ViperGPT: Visual Inference via Python Execution for Reasoning

ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.


Github:
https://github.com/cvlab-columbia/viper

Paper:
https://arxiv.org/pdf/2303.08128.pdf

Project:
https://paperswithcode.com/dataset/beat

👉@computer_science_and_programming

Test of Time: Instilling Video-Language Models with a Sense of Time

GPT-5 will likely have video abilities, but will it have a sense of time? Here is answer to this question in #CVPR2023 paper by student of University of Amsterdam to learn how to instil time into video-language foundation models.

Paper:
https://arxiv.org/abs/2301.02074

Code:
https://github.com/bpiyush/TestOfTime

Project Page:
https://bpiyush.github.io/testoftime-website/

👉 @computer_science_and_programming

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Paper:
https://arxiv.org/abs/2305.10973

Github:
https://github.com/XingangPan/DragGAN

Project page:
https://vcai.mpi-inf.mpg.de/projects/DragGAN/

👉 @computer_science_and_programming

🔭 GRES: Generalized Referring Expression Segmentation

New benchmark (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects.

🖥 Github: https://github.com/henghuiding/ReLA

⏩ Paper: https://arxiv.org/abs/2306.00968

🔎 Project: https://henghuiding.github.io/GRES/

📌 New dataset: https://github.com/henghuiding/gRefCOCO

👉 @computer_science_and_programming

80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife

Classification library
https://github.com/Tessellate-Imaging/monk_v1

Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo

Detection and Segmentation Library
https://github.com/Tessellate-Imaging/

Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

👉 @computer_science_and_programming

Backend Burger 🍔

Wondering how C++, Java, Python Work?

🔵 C++
C++ is like the superhero of programming languages. It's a compiled language, meaning your code is transformed into machine code that your computer can understand before it runs. This compilation process is crucial for efficiency and performance. C++ gives you precise control over memory and hardware, making it a top choice for systems programming and game development. It's like wielding a finely-tuned instrument in the world of code! 🎸💻

🔴 Java
Java, on the other hand, is the coffee of programming languages. It's a compiled language too but with a twist. Java code is compiled into bytecode, which runs on the Java Virtual Machine (JVM). This bytecode can run on any platform with a compatible JVM, making Java highly portable and platform-independent. It's a bit like sending your code to a virtual coffee machine that serves it up just the way you like it on any OS! ☕️💼

🐍 Python
Python is the friendly neighborhood programming language. It's an interpreted language, which means there's no compilation step. Python code is executed line by line by the Python interpreter. This simplicity makes it great for beginners and rapid development. Python's extensive library ecosystem and easy syntax make it feel like you're scripting magic spells in a magical world! 🪄🐍

In the end, the choice of programming language depends on your project's needs and your personal preferences. Each language has its strengths and weaknesses, but they all share the goal of bringing your ideas to life through code. 🚀💡

So, whether you're crafting the perfect C++ masterpiece, brewing up Java applications, or scripting Python magic, remember that programming languages are the tools that empower us to create amazing things in the digital realm. Embrace the language that speaks to you and keep coding! 💻🌟

What is Kafka?

Kafka is an open-source, distributed event streaming platform that serves as the central nervous system for data in modern enterprises. It's designed to handle real-time data feeds, process them efficiently, and make them available for a variety of applications in real-time.

🛠 Use Cases:
- Real-time Analytics
- Log Aggregation
- Event Sourcing
- Data Integration
- Machine Learning Pipelines

Docker Architecture and Components

1. Docker Daemon (dockerd):
- 𝗥𝗼𝗹𝗲: Manages Docker containers on a system.
- 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: Building, running, and managing containers.

2. Docker Client (docker):
- 𝗥𝗼𝗹𝗲: Interface through which users interact with Docker.
- 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀: build, pull, run, etc.

3. Docker Images:
- 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻: Read-only templates used to create containers.
- 𝗥𝗼𝗹𝗲: Serve as the basis for creating containers.
- 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆/𝗛𝘂𝗯: A storage and distribution system for Docker images.

4. Docker Containers:
- 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻: Runnable instances of Docker images.
- 𝗥𝗼𝗹𝗲: Encapsulate the application and its environment.

5. Docker Registry:
- 𝗥𝗼𝗹𝗲: Store Docker images.
- 𝗣𝘂𝗯𝗹𝗶𝗰 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆: Docker Hub.
- 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆: Can be hosted by users.

Top 12 Tips for API Security:

- Use HTTPS
- Use OAuth2
- Use WebAuthn
- Use Leveled API Keys
- Authorization
- Rate Limiting
- API Versioning
- Whitelisting
- Check OWASP API Security Risks
- Use API Gateway
- Error Handling
- Input Validation

Angular 17 and the new angular.dev site has been officially released.

Here's a summary of what's new.

Is AI making a real impact in the way you work or is it all hype? Stack Overflow recaps some of the top insights from their 2023 Developer Survey.💡

Explore what developers are thinking about the benefits, accuracy, and use cases for GenAI here.

Prod Software Release

1. Planning:
- Identify the goals and features for the upcoming release.
- Prioritize tasks based on importance and dependencies.
- Define timelines and allocate resources accordingly.

2. Development:
- Programmers start coding based on the planned features.
- Regular code reviews and collaboration to maintain code quality.
- Version control systems track changes for better collaboration.

3. Building Artifact:
- Compile the source code into executable or deployable artifacts.
- Generate documentation and other necessary files.
- Automation tools can be used to streamline this process.

4. Testing:
- Conduct various levels of testing (unit, integration, system, etc.).
- Identify and fix bugs or issues.
- Ensure compatibility with different platforms and configurations.

5. Release:
- Once testing is successful, prepare for the release.
- Generate release notes documenting changes and updates.
- Coordinate with other teams for a smooth rollout.

Environments:
- Set up different environments for development, testing, and production.
- Ensure consistency across environments to minimize deployment issues.
- Monitor and troubleshoot any discrepancies between environments.

🔵 REST vs 🟣 GraphQL

🔵 REST:
👉 Stands for Representational State Transfer
👉 Well-established and widely adopted
👉 Uses predefined endpoints for data retrieval
👉 Great for simple, stateless operations

🟣 GraphQL:
👉 A modern query language for APIs
👉 Allows clients to request exactly what they need
👉 Reduces over-fetching and under-fetching of data
👉 Empowers front-end developers with data control

Which one is right for your project? 🤔

Use 🔵 REST if:

Simplicity and Convention: REST is straightforward and relies on a set of conventions. If you have a simple API with well-defined endpoints and actions, REST might be a good choice.

Caching: RESTful APIs are typically easier to cache because the URLs for resources remain consistent. This can lead to better performance in scenarios where caching is crucial.

Existing Ecosystem: If you're working with legacy systems or integrating with third-party APIs that follow REST principles, it may make sense to stick with REST for consistency.

Use 🟣 GraphQL if:

Flexibility: GraphQL allows clients to request exactly the data they need, which can lead to reduced over-fetching and under-fetching of data. This flexibility is especially beneficial for complex applications with varying data requirements.

Efficiency: With GraphQL, you can often make a single request to fetch related
data, reducing the number of API calls required compared to REST, which might require multiple requests to different endpoints.

Real-time Data: If you need real-time updates and subscriptions, GraphQL's ability to provide live data can be a significant advantage.

Team Expertise: If your development team is comfortable with GraphQL and prefers its query language, it might lead to faster development and easier maintenance.