u/falaq-ai

I’m working on Daqa, a waitlist-stage workspace for teams preparing AI training datasets, and I’m trying to sanity-check the computer vision side with people who actually build image/video datasets.

The workflow I’m looking at is everything around annotation: sourcing or uploading data, profiling quality issues, cleaning/deduping, generating missing cases, labeling/reviewing, tracking provenance/license evidence, validating the dataset, and exporting in formats like COCO, YOLO, or image manifests.

I’d really value feedback on four things:

• What feature would you most want to see in a tool for this workflow?
• Does the pricing on https://daqa.ai/ make sense for CV dataset prep?
• What would you need to see before joining a waitlist or trying it?
• What tools do you use today for this use case, such as CVAT, Roboflow, Label Studio, FiftyOne, scripts/notebooks, etc., and what do they still lack?

I’m especially trying to understand whether the pain is annotation itself, or the surrounding workflow: source tracking, review, dataset versioning, validation, and clean export.

I’m working on Daqa, a waitlist-stage workspace for teams preparing AI training or evaluation datasets, and I’m trying to sanity-check the workflow with people who actually manage ML/data pipelines.

The problem I’m looking at is the messy path from source data to model-ready export: finding data, uploading/importing it, profiling quality issues, approving cleaning/transforms, generating missing examples, labeling/reviewing, tracking provenance/license evidence, validating, and exporting in the right format.

I’d really value feedback on four things:

• What feature would you most want in a tool for this workflow?
• Does the pricing on https://daqa.ai/ make sense for this kind of dataset ops tool?
• What would you need to see before joining a waitlist or trying it?
• What tools are you using now for this use case, and what do they still lack?

I’m especially interested in whether this should start as a broad dataset operations workspace or a narrower wedge around provenance, eval datasets, labeling/export, or synthetic data review.

I’m building Daqa, a waitlist-stage product for teams preparing AI training data.

The pitch is:

Daqa helps teams find source data, clean it, generate missing records, label what matters, and export reviewed Data Packs from one workspace.

It is meant for the messy middle between “I need training data” and “I have a clean dataset ready for my model.” That usually involves public dataset search, uploads, notebooks, cleaning scripts, annotation tools, synthetic data experiments, provenance notes, and export formatting.

The waitlist page is here: https://daqa.ai/

I’d love feedback from anyone who has had to prepare training or evaluation data:

• What feature would you most want to see in a tool like this?
• Does the pricing on the page make sense for this kind of workflow?
• What would you need to see before joining the waitlist?
• What tools do you use today for this use case, and what do they still lack?

I published a React Native package called react-native-client and would like feedback from people who have shipped native/mobile packages.

NPM: https://www.npmjs.com/package/react-native-client

GitHub: https://github.com/zraisan/react-native-client

The package came out of building Orb, my private offline AI app for Android. Orb needs to download local model files that can be multiple GB, so I needed something more reliable than a simple JS download flow.

Current stable API: downloadFile

What it supports right now:

• native direct-to-file downloads
• OkHttp on Android
• URLSession on iOS
• progress callbacks
• HTTP Range resume
• Content-Range validation before appending partial files
• Android foreground-service background mode
• iOS background URLSession
• Nitro Modules typed API

What it does not support yet:

• task IDs
• cancel API
• persistent queue
• checksum verification
• uploads
• general request/response API

Orb is the first real product use case: https://play.google.com/store/apps/details?id=com.falaq.orb

I’m mainly looking for API/package feedback before expanding the surface area.

I built Orb because I wanted AI on my phone that still worked when the network disappeared.

Most AI apps send every prompt to a server. Orb runs local models directly on Android, so after the one-time model download it works without Wi-Fi or mobile data. No account, no subscription, no ads.

What it does:

• Offline AI chat
• Image analysis on-device
• Document upload for private local Q&A
• Voice input and text-to-speech
• No login, email, API key, or cloud chat backend
• CPU fallback, GPU where supported, and experimental Snapdragon NPU paths on supported devices

Supported models include Qwen3-VL 2B, Qwen3-VL 4B, MiniCPM-V 4.6, and Gemma 4 E2B.

It is Android / Google Play only for now. Current price is $0.99 one-time: https://play.google.com/store/apps/details?id=com.falaq.orb

I’d love feedback from people who care about private/offline tools: is the value clear from the listing, and what device compatibility info would you want before buying?

https://preview.redd.it/dntqfje8jfah1.png?width=512&format=png&auto=webp&s=719728bdb3401c8d28a95c395db69a09c3645ca8

https://preview.redd.it/eci5j7y9jfah1.png?width=288&format=png&auto=webp&s=113ea7c02181c4a2c5d432f8dcc059ef5fdde103

Need 12 testers to test my application for 14 days; Basically just opt-in and interact with the app briefly during the time period and send me your feedback, you know the drill. I of course will test your app back.

Join the google group first: https://groups.google.com/g/falaq-group

Then:

Join on Android: https://play.google.com/store/apps/details?id=com.falaq.orb

Or

Join on Web: https://play.google.com/apps/testing/com.falaq.orb