using synthetic rendering is lower than commonly assumed.
 
+Where DiffusionRenderer changes the picture: NVIDIA's
+DiffusionRenderer (CVPR 2025 Oral) handles both inverse rendering
+(video → PBR maps) and forward rendering (PBR maps + lighting →
+relit output) in a single open-source system. This is the first
+open-source tool that directly replicates Beeble's entire core
+pipeline, including relighting. It is backed by NVIDIA's resources,
+uses Apache 2.0 licensing for code, and has a ComfyUI integration
+available.
+
 Where open-source wins on flexibility: manual relighting in
 Blender/Nuke with the extracted PBR passes gives full artistic control
 that Beeble's automated pipeline does not offer.
@@ -369,9 +462,11 @@ need high-quality material properties, Beeble's model still has an
 edge due to its portrait-specific training data. But the gap is
 narrowing as models like CHORD improve.
 
-If you use Beeble for one-click relighting, IC-Light provides a
-different but functional alternative, and manual PBR relighting in
-Blender/Nuke gives you more control.
+If you use Beeble for one-click relighting, NVIDIA's
+DiffusionRenderer is a direct open-source competitor that handles both
+PBR decomposition and relighting in a single system. IC-Light provides
+a diffusion-based alternative, and manual PBR relighting in
+Blender/Nuke gives you full artistic control.
 
 The core value proposition of Beeble Studio--beyond the models
 themselves--is convenience. It packages everything into a single

docs/LICENSE_ANALYSIS.md

@@ -235,6 +235,14 @@ into which tracker variant Beeble uses, the exact license obligation
 cannot be determined. If an AGPL-3.0 tracker is used, the license
 requirements would be significantly more restrictive than MIT.
 
+If an AGPL-3.0 tracker is used, the implications would extend far
+beyond attribution. AGPL-3.0 requires making the complete source code
+of the incorporating application available to users--effectively
+requiring Beeble to open-source its entire application. This is among
+the most restrictive open-source licenses and represents a
+significantly different risk profile than the MIT/Apache non-compliance
+discussed elsewhere in this document.
+
 ### DexiNed (edge detection)
 
 - **Repository**: https://github.com/xavysp/DexiNed

docs/METHODOLOGY.md

@@ -113,6 +113,14 @@ website, documentation, FAQ, and research pages were reviewed to
 understand how the technology is described to users. All public
 claims were archived with URLs and timestamps.
 
+The manifest confirms Python 3.11 as the runtime (via the presence of
+`libpython3.11.so.1.0` in the downloaded files). TensorRT 10.12.0 was
+also identified, and notably, builder resources are present alongside
+the runtime--not just inference libraries. The presence of TensorRT
+builder components suggests possible on-device model compilation,
+meaning TensorRT engines may be compiled locally on the user's GPU
+rather than shipped as pre-built binaries.
+
 
 ## What was not done
 

docs/REPORT.md

@@ -15,9 +15,14 @@ maps, base color, roughness, specular, and metallic passes, along
 with AI-driven relighting capabilities.
 
 Beeble markets its pipeline as being "Powered by SwitchLight 3.0,"
-their proprietary video-to-PBR model published at CVPR 2024. The
-application is sold as a subscription product, with plans starting
-at $42/month.
+their proprietary video-to-PBR model. The original SwitchLight
+architecture was published at CVPR 2024 as a highlight paper (top
+~10% of accepted papers), but that paper describes SwitchLight 1.0.
+The product has since gone through at least two major rebuilds:
+SwitchLight 2.0 (June 2025, described by Beeble as a "complete
+architecture rebuild") and SwitchLight 3.0 (November 2025, marketed
+as a "true video model"). The application is sold as a subscription
+product, with plans starting at $42/month.
 
 This analysis was prompted by observing that several of Beeble
 Studio's output passes closely resemble the outputs of well-known
@@ -423,8 +428,18 @@ standard in ML applications:
 | Flet | Apache 2.0 | Cross-platform GUI framework |
 | SoftHSM2 / PKCS#11 | BSD 2-Clause | License token validation |
 | OpenSSL 1.1 | Apache 2.0 | Cryptographic operations |
+| botocore (AWS SDK) | Apache 2.0 | Cloud connectivity (1,823 service files) |
 
-Two entries deserve mention. **Pyarmor** (runtime ID
+Three entries deserve mention. **botocore** is the AWS SDK core
+library, bundled with 1,823 service definition files covering 400+
+AWS services. For an application whose product page states "Your
+files never leave your machine," the presence of the full AWS SDK
+raises questions about what network connectivity the application
+maintains. This analysis did not perform network monitoring to
+determine what connections, if any, the application makes during
+normal operation.
+
+**Pyarmor** (runtime ID
 `pyarmor_runtime_007423`) is used to encrypt all of Beeble's custom
 Python code--every proprietary module is obfuscated with randomized
 names and encrypted bytecode. This prevents static analysis of how
@@ -456,6 +471,17 @@ This is presented as a unified system where intrinsic decomposition
 step in the relighting pipeline. The paper's novelty claim rests
 partly on this physics-driven architecture.
 
+An important caveat: the CVPR paper describes SwitchLight 1.0.
+The shipped product is SwitchLight 3.0, which Beeble says went
+through two major rebuilds. SwitchLight 2.0 (June 2025) was described
+as a "complete architecture rebuild" that removed the alpha mask
+requirement and extended from isolated humans to full scenes.
+SwitchLight 3.0 (November 2025) was described as a "true video
+model" with multi-frame processing, replacing the per-frame
+architecture. The paper's physics-based architecture may not reflect
+what is currently deployed. The binary analysis that follows applies
+to the deployed product, not the CVPR paper.
+
 ### 4.2 What the binary contains
 
 A thorough string search of the 2GB process memory dump and the 56MB
@@ -604,7 +630,7 @@ available evidence, not as a certainty.
 Beeble uses two layers of protection to obscure its pipeline:
 
 **Model encryption.** The six model files are stored as `.enc`
-files encrypted with AES. They total 4.4 GB:
+files encrypted with AES. They total 4.3 GB:
 
 | File | Size |
 |------|------|
@@ -762,16 +788,58 @@ replicating each stage of the pipeline with open-source tools.
 There is a common assumption that the training data represents a
 significant barrier to replication--that lightstage captures are
 expensive and rare, and therefore the trained weights are uniquely
-valuable. This may overstate the difficulty. For PBR decomposition
-training, what you need is a dataset of images paired with
-ground-truth PBR maps (albedo, normal, roughness, metallic). Modern
-3D character pipelines--Unreal Engine MetaHumans, Blender character
-generators, procedural systems in Houdini--can render hundreds of
-thousands of such pairs with varied poses, lighting, skin tones, and
-clothing. The ground truth is inherent: you created the scene, so you
-already have the PBR maps. With model sizes under 2 GB and standard
-encoder-decoder architectures, the compute cost to train equivalent
-models from synthetic data is modest.
+valuable. As of late 2025, this assumption is increasingly difficult
+to sustain.
+
+Multiple public datasets now provide the kind of paired image +
+ground-truth PBR data needed for training:
+
+- **POLAR** (December 2025): 220 subjects, 156 light directions, 32
+  views, 4K resolution, 28.8 million images. This is comparable in
+  scale to the 287 subjects cited in Beeble's CVPR paper.
+- **HumanOLAT** (ICCV 2025): the first public full-body lightstage
+  dataset, 21 subjects, 331 OLAT lighting conditions.
+- **OpenHumanBRDF** (July 2025): 147 human models with full PBR
+  properties (diffuse, specular, SSS) in Blender.
+- **MatSynth** (CVPR 2024): 433 GB of CC0/CC-BY PBR material maps,
+  used to train Ubisoft's CHORD model.
+
+Published results further undermine the lightstage data moat.
+**SynthLight** (CVPR 2025) trained purely on ~350 synthetic Blender
+heads and matched the quality of lightstage-trained methods. **NVIDIA
+Lumos** (SIGGRAPH Asia 2022) matched state-of-the-art with 300,000
+synthetic samples. **DiFaReli++** outperformed lightstage baselines
+using only 2D internet images.
+
+The cost estimates are modest. Ubisoft's CHORD model was trained in
+5.2 days on a single H100 GPU (~$260-500 in cloud compute). A full
+replication effort--synthetic dataset generation plus model training
+--has been estimated at $4,500-$18,000, a fraction of Beeble's $4.75M
+seed round.
+
+Note: Unreal Engine MetaHumans, while visually excellent, cannot
+legally be used for AI training. Epic's MetaHuman EULA explicitly
+prohibits "using the Licensed Technology as a training input...into
+any Generative AI Program." Blender with the MPFB2 plugin is a
+viable alternative for synthetic data generation without license
+restrictions.
+
+The competitive landscape shifted significantly in 2025. NVIDIA's
+**DiffusionRenderer** (CVPR 2025 Oral--the highest honor) performs
+both inverse rendering (video → PBR maps) and forward rendering (PBR
+maps + lighting → relit output) using video diffusion models. It is
+open source (Apache 2.0 code, NVIDIA Open Model License for weights)
+and has a ComfyUI integration. This is the first open-source system
+that directly replicates Beeble's entire core pipeline, including
+relighting, backed by NVIDIA's resources. See
+[COMFYUI_GUIDE.md](COMFYUI_GUIDE.md) for integration details.
+
+No patent applications were found for Beeble or its founders related
+to SwitchLight, relighting, or inverse rendering (searched USPTO and
+Google Patents, January 2026; note the 18-month publication delay for
+recent filings). The CVPR 2024 paper has no associated code release.
+Together with the architecture findings in section 4, this suggests
+limited defensibility against open-source replication.
 
 None of this means Beeble has no value. Convenience, polish, and
 integration are real things people pay for. But the gap between
@@ -824,12 +892,12 @@ Electron app's 667 JavaScript files. It is a marketing name that
 refers to no identifiable software component.
 
 The CVPR 2024 paper describes a physics-based inverse rendering
-architecture. The deployed application contains no evidence of
-physics-based rendering code at inference time. The most likely
-explanation is that the physics (Cook-Torrance rendering) was used
-during training as a loss function, and the deployed model is a
-standard feedforward network that learned to predict PBR channels
-from that training process.
+architecture for SwitchLight 1.0. The deployed product is SwitchLight
+3.0, which went through at least two "complete architecture rebuilds."
+The application contains no evidence of physics-based rendering code
+at inference time. This could mean the physics (Cook-Torrance
+rendering) was used during training as a loss function, that the
+architecture was replaced during the rebuilds, or both.
 
 Beeble's marketing attributes the entire pipeline to SwitchLight
 3.0. The evidence shows that alpha mattes come from InSPyReNet, depth

docs/VERIFICATION_GUIDE.md

@@ -234,6 +234,13 @@ When running these commands, look for:
   directory versus total packages reveals the scope of missing
   attribution
 
+- **AWS SDK presence**: The application bundles botocore with 1,823
+  service definition files for 400+ AWS services. For an application
+  that claims "Your files never leave your machine," the presence of
+  the full AWS SDK raises questions about network connectivity.
+  Network monitoring during normal operation would reveal what
+  connections the application makes.
+
 
 ## What not to do
 

evidence/marketing_claims.md

@@ -152,6 +152,171 @@ As of January 2026, the company appears to have approximately 9
 employees.
 
 
+## Version history
+
+| Version | Approximate date | Key changes |
+|---------|-----------------|-------------|
+| SwitchLight (mobile app) | 2022-2023 | Photo relighting app for iOS, 3M+ downloads claimed. Selfie/portrait focus. |
+| SwitchLight 1.0 | Late 2023 - early 2024 | First VFX tool. Required alpha mask input. Isolated humans only. Architecture described in CVPR 2024 paper. Per-frame processing. |
+| SwitchLight 2.0 | June 30, 2025 | "Complete architecture rebuild." No alpha mask required. Full-scene PBR maps (not just isolated subjects). Claimed 10x larger model, 13x more training data than 1.0. Still per-frame with post-processing deflicker. 2K resolution limit (cloud). 8-bit PNG output. User data training controversy (see below). |
+| SwitchLight 3.0 | November 5, 2025 | Marketed as "true video model" with multi-frame processing. Claimed 10x more training data than 2.0 (130x more than 1.0). 4K resolution support. 16-bit EXR output. Desktop app (Beeble Studio) launched for local GPU processing. Paid users exempt from data training. |
+
+Source: beeble.ai/research/switchlight-2-0-is-here,
+beeble.ai/research/switchlight-3-0-is-here
+
+
+## Team and leadership
+
+Beeble was founded in 2022 in Seoul, South Korea by five co-founders
+who previously worked at the AI research and machine learning team of
+Krafton Inc., a South Korean game publisher.
+
+**CEO: Hoon Kim**
+- B.S. and M.S. in Electrical Engineering from KAIST (2012-2019)
+- Research scientist at Lunit (medical AI, 2019-2020)
+- Deep learning research scientist at Krafton Inc. (voice synthesis
+  team leader, 2020-2022)
+- 6 peer-reviewed papers at ICLR, AAAI, ICML workshop
+- Prior research was in autonomous driving (sim-to-real transfer,
+  vehicle collision prediction) and voice synthesis--not in computer
+  vision, relighting, or PBR decomposition
+- The SwitchLight paper is his first publication in relighting
+
+Source: gnsrla12.github.io/About-myself/
+
+**Paper co-author: Sanghyun Woo** (last/senior author on the CVPR paper)
+- Ph.D. from KAIST, currently Senior Research Scientist at Google
+  DeepMind
+- Previously Faculty Fellow at NYU Courant (hosted by Saining Xie)
+- Creator of CBAM (Convolutional Block Attention Module, ECCV 2018)
+  with 34,000+ citations
+- Co-author on ConvNeXt V2 (CVPR 2023), Cambrian-1 (NeurIPS 2024 Oral)
+- Listed as affiliated with NYU on the SwitchLight paper, not Beeble
+- Whether his involvement extends beyond the CVPR 2024 paper is
+  unknown
+
+Source: sites.google.com/view/sanghyunwoo/
+
+**Team size:** 9 employees as of early 2026. With 5 co-founders, this
+means approximately 4 non-founder employees.
+
+
+## Products and pricing
+
+**Beeble Cloud** (web app): Credit-based processing. Free tier
+(15-second clips), Creator $19/month, Professional $75/month.
+
+**Beeble Studio** (desktop app): Local GPU processing.
+Indie $504/year ($42/month, for studios under $200K revenue),
+Standard $3,000/year ($250/month).
+
+**SwitchLight API**: Available at switchlight-api.beeble.ai for
+developer integration.
+
+**Mobile app**: SwitchLight photo editor on iOS, 3M+ downloads
+claimed. This is a consumer selfie relighting app, not a professional
+VFX tool.
+
+**Plugins**: Nuke, Blender, Unreal Engine integration.
+
+The original SwitchLight Studio product was shut down and merged into
+Beeble Studio. The switchlight-studio.beeble.ai domain displays a
+"Closing" notice.
+
+Source: beeble.ai/pricing, beeble.ai/pricing-cloud
+
+
+## User data training controversy
+
+When SwitchLight 2.0 launched in mid-2025, CG Channel reported that
+Beeble's terms of use allowed user-uploaded content to be used for AI
+training. This caused significant backlash in the VFX community, where
+studios are protective of proprietary footage.
+
+Beeble responded by changing policy: paid subscribers' content is no
+longer used for training (as of the SwitchLight 3.0 launch in November
+2025). Free tier uploads may still be used. The Beeble Studio desktop
+app was positioned as the privacy-focused alternative, with all
+processing running locally.
+
+Source: cgchannel.com/2025/11/beeble-launches-switchlight-3-0/
+
+
+## Interview statements
+
+In a September 2025 interview with Digital Production magazine
+("We have to talk about Switchlight 2.0"), CEO Hoon Kim stated:
+
+> At its core, it's a neural net doing the heavy lifting.
+
+When asked for architecture details, he said he "couldn't share more
+details beyond that."
+
+On training data, Kim stated all data was "created in-house using
+scans of real humans and objects" with "no movies, films, or
+third-party content used."
+
+Source: digitalproduction.com/2025/09/05/we-have-to-talk-about-switchlight-2-0/
+
+
+## Production credits
+
+Boxel Studio used SwitchLight for VFX relighting sequences on
+*Superman & Lois*. This appears to be Beeble's most prominent
+production credit.
+
+Source: boxelstudio.com/beeble-switchlight/
+
+
+## Patent filings
+
+No patent applications or grants were found for Beeble Inc. or any of
+its founders related to SwitchLight, relighting, or inverse rendering.
+Searches were conducted on USPTO Patent Public Search and Google
+Patents for "Beeble," "Hoon Kim," "SwitchLight," and "portrait
+relighting neural network."
+
+Note: Patent applications have an 18-month publication delay from
+filing, so recent applications may not yet be visible.
+
+Searched: January 2026
+
+
+## CVPR paper reception
+
+The SwitchLight paper was accepted as a CVPR 2024 highlight paper
+(top ~10% of accepted papers out of 11,532 submissions). Beeble
+claimed perfect 5/5/5 reviewer scores.
+
+The paper is cited as "state-of-the-art" in IC-Light's ICLR 2025
+paper and is referenced in the Awesome-Relighting curated list.
+No public criticisms of the paper were found, though CVPR reviews
+are confidential.
+
+The paper has no associated code release. The beeble-ai/SwitchLight-
+Studio GitHub repository contains only desktop application scripts
+and integration helpers, not model code. For a CVPR highlight paper,
+the absence of a code release is notable.
+
+The arXiv version is licensed CC BY-NC-SA 4.0 (non-commercial).
+
+Source: x.com/beeble_ai/status/1763564054529159548
+
+
+## Community presence
+
+As of January 2026, there is minimal Reddit discussion of Beeble or
+SwitchLight on r/vfx, r/compositing, or r/NukeVFX. For a tool that
+has been used on a major television production (Superman & Lois) and
+claims 3M mobile app downloads, the lack of organic community
+discussion is notable.
+
+There is a separate, unrelated company called "Beeble" (based in
+Latvia, offering encrypted email and cloud storage) that has an
+AppSumo listing with poor reviews. This is not the same as Beeble AI
+but creates brand confusion.
+
+
 ## Notable patterns
 
 Beeble's marketing consistently attributes the entire Video-to-VFX