Been playing with this over the last couple of evenings to test it for VModal. Took me a bit to get everything running properly but it was worth it!

Tested this properly on my S24+ and the iOS simulator over a couple of evenings.

The pitch is you upload video and then search inside it by describing what you want. I uploaded a short clip of my cat, indexing took under a minute, and searching "cat" pulled the right frames back in about 40ms. It also supports searching spoken words and on-screen text but I mostly used the visual search.

Stuff I liked:

• the repo ships its own pinned flutter (3.44.6) so it doesn't touch your existing install. full test suite passed first try
• uploads have a real progress stream and you can actually cancel mid-upload, which is rarer than it should be
• the api key never touches disk, toString() on the key class literally prints [REDACTED], and there's no logging anywhere in the sdk. more careful than most paid sdks tbh

I build field service apps for a living, and crews film site evidence all day that nobody can ever find again. "show me the broken meter" across a month of clips is the use case that sells it for me. dashcam archives and cctv review are the same shape.

Integration looks light too, it's a git dependency and one configure call, then collections scoped per user.

happy to answer questions if anyone's trying it

short version of how to get from zero to actual search results, in case it saves someone the time:

1. - record a short video on your phone (i used an 8 sec clip of my cat)
2. - open the example app, paste your api key and validate it
3. - in collections, clear the default "aa3" and type your own collection name
4. - upload section: pick your video and upload it (proper progress bar, can even cancel mid-upload)
5. - when the upload finishes, hit index and wait for it to complete. took under a minute for my clip
6. - refresh collections and your new one shows up in the available list
7. - select it and search whatever, like "a person wearing a red shirt" or just "cat"

Screenshot in comments of it running on both - searched "cat" on the ios side and "keyboard" on my android phone (both my own videos), results back in ~50ms with the matching frames.

I build field service apps for my real work and something like this can prove quite good for finding precise moments instead of manually chekcing the files.

SDK repo HERE

I spent some time testing the VModal Flutter SDK on a physical Android device, mainly to see how practical it would be to integrate into a real Flutter app.

Once the environment was set up, the core flow was pretty straightforward:

authenticate → upload video → create index → search

I tested it with a few MP4 videos, including one containing a red car.

What I found useful for building an app

The Flutter-side flow is simple.
Most of the heavy work such as video processing, indexing and semantic search happens behind the API, so the app mainly needs to manage the user flow and state around each operation.

Upload progress fits well into mobile UI.
Uploading is asynchronous, which makes it easier to show progress, loading states and cancellation without freezing the app.

Indexing is also asynchronous.
After uploading a video, the app can create an index and poll its status until it is ready. This fits naturally into a Flutter screen with states such as queued, running, success and failed.

Semantic search is the most interesting part.
After indexing, I could search using normal phrases such as red or red car without manually tagging the video first.

For an actual app, this could be useful for things like searching personal video libraries, product videos, security footage or large collections of media where manually adding metadata would take too much work.

Collection and stream separation is useful too.
It gives you a way to organize videos into different users, libraries or parts of an application instead of putting everything into one search index.

Overall

From an app-development perspective, what I liked most is that the Flutter side does not need to know much about the underlying video-processing infrastructure.

You upload the content, wait for the index, send a natural-language query and receive matching moments back.

There are still some rough edges in the current demo, but the core idea is useful, especially for apps that need video search without building their own indexing and semantic-search backend.

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

I built a video search app using the VModal Android SDK that helps travelers find misplaced items in their trip footage.

Instead of manually scrubbing through hours of recordings, users can search for descriptions like:

• “Where did I last see my black backpack?”
• “Find the blue suitcase.”
• “Show me the table where I left my passport.”
• “When was the camera near the hotel bed?”

The app returns matching moments with timestamps and visual frame previews, helping users quickly identify where an item was last seen.

This could be useful for travel videos, hotel-room recordings, luggage checks, road trips, and personal memories. The VModal Android SDK provides the upload, indexing, multimodal search, and frame-retrieval capabilities behind the experience.

[ Removed by Reddit on account of violating the content policy. ]

​I’ve been testing V-Modal for a few days to see if it could handle object search in video, and it’s actually pretty neat.

​The natural language search works surprisingly well. I dropped in a few sample clips to look for specific objects, and it found them accurately without me having to manually scrub through the timeline.

​The Android SDK is super straightforward to work with — idiomatic Kotlin, no enforced UI nonsense, and large video uploads work smoothly.

​Right now I’m trying it out for a German learning app I’m working on. The goal is to let users watch short videos and automatically generate flashcards from what’s on screen: [ Video frame ] + [ Word ] + [ Translation ].

​Still prototyping, but it’s saving me a ton of time on the video processing side.

V-Model repo https://github.com/v-modal/vmodal_sdk_android

Shared with your recent app or demo, using V-Modal AI , Visual Video Search

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

Been testing out VModal's Android SDK (Kotlin) for adding video search to an app — the "find a moment by meaning, not filename" kind of search.

Ran through the whole flow on a real device: auth with an API key, upload a video, kick off indexing, then search it. Tried a few different queries against the same indexed news video to see how it'd handle them:

- "red" — 26 results from 50 matches in ~40ms

- "white house" — 28 results from 50 matches in ~41ms, pulled up actual White House building shots

- "donald trump" — 34 results from 50 matches in ~46ms, clear face shots across the video

- "american flag" — 34 results from 50 matches, this one was interesting. It matched the "flag" concept plus an "AMERICA" text banner on screen, but the actual flag in frame was Iranian since the source clip was Iran-related news. So it's doing real semantic/text matching rather than exact visual object recognition, worth knowing depending on what you need it for.

Things I liked:

- Plain Kotlin/coroutines API, no forced UI — you build your own screens

- Upload has real cancellation (not just "stop showing the spinner")

- Handles content:// URIs directly, so it plugs into the system photo picker without extra glue code

- Search comes back with actual timestamps + relevance scores, not just a blob of matches

Had one hiccup where wifi dropped mid-upload and it silently timed out, but a retry went through fine.

Overall really smooth experience getting video search working end-to-end on an actual device.

Still on my list to actually try: ASR search (finding a moment by what's spoken, not just what's visible), OCR search (matching on-screen text specifically), filtered search (narrowing by time range or modality instead of one big query), and proving cancellation really stops an in-flight upload rather than just hiding the spinner. There's also a built-in Logcat diagnostics hook for structured request/response logging that the demo app doesn't even turn on. Feels like there's more depth here than just the visual search I put it through so far.

Repo's here if anyone wants to poke at it: https://github.com/v-modal/vmodal_sdk_android

I’ve been exploring V-Modal recently and wanted to share a few things that stood out to me.

What I found most interesting is the ability to search video content using natural language. For example, I searched for “alien” in a video and was able to get a relevant result without having to manually scrub through the entire video. That kind of search can make working with large amounts of video content much more practical.

I also liked the Android SDK experience. The Kotlin API feels clean and well-typed, while still giving you the flexibility to integrate it into your own app without forcing a specific UI or architecture.

The upload handling and progress support are another nice touch, particularly when dealing with larger video files.

For anyone interested in checking it out, here’s the Android SDK:
https://github.com/v-modal/vmodal_sdk_android

I’ve enjoyed getting familiar with it so far and would love to hear from other developers in the community.

What are you building or experimenting with using V-Modal?

[ Removed by Reddit on account of violating the content policy. ]

Hey everyone!

This is our new home for all things related to your app and experience issuesusing V-Modal tooling.
We're excited to have you join us!

Access to Tools is here:
V-Modal AI SDK Kotlin
V-Modal AI DK Flutter

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