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Getting Started with Ollama REST APIs

This article provides a detailed guide on working with Ollama's REST APIs, specifically focusing on the /api/generate endpoint for AI model responses.
The /api/generate endpoint offers standard and advanced parameters for customization, supporting features like suffix, images, format selection, and more.
An example request to generate a joke using the llama3.2 model is demonstrated, showing how the response is streamed as incremental JSON objects.
The response culminates in delivering the full joke 'Why don't eggs tell jokes? Because they'd crack each other up!' as concatenated pieces.
Key fields in the response JSON include model, created_at timestamp, response content, and indicators like done status and done_reason for completion.
The article explains the streaming nature of the API in generating responses piece by piece and concludes with how to receive the response as a single JSON object.
Additionally, it delves into the /api/chat endpoint usage for generating conversation responses, detailing the parameters and an example request.
The response format, timing information, and key elements like the response message, done status, and token statistics are highlighted for understanding the chat API.
Lastly, the article covers how to request responses in JSON format using the /api/generate endpoint and receiving the complete joke response in one JSON object.
Overall, this guide provides insights into leveraging Ollama's REST APIs effectively for generating AI responses and controlling response delivery.
Source: https://github.com/ollama/ollama/blob/main/docs/api.md

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Dev

358

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How I Set Up CI/CD with GitHub Actions in a Real Node.js Project

GitHub Actions is used to automate CI/CD for the BinaryStudioAcademy/bsa-2022-streamlet project
They wanted to run tests, validate code builds, lint for consistency, and deploy using GitHub Actions
A minimal setup for Node.js in GitHub Actions workflow is demonstrated
Tips and lessons learned from the experience are shared

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Self-Learning-Java

128

Creating a Custom Model with Ollama: Alice, Your Smart Assistant

This guide demonstrates how to create a custom AI model using Ollama, specifically configuring it to behave as a highly effective and informative assistant named "Alice".
Step 1: Create a Model File 'aliceModel'
Step 2: Create the Model
Step 3: Verify the Model Creation.

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Dev

323

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Creative Work: ChatGPT-4 and the Future of Design

ChatGPT-4 is revolutionizing AI-driven image generation in creative industries.
Features of ChatGPT-4 include diverse style adaptation, enhanced instruction-following, and high-resolution outputs.
Vibe marketing, leveraging AI-generated visuals, is a rising design trend.
Creative professionals should adapt by using AI as an assistant, expanding skill sets, and focusing on conceptual thinking.

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19 Likes

Medium

363

Image Credit: Medium

15 Little-Known C# Array Tips That Will Supercharge Your Coding

Arrays in C# offer advanced features to optimize coding efficiency.
Array.Fill() can be used for quick initialization, avoiding manual value setting.
ArraySegment or Span can create array slices without copying data, reducing memory overhead.
Array.Resize() allows dynamically adapting the size of an array as needed.

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Self-Learning-Java

141

How to Use the ollama rm Command to Delete a Model?

The ollama rm command is used to delete a model from your Ollama environment.
To delete a model, simply use the following command:ollama rm llama3.2
With the ollama rm command, removing a model is quick and easy, ensuring that you can keep your system organized.
Be cautious while using this command, as it permanently deletes the specified models!

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Self-Learning-Java

190

How to List All Models Using Ollama?

The ollama list command provides a comprehensive list of all the models installed in your Ollama setup.
The output includes details about each model, such as its name, version, and any other metadata.
The NAME column lists the name and tag of each model, allowing you to specify versions or different configurations of the same model.
The ID column contains a unique identifier (UUID) for each model, which can be used in various Ollama commands.

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Self-Learning-Java

221

ollama help: get information about any Ollama command

The ollama help command is your go-to tool for getting information about any Ollama command.
Usage: ollama [flags] ollama [command]
ollama help provides a simple and structured guide to assist you in using specific Ollama commands.
To get more detailed help on a specific command, you can run: ollama [command] --help

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Self-Learning-Java

336

Mastering Basic Commands in the Ollama Shell: A Quick Guide

When working with the Ollama shell, knowing the essential commands can make your experience seamless and efficient.
1. Get Help: /?, /help. Use these commands to get a list of available commands and their purposes.
2. Show Model Information: /show. Use this command to explore details about the current model you're working with.
3. Clear Session Context: /clear. Use this command to reset the session context and start fresh.

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Self-Learning-Java

283

Exploring KAPLAY Components: The Building Blocks for Dynamic Game Objects

In KAPLAY, components are essential for creating game objects, controlling their behavior in terms of movement, appearance, and interaction.
Key components in KAPLAY include pos(x, y) for object positioning, sprite() for visuals, area() for collision detection, and text() for displaying information.
Other components like scale(), rect(width, height), and color(r, g, b) offer options for resizing, drawing shapes, and changing colors of game objects.
By combining these components creatively, developers can bring game objects to life and enhance player experiences through varied interactions.
The step-by-step procedure for building an application using KAPLAY components involves creating a project, updating the main.js file, and executing commands for running the application.
The application allows for the dynamic creation of player objects with random attributes such as position, appearance, and movement controls.
The use of Random utility functions adds spontaneity to the game by generating random values for properties like position, color, and direction.
The demo provided gives a visual representation of how these components work together to create a dynamic and interactive gameplay experience.
KAPLAY components simplify the process of building and managing game objects, offering a user-friendly approach to game development with versatile tools.
The integration of components like pos, sprite, area, and text enables developers to customize game elements and enhance gameplay engagement.

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Dev

Image Credit: Dev

What if React never needed JavaScript? Introducing NO JS 🧠

NO JS is a meaning-driven syntax OS that allows you to build full web apps without using JavaScript.
You write .nojs files to describe the structure, state, and flow, then compile them to pure HTML using Zig.
NO JS eliminates the need for JavaScript, runtime, and frameworks.
The author built NO JS with the idea of writing the web using just meaning and without the complexities of modern web tools.

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5 Likes

Self-Learning-Java

350

Game Objects in KAPLAY

Game objects are the core components that represent all entities in the game.
Key features of game objects include acting, components, interaction, and behavior.
Game objects are created using components and the add() function in KAPLAY.
The project example demonstrates the creation of rectangles, circles, and sprites in the KAPLAY game engine.

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21 Likes

Self-Learning-Java

195

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Getting Started with Kaboom.js: Build Your First Platformer Game with Gravity, Jumping, and Movement

This article introduces Kaboom.js, a beginner-friendly library for creating games, by creating a platformer game with movement and jumping features.
Key learnings include utilizing gravity, adding player sprites, implementing physics, creating platforms, and enabling collision detection.
The 'body()' method in Kaboom.js simulates real-world physics like gravity, velocity, momentum, and forces for game objects.
The 'area()' method acts as a collision detector, defining interaction spaces for objects in the game.
Static platforms can be created using 'body({ isStatic: true })' for objects unaffected by gravity, aiding player movement and interactions.
Player behaviors like jumping, horizontal movement, and screen boundaries are defined in the code to enhance gameplay.
Running the Kaboom.js project involves initializing the canvas, setting gravity, loading sprites, and adding interactive elements.
The provided code snippets and explanations help beginners understand game development concepts and Kaboom.js functionality.
By following the step-by-step instructions, users can build a complete platformer game using Kaboom.js.
Finally, the article offers a demo link and resources for further exploration and learning in game development.

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11 Likes

Dev

181

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Apache Pyspark

Apache PySpark is a fast and general-purpose distributed computing system for big data processing.
Key features of PySpark include in-memory processing, scalability, ease of use, and a unified analytics engine.
Spark, with its in-memory computations, offers faster processing than disk-based frameworks like Hadoop MapReduce.
PySpark is the Python API for Apache Spark, allowing users to leverage Spark's capabilities using Python.

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10 Likes

Dev

394

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2140. Solving Questions With Brainpower

Given a 0-indexed 2D integer array 'questions' representing an exam where each question has points and brainpower constraints.
The task is to maximize points by solving questions considering brainpower limitations.
Dynamic Programming is suggested to decide the most optimal option for each question.
Solution involves DP array to track maximum points starting from each question.
For each question, two choices exist: solve with constraints or skip to the next.
Reverse iteration helps in considering each question's impact on future questions.
The algorithm efficiently determines the maximum points earned from solving exam questions.
Initialization of DP array and iterating from last question aids in optimal point calculation.
The approach computes the solution in O(n) time and space complexity for large input sizes.
Dynamic Programming is a powerful technique for optimizing decision-making processes in various scenarios.

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