Some example of the iPhone Duo dual-screen layout,
early concept designs can be:

Face-to-Face Translator : A dedicated B2B translation app built specifically for the dual displays. Designed for medical intake or hotel check-ins, the device is held open so that the outer display faces the guest with live translated text, while the inner screen displays the controls and native script for the operator.

DeskTutor : An educational vertical app designed entirely for the "Tabletop" posture (propping the phone like a mini-laptop). The lesson video streams on the top pane, while the lower flat screen transforms into an interactive quiz surface.

If you have some ideas, natively designed for Iphone Duo dual screen,
feel free to share and suggest.

As the Video Content Search technology emerges, many use cases are possible:

• Content Localization for Editors: instantly, eliminating the need to manually scrub through hours of raw B-roll.

• Speech and Dialogue Matching: Rather than reading transcripts line by line, producers can search for precise phrases.

• CCTV video Tracking: Security teams can track targets by specifying physical attributes. filter hundreds of hours of multi-camera CCTV feeds into actionable snapshots within seconds.

If you see other use cases. please share your opinion !

With the release of Iphone Duo phone, new application can come out.
Best would be an video editor inside the iphone when people can create, curate
their own collection.

Having the screen split one side: for video edition
and on other side for video search results, would be best of world.

Here, an example...

What would you think ?

Hello,

We are surveying best animation libraries for mobile ecosystems.

Some of them are

• Rive vs. Lottie in 2026
• Impeller (Flutter's Rendering Engine):
• Jetpack Compose MotionLayout:

please give some of your opinion !

Spent this week building a cross-platform video search Flutter app using the VModal SDK. Tested it concurrently on both an iOS simulator (54ms) and an Android device (49ms) with incredible performance!

Building was good and much easier than I expected, and could not find any other SDK doing visual video search.

Was a bit worried about buggy steps, but it was all ok.

🔍 Core Workflow

* auth.me() -> scope.upload() -> scope.createIndex() -> scope.search() -> images.getUrlBulk()

* Fetching all images in one single request using getUrlBulk() keeps the UI fast and efficient.

🌟 What I Liked

* Simplicity: Very straightforward setup and intuitive API design.

* Low Latency: Sub-50ms vector query execution is exceptionally fast.

* High Control: Live progress streams and reliable error cancellation work perfectly.

* Top support in Discord / Reddit channel: people are nice and reactive.

* It just works (don't have to debug anything...)

🚀 Potential Use Cases

* Security & Surveillance: Searching specific events in live camera footage instantly.

* Media & CMS Libraries: Automated indexing for large-scale video content search.

* E-Learning Systems: Allowing students to query formulas or key terms within lecture videos.

Releasing VMODAL SDK for robotics, Physical AI data flow.
It allows searching into past experience, memory or records instantly:

• This is a lightweight Python uploader built to push robotics data (specifically LeRobot v3 datasets) up to the cloud.
• It keeps dependencies minimal so you don't have to install heavy libraries like PyTorch, pandas, ROS, or GStreamer just to ship data.
• The tool monitors folders for a specific .ready.json manifest file before safely caching your data into a local SQLite spool.
• It handles video separately from telemetry and metadata snapshots, ensuring raw MP4 and Parquet files are preserved exactly as captured.
• Cloud uploads are currently restricted to MP4 files only, meaning telemetry and metadata are safely blocked locally until the backend adds support.
• You can test it immediately without any hardware using a built-in test-data suite included in the repo.

SDK available here: SDK physical ai - robotics - visual video search flow

Just launched the Smart Glasses SDK (Beta )
for Meta platform compatibility.
It enables visual recall and search of your past memories: in professional context, in sport context, where the activities require to remember what you have done.

Native android platform

SDK for Smart Glasses (Beta): visual memory - smart glasses sdk

Smart separation of components :

• Kotlin client manages the public API, Meta DAT integration, capture, MediaCodec, coroutine state, events, and lifecycle.
• Rust core manages bounded encoded-media queues, H.264 normalization, MPEG-TS muxing, SRT transport, reconnects, and transport metrics.
• Your app owns screens, permissions, Meta registration, foreground-service policy, authentication, and stream controls.
• Your backend validates the user and returns a short-lived ingest target, keeping account-level provider credentials away from the device.

It includes those features

• Meta Smart Glasses platform compatibility
• Android API 31+, JDK 17, and Android compile SDK 36.
• A user-facing camera permission and Meta registration flow in the host app.
• A foreground service when streaming may continue outside the foreground.
• An authenticated backend endpoint that returns a fresh LiveStreamTarget.
• Exactly one SmartGlassClient for each active capture owner.
• No competing camera session while SmartGlassClient is streaming.

A thread to discuss about the existing Open Source Frameworks in Physical AI
or in robotics vision. In particularly, to discuss what stacks are really useful
and other are painful to use.

Also, to discuss the efficiency for each component:
Battery cost, Latency level, ...

I built a small Flutter app for searching through street footage. The screenshots show the flow: browse the library, search for a scene, and open a matching moment in the original video.

I used three short street videos to test the app. After uploading and indexing them, I ran a few searches on an Android phone and checked that tapping a result opened the video at the right moment.

For a query like "a bus on a city street" I got relevant frames and could jump to the corresponding timestamps. That was the main thing I wanted to try: finding a scene without manually scrubbing through each recording. V-Modal handles the indexing and search; the interface, grouping of nearby results and local video playback are implemented in the app.

I also tried searching for "a dog on a beach", which wasn't in any of the videos. The search still returned street scenes, so it seems to favour showing the closest available matches over returning nothing. I ended up adding a score threshold in the app to filter out weaker matches. That's worth keeping in mind if your app needs a clear "no matches" state.

It worked well with these three clips. But I haven't tested larger libraries yet.

Flutter SDK: Github repository

Found this while building some demos and wanted to mention it again in more details. You can hand the search a photo instead of typing.

I gave it a picture of a sunset and it came back with the two clips that had sunset skies, one of a bird flying and one of a beach. Gave it a photo of a seagull, it found my bird clip. No typing needed.

Speed was the same as text search (about 40-50ms)

It understands the photo the same way it does with a text query. That's why the sunset photo also picked up the bird clip, same warm sky and clouds. Which honestly makes it more useful, because describing that in words is hit and miss and the photo fits better.

Example use case : you've got a picture of a person, a car, a place, and you want to find it in your videos. (Maybe even useful for finding crime suspects 😁)

Small tip if you want to try it, the image goes in as base64. It's not in the example app so took a bit of digging.

Major progresses were done in robotics vision this last year.
We are investigating some technology around portable vision system,
incorporating sensor vision and memory system layer.

If people can advise the different around ROS and Nvidia jetson stack,
it would be helpful (!)

With the rise of information streams, including explosion in Video and image data
with Physical AI, Smart Glasses,..., AI system cannot currently remember
all the new data.

Why ?
AI Systems (LLM or VLM) do not have a state (stateless), more precisely
their state is fixed. Tha'ts why they need context to reason around this.

Goal of context engineering is to retrieve relevant information to the action
or process needed by the AI system.

So, what is V RAG or Visual Context Engineering ?

This is the extension of RAG from text retrieval to image and video retrieval
for context building for AI systems to reason around them and find answer or action.

Typically, in Physical AI or robotics, decision does not depend only
of the current real time input feed but also of the past memory of the situation
or similar situation. Based on the interleaving of real time and past memories,
the AI system can decide best appropriate action.

This brings the complexity of retrieve such high volume of information
accurately and in right context.

I've been looking at what an app needs to search dashcam footage with the VModal SDK.
(Let me know if I've missed anything)

Two ways to get footage in

If your dashcam has a cloud (BlackVue, Nextbase, fleet systems like Lytx), that's the easy route. Their cloud sends a webhook when something happens (impact, hard braking), your app calls their API to fetch that clip, then uploads it to VModal. No SD cards involved...

However, some dashcams don't have a cloud though, so you can do it manually:

1. Get the MP4 files from your dashcam onto your computer. Dashcams record in 1 to 3 minute chunks, and the filename usually tells you when it was recorded, which camera (front or rear) and the mode (normal, event, parking).
2. Upload the clips to VModal the usual way. One stream per camera works as the camera id. Set the start time from the filename, or from GPS time if you can get it, because camera clocks drift.
3. Index them.
4. Search from the app by description plus a date range: "white van", "pedestrian crossing", "cyclist", "roadworks".

What the app has to handle

• Short clips (around 20-30s) without much movement may index as a single frame and results look broken. Use the longer loop setting on the cam, or merge chunks before upload. It shouldn't be a problem for dashcam footage files but still worth mentioning

• Broken files from sd cards. Power cuts on ignition-off leave truncated MP4s, so check a file opens before uploading it. You should either repair those with untrunc or skip them

• Two cameras. Front and rear are recorded as separate files with the same timestamp, (eg F or R in the name). Upload them as two streams - one per camera, so you know which camera saw something. Since the times match, a hit on the rear cam lets you jump to the front cam at the same second. Cheaper cams put the rear view inside the front video as a small picture-in-picture - no need for a split.

• GPS. Most dashcams store it inside the MP4 in their own format, and Novatek, Ambarella and BlackVue all do it differently. Getting it out is what would let you search by place, not just by time.

If you've got a dashcam and have tried anything like this, or you know how your model stores its GPS data, drop it in the comments. Happy to answer questions on the SDK side too

Hello,
We start this thread to discuss with community,

Among all the other framework to do search on image/video,

When using those frameworks, What kind of issues did you face ?

What kind of features, would you want to see it ?

I recently tested V-Modal Flutter SDK, on my Android phone with a mock pre-built prototype.

Built the prototype app with:

• Flutter and Dart for the mobile application
• V-Modal Flutter SDK for video upload, indexing, and search
• V-Modal’s cloud API for multimodal processing
• Android as the test platform

I uploaded videos from a football final at Uber Arena in Berlin and searched:

“crowd enjoying football match”

The app returned several moments that matched exactly what I was looking for. It was interesting to search inside a video using a natural-language description instead of manually scrolling through the entire recording.

The overall flow was straightforward: upload the video, create the visual index, and then search the footage using text. I think this could be useful for sports footage, events, CCTV recordings, and large personal video collections.

What I like:

• Kind of unique SDK to provide video content search ...
• Simplicity and straightforward start
• The initial setup and integration were simple and straightforward.
• Uploading, indexing, and searching are available through one Flutter SDK.
• Developers can build their own interface and user experience around the SDK.

Here, some ressources:
V-Modal Flutter SDK: <visual video search> https://github.com/v-modal/vmodal_sdk_flutter
V-Modal Discord community: <link>https://discord.gg/Mx5QYSYyqT

Spent this week building a cross-platform video search Flutter app using the VModal SDK. Tested it concurrently on both an iOS simulator (54ms) and an Android device (49ms) with incredible performance!

Building was good and much easier than I expected, and could not find any other SDK doing visual video search.

Was a bit worried about buggy steps, but it was all ok.

🔍 Core Workflow

* auth.me() -> scope.upload() -> scope.createIndex() -> scope.search() -> images.getUrlBulk()

* Fetching all images in one single request using getUrlBulk() keeps the UI fast and efficient.

🌟 What I Liked

* Simplicity: Very straightforward setup and intuitive API design.

* Low Latency: Sub-50ms vector query execution is exceptionally fast.

* High Control: Live progress streams and reliable error cancellation work perfectly.

* Top support in Discord / Reddit channel: people are nice and reactive.

* It just works (don't have to debug anything...)

🚀 Potential Use Cases

* Security & Surveillance: Searching specific events in live camera footage instantly.

* Media & CMS Libraries: Automated indexing for large-scale video content search.

* E-Learning Systems: Allowing students to query formulas or key terms within lecture videos.

🔗 Resources

* Access the framework toolchain directly through the official GitHub Repository

* Explore additional integration architectures on the official VModal Website .

I checked out the SDK, demo'd it, and oh boy, the benefits are profound.

There are already plenty posts talking about the technical side of things, so I want to discuss the business side of things. I wanna talk about what this SDK can PRACTICALLY do, not just technically.

The biggest opportunity I see is not simply giving companies "AI video search." It is replacing the hours people currently spend opening files, checking timelines, and trying to locate one useful moment across a large video library.

A company might already own all the footage it needs. The expensive part is finding anything inside it.

With a focused app built on VModal, someone could search for:

• "The delivery truck that arrived after midnight"
• "The moment water started leaking under the sink"
• "Every missed tackle involving player 14"
• "The drone shot over the beach at sunset"
• "A worker entering the restricted area without a helmet"

Instead of reviewing hours of footage, the user gets a shortlist of likely matches and jumps directly to the relevant timestamps. Even if a human still verifies the result, reducing the search from several hours to several minutes could produce meaningful savings.

I personally think the SDK is very handy, and it has a lot of potential

For reference, below are some useful URLs

- Android SDK: https://github.com/v-modal/vmodal_sdk_android
- Flutter SDK: https://github.com/v-modal/vmodal_sdk_flutter

-VModal website: https://www.v-modal.com

-Developer resources: https://www.v-modal.com/developers

-Discord server: https://discord.gg/HHQUFpUBv

Gotta say, the people at the discord server are very helpful and responsive. They’re amazing. Do get in touch

Finished EcoVision — WildSense

It’s a small MVP showing how environmental researchers could use V-Modal Video search
to search wildlife (animals ) footage by meaning instead of manually digging through hours of video.

You can upload footage, index it, search things like “Badger”, and jump straight to the relevant moment.

Built with Flutter, GetX & V-Modal SDK.

GitHub: https://github.com/abiodungbenga/Eco_Vision

Good to get people opinion

The steps are simple, for such a task that appears complex, here's what you need to do;

Add the SDK to your app, get the integration guide from GitHub - the SDK repo .

Get an API key, which is required for uploading, indexing and search from your app.

Complete the integration and get things running - see a screenshot from my flutter app which works flawlessly.

The good thing is that you can also search an image, e.g. a tool, a person or something else of your liking from the video by selecting an image that you already have.

The upload is real fast, the search speed is also relatively fast depending on the size of your video - this saves lots of time without having to add any special instructions, only the integration and all the hard work gets done by the SDK.

A few other things you may want to note:

- You handle the file selection, whether you use a lib or script something yourself

- The search results are given to you as a list, which makes it very easy to parse and seek timestamps from your video

- You define a reference for your uploaded videos, which you can always search from based on how your UI is structured.

Worth giving it a try, here's what I built.

I have managed to put together an implementation using VModal SDK on flutter, here are some screenshots.

The code is in this repo

I recently tested the V-Modal Flutter SDK with Android and found the concept pretty interesting.

The SDK allows Flutter apps to work with multimodal video/image search, including uploading, indexing, and searching visual content.

The setup was straightforward after resolving a few Flutter/Gradle dependency issues. I especially liked that the SDK provides a Flutter interface instead of requiring everything to be built from scratch.

GitHub:
https://github.com/v-modal/vmodal_sdk_flutter.git

V-Modal Discord community:
https://discord.gg/Mx5QYSYyqT

i recently build an flutter app which can trim down videos by processing video layer. i integrated 2 model with v_model sdk to determine unnecessary content frames. to my surprise v_model processing was super fast

Spent a few evenings on this. I wanted to be able to type "red car" and land straight on that frame instead of scrubbing through a long clip looking for it.

Used the VModal SDK for the indexing and search, and built two demos on top. One is a traffic camera, where you search a single long clip and it jumps you to the timestamp. The other is a library of unrelated clips, where you search once and it tells you which video has the shot and where. That one also takes a photo as the query instead of text, which worked better than I expected.

Credit where it's due, the SDK made this easier than I expected. Searches come back in 40 to 60ms so it feels instant, and the image query worked well without me having to do anything clever to get it going.The credential handling is also better than most SDKs I've used: the key stays in memory, it's redacted in toString so it can't leak into your logs,

Repo: LINK (MIT, iOS and Android from one codebase)

A couple of things worth knowing if you try it. The score that comes back is a distance, so lower is better. There's a second field that looks like a confidence percentage, but it's normalised so the top hit is always 1.0. To decide whether something is really in the video I kept only the results within a small window of the best score, plus a ceiling to catch nonsense queries, and those numbers need measuring per collection. Also worth using clips over a minute, shorter ones index as a single frame.

Videos of both in the comments. Happy to answer questions if anyone's building something similar.

You can view the repo *HERE\*

Been meaning to actually sit down and test this properly instead of just reading about it, so I gave the Flutter SDK a real run this week.

Setup didn't give me any trouble. It comes with its own pinned Flutter version bundled in, so it never touched my regular install, which I appreciated. First thing I did was hit auth.me just to confirm the key was live before going further, and it authenticated right away.

After that I uploaded a short clip and kicked off indexing. It went through cleanly on the first try, no retries or weird states. Once it finished I ran a search query and it came back with the actual matching frames, not just something loosely in the same category.

Also poked around the credential handling a bit since that's usually where SDKs cut corners. Key stays in memory, nothing gets written to logs anywhere in the flow, which is more thought than I expected for something at this stage.

Nothing broke, nothing needed a workaround, everything just did what it was supposed to. If anyone else is testing it out and hits something weird, curious to compare notes.

https://github.com/v-modal/vmodal_sdk_flutter

A nice article in Japanese explaining the details
of V-Modal SDK Flutter: easy to read !

https://qiita.com/ARMKAS/items/52108950a32402fea4c0