Keeping documentation and SDK updates aligned with evolving "LLM contexts" can quickly overwhelm dev teams. At VideoDB, we've built an open-source solution—Agent Toolkit—that automates syncing your docs, SDK versions, and examples, making your dev content effortlessly consumable by Cursor, Claude AI, and other agents. Ready-to-use template available.
A new benchmark study evaluates Vision-Language Models (Claude-3, Gemini-1.5, GPT-4o) against traditional OCR tools (EasyOCR, RapidOCR) for extracting text from videos. The findings show VLMs outperforming OCR in many cases but also highlight challenges like hallucinated text and handling occluded/stylized fonts.
The dataset (1,477 manually annotated frames) and benchmarking framework are publicly available to encourage further research.
This open-source agent framework is like ChatGPT, but for videos. It simplifies complex video tasks like search, editing, compilation, and—best of all—generation. The results stream instantly. You can even extend the agents to suit your needs and build custom automated workflows.
The framework is fully open source and uses a VideoDB key for cloud-based video storage, processing, and streaming. It seamlessly integrates with tools like Stable Diffusion, Eleven Labs, Kling, Replicate, and more.
Looking for collaboration with GenAI audio/ video teams and feedback from amazing devs out here.
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LLMs are great with text, but they don't help you consume or create video clips. Checkout PromptClip
- Use natural language to describe the what you want.
- Instantly get video clips with the help of LLMs like OpenAI or Claude.
Few interesting prompts we tried while building it and loved the results. There's no limit to creativity with this.
So it's still just analysing the transcript, not using GPT4-V or OCR in any way?
Can you confirm if I could skip using VideoDB by using Whisper to transcribe the video, and then use that transcript with LLaMa to extract the important parts?
It analyse the transcript, but there is no way to get back the video clip without building your own video infra. We at Videodb are solving the exact problem.
Build custom GPT on your video data with StreamRAG in 2 mins.This search agent find relevant moments across hundreds of hours of content and return a video clip instantly.
RAG applications are great with text, but with video they can't support simple requests like "show me where sleep improvement is discussed"
The cloud providers more or less just give you a Kubernetes cluster. You can then connect this cluster (or any other cluster in a private cloud) to DevSpace Cloud, which allows developers to create their own namespaces on demand in the cluster.
DevSpace CLI is supposed to enable developers to work with Kubernetes regardless of their experience in working with k8s.
Overall, DevSpace does not replace the cloud providers but makes their Kubernetes offer accessible (DevSpace Cloud) and easy to use (DevSpace CLI) for developers.
