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Video Converter Software
Video converter software, also known as video encoding or video transcoding software, allows users to convert video files from one format to another, ensuring compatibility with various devices, platforms, or media players. These platforms typically support a wide range of video formats, such as MP4, AVI, MOV, MKV, and more, enabling users to adjust resolution, bitrate, and other settings during the conversion process. Video converter software often includes additional features like batch conversion, video trimming, and audio extraction, allowing for greater flexibility. By using this software, users can efficiently prepare videos for different uses, whether for sharing, editing, or playback on various devices.
Artificial Intelligence Software
Artificial Intelligence (AI) software is computer technology designed to simulate human intelligence. It can be used to perform tasks that require cognitive abilities, such as problem-solving, data analysis, visual perception and language translation. AI applications range from voice recognition and virtual assistants to autonomous vehicles and medical diagnostics.
  • 1
    Vectara

    Vectara

    Vectara

    ...Developers can embed the most advanced NLP models for app and site search in minutes. Vectara automatically extracts text from PDF and Office to JSON, HTML, XML, CommonMark, and many more. Encode at scale with cutting edge zero-shot models using deep neural networks optimized for language understanding. Segment data into any number of indexes storing vector encodings optimized for low latency and high recall. Recall candidate results from millions of documents using cutting-edge, zero-shot neural network models. Increase the precision of retrieved results with cross-attentional neural networks to merge and reorder results. ...
    Starting Price: Free
  • 2
    Shap-E

    Shap-E

    OpenAI

    ...To get the best result, you should remove the background from the input image. Load 3D models or a trimesh, and create a batch of multiview renders and a point cloud encode them into a latent and render it back. For this to work, install Blender version 3.3.1 or higher.
    Starting Price: Free
  • 3
    Towhee

    Towhee

    Towhee

    ...From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model inference, making your pipeline execution 10x faster. Towhee provides out-of-the-box integration with your favorite libraries, tools, and frameworks, making development quick and easy. Towhee includes a pythonic method-chaining API for describing custom data processing pipelines. We also support schemas, making processing unstructured data as easy as handling tabular data.
    Starting Price: Free
  • 4
    Swarm

    Swarm

    OpenAI

    ...It is designed to be scalable and highly customizable, making it suitable for scenarios involving a large number of independent capabilities and instructions that are challenging to encode into a single prompt. Swarm operates entirely on the client side and, like the Chat Completions API it utilizes, does not store state between calls. This stateless nature allows for the construction of scalable, real-world solutions without a steep learning curve. Swarm agents are distinct from assistants in the assistants API; they are named similarly for convenience but are otherwise completely unrelated. ...
    Starting Price: Free
  • 5
    DeepSeek-VL

    DeepSeek-VL

    DeepSeek

    ...The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead.
    Starting Price: Free
  • 6
    Pinecone Rerank v0
    Pinecone Rerank V0 is a cross-encoder model optimized for precision in reranking tasks, enhancing enterprise search and retrieval-augmented generation (RAG) systems. It processes queries and documents together to capture fine-grained relevance, assigning a relevance score from 0 to 1 for each query-document pair. The model's maximum context length is set to 512 tokens to preserve ranking quality.
    Starting Price: $25 per month
  • 7
    ColBERT

    ColBERT

    Future Data Systems

    ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. It relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings. At search time, it embeds every query into another matrix and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. These rich interactions allow ColBERT to surpass the quality of single-vector representation models while scaling efficiently to large corpora. ...
    Starting Price: Free
  • 8
    Diffgram Data Labeling
    ...More degrees of freedom through: Radio buttons. Multiple select. Date pickers. Sliders. Conditional logic. Directional Vectors. And more! You can capture complex knowledge and encode it into your AI. Streaming Data Automation Up to 10x Faster then manual labeling
    Starting Price: Free
  • 9
    Depthify

    Depthify

    Depthify

    ...We first run a monocular depth network which predicts the metric depth of each pixel in each image. Next, we convert the RGB and depth images into the left and right eyes of a stereo image. Finally, we encode the results into either an .HEIC image or MV-HEVC video which can be viewed on the Apple Vision Pro or Meta Quest. Converting RGB images to spatial photos is useful for various computer vision and 3D modeling applications. It enables the creation of depth maps, stereo images, and HEIC files for use with Apple Vision Pro and Meta Quest. ...
  • 10
    Klee

    Klee

    Klee

    ...This means you can keep sensitive data on-premises while leveraging it to enhance the model‘s response capabilities. To implement RAG locally, you first need to segment documents into smaller chunks and then encode these chunks into vectors, storing them in a vector database. These vectorized data will be used for subsequent retrieval processes. When a user query is received, the system retrieves the most relevant chunks from the local knowledge base and inputs these chunks along with the original query into the LLM to generate the final response. ...
  • 11
    MiniMax Audio

    MiniMax Audio

    MiniMax Audio

    ...Users can quickly generate lifelike audio samples via long-text mode, URL input, or voice cloning, capturing a unique voice in as little as 10 seconds, without needing transcription. The underlying technology incorporates cutting-edge AI such as transformer-based TTS models, a learnable speaker encoder, and Flow-VAE architectures, enabling zero- or one-shot voice cloning with high fidelity and expressive control, and it ranks at the top of public voice cloning benchmarks.
    Starting Price: Free
  • 12
    LLaVA

    LLaVA

    LLaVA

    LLaVA (Large Language-and-Vision Assistant) is an innovative multimodal model that integrates a vision encoder with the Vicuna language model to facilitate comprehensive visual and language understanding. Through end-to-end training, LLaVA exhibits impressive chat capabilities, emulating the multimodal functionalities of models like GPT-4. Notably, LLaVA-1.5 has achieved state-of-the-art performance across 11 benchmarks, utilizing publicly available data and completing training in approximately one day on a single 8-A100 node, surpassing methods that rely on billion-scale datasets. ...
    Starting Price: Free
  • 13
    Qwen3-VL

    Qwen3-VL

    Alibaba

    ...Qwen3-VL delivers major advances in spatial reasoning, visual perception, and multimodal reasoning; the model architecture incorporates several innovations such as Interleaved-MRoPE (for robust spatio-temporal positional encoding), DeepStack (to leverage multi-level features from its Vision Transformer backbone for refined image-text alignment), and text–timestamp alignment (for precise reasoning over video content and temporal events). These upgrades enable Qwen3-VL to interpret complex scenes, follow dynamic video sequences, read and reason about visual layouts.
    Starting Price: Free
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