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In this blog, we will build live image search and query it with natural language. For example, you can search for "an elephant", or a "cute animal" with a list of images as input. We are going to use multimodal embedding model to understand and embed the image, and build a vector index for efficient retrieval. We are going use CocoIndex to build the indexing flow, it is a ultra performant real-time data transformation framework. During running, you can add new files to the folder and it only process changed files and will be indexed within a minute. It'd mean a lot to us if you could drop a star at CocoIndex on Github, if this tutorial is helpful. CocoIndex on Github Technologies CocoIndex CocoIndex is an ultra performant real-time data transformation framework for AI. CocoIndex CLIP ViT-L/14 CLIP ViT-L/14 is a powerful vision-language model that can understand both images and texts. It's trained to align visual and textual representations in a shared embedding space, making it perfect for our image search use case. CLIP ViT-L/14 In our project, we use CLIP to: Generate embeddings of the images directly
Convert natural language search queries into the same embedding space
Enable semantic search by comparing query embeddings with caption embeddings Generate embeddings of the images directly Convert natural language search queries into the same embedding space Enable semantic search by comparing query embeddings with caption embeddings Qdrant Qdrant is a high performance vector database. We use it to store and query the embeddings. Qdrant FastAPI FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. We use it to build the web API for the image search. FastAPI Prerequisites Install Postgres. CocoIndex uses Postgres to keep track of data lineage for incremental processing.
Install Qdrant. Install Postgres. CocoIndex uses Postgres to keep track of data lineage for incremental processing. Install Postgres Install Qdrant. Install Qdrant Define Indexing Flow Flow Design The flow diagram illustrates how we'll process our codebase: Read image files from the local filesystem
Use CLIP to understand and embed the image
Store the embeddings in a vector database for retrieval Read image files from the local filesystem Use CLIP to understand and embed the image Store the embeddings in a vector database for retrieval 1. Ingest the images. @cocoindex.flow_def(name="ImageObjectEmbedding")
def image_object_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    data_scope["images"] = flow_builder.add_source(
        cocoindex.sources.LocalFile(path="img", included_patterns=["*.jpg", "*.jpeg", "*.png"], binary=True),
        refresh_interval=datetime.timedelta(minutes=1)  # Poll for changes every 1 minute
    )
    img_embeddings = data_scope.add_collector() @cocoindex.flow_def(name="ImageObjectEmbedding")
def image_object_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    data_scope["images"] = flow_builder.add_source(
        cocoindex.sources.LocalFile(path="img", included_patterns=["*.jpg", "*.jpeg", "*.png"], binary=True),
        refresh_interval=datetime.timedelta(minutes=1)  # Poll for changes every 1 minute
    )
    img_embeddings = data_scope.add_collector() flow_builder.add_source will create a table with sub fields (filename, content), we can refer to the documentation for more details. flow_builder.add_source filename content documentation 2. Process each image and collect the information. 2.1 Embed the image with CLIP @functools.cache
def get_clip_model() -> tuple[CLIPModel, CLIPProcessor]:
    model = CLIPModel.from_pretrained(CLIP_MODEL_NAME)
    processor = CLIPProcessor.from_pretrained(CLIP_MODEL_NAME)
    return model, processor @functools.cache
def get_clip_model() -> tuple[CLIPModel, CLIPProcessor]:
    model = CLIPModel.from_pretrained(CLIP_MODEL_NAME)
    processor = CLIPProcessor.from_pretrained(CLIP_MODEL_NAME)
    return model, processor The @functools.cache decorator caches the results of a function call. In this case, it ensures that we only load the CLIP model and processor once. @functools.cache @cocoindex.op.function(cache=True, behavior_version=1, gpu=True)
def embed_image(img_bytes: bytes) -> cocoindex.Vector[cocoindex.Float32, Literal[384]]:
    """
    Convert image to embedding using CLIP model.
    """
    model, processor = get_clip_model()
    image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        features = model.get_image_features(**inputs)
    return features[0].tolist() @cocoindex.op.function(cache=True, behavior_version=1, gpu=True)
def embed_image(img_bytes: bytes) -> cocoindex.Vector[cocoindex.Float32, Literal[384]]:
    """
    Convert image to embedding using CLIP model.
    """
    model, processor = get_clip_model()
    image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        features = model.get_image_features(**inputs)
    return features[0].tolist() embed_image is a custom function that uses the CLIP model to convert an image into a vector embedding. It accepts image data in bytes format and returns a list of floating-point numbers representing the image's embedding. embed_image The function supports caching through the cache parameter. When enabled, the executor will store the function's results for reuse during reprocessing, which is particularly useful for computationally intensive operations. For more information about custom function parameters, refer to the documentation. cache documentation Then we are going to process each image and collect the information. with data_scope["images"].row() as img:
    img["embedding"] = img["content"].transform(embed_image)
    img_embeddings.collect(
        id=cocoindex.GeneratedField.UUID,
        filename=img["filename"],
        embedding=img["embedding"],
    ) with data_scope["images"].row() as img:
    img["embedding"] = img["content"].transform(embed_image)
    img_embeddings.collect(
        id=cocoindex.GeneratedField.UUID,
        filename=img["filename"],
        embedding=img["embedding"],
    ) 2.3 Collect the embeddings Export the embeddings to a table in Qdrant. img_embeddings.export(
    "img_embeddings",
    cocoindex.storages.Qdrant(
        collection_name="image_search",
        grpc_url=QDRANT_GRPC_URL,
    ),
    primary_key_fields=["id"],
    setup_by_user=True,
) img_embeddings.export(
    "img_embeddings",
    cocoindex.storages.Qdrant(
        collection_name="image_search",
        grpc_url=QDRANT_GRPC_URL,
    ),
    primary_key_fields=["id"],
    setup_by_user=True,
) 3. Query the index Embed the query with CLIP, which maps both text and images into the same embedding space, allowing for cross-modal similarity search. def embed_query(text: str) -> list[float]:
    model, processor = get_clip_model()
    inputs = processor(text=[text], return_tensors="pt", padding=True)
    with torch.no_grad():
        features = model.get_text_features(**inputs)
    return features[0].tolist() def embed_query(text: str) -> list[float]:
    model, processor = get_clip_model()
    inputs = processor(text=[text], return_tensors="pt", padding=True)
    with torch.no_grad():
        features = model.get_text_features(**inputs)
    return features[0].tolist() Defines a FastAPI endpoint /search that performs semantic image search. /search @app.get("/search")
def search(q: str = Query(..., description="Search query"), limit: int = Query(5, description="Number of results")):
    # Get the embedding for the query
    query_embedding = embed_query(q)
    
    # Search in Qdrant
    search_results = app.state.qdrant_client.search(
        collection_name="image_search",
        query_vector=("embedding", query_embedding),
        limit=limit
    ) @app.get("/search")
def search(q: str = Query(..., description="Search query"), limit: int = Query(5, description="Number of results")):
    # Get the embedding for the query
    query_embedding = embed_query(q)
    
    # Search in Qdrant
    search_results = app.state.qdrant_client.search(
        collection_name="image_search",
        query_vector=("embedding", query_embedding),
        limit=limit
    ) This searches the Qdrant vector database for similar embeddings. Returns the top limit results limit # Format results
out = []
for result in search_results:
    out.append({
        "filename": result.payload["filename"],
        "score": result.score
    })
return {"results": out} # Format results
out = []
for result in search_results:
    out.append({
        "filename": result.payload["filename"],
        "score": result.score
    })
return {"results": out} This endpoint enables semantic image search where users can find images by describing them in natural language, rather than using exact keyword matches. Application Fast API app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
# Serve images from the 'img' directory at /img
app.mount("/img", StaticFiles(directory="img"), name="img") app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
# Serve images from the 'img' directory at /img
app.mount("/img", StaticFiles(directory="img"), name="img") FastAPI application setup with CORS middleware and static file serving The app is configured to: Allow cross-origin requests from any origin
Serve static image files from the 'img' directory
Handle API endpoints for image search functionality Allow cross-origin requests from any origin Serve static image files from the 'img' directory Handle API endpoints for image search functionality @app.on_event("startup")
def startup_event():
    load_dotenv()
    cocoindex.init()
    # Initialize Qdrant client
    app.state.qdrant_client = QdrantClient(
        url=QDRANT_GRPC_URL,
        prefer_grpc=True
    )
    app.state.live_updater = cocoindex.FlowLiveUpdater(image_object_embedding_flow)
    app.state.live_updater.start() @app.on_event("startup")
def startup_event():
    load_dotenv()
    cocoindex.init()
    # Initialize Qdrant client
    app.state.qdrant_client = QdrantClient(
        url=QDRANT_GRPC_URL,
        prefer_grpc=True
    )
    app.state.live_updater = cocoindex.FlowLiveUpdater(image_object_embedding_flow)
    app.state.live_updater.start() The startup event handler initializes the application when it first starts up. Here's what each part does: load_dotenv(): Loads environment variables from a .env file, which is useful for configuration like API keys and URLs
cocoindex.init(): Initializes the CocoIndex framework, setting up necessary components and configurations
Qdrant Client Setup:

Creates a new QdrantClient instance
Configures it to use the gRPC URL specified in environment variables
Enables gRPC preference for better performance
Stores the client in the FastAPI app state for access across requests


Live Updater Setup:

Creates a FlowLiveUpdater instance for the image_object_embedding_flow
This enables real-time updates to the image search index
Starts the live updater to begin monitoring for changes load_dotenv(): Loads environment variables from a .env file, which is useful for configuration like API keys and URLs load_dotenv() cocoindex.init(): Initializes the CocoIndex framework, setting up necessary components and configurations cocoindex.init() Qdrant Client Setup:

Creates a new QdrantClient instance
Configures it to use the gRPC URL specified in environment variables
Enables gRPC preference for better performance
Stores the client in the FastAPI app state for access across requests Creates a new QdrantClient instance
Configures it to use the gRPC URL specified in environment variables
Enables gRPC preference for better performance
Stores the client in the FastAPI app state for access across requests Creates a new QdrantClient instance QdrantClient Configures it to use the gRPC URL specified in environment variables Enables gRPC preference for better performance Stores the client in the FastAPI app state for access across requests Live Updater Setup:

Creates a FlowLiveUpdater instance for the image_object_embedding_flow
This enables real-time updates to the image search index
Starts the live updater to begin monitoring for changes Creates a FlowLiveUpdater instance for the image_object_embedding_flow
This enables real-time updates to the image search index
Starts the live updater to begin monitoring for changes Creates a FlowLiveUpdater instance for the image_object_embedding_flow FlowLiveUpdater image_object_embedding_flow This enables real-time updates to the image search index Starts the live updater to begin monitoring for changes This initialization ensures that all necessary components are properly configured and running when the application starts. Frontend you can check the frontend code here. We intentionally kept it simple and minimalistic to focus on the image search functionality. here Time to have fun! Create a collection in Qdrant
curl -X PUT 'http://localhost:6333/collections/image_search' \
-H 'Content-Type: application/json' \
-d '{
    "vectors": {
    "embedding": {
        "size": 768,
        "distance": "Cosine"
    }
    }
}'



Setup indexing flow
cocoindex setup main.py

It is setup with a live updater, so you can add new files to the folder and it will be indexed within a minute.


Run backend
uvicorn main:app --reload --host 0.0.0.0 --port 8000



Run frontend
cd frontend
npm install
npm run dev Create a collection in Qdrant
curl -X PUT 'http://localhost:6333/collections/image_search' \
-H 'Content-Type: application/json' \
-d '{
    "vectors": {
    "embedding": {
        "size": 768,
        "distance": "Cosine"
    }
    }
}' Create a collection in Qdrant curl -X PUT 'http://localhost:6333/collections/image_search' \
-H 'Content-Type: application/json' \
-d '{
    "vectors": {
    "embedding": {
        "size": 768,
        "distance": "Cosine"
    }
    }
}' curl -X PUT 'http://localhost:6333/collections/image_search' \
-H 'Content-Type: application/json' \
-d '{
    "vectors": {
    "embedding": {
        "size": 768,
        "distance": "Cosine"
    }
    }
}' Setup indexing flow
cocoindex setup main.py

It is setup with a live updater, so you can add new files to the folder and it will be indexed within a minute. Setup indexing flow cocoindex setup main.py cocoindex setup main.py It is setup with a live updater, so you can add new files to the folder and it will be indexed within a minute. Run backend
uvicorn main:app --reload --host 0.0.0.0 --port 8000 Run backend uvicorn main:app --reload --host 0.0.0.0 --port 8000 uvicorn main:app --reload --host 0.0.0.0 --port 8000 Run frontend
cd frontend
npm install
npm run dev Run frontend cd frontend
npm install
npm run dev cd frontend
npm install
npm run dev Go to http://localhost:5174 to search. http://localhost:5174 Now add another image in the img folder, for example, this cute squirrel, or any picture you like. Wait a minute for the new image to be processed and indexed. img cute squirrel If you want to monitor the indexing progress, you can view it in CocoInsight cocoindex server -ci main.py . cocoindex server -ci main.py Finally - we are constantly improving, and more features and examples are coming soon. If you love this article, please give us a star ⭐ at GitHub to help us grow. Thanks for reading! Finally - we are constantly improving, and more features and examples are coming soon. If you love this article, please give us a star ⭐ at GitHub to help us grow. Thanks for reading! GitHub

How to Build Live Image Search With Vision Model and Query With Natural Language

Sådan opbygges Live Image Search med Vision Model og Query med Naturligt Sprog

Cómo construir la búsqueda de imagen en vivo con el modelo de visión y la consulta con el lenguaje natural

Kuinka rakentaa live-kuvanhaku visio-mallilla ja kyselyllä luonnollisella kielellä

Ki jan yo bati rechèch imaj viv ak modèl vizyon ak kesyon ak lang natirèl

自然言語でビジョンモデルとクエリを使用してライブイメージ検索を構築する方法

Na na na na na na na na na na na na na na na

Kā izveidot dzīvu attēlu meklēšanu ar redzes modeli un vaicājumu ar dabisko valodu

चन्द्र रानोहोंछाका कवितामा पाइने सबैभन्दा सुन्दर पक्ष हो- भाषाको प्रयोग र त्यसको शसक्तता ।

Как создать поиск живых изображений с помощью модели зрения и запроса с помощью естественного языка

Hur man bygger Live Image Search med Vision Model och Query med naturligt språk

如何用自然语言构建视觉模型和查询的现场图像搜索

Indlela yokwakha ukubuyekeza imifanekiso Live nge-Vision Model kanye ne-Query ne-Natural Language

ScyllaDB Hits Fourth Generation with Raft, Tablets, and a Cloud-First Vision

**PANAMA CITY, PANAMA, May 20th, 2025/Chainwire/--**BingX, a leading cryptocurrency exchange and Web3 AI company, is excited to announce a bold, incremental strategy BingX AI Evolution to mark its 7th anniversary. BingX has outlined a strategic initiative to integrate artificial intelligence across its platform and ecosystem, allocating $300 million over the next three years to support this development. BingX AI Evolution BingX AI Evolution This milestone initiative underscores BingX's mission to redefine crypto with AI, driving meaningful progress across the Web3 space by embedding AI into every aspect of user experience, exchange operations, and future industry collaboration. "We are thrilled to share this evolution as we celebrate our 7th anniversary. This is not just a product update — it's a strategic reorientation," said Vivien Lin, Chief Product Officer at BingX. "In the future, AI will become a defining engine of our evolution, enabling faster and smarter experiences. We want BingX to be more than just a trading platform — we want it to be a place where users feel empowered and genuinely supported by intelligent tools at every step of their journey. As AI grows more powerful, it's crucial that innovation remains aligned with user needs and long-term trust. This drives our commitment to leading AI integration in crypto with both ambition and responsibility." "We are thrilled to share this evolution as we celebrate our 7th anniversary. This is not just a product update — it's a strategic reorientation," said Vivien Lin, Chief Product Officer at BingX. "In the future, AI will become a defining engine of our evolution, enabling faster and smarter experiences. We want BingX to be more than just a trading platform — we want it to be a place where users feel empowered and genuinely supported by intelligent tools at every step of their journey. As AI grows more powerful, it's crucial that innovation remains aligned with user needs and long-term trust. This drives our commitment to leading AI integration in crypto with both ambition and responsibility." Vivien Lin Vivien Lin AI as a Companion in Every User Journey BingX will deploy AI across every stage of the user experience, beginning with an AI-powered tool to support users across various scenarios. From real-time market insights and position diagnostics to intelligent trade suggestions and trader analysis, the tool is designed to help users trade smarter and feel more confident. It addresses common concerns like market uncertainty and complex strategies. This marks the first step in making AI a true companion in every user journey. Meanwhile, BingX Labs will accelerate investments in AI-native Web3 projects, incubating innovations that bridge decentralized technologies with machine learning capabilities. AI at the Core of BingX DNA Looking ahead, BingX is set to become an AI-native company in its operations, culture, and decision-making. 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BingX's growth underscores its commitment to this AI evolution. The platform now serves over 20 million users worldwide and ranks among the Top 5 crypto derivatives trading platforms. Through BingX Labs, over $15 million has already been invested to accelerate innovation and support the broader Web3 ecosystem. As BingX moves forward, its AI-driven approach will continue to shape a more intelligent, inclusive, and connected digital economy — leading with purpose, inspiring progress, and unlocking smarter ways to AI-native future of crypto. About BingX Founded in 2018, BingX is a leading crypto exchange and Web3 AI company, serving a global community of over 20 million users. With a comprehensive suite of AI-powered products and services, including derivatives, spot trading, and copy trading, BingX caters to the evolving needs of users across all experience levels, from beginners to professionals. BingX BingX Committed to building a trustworthy and intelligent trading platform, BingX empowers users with innovative tools designed to enhance performance and confidence. In 2024, BingX proudly became the official crypto exchange partner of Chelsea Football Club, marking an exciting debut in the world of sports sponsorship. For media inquiries, users can contact: media@bingx.com media@bingx.com For more information, users can visit: https://bingx.com/ https://bingx.com/ https://bingx.com/ Contact BingX Media media@bingx.com This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program. This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program. Program Program

BingX AI Evolution Begins: A $300M Vision To Build The Future Of AI-Powered Crypto

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BingX AI Evolution เริ่มต้น: วิสัยทัศน์ 300 ล้านดอลลาร์เพื่อสร้างอนาคตของ Crypto ที่ขับเคลื่อนด้วย AI

These 5 Unexpected Use Cases Prove WebRTC Has Outgrown Its Roots as "Just" a Video-calling Tool

CapCut has become one of the most popular video editing apps for mobile content creators. Whether you are a YouTuber, a TikTok star, or just love creating cinematic edits on your phone, CapCut offers powerful tools right at your fingertips. But are you really using CapCut to its full potential? In this post, we will uncover 10 hidden features in CapCut that can take your video editing skills to the next level. Video Editing: A Quick Guide for Newbies Video Editing: A Quick Guide for Newbies 1. Keyframe animations beyond zoom Most users use keyframes to zoom in or out, but CapCut's keyframe system allows for so much more. You can animate position, rotation, scale, and even opacity. For example, you can create smooth pans over a still photo or make text follow a motion path. Pro Tip: Use subtle keyframe rotation to simulate natural handheld camera motion. Pro Tip: 17 Best AI Animation Software for Beginners 17 Best AI Animation Software for Beginners 2. Custom curve speed control Instead of the standard speed up or slow down, CapCut offers a custom curve speed feature. This allows you to control the speed of a clip at different points, creating dramatic slow-motion effects or snappy speed ramps—perfect for transitions and action sequences. Pro Tip: Use S-curve patterns for smooth cinematic motion transitions. Pro Tip: 3. Removing background without Green Screen CapCut uses AI to remove backgrounds from your videos, no green screen required. This feature is especially useful for creating reels or shorts with clean, professional-looking subjects separated from busy backgrounds. Pro Tip: Combine with blur or gradient background for a studio-style effect. Pro Tip: 5 Best Apps to Make Your Social Media Reels and Stories Glow 5 Best Apps to Make Your Social Media Reels and Stories Glow 4. Masking for split-screen or Creative Cuts CapCut’s masking tool lets you isolate parts of a video using shapes like linear, mirror, or circle. One creative application: film yourself twice in different parts of the frame and use masking to combine them to get a clone effect. Pro Tip: Feather the mask edges for smoother blending. Pro Tip: 5. Blending modes for Pro transitions Blending modes such as "Screen," "Overlay," and "Multiply" allow you to mix layers in creative ways. Combine a light leak overlay with the "Screen" mode and your transitions will look professionally graded. Pro Tip: Add VHS overlays with "Overlay" mode for a retro vibe. Pro Tip: 6. Reverse and replay tricks Use CapCut’s reverse function to make time flow backward, creating compelling or humorous effects. Combine it with speed ramping or bounce transitions for extra impact. Pro Tip: Loop short reversed clips for a unique replay effect. Pro Tip: 7. Voice effects and audio sync tools Voice changer effects can add humor or disguise a voice. CapCut also helps you sync clips to beats automatically—ideal for music videos or dance edits. Pro Tip: Use auto-beat sync with text animations for catchy lyric videos. Pro Tip: 10 Best Exciting TikTok Video Ideas To Make Your Videos Stand Out 10 Best Exciting TikTok Video Ideas To Make Your Videos Stand Out 8. Smart cutouts for dynamic captions The cutout tool can help you create depth by placing captions behind people or objects. Just mask the subject and position your text strategically for a dynamic, layered look. Pro Tip: Combine cutouts with motion blur to simulate depth of field. Pro Tip: 9. AI Captions and Subtitles CapCut’s auto-captioning feature can generate subtitles instantly. Though it doesn't yet support all languages, it's a huge time-saver. You can adjust the font, color, and timing to fit your style. Pro Tip: Use bold captions in reels to keep viewers engaged without sound. Pro Tip: 10. Use of Overlays for Cinematic Look By adding overlays like film grain, light flares, or dust particles, you can give your videos a cinematic edge. Pair this with filters or LUTs to complete the look. Pro Tip: Layer multiple overlays at low opacity for a textured, filmic feel. Pro Tip: Comparison Table: CapCut vs. other mobile editors Feature

CapCut

InShot

Premiere Rush



Keyframe Animation

✔️ Full control

❌ Basic only

✔️ Limited



Curve Speed Control

✔️ Customizable

❌ No

✔️ Partial



AI Background Removal

✔️ Yes

❌ No

❌ No



Blending Modes

✔️ Multiple

❌ None

✔️ Basic



Auto Subtitles

✔️ Yes

❌ No

✔️ Yes Feature

CapCut

InShot

Premiere Rush



Keyframe Animation

✔️ Full control

❌ Basic only

✔️ Limited



Curve Speed Control

✔️ Customizable

❌ No

✔️ Partial



AI Background Removal

✔️ Yes

❌ No

❌ No



Blending Modes

✔️ Multiple

❌ None

✔️ Basic



Auto Subtitles

✔️ Yes

❌ No

✔️ Yes Feature

CapCut

InShot

Premiere Rush Feature Feature CapCut CapCut InShot InShot Premiere Rush Premiere Rush Keyframe Animation

✔️ Full control

❌ Basic only

✔️ Limited Keyframe Animation Keyframe Animation ✔️ Full control ✔️ Full control ❌ Basic only ❌ Basic only ✔️ Limited ✔️ Limited Curve Speed Control

✔️ Customizable

❌ No

✔️ Partial Curve Speed Control Curve Speed Control ✔️ Customizable ✔️ Customizable ❌ No ❌ No ✔️ Partial ✔️ Partial AI Background Removal

✔️ Yes

❌ No

❌ No AI Background Removal AI Background Removal ✔️ Yes ✔️ Yes ❌ No ❌ No ❌ No ❌ No Blending Modes

✔️ Multiple

❌ None

✔️ Basic Blending Modes Blending Modes ✔️ Multiple ✔️ Multiple ❌ None ❌ None ✔️ Basic ✔️ Basic Auto Subtitles

✔️ Yes

❌ No

✔️ Yes Auto Subtitles Auto Subtitles ✔️ Yes ✔️ Yes ❌ No ❌ No ✔️ Yes ✔️ Yes Frequently Asked Questions (FAQ) Q: Can you use CapCut without a watermark? Q: A: Yes, CapCut does not add a watermark by default unless using certain templates. A: Q: Is CapCut good for professional editing? Q: A: Absolutely. While it's a mobile app, its features rival many desktop editors for content creators. A: Q: Does CapCut support 4K exports? Q: A: Yes, CapCut allows export in up to 4K resolution, depending on your device. Conclusion CapCut isn’t just a basic editing tool—it’s a full-featured mobile editing powerhouse. By exploring these hidden features, you will unlock new creative possibilities and stand out from the crowd. Start experimenting today, and see where your video editing journey takes you.

10 Hidden CapCut Features Every Mobile Video Editor Should Know

Turning Flat Images into 3D Worlds: The Math and Mechanics Behind Stereo Vision

Bold visions are common; but transforming those visions into tangible outcomes is where true leadership is tested. For Dima Rukin, CEO of LaFinteca, the challenge is clear: building a company that not only imagines a world of inclusive financial ecosystems without borders, but one that delivers it through daily execution. According to Rukin, moving from vision to velocity requires more than ambition. It demands discipline, clarity, and a deep commitment to operational structure. Based on his experience leading LaFinteca’s expansion across Latin America, he highlights five key principles that have shaped the company’s strategic execution. 1. Vision as a Strategic Filter Rukin believes that a company’s vision must serve as a practical filter for decision-making. At LaFinteca, every initiative—whether product development, partnerships, or market entry—is evaluated based on its alignment with the goal of making cross-border payments more accessible and inclusive. “A clear vision helps avoid distraction and keeps teams focused on what truly moves the needle,” he explains. “A clear vision helps avoid distraction and keeps teams focused on what truly moves the needle,” he explains. 2. Structured, Rhythmic Execution Execution Rukin argues, is not about speed alone—it’s about structure. LaFinteca operates on a system of quarterly priorities and measurable OKRs that cascade across teams. This rhythm allows for fast iteration without losing alignment. Strategic goals are translated into clear team objectives, ensuring that every department—from tech to compliance—knows how their work connects to the broader mission. Empowerment Through Context One of the most important lessons Rukin shares is the shift from control to context. “In a cross-border business, centralized decision-making slows everything down,” he says. “In a cross-border business, centralized decision-making slows everything down,” he says. Instead, LaFinteca equips teams with deep context about the company’s goals and challenges, enabling faster, smarter local decisions, especially critical in diverse regulatory and cultural environments across LATAM. Learning-Driven Feedback Loops For Rukin, agility doesn’t mean changing course constantly—it means learning quickly. That’s why LaFinteca invests in robust feedback loops, including real-time performance data, customer feedback, and regular cross-functional reviews. These insights allow the company to adapt strategy without losing direction. Reinforcing the Vision Daily Finally, Rukin emphasizes that vision must be embedded into the company’s everyday language, rituals, and decisions. From internal communications to onboarding new hires, LaFinteca continuously reinforces that it is not just building infrastructure—it is enabling inclusion, access, and trust across borders. In an industry full of disruption and noise, Dima Rukin’s approach offers a grounded blueprint: combine vision with systems, context, and constant alignment. It’s a formula that has helped LaFinteca grow with purpose—and one that other fintech leaders may look to as they scale across complex, fragmented markets. Also read Dmytro Rukin’s thoughts here: “What Drives a Fintech CEO: 5 Numbers That Matter Every Morning” Also read Dmytro Rukin’s thoughts here: “What Drives a Fintech CEO: 5 Numbers That Matter Every Morning” What Drives a Fintech CEO: 5 Numbers That Matter Every Morning

Dmytro Rukin, CEO of LaFinteca: From Vision to Velocity - Turning Strategy into Execution

Dmytro Rukin, CEO de LaFinteca: De la visión a la velocidad - Transformando la estrategia en ejecución

LaFintecan toimitusjohtaja Dmytro Rukin: Visioista nopeuteen – strategia toteutukseen

Dmytro Rukin, PDG de LaFinteca : De la vision à la vitesse – Transformer la stratégie en exécution

Dmytro Rukin, CEO של LaFinteca: From Vision to Velocity – Turning Strategy into Execution

Dmytro Rukin, izvršni direktor tvrtke LaFinteca: Od vizije do brzine - pretvaranje strategije u izvršenje

Dmytro Rukin, CEO LaFinteca: Dari Visi ke Kecepatan - Mengubah Strategi Menjadi Eksekusi

LaFinteca CEO Dmytro Rukin: From Vision to Velocity - 戦略を実行に変える

Dmytro Rukin, CEO LaFinteca: From Vision to Velocity - სტრატეგიის გაკეთება

Dmytro Rukin, Ketua Pegawai Eksekutif LaFinteca: Daripada Visi kepada Kelajuan - Menukar Strategi kepada Eksekusi

Dmytro Rukin, CEO ee LaFinteca: From Vision to Velocity - Shirkadda Strategy in Execution

Dmytro Rukin, CEO LaFinteca: Vision to Velocity – Strategy to Execution – strategiyadan qaytaradi

I-Dmytro Rukin, CEO ye-LaFinteca: ukusuka kwi-Vision ukuya kwi-Velocity - Ukuguqulwa kwe-Strategy kwi-Execution

Why is a crypto exchange investing $2 billion in trust? Why is a crypto exchange investing $2 billion in trust? At TOKEN2049 Dubai, KuCoin CEO BC Wong announced the "Trust Project," a $2 billion initiative aimed at enhancing transparency, security, and compliance in the cryptocurrency industry.  This move comes as the crypto sector faces increased regulatory scrutiny and a need for greater user confidence. KuCoin announced Reframing the Role of Crypto Exchanges In a panel titled "What’s Next for Crypto Markets: The Exchange Perspective," BC Wong emphasized that crypto exchanges are evolving from mere trading platforms to integral components of the financial infrastructure. He highlighted KuCoin's investments in overhauling its wallet architecture, developing real-time data systems, and enhancing security frameworks. With more than 1,000 global employees, the majority in engineering roles, KuCoin is focusing on technical excellence and scalability. Wong stated, "The role of exchanges has changed. It's no longer enough to launch fast and fix later. We must collaborate with regulators from day one—because trust isn’t just earned from users; it must be built with the entire financial ecosystem." "The role of exchanges has changed. It's no longer enough to launch fast and fix later. We must collaborate with regulators from day one—because trust isn’t just earned from users; it must be built with the entire financial ecosystem." The Trust Project: A $2 Billion Commitment The Trust Project is KuCoin's response to the industry's trust deficit. The initiative focuses on four key areas: transparency, compliance, user protection, and long-term sustainability. By investing in these areas, KuCoin aims to set new standards for the industry. Oliver Stauber, CEO of KuCoin EU, joined Wong on stage to present the initiative, emphasizing the need for platforms that deserve user trust. The project also aims to integrate KuCoin's native token, KCS, more deeply into the ecosystem, enhancing its utility and governance functions. Navigating Regulatory Landscapes KuCoin's strategy involves proactive engagement with regulators. The company has assembled a team of over 20 scientists and analysts focused on compliance, proof-of-reserve systems, and transparent reporting. Wong noted that in some markets, regulators expect involvement from the earliest stages of product development. He also mentioned plans to reenter South Korea after securing compliance in major jurisdictions like the United States, EU, China, and India. Looking ahead, KuCoin plans to launch dedicated, compliant platforms in Europe and Australia in the second half of 2025. These platforms will be part of the broader effort to expand the KCS ecosystem and bring more utility to global users.  The company believes that regulation should be seen as a catalyst for long-term credibility rather than a constraint. By leading in transparency, risk management, and regulatory collaboration, KuCoin aims to shape the next chapter of the crypto industry. Final Thoughts KuCoin's $2 billion Trust Project represents a significant investment in the future of cryptocurrency. By focusing on transparency, security, and compliance, the company is taking proactive steps to address the industry's challenges. This initiative could set a precedent for other exchanges and contribute to a more mature and credible crypto environment. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO business blogging program business blogging program

How KuCoin Plans to Rebuild Trust in Crypto: Inside BC Wong’s $2B Vision

“KuCoin”ın “Crypto”ya güvənməyi necə planlaşdırır: “BC Wong”ın “2B $” viziyası

Jak KuCoin plánuje obnovit důvěru v kryptoměny: Vnitřní vize 2B USD od BC Wong

Cómo KuCoin planea reconstruir la confianza en Crypto: dentro de la visión de $ 2B de BC Wong

Come KuCoin pianifica di ricostruire la fiducia in Crypto: all'interno della visione di 2 miliardi di dollari di BC Wong

KuCoinがCryptoへの信頼を再構築する方法:BC Wongの2Bドルのビジョン

«Қазақтелеком» АҚ-ның телекоммуникациялар желісін қуаттылығы 1 млн. абонент Next Generation Network (NGN) технологиясына көшіру

តើធ្វើដូចម្តេចដែល KuCoin បានរចនាឡើងដើម្បីបង្កើតឥតគិតថ្លៃនៅក្នុង Crypto: នៅក្នុងគំនិត $ 2B នៃ BC Wong

Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela Kofutela

Cum KuCoin intenționează să-și recâștige încrederea în Crypto: În interiorul viziunii 2B a lui BC Wong

Ako KuCoin plánuje obnoviť dôveru v kryptomeny: Vnútri vízie BC Wong 2B dolárov

KuCoin o‘z bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan.

Indlela KuCoin Ukulungiselela Ukuguqulwa kwe-Crypto: Ngaphakathi kwe-BC Wong's $ 2B Vision

The Visionary Behind the Platform At the forefront of innovation in the field of IT staffing is YESHWANTH VASA, Chief Technology Officer and Head of Business & Technology Operations of Static IQ Corp. With a reputation for his capability to bring together extensive technical research and business implementation, YESHWANTH VASA is revolutionizing how staffing is done—by doing it smarter, faster, and far more secure. “We’re not just matching resumes to job descriptions,” YESHWANTH VASA says. “We’re rethinking the system from the ground up—how talent is evaluated, how data is secured, and how bias is eliminated.” The Birth of Job Clicks That philosophy translated into Job Clicks, a recruitment automation platform that was developed during his leadership. It uses AI to sort through applicants with minimal human touch—but what sets it apart is its security and resilience underpinnings. Fundamentally, Job Clicks employs adversarial attacks against generative models and robustness to render it resilient to false positives and fake profiles. With the era of AI-generated content and faceless profiles, we need models that can't be manipulated," he adds. "The adversarial methodology gives our platform strength—candidates can't fake the system, and companies can believe what they see." Research-Backed, Industry-Ready Job Clicks isn’t built on generic code. It’s deeply rooted in YESHWANTH VASA’s own academic work—most notably his published paper, “Automated Machine Learning Framework Using Large Language Models for Financial Security in Cloud Observability.” “We applied the same logic we use in cloud security to staffing,” Yeshwanth Vasa notes. “If you can protect financial data in unpredictable environments, you can certainly protect hiring pipelines from threats—intentional or accidental.” Security as a Differentiator What makes Job Clicks stand out is its security-first architecture. It doesn’t just match resumes; it actively guards data, flags anomalies, and secures every interaction in the hiring workflow. “When people think of IT staffing, they rarely think about security,” Yeshwanth Vasa says. “But we’re dealing with high-value data—resumes, contact details, sometimes even confidential projects. That needs to be guarded just like any other critical system.” This holistic approach transforms Job Clicks from a basic tool into a comprehensive hiring intelligence system. Looking Ahead: Empowering, Not Replacing For Yeshwanth Vasa, the mission isn’t to remove humans from the hiring process—it’s to make them exponentially more effective with the right tools. “Our goal isn’t to replace people in hiring,” he says. “It’s to empower them with tools that are faster, smarter, and far more secure. That’s the future of staffing, and we're building it.” Static IQ Corp and its quietly trailblazing CTO are proving that true innovation doesn’t need loud headlines—it just needs results that speak for themselves. The Visionary Behind the Platform At the forefront of innovation in the field of IT staffing is YESHWANTH VASA, Chief Technology Officer and Head of Business & Technology Operations of Static IQ Corp. With a reputation for his capability to bring together extensive technical research and business implementation, YESHWANTH VASA is revolutionizing how staffing is done—by doing it smarter, faster, and far more secure. “We’re not just matching resumes to job descriptions,” YESHWANTH VASA says. “We’re rethinking the system from the ground up—how talent is evaluated, how data is secured, and how bias is eliminated.” The Birth of Job Clicks The Birth of Job Clicks Job Clicks That philosophy translated into Job Clicks, a recruitment automation platform that was developed during his leadership. It uses AI to sort through applicants with minimal human touch—but what sets it apart is its security and resilience underpinnings. Fundamentally, Job Clicks employs adversarial attacks against generative models and robustness to render it resilient to false positives and fake profiles. With the era of AI-generated content and faceless profiles, we need models that can't be manipulated," he adds. "The adversarial methodology gives our platform strength—candidates can't fake the system, and companies can believe what they see." Research-Backed, Industry-Ready Research-Backed, Industry-Ready Job Clicks isn’t built on generic code. It’s deeply rooted in YESHWANTH VASA ’s own academic work—most notably his published paper, “Automated Machine Learning Framework Using Large Language Models for Financial Security in Cloud Observability.” YESHWANTH VASA “Automated Machine Learning Framework Using Large Language Models for Financial Security in Cloud Observability.” “We applied the same logic we use in cloud security to staffing,” Yeshwanth Vasa notes. “If you can protect financial data in unpredictable environments, you can certainly protect hiring pipelines from threats—intentional or accidental.” Yeshwanth Vasa Security as a Differentiator Security as a Differentiator What makes Job Clicks stand out is its security-first architecture. It doesn’t just match resumes; it actively guards data, flags anomalies, and secures every interaction in the hiring workflow. “When people think of IT staffing, they rarely think about security,” Yeshwanth Vasa says. “But we’re dealing with high-value data—resumes, contact details, sometimes even confidential projects. That needs to be guarded just like any other critical system.” Yeshwanth Vasa This holistic approach transforms Job Clicks from a basic tool into a comprehensive hiring intelligence system. Looking Ahead: Empowering, Not Replacing Looking Ahead: Empowering, Not Replacing For Yeshwanth Vasa , the mission isn’t to remove humans from the hiring process—it’s to make them exponentially more effective with the right tools. Yeshwanth Vasa “Our goal isn’t to replace people in hiring,” he says. “It’s to empower them with tools that are faster, smarter, and far more secure. That’s the future of staffing, and we're building it.” Static IQ Corp and its quietly trailblazing CTO are proving that true innovation doesn’t need loud headlines—it just needs results that speak for themselves.

Powered by AI, Driven by Vision: A New Era of Staffing

Powered by AI, Driven by Vision: Una nueva era de personalización

Impulsado por IA, impulsado por visión: unha nova era de persoal

Powered by AI, Driven by Vision: Una nuova era del personale

タイトル: Powered by AI, Driven by Vision: A New Era of Staffing

Алдыңғы мақалаЧТО НАДО ДЕЛАТЬ ДЛЯ РАЗВИТИЯ ПРОМЫШЛЕННОСТИ?

인공지능에 의해 구동, 비전에 의해 구동: 인력 개발의 새로운 시대

Водени од АИ, водени од визија: Нова ера на персонал

AI-ийн дэмжлэгтэй, Vision-ийн дэмжлэгтэй: Ажлын шинэ эрт

Dirancang oleh AI, Driven by Vision: A New Era of Staffing

ஆங்கிலத்தில் இதை Single Orgasm, Multiple Orgasm என்றும் கூறுகிறார்கள்.

IA tarafından desteklenen, vizyon tarafından desteklenen: Yeni bir personel çağı

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