#github prompt engineering guide

[callofdutymobileindia]

How to Choose the Right Generative AI Course? Key Factors to Consider

In today’s rapidly evolving tech landscape, Generative AI is transforming industries—from art and marketing to finance and healthcare. Tools like ChatGPT, Midjourney, DALL·E, and Runway ML are reshaping how we generate content, design products, and solve problems. As demand for AI skills skyrockets, enrolling in a Generative AI course can be a smart move for professionals and beginners alike.

But with countless programs available—online and offline—it’s essential to know how to choose the right course that matches your goals, learning style, and future career prospects. In this guide, we’ll walk you through the key factors to consider when selecting a Generative AI course so you can make an informed decision.

1. Understand What a Generative AI Course Offers

Before choosing a program, understand what a Generative AI course typically includes. These courses are designed to teach:

Foundations of AI and Machine Learning

Deep Learning models (especially GANs and transformer-based models)

Text, image, audio, and video generation tools

Prompt engineering techniques

Real-world projects using tools like ChatGPT, DALL·E, Midjourney, Runway, etc.

The best courses blend theory with hands-on experience, ensuring you not only learn the concepts but also know how to apply them effectively.

2. Define Your Learning Goals

The right Generative AI course depends largely on your personal or professional goals. Ask yourself:

Are you a beginner looking to explore the field?

Are you a developer or data scientist aiming to upskill?

Are you in marketing, design, or content creation and want to leverage AI tools?

Are you planning to build AI-powered products or startups?

Clearly defining your goal will help you choose a course that focuses on either the technical aspects (e.g., coding, model training) or practical applications (e.g., content generation, automation).

3. Check the Course Curriculum in Detail

Not all Generative AI courses are created equal. A high-quality curriculum should include the following:

Core Modules:

Introduction to Generative AI

Basics of Machine Learning & Deep Learning

Generative Adversarial Networks (GANs)

Transformers and Large Language Models (LLMs)

Ethics and Responsible AI

Tool-Based Training:

ChatGPT and Prompt Engineering

DALL·E and Text-to-Image generation

Midjourney or Stable Diffusion

Runway ML and video generation tools

Hands-On Projects:

AI Art Generation

AI Text Summarization & Copywriting

AI Music or Video Creation

Custom Chatbot or Application Building

Tip: Prefer a course that includes project-based learning, as this improves retention and builds a portfolio you can showcase to potential employers.

4. Assess the Instructor’s Expertise

An excellent instructor can make complex concepts easier to grasp. Look for courses taught by industry practitioners, AI researchers, or certified educators with:

Real-world AI experience

Published work or thought leadership in Generative AI

Teaching credentials or testimonials from past students

Check their LinkedIn profile, GitHub contributions, or YouTube tutorials to verify their authority in the field.

5. Evaluate Course Format: Self-Paced vs Instructor-Led

Depending on your schedule and learning preference, choose between:

Self-Paced Courses:

Learn anytime, at your own speed

Usually more affordable

Ideal for working professionals

Instructor-Led Courses:

Scheduled live classes with Q&A sessions

Often includes peer discussions, mentorship, and evaluations

Better for structured learners or beginners

Some hybrid programs offer recorded content + live doubt-clearing sessions, giving you the best of both worlds.

6. Look for Industry Recognition or Certification

A Generative AI course certification from a reputed institute adds value to your resume and LinkedIn profile. Recognized names like Boston Institute of Analytics, Coursera, edX, or Google-backed programs often carry more weight in hiring processes.

Ensure the certificate:

Is verifiable

Comes from a credible institution

Demonstrates skills employers are currently seeking

7. Read Student Reviews and Alumni Success Stories

Before enrolling, check testimonials, Google reviews, or Reddit discussions about the course. Look for answers to:

Did students find the content useful and up-to-date?

Did it help them apply skills in real-world projects or jobs?

Are alumni now working in AI, marketing, or tech fields?

Alumni case studies or LinkedIn mentions can give you real-world proof of course outcomes.

8. Check for Career Support and Job Placement Assistance

If you're taking a Generative AI course to switch careers or land a new role, see whether the course offers:

Resume building and LinkedIn optimization

Portfolio development (via hands-on projects)

Interview preparation

Placement opportunities or industry connections

Institutes like Boston Institute of Analytics often provide placement guidance and mentorship, which can significantly ease your transition into the AI industry.

9. Consider the Cost and ROI

Prices for Generative AI courses can range from ₹5,000 to ₹1,50,000+ depending on the platform, format, and institute. Ask yourself:

Is the curriculum comprehensive for the price?

Do you get lifetime access to materials?

Are there discounts, EMI options, or scholarships?

Sometimes, paying a bit more for a course with mentorship + certification + career support can deliver far greater value and return on investment (ROI) than a free YouTube playlist.

10. Ensure You Get Hands-on with Generative AI Tools

One of the biggest mistakes learners make is choosing a theoretical course. Generative AI is a practical field—you must work with:

ChatGPT: for chatbots, content generation, customer support

DALL·E & Midjourney: for marketing visuals, branding, and product design

Runway ML: for video editing and synthetic media

Custom APIs & Code: to integrate Generative AI into apps or websites

Look for a course that gives tool access, sandbox environments, or downloadable code notebooks for experimentation.

11. Stay Updated: Is the Course Aligned with 2025 Trends?

Generative AI is evolving fast. Courses designed even two years ago may now be outdated. Make sure your chosen course includes:

Latest LLMs like GPT-4.5, Claude 3, or Gemini

Updated best practices in prompt engineering

Emerging use-cases like Agentic AI, multimodal AI, or enterprise applications

Also, ensure your course provides updates or free future modules to stay current.

12. Bonus: Community Access and Networking

A strong peer and mentor community helps in:

Sharing prompts, tools, and techniques

Collaborating on real-world AI projects

Getting feedback on your work

Finding freelance gigs or job referrals

Courses that offer Slack groups, Discord servers, or alumni communities provide immense long-term value.

Final Thoughts

Choosing the right Generative AI course is more than just clicking “Enroll.” It’s about identifying a program that aligns with your goals, learning style, and career path. Whether you're a content creator, designer, engineer, or entrepreneur, investing in the right course can unlock tremendous opportunities in this fast-growing field.

Look for a course with practical projects, expert guidance, tool mastery, and career support. If you're seeking a future-ready program that covers all these aspects, the Boston Institute of Analytics offers one of the most comprehensive and industry-relevant Generative AI certification courses available today. With expert faculty, hands-on training, and placement support, it could be your ideal launchpad into the world of AI innovation.

[govindhtech]

Microsoft Introduces Agentic DevOps for Software Development

DevOps agent

Agentic DevOps: Microsoft Azure and GitHub Copilot for software development

Agentic DevOps: Microsoft's Next Software Development Trend at Build 2025

Microsoft launched Agentic DevOps, a new technique that leverages intelligent agents to rethink application creation and maintenance, marking a major milestone in the software development lifecycle. Agentic DevOps, the “next evolution of DevOps,” envisions AI agents working with developers and each other to automate and optimise the software lifecycle.

As it celebrates its 51st anniversary, the company is reimagining its founding as a “software factory” developed by developers for developers. The idea is to help engineers “break free from the grind,” restoring the “joy, your flow, and the magic of building” lost in rising demands, complexity, and technological debt. Besides coding, developers spend a lot of time designing systems, going through documentation, troubleshooting, refactoring, and fighting legacy code.

Agentic workflows are offered by GitHub Copilot, which is crucial to this shift. GitHub Copilot, already used by 15 million developers to speed up development, is expanding beyond code completion to enable agentic workflows, which help teams move from idea to production faster, improve code quality, collaborate faster, fortify security, pay off technical debt, and maintain app smoothness.

Microsoft is making GitHub Copilot available to all Visual Studio Code users to promote openness and community-driven innovation.

The Agentic DevOps announcement includes new developer-focused agents. A new coding agent elevates GitHub Copilot from pair programming to “peer” status in the development team. This agent can manage complex, multi-step coding jobs with other agents. Developers may assign it code reviews, test authoring, issue solutions, and full specification implementation.

It works across files and recommends terminal commands from a single prompt. Popular code editors including Microsoft Visual Studio Code, JetBrains, Eclipse, and Xcode offer “Agent mode”. Due to its audit logs and branch controls, this agent's suggested changes must be evaluated before deployment.

New Azure Site Reliability Engineering (SRE) Agents go beyond development. A 24/7 production system monitor spares developers of late-night warnings. It automatically diagnoses and fixes problems. The SRE Agent evaluates application health and performance in Azure Kubernetes Service (AKS), Azure App Service, serverless, and databases using Microsoft's worldwide experience.

It can solve problems itself or guide developers through root cause analysis. Note that repair items and remedial activities are documented as GitHub issues for team follow-up. Resilient, self-healing technologies allow teams to relax and focus, recover faster, and have fewer wake-up calls.

Agentic DevOps aims to address technical debt. With mainframe modernisation imminent, GitHub Copilot is offering new app modernisation services to help developers update stacks, focussing on legacy Java and.NET workloads. These features enable code assessments, dependency updates, and remediation by providing visibility and control over changes and automatically building and executing update plans. The goal is to produce cost-effective, safe, and stable apps so developers can focus on innovation rather than the past.

Azure's platform supports these capabilities with AI + machine learning services like Azure AI Foundry and Azure OpenAI in Foundry Models, containers (Azure Kubernetes Service, Azure App Service), databases (Azure Cosmos DB, Azure DB for PostgreSQL), and monitoring (Azure Monitor). Azure AI Foundry is a “AI App and agent factory” that lets you test cutting-edge models from several providers in the GitHub workflow with a new native integration. This integration lets GitHub Actions users invoke, compare, and exchange models via a single API.

Microsoft calls Agentic DevOps “as transformative as the shift to the cloud” and “seismic shift.” It promises to minimise friction, simplify processes, and rewrite decades-old cost structures that hampered teams. Agentic DevOps automates monotonous tasks to free developers to create the future and enhance productivity.

Microsoft Build 2025 guests are encouraged to explore this future with talks on agentic AI, GitHub Copilot, faster Azure development, app modernisation with AI, and agent mode in action.

[benmuzzio]

How AI Revolutionizes Application Development

Artificial Intelligence (AI) has revolutionized software development, altering how developers build applications from design to deployment. This paradigm shift stretches back to the dawn of programming - from Ada Lovelace's 1843 vision of utilizing punched cards for mathematical computations to today's sophisticated AI systems that interpret natural language into executable code.

AI in software development has brought unprecedented efficiency and ease of use. Up to 82 percent of developers already utilize AI tools to write code, manage debugging, complete documentation, and search for answers. Nearly half of the respondents had an interest in using AI to test code, while 40 percent had an interest in using it for code review, predictive analytics, and deployment monitoring.

These AI-powered tools tackle different functions in the development process. In code generation, systems like GitHub Copilot use large language models to translate natural language prompts into code in multiple programming languages. It automates redundant processes and allows developers to focus on more creative programming tasks.

Moreover, AI systems can recognize patterns and issues before code reaches deployment, transforming the debugging process. This proactive system significantly reduces the time traditionally spent on manual debugging and code reviews. In testing and quality assurance, AI automates repetitive test cases and helps QA specialists prioritize issue areas, making the process more efficient and effective.

Beyond its technical advantages, AI enhances project management by automating tasks, reducing workflows, and optimizing the allocation of resources. Predictive analytics enable teams to learn how users behave and prioritize features based on anticipated user demand. Natural language processing enables greater user personalization by better understanding feedback and developing sophisticated chatbots that understand the context and provide accurate responses.

Nevertheless, several myths about AI in software development exist. Contrary to popular belief, AI will not replace software engineers but help them improve their abilities and code quality. While AI can help with code generation, it does not generate flawless code on its own. Its capabilities rely on training data. Therefore, it produces code that humans must review.

AI-powered tools allow people to program using natural language rather than traditional programming languages. Developers can now type their prompts in a conversational form, and AI translates them into executable code. Low-code and no-code platforms like Microsoft Power Platform empower billions of people to build applications that solve their unique problems. New features like Power Apps use AI to convert drawings, images, and design files into working applications without requiring users to master advanced programming concepts.

The future of AI in software development is promising. Emerging tools will sanitize data, proof code for errors, debug programs and explain chunks of code in natural language. The goal is for AI to become an assistant that guides developers to solutions and allows anyone to translate ideas into functional software.

As the world becomes increasingly software-dependent, these AI-powered development tools will make coding more accessible, opening the developer community to more people from diverse backgrounds. They will also capture broader perspectives on problem-solving and creating prosperity through technology for engineers and others.

[techit-rp]

Prompt Engineering: The New Digital Superpower in the Age of Generative AI

In a world where artificial intelligence can write novels, design logos, compose music, and generate photorealistic videos, one question rises above all: How do you tell the AI what you want?

The answer lies in a skill that’s becoming as important as coding — prompt engineering.

This may sound like jargon, but it’s quickly turning into one of the most in-demand abilities of 2025. Prompt engineering is the art of communicating effectively with AI models to get the exact output you want. Whether it’s generating marketing copy, building virtual worlds, or writing code, how you phrase your request can make or break the result.

As more people turn to tools like ChatGPT, Midjourney, and Sora, mastering prompts is no longer optional. It’s a digital superpower, and it’s transforming careers, industries, and the future of creativity.

Let’s dive into why prompt engineering matters, what it looks like in action, and how Generative AI training online is helping thousands gain this futuristic skill.

What Is Prompt Engineering?

At its core, prompt engineering is the skill of crafting effective inputs (or prompts) to guide the behavior of AI systems like language models, image generators, and code assistants. These models respond to instructions, but the quality, structure, and clarity of your prompt directly influence the output.

Think of it like talking to a genie. You only get what you wish for — literally. So you better be specific, clear, and creative.

For example:

A vague prompt: “Write a blog.”

A strong prompt: “Write a 1000-word blog in a conversational tone about how prompt engineering is shaping the future of AI content creation. Include real-world examples and keep it beginner-friendly.”

See the difference? The second prompt gives the AI a roadmap to follow.

Why Prompt Engineering Is Exploding in 2025

In just the past year, we've seen an explosion of tools powered by Generative AI. From ChatGPT and Claude to image and video models like Midjourney and Runway, the ability to generate high-quality content with AI has never been easier.

But here’s the twist — the real value comes from knowing how to use them well.

Prompt engineering is like knowing the secret language that unlocks the true potential of these tools. And as organizations increasingly embed AI into their workflows, they need professionals who can command these systems effectively.

This is why roles like Prompt Engineer, AI Content Strategist, and AI Interaction Designer are appearing across job boards. Employers are no longer just looking for technical talent — they want creative thinkers who can talk to machines in the most efficient, productive way.

Real-World Applications of Prompt Engineering

Prompt engineering isn’t just for AI researchers or data scientists. It’s being used every day by professionals in marketing, design, education, finance, and more.

Here’s how:

1. Marketing and Copywriting

Marketers use AI tools to generate headlines, product descriptions, and social media posts. A well-engineered prompt can produce brand-consistent content in seconds. Imagine saving hours on campaign brainstorming just by refining a few lines of text.

2. Design and Visual Art

Image generators like Midjourney or DALL·E can create stunning visuals. But describing what you want — the composition, lighting, mood, color scheme — requires detailed prompting. Designers who learn prompt engineering can explore endless concepts without ever opening Photoshop.

3. Education and Training

Teachers and course creators use prompts to build custom quizzes, explain concepts in simple terms, or generate lesson plans tailored to different learning levels. A smart prompt can create a personalized tutor in real time.

4. Software Development

Tools like GitHub Copilot and ChatGPT can now help write code, debug scripts, and suggest improvements. But how well they perform depends on how you frame the problem. Developers who write thoughtful, context-rich prompts can drastically speed up coding tasks.

5. Entrepreneurship

Startups use AI to generate pitch decks, conduct competitor research, simulate user feedback, and even design mock apps. Prompt engineering allows founders to test ideas quickly without hiring a large team.

How to Learn Prompt Engineering

The good news? You don’t need to be a programmer or AI expert to learn prompt engineering. It’s a skill rooted in language, logic, and creativity — not just code.

And thanks to the boom in e-learning, you can now access high-quality Generative AI training online that focuses specifically on prompt engineering. These programs often include:

Practical projects using ChatGPT, Midjourney, DALL·E, and others

Prompt refinement techniques

Real-world use cases

Prompt libraries and templates

Ethics and responsible AI use

Some even offer certifications that are now recognized by employers eager to integrate Gen AI into their operations.

So whether you're a student, freelancer, or working professional, learning prompt engineering online is one of the smartest investments you can make in 2025.

Tips to Start Prompt Engineering Today

If you're curious to try prompt engineering on your own, here are a few tips to get started:

1. Be Clear and Specific

Vague prompts lead to vague answers. Add as much relevant detail as you can, especially the style, format, tone, and intent.

2. Use Examples

If you want AI to mimic a certain style, give it an example. For instance, “Write this in the style of The New York Times” gives the AI a clear reference point.

3. Iterate

Don’t settle for the first response. Tweak your prompt, reword your request, or add context. Prompt engineering is as much about iteration as it is instruction.

4. Structure Matters

Break down complex tasks into parts. Instead of one long prompt, try multiple prompts or step-by-step instructions.

5. Explore Pre-built Prompt Libraries

Many online platforms and communities now share prompt libraries for various use cases. These are great for learning and inspiration.

The Human Touch in a Machine World

As powerful as AI becomes, it still relies on human creativity to guide it. Prompt engineering bridges the gap between human intention and machine execution. It’s the translator, the conductor, the magician behind the curtain.

In the coming years, it won't be enough to just use AI tools. The edge will belong to those who can guide them skillfully.

That’s why enrolling in Generative AI training online is no longer just a tech interest — it's a career upgrade, a creative boost, and a future-proof decision.

Conclusion

Prompt engineering is not a passing trend. It’s the new literacy of the digital age. As Generative AI continues to evolve, those who know how to speak its language will shape the future of work, art, and innovation.

So whether you're building a business, designing a campaign, or teaching a class, this new digital superpower is yours to master. And the best part? You don’t need a computer science degree — just curiosity, creativity, and the right training.

[keploy]

Prompt Engineering for Python Code Generation: Techniques and Best Practices

Prompt Engineering for Python Code Generation

Prompt engineering is an essential technique for optimizing AI-generated Python code, ensuring accurate, efficient, and context-aware outputs. As AI-powered tools like OpenAI’s GPT, GitHub Copilot, and Keploy gain popularity in software development, crafting well-structured prompts becomes crucial for generating high-quality Python code. This guide explores the fundamentals of prompt engineering and best practices for AI-driven Python code generation.

What is Prompt Engineering?

Prompt engineering involves designing and structuring inputs to guide AI models in producing high-quality responses. In the context of Python code generation, well-crafted prompts ensure the AI understands the intent and delivers precise results. By refining how queries are structured, developers can enhance the quality of AI-generated code, making it more readable, maintainable, and efficient.

Why is Prompt Engineering Important for Python Code Generation?

AI models rely on prompts to generate relevant and accurate code. A poorly structured prompt can lead to ambiguous or incorrect outputs, requiring additional effort to debug and refine. A well-structured prompt reduces ambiguity, improves efficiency, and minimizes the need for extensive post-processing. Prompt engineering is especially crucial for automated test generation tools like Keploy, which leverage AI to create reliable test cases for applications.

Key Strategies for Effective Prompt Engineering

To generate better Python code with AI, consider the following strategies:

1. Be Specific and Contextual

Providing clear instructions and specifying the expected output format helps AI models generate more accurate results. Instead of asking, "Write a Python function," a better prompt would be, "Write a Python function that takes a list of numbers and returns a sorted list using the quicksort algorithm."

2. Use Examples to Guide the AI

Including input-output examples in your prompt helps AI understand patterns and expectations for code generation. For instance:

Prompt: Write a Python function to check if a string is a palindrome.

Example Input: 'radar'

Example Output: True

By providing examples, AI can better align with the expected behavior.

3. Define Constraints and Requirements

Explicitly stating programming constraints, such as using specific libraries or avoiding certain functions, refines the generated output. If you want to generate a machine learning model using TensorFlow instead of PyTorch, include that in the prompt.

4. Break Down Complex Tasks into Steps

Splitting a large problem into smaller tasks improves AI performance and ensures structured code generation. Instead of asking AI to "build a chatbot," break it down into "write a Python function to handle user input," "generate responses using an NLP model," and "deploy the chatbot using Flask."

Common Challenges in AI-Generated Python Code

Despite advancements, AI-generated Python code may have challenges such as logical errors, lack of optimization, or missing edge cases. Effective prompt engineering can mitigate these issues by providing clear instructions and expected behavior. Keploy, an AI-powered test case generation tool, can help validate and refine AI-generated code by automating the testing process and identifying potential issues.

Practical Examples of Prompt Engineering for Python Code

Example 1: Generating a Simple Function

Prompt:

Write a Python function that takes a list of numbers and returns the maximum value.

Generated Code:

def find_max(numbers):

    return max(numbers)

Example 2: Enforcing Coding Standards

Prompt:

Write a Python function that calculates the factorial of a number, following PEP 8 style guide.

Generated Code:

def factorial(n):

    """Returns the factorial of a given number."""

    if n == 0:

        return 1

    return n * factorial(n - 1)

Example 3: Using External Libraries

Prompt:

Write a Python function that uses NumPy to generate a random array of size 10.

Generated Code:

import numpy as np

def random_array():

    return np.random.rand(10)

Tools for Python Code Generation with AI

Several AI-powered tools assist in generating Python code efficiently. Some of the notable tools include:

OpenAI’s GPT – Generates Python code based on structured prompts.

GitHub Copilot – Suggests code snippets within the IDE.

Keploy – Automates test case generation and ensures AI-generated code is reliable and functional.

Best Practices for Refining AI-Generated Code

Review and Validate Output – Always test AI-generated code for correctness and efficiency.

Optimize for Readability – Ensure the generated code follows best practices for maintainability.

Use AI for Assistance, Not Replacement – AI can enhance productivity but should complement human expertise.

Leverage Keploy for Test Generation – AI-generated code should be tested to ensure robustness, and Keploy can automate test creation for better validation.

Conclusion

Prompt engineering is a powerful skill that enhances AI-driven Python code generation. By crafting precise, structured prompts, developers can improve AI accuracy, efficiency, and reliability in coding tasks. Whether generating new functions, enforcing coding standards, or integrating external libraries, well-designed prompts ensure optimal AI-generated output. Additionally, tools like Keploy help validate and test AI-generated code, ensuring its reliability in real-world applications. Mastering prompt engineering not only improves the quality of AI-generated Python code but also enhances overall development efficiency, making AI an invaluable assistant in modern software engineering.

[davidwyatt309]

Step-by-Step Guide to Hiring Dedicated Developers

In the ever-evolving realm of software development, finding the perfect talent feels like forging your own Excalibur – a mythical quest demanding strategy, discernment, and a touch of magic. But fear not, intrepid leader! This step-by-step guide will equip you with the knowledge to hire dedicated developers, the skilled artisans who will craft your digital vision into a reality.

Step 1: Unearth the Quest: Define Your Project Needs

Before venturing forth, embark on a journey of self-discovery. Clearly define your project's scope. Are you building the next groundbreaking mobile game, or perhaps a revolutionary AI-powered application that requires the expertise of hire prompt engineers? Identifying your specific needs – whether you require a team of hire Kotlin developers or specialists in cross-platform development – is the cornerstone of attracting the right talent pool.

Step 2: Chart Your Course: Scouting Grounds for Top Talent

The digital world offers a vast landscape for recruiting skilled developers. Platforms like LinkedIn and GitHub are teeming with experienced professionals. Consider developer communities or forums specific to your project's needs. If you require a team of wizards versed in the language of Kotlin, for instance, explore Kotlin-focused communities to find developers passionate about this technology.

Step 3: Craft Your Call to Arms: The Compelling Job Description

Your job description is your siren song, so make it resonate! Clearly outline the project requirements, desired skillsets (including specific programming languages and frameworks), and a glimpse into your company culture. Don't forget to showcase the project's most exciting aspects – what will truly motivate a talented developer to join your quest?

Step 4: The Gauntlet of Interviews: Unearthing Hidden Gems

Resumes are just the first impression. Design a series of interviews that delve deeper than technical prowess. Assess a candidate's problem-solving abilities, communication style, and cultural fit. Technical coding challenges can be a great way to gauge their skills, but also throw in some curveballs to see how they think critically under pressure.

Step 5: Trial by Code: The Power of Test Projects

Sometimes, the best way to assess a developer's fit is through a well-defined test project. This allows you to evaluate their coding style, ability to meet deadlines, and communication skills within a low-stakes environment.

Step 6: The Pact of Trust: References and Portfolio Power

Seek out references from past clients and employers. A developer's portfolio is their arsenal – scrutinize it carefully to see if their past projects align with your needs.

Step 7: Beyond the Code: Building Bridges of Communication

Technical skills are crucial, but a developer who can't communicate effectively is a recipe for disaster. Prioritize clear and concise communication throughout the hiring process. Look for developers who can articulate complex ideas, actively listen to feedback, and collaborate seamlessly within a team.

Step 8: The Contract Negotiation: Striking a Fair Bargain

Once you've found your ideal candidate, it's time to discuss compensation. Research average rates for developers with the desired skillset in your chosen location (be it local or remote). Remember, competitive compensation is key to attracting and retaining top talent.

Step 9: Building Your Fellowship: Fostering a Culture of Collaboration

Company culture is more than just virtual water coolers and online game nights. It's about shared values, work ethic, and a collaborative environment. Ensure your chosen developers align with your company culture for a harmonious and productive working relationship.

Step 10: The Path of Onboarding: Equipping Your Champions

Don't just throw your new recruits into the digital battlefield. A well-structured onboarding process is essential. Provide them with the tools, resources, and mentorship they need to integrate seamlessly into your team and start contributing effectively from day one.

By following these steps, you'll be well on your way to assembling a fellowship of dedicated developers – your digital knights who will wield their coding prowess to bring your project to life. Remember, the quest for exceptional talent is an ongoing journey. But with the right approach, you'll forge a team that empowers you to conquer the digital landscape and claim your own place in the ever-evolving world of technology.

[toppromptengineeringcourse]

What is Prompt Engineering? A Beginner’s Overview

Prompt Engineering Course, With the rise of artificial intelligence (AI) and natural language processing (NLP), Prompt Engineering has emerged as a critical skill. It involves crafting effective prompts to guide AI models like GPT-4 in generating accurate and relevant responses. Whether for chatbots, content creation, coding, or automation, mastering Prompt Engineering allows users to maximize AI efficiency. Prompt Engineering Training

Why is Prompt Engineering Important?

Prompt Engineering is vital for optimizing AI-generated outputs. AI models, including large language models (LLMs), rely on well-structured prompts to provide useful responses. Poorly designed prompts can result in vague, irrelevant, or misleading information. Through Prompt Engineering, users can:

Improve AI accuracy and relevance.

Reduce biases and inconsistencies in AI-generated content.

Streamline workflows across various industries, from customer service to healthcare. Prompt Engineering Course

Core Principles of Prompt Engineering

To master Prompt Engineering, beginners should understand its foundational principles:

1. Clarity and Specificity

A prompt should be clear and concise to ensure accurate AI responses. Instead of "Explain machine learning," a better prompt is "Summarize the key principles of machine learning in 200 words."

2. Contextual Framing

Providing context enhances AI comprehension. For example, instead of "Translate this text," a more effective prompt is "Translate this French text into English, keeping a formal tone." Prompt Engineering Training

3. Iterative Refinement

Fine-tuning prompts through testing and feedback is crucial. Users should experiment with different phrasing and structures to achieve optimal results.

Applications of Prompt Engineering

Prompt Engineering is widely used across multiple industries. Some key applications include:

1. Content Creation

AI-driven tools like ChatGPT and Jasper rely on Prompt Engineering for generating blog posts, product descriptions, and marketing copies. A well-designed prompt can yield creative, engaging, and SEO-optimized content.

2. Coding Assistance

Developers leverage Prompt Engineering to generate code snippets, debug errors, and automate repetitive tasks using AI-powered tools like GitHub Copilot.

3. Customer Support

Companies use AI chatbots trained with Prompt Engineering to handle customer queries, improving response times and service quality.

4. Research and Data Analysis

Data professionals use AI-driven tools to summarize reports, analyze trends, and extract insights using well-crafted prompts.

Best Practices for Effective Prompt Engineering

To enhance Prompt Engineering efficiency, follow these best practices:

Use step-by-step prompts: Breaking down tasks into sequential steps improves AI comprehension.

Incorporate examples: Providing examples within the prompt improves response accuracy.

Define the output format: If a specific format is needed, mention it (e.g., "List five key benefits of AI in bullet points").

Test and iterate: Regular experimentation helps refine prompt effectiveness.

The Future of Prompt Engineering

As AI continues to evolve, Prompt Engineering will become an essential skill for professionals across industries. Advanced AI models will require more precise prompts to achieve human-like reasoning and creativity. Additionally, Prompt Engineering will shape how businesses interact with AI, influencing automation, personalization, and data-driven decision-making.

Conclusion

Prompt Engineering is a game-changer in the AI-driven world, enabling users to interact effectively with language models. From content generation to automation, it has a broad range of applications that enhance productivity and efficiency. By understanding and applying the core principles of Prompt Engineering, beginners can unlock the full potential of AI tools and stay ahead in the ever-evolving tech landscape.

Visualpath is the Leading and Best Institute for learning in Hyderabad. We provide Prompt Engineering courses online. You will get the best course at an affordable cost.

Call on – +91-7032290546

Visit: https://www.visualpath.in/prompt-engineering-course.html

[secourses]

Kling AI Video Has Finally Been Released Globally (Available in All Countries), Free to Use and Astonishing - Comprehensive Guide

Complete Tutorial Link ▶️ https://youtu.be/zcpqAxYV1_w

You've likely encountered those astounding AI-generated videos. The moment has arrived. The renowned Kling AI is now accessible worldwide at no cost. In this instructional video, I'll demonstrate how to register for Kling AI for free using only an email address and utilize its impressive text-to-video animation, image-to-video animation, text-to-image, and image-to-image capabilities. This video will present non-cherry-picked results, giving you an accurate understanding of the model's actual quality and capabilities, unlike those highly selective example demonstrations. Nevertheless, #KlingAI remains the sole #AI model that rivals OpenAI's #SORA and is available for real-world use.

🔗 Kling AI Official Website ⤵️ ▶️ https://www.klingai.com/

🔗 SECourses Discord Channel for Comprehensive Support ⤵️ ▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388

🔗 Our GitHub Repository ⤵️ ▶️ https://github.com/FurkanGozukara/Stable-Diffusion

🔗 Our Reddit ⤵️ ▶️ https://www.reddit.com/r/SECourses/

0:00 Introduction to Kling AI - premier video generator AI model 0:28 Kling AI free registration process 1:17 Generating a prompt idea using Claude 3.5 for free to use with Kling AI for video creation 1:54 Testing a challenging prompt on Kling AI with various parameters 2:56 Optimizing LLM-generated prompts for text-to-video (AI) platforms 3:20 Daily free video generation limit and Kling AI's credit system 3:48 Generating multiple videos simultaneously 4:21 Maximum video duration possible with Kling AI's free version 4:54 Comparing different configurations for text-to-video generation on Kling AI 5:38 Crafting a prompt for image-to-image video/animation generation 5:55 Creating an AI video from an input image 7:11 Comparing various configurations for image-to-video generation on Kling AI 8:50 Optimal image-to-video animation configuration for Kling AI 9:45 Utilizing Kling AI's text-to-image feature

Kuaishou Initiates Comprehensive Public Testing of 'Kling AI' for Global Users, Enhances Model Capabilities

Kuaishou Technology (HKD Counter Stock Code: 01024 / RMB Counter Stock Code: 81024) (along with its subsidiaries and consolidated affiliated entities, hereafter referred to as "Kuaishou" or the "Company"), a prominent content community and social platform, recently announced significant upgrades to the foundation model of its "Kling AI" (可灵AI) video generation model, with the beta version now accessible to users worldwide via web portal (Chinese version: https://klingai.kuaishou.com/ ; English version: https://klingai.com/).

In response to growing demand from its extensive roster of content creators, Kuaishou has not only initiated beta testing of Kling AI for a broad audience but also launched a subscription program for users in mainland China, offering Kling AI users more tailored features across different subscription tiers. The Company anticipates launching international subscriptions in the near future.

Improved Foundation Model Enhances User Experience

In the month since its unveiling, Kling AI has undergone multiple enhancements. With the introduction of the subscription program, the foundation model now offers even more upgraded features. The latest round of improvements significantly enhances overall video quality. Videos produced by the upgraded model exhibit improved composition and color tone, boasting superior overall aesthetics. Motion performance has also been considerably enhanced, with greater range and accuracy of movement.

Earlier versions of Kling AI offered capabilities such as image-to-video generation and video extension. At the recent World Artificial Intelligence Conference, Kling AI was officially launched on the web along with several new features, including extending the text-to-video generation duration to 10 seconds. With the latest upgrade, users can expect an even more refined AI video-generating experience.

Full Beta Testing Launch and Limited-Time Subscription Discount

As the world's first accessible, real-image-level video generation large model for ordinary users, Kling AI has been immensely popular since it began accepting applications on June 6. After receiving over one million applications, more than 300,000 users were granted early access. With today's announcement, Kuaishou has fully launched the beta version to everyone, bringing the exciting Kling AI experience to a wider audience. Users will receive 66 daily "Inspiration Credits" that can be used to redeem specific functions or value-added services on the Kling AI platform, equivalent to producing about six free videos.

Alongside the upgrade, Kling AI has also officially introduced an all-new subscription program for users in mainland China. Users can select from three subscription tiers on Kling AI's official website: Gold, Platinum and Diamond, with monthly prices of RMB66, RMB266 and RMB666, respectively.

[callofdutymobileindia]

How Generative AI Training in Bengaluru Can Boost Your Tech Career?

In recent years, Generative AI has emerged as one of the most disruptive technologies transforming industries across the globe. From personalized content creation to AI-driven design, code generation, and even advanced medical imaging—Generative AI is revolutionizing how we work, interact, and innovate.

And if you are a tech enthusiast or working professional based in India’s Silicon Valley, you’re in the perfect place to jump into this exciting field. Generative AI Training in Bengaluru offers a unique blend of industry exposure, expert-led education, and career acceleration opportunities. This blog will guide you through the benefits of pursuing generative AI training in Bengaluru and how it can supercharge your career in the tech domain.

What is Generative AI?

Before diving into the career benefits, let’s define what Generative AI actually is. Generative AI refers to a class of artificial intelligence models capable of generating new content—text, images, audio, video, or code—based on patterns learned from existing data.

Popular tools and models include:

ChatGPT (OpenAI) – for conversational AI and text generation

DALL·E & Midjourney – for AI-generated images

Codex & GitHub Copilot – for AI-assisted programming

Runway ML & Sora – for generative video

Stable Diffusion – for open-source creative tasks

Industries are actively seeking professionals who can understand, implement, and innovate with these tools. That’s where Generative AI training comes in.

Why Choose Bengaluru for Generative AI Training?

Bengaluru is more than just a city—it’s the beating heart of India’s tech ecosystem. Here’s why enrolling in a Generative AI training in Bengaluru program can be a game-changer:

1. Home to India’s Leading Tech Companies

From Infosys and Wipro to Google, Microsoft, and OpenAI-partnered startups—Bengaluru hosts a vast number of AI-focused organizations. Training in the city means you’re close to the action, with easier access to internships, workshops, and networking events.

2. Cutting-Edge Training Institutes

Bengaluru boasts some of the top AI and ML training providers in India. These institutions offer hands-on experience with real-world projects, industry mentorship, and certifications that are recognized globally.

3. Startup Ecosystem

With a thriving startup culture, Bengaluru is a breeding ground for innovation. After completing your training, you’ll find ample opportunities in early-stage ventures working on next-gen generative AI products.

4. Tech Community and Events

The city is buzzing with meetups, hackathons, AI summits, and conferences. This vibrant tech community provides a great platform to learn, collaborate, and grow.

What Does Generative AI Training in Bengaluru Include?

Most leading programs in Bengaluru offer comprehensive coverage of the following:

✅ Core AI and ML Concepts

Understanding the foundational building blocks—supervised/unsupervised learning, deep learning, and neural networks.

✅ Generative Models

Focused training on GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and Diffusion Models.

✅ Large Language Models (LLMs)

Working with GPT-3.5, GPT-4, Claude, LLaMA, and other state-of-the-art transformer-based models.

✅ Prompt Engineering

Learning the art and science of crafting prompts to generate better, controlled outputs from AI models.

✅ Toolkits and Platforms

Hands-on experience with tools like OpenAI APIs, Hugging Face, TensorFlow, PyTorch, GitHub Copilot, and LangChain.

✅ Capstone Projects

End-to-end implementation of real-world generative AI projects in areas like healthcare, e-commerce, finance, and creative media.

How Generative AI Training in Bengaluru Can Boost Your Tech Career?

Let’s get to the heart of it—how can this training actually boost your career?

1. Future-Proof Your Skill Set

As automation and AI continue to evolve, companies are constantly seeking professionals with AI-forward skills. Generative AI is at the forefront, and training in it makes you an in-demand candidate across industries.

2. Land High-Paying Roles

According to industry data, professionals with Generative AI skills are commanding salaries 20-30% higher than traditional tech roles. Roles such as:

AI Product Engineer

Prompt Engineer

Machine Learning Scientist

Generative AI Researcher

AI Consultant

LLM Application Developer

are on the rise.

3. Open Doors to Global Opportunities

With Bengaluru's global tech footprint, professionals trained here can easily transition to remote roles, international placements, or work with multinational companies using generative AI.

4. Enhance Your Innovation Quotient

Generative AI unlocks creativity in code, design, storytelling, and more. Whether you're building an app, automating a workflow, or launching a startup, these skills amplify your ability to innovate.

5. Accelerate Your Freelancing or Startup Journey

Many tech professionals in Bengaluru are turning into solopreneurs and startup founders. With generative AI, you can quickly prototype MVPs, create content, or offer freelance services in writing, video creation, or coding—all powered by AI.

Who Should Enroll in a Generative AI Training Program?

A Generative AI training in Bengaluru is ideal for:

Software Developers & Engineers – who want to transition into AI-focused roles.

Data Scientists – looking to expand their capabilities in creative and generative models.

Students & Graduates – aiming for a future-proof tech career.

Designers & Content Creators – interested in AI-assisted creation.

Entrepreneurs & Product Managers – who wish to integrate AI into their offerings.

IT Professionals – looking to reskill or upskill for better job roles.

Why Choose a Professional Institute?

While there are many online courses available, a classroom or hybrid program in Bengaluru often provides:

Mentorship from industry experts

Collaborative learning environment

Real-time feedback and doubt-solving

Placement support and career counseling

Live projects using real-world datasets

One such reputed name is the Boston Institute of Analytics, which offers industry-relevant, project-based Generative AI training programs in Bengaluru. Their curriculum is tailored to meet evolving market demands, ensuring students graduate job-ready.

Final Thoughts

In today’s fast-evolving tech landscape, staying ahead means staying adaptable—and Generative AI is the perfect skill to future-proof your career. Whether you want to break into AI development, build cutting-edge products, or simply enhance your tech toolkit, enrolling in a Generative AI Training in Bengaluru can set you on a path of accelerated growth and innovation.

Bengaluru’s thriving tech ecosystem, access to global companies, and expert-led training institutions make it the ideal place to begin your generative AI journey. Don’t wait for the future—start building it now with the right training, tools, and support.

[adafruit]

JOIN US! TONIGHT 9PM ET Sept 3, 2023 - Desk of Ladyada - ChatGPT-assisted driver programming & I2C Infrared Proximity Sensors ����🤖🔍

This week at the Desk of Ladyada, we've been on a "try to use ChatGPT every day" experiment to see how to use LLM tools for engineering. This week, we had fun using it to write Arduino drivers for chips by uploading the chip datasheet for analysis and guiding the LLM through how to write an 'adafruit' style driver. After all, it's been trained on hundreds of ladyada-written libraries on GitHub. https://github.com/orgs/adafruit/repositories. 

Here's the chatGPT log for a video we published last night https://chat.openai.com/c/f740eb57-17a6-41e3-ae0a-12da959a1f4c - and here's a previous one that is more 'complete' https://chat.openai.com/share/f44dc335-7555-4758-b2f9-487f9409d556. The amount of time it takes for ChatGPT to write a driver is about the same as it would take manually, and you definitely need to be eagle-eyed to redirect the AI if it starts making mistakes... but we think with some prompt hacking, we can speed things up by front-loading some of our requirements instead. It's still very early in our experiments, and many things are challenging/hard for GPT 4 to do, so you just have to try them!

The Great Search - I2C Infrared Proximity Sensor

The driver that we used ChatGPT 4 to write is for the VCNL4020 https://www.digikey.com/short/dpz897jj, an 'all in one' IR proximity sensor. These sensors work by bouncing bright IR light off a surface and measuring how much returns. The IR light is modulated and relatively resistant to ambient light interaction. One of the first sensors we ever used was the Sharp GP2Y https://www.adafruit.com/product/164. It is famous for its ease of use in the days before cheap microcontrollers provided 5V power, and an analog signal that roughly maps with distance is emitted. While you can still get analog distance sensors https://www.digikey.com/short/d21wnm5c, I2C interfacing lets you get data quickly and adjust for lower power usage. Let's look at some digital IR proximity sensors and other distance sensors (and the differences between them!)

[shiftasia]

Harnessing the Power of Automation in Modern Software Testing

In the rapidly evolving landscape of contemporary software development, the spotlight is firmly on automation’s role in software quality testing. This transformative practice is reshaping the traditional testing paradigm within the software development life cycle (SDLC). Let’s dive deeper into the world of software testing automation, uncovering its benefits, addressing challenges, and seamlessly integrating it into the ever-changing world of technology.

Transitioning from Manual Testing

The era of labor-intensive manual testing dominating the software quality assurance process is a thing of the past. As competitive pressures and customer expectations have soared, companies have sought faster, more efficient ways to deliver exceptional products. What was once the norm – manual testing – became a bottleneck in the fast-paced environment of today. This shift in perspective prompted the adoption of software testing automation as a pivotal tool for staying competitive and meeting market demands.

Adapting to Shifting Industry Dynamics

The technology sector is undergoing rapid transformation, marked by increasing standards and a fluid competitive landscape. Modern software quality assurance companies need agility to adapt swiftly to changes while maintaining cost-effectiveness in their development processes. With the rise of agile methodologies, DevOps practices, and continuous integration/continuous delivery (CI/CD) pipelines, the limitations of manual testing have become apparent. Its time-consuming nature and potential for errors clash with these innovative approaches, underscoring the need for a streamlined testing process.

Embracing the Benefits

The journey towards automated testing has been underway for years, and its benefits are profound. Automation brings reliability, speed, and scalability to the forefront. Traditional manual testing often required dedicated software testing and quality assurance teams, leading to extended development cycles and increased costs. In contrast, automated tests execute swiftly and can scale to handle large workloads. This not only accelerates testing but also frees IT personnel for more strategic endeavors.

Moreover, automated tests seamlessly integrate into CI/CD pipelines, enabling early and frequent end-to-end tests. This “shifting left” approach facilitates the early detection of issues and cost-effective resolutions, ultimately enhancing overall software quality.

Harmonizing with CI/CD Workflows

Automated tests are an integral part of CI/CD workflows. Developers push updates to repositories like GitHub, triggering automated tests. Test results guide quality engineers in resolving issues before merging updates into the production code. This integration ensures software quality aligns with benchmarks and user expectations.

Navigating Challenges

While software testing automation offers substantial benefits, it’s not a universal solution. Not all tests can or should be automated. Tasks involving exploratory testing or those requiring visual design evaluation may necessitate manual testing. Setting up automation can be time-intensive, but it yields returns through test reuse. Developing, reviewing, and updating automated checks require maintenance and coding skills. Thoughtful tool selection is essential, given the varying effectiveness of testing tools.

A Paradigm Shift

In today’s software landscape, software testing automation is more than just a tool; it signifies a paradigm shift. It accelerates testing, aligns seamlessly with agile practices, and elevates software quality. By harnessing automation’s power, companies can expedite development cycles, reduce costs, and ensure the delivery of high-quality, competitive products to a demanding market.

If your company grapples with resource constraints for conducting automated testing campaigns and seeks to optimize costs in this process, considering options for outsourcing automated testing is a judicious move. SHIFT ASIA stands as a reputable player in the software quality assurance companies, worthy of consideration. Their expertise and proficiency position them as a favorable choice for your automated testing needs. Partnering with a respected outsourcing company like SHIFT ASIA enables your company to streamline testing processes and ensure the delivery of quality software products while optimizing expenditures.

[blabbin]

Disco Elysium Fandom Masterpost

Decided to compile all the cool projects fans have made for Disco Elysium! This includes game resources, fan games, and fun collaboration events!

If you want to be notified of any updates, you can click on 'subscribe to conversation' for this post!

If you've got any corrections/updates/suggestions, reach out to let me know! ((Last updated: January 29th, 2023))

Game Information/Archives/Resources

Disco Elysium Wiki (Literally contains most everything you'd want to know about the game, including a ton of image resources)

FAYDE On-Air by Morgue, Pieartsy, and Sphinx111 (Search engine tool to easily find any in-game dialogue)

Disco Reader by xyrilyn + Guide on how to use it (Search engine AND text visualization tool. You can see the full dialogue trees as flowcharts)

Disco Elysium Explorer by Leerion + Github (Inspired by Disco Reader. It works much the same way but it ALSO includes the audio clips for every piece of dialogue!! + the text for every language released so far)

Disco Elysium Checklist compiled by MentalPrinciple0 (Includes all red/white checks, their difficulty level, all possible modifiers, as well as items, tasks, thoughts, and their modifiers)

Complete OST + Ambient Mix compiled by OnlySheStandsHere (Includes everything added for The Final Cut)

High Quality Full Map of Martinaise compiled by For Lack Of A Title

Map of Revachol West vectorized by freezebobs + svg file

Disco Elysium: Country/Cultural Mappings compiled by Limey (List of all the geographical places named in-game, what we know about them, and what some real-world parallels are)

Disco Elysium: A Timeline compiled by six and brenn (A complete timeline of every event mentioned in the game, from the creation of the universe to the events of The Hanged Man case)

Theoretical Entroponetics by 57sfinest (Everything we know about The Pale + OP's own theory)

Fanworks

Fandom Collaboration Projects

((since these are big collaboration projects, I unfortunately can't properly list the credits on this post. Make sure to check out the respective credit pages of the projects!! So many talented peeps!!!))

Disco Elysium Fan Colouring Book (SFW)

Disco Elysium Fan Colouring Book (NSFW) [[18+]]

Social media tag: #EspritDeCouleur

Fire Escape in The Sea Montage-Disco Elysium

Social media tag: #FEITSmontage

Disco Elysium Montage/Cleaning Out The Rooms

Social media tag: #COTRmontage

Disco Elysium Secret Santa 2022

Ao3 Collection

Bonus Round! Prompt List is open for anyone to fill if something grabs your interest!

Social media tag: #DESecretSanta2022

Disco Elysium Rarepair Fanworks Exchange

Ao3 Collection

Prompt List! Open for anyone to fill if something grabs your interest!

((There's no tag, everything is compiled on the twitter account. If you post anything new, make sure to @ the account to be RTed!))

Disco Elysium Tarot, the Major Arcana

Social Media tag: #DiscoTarot

An art challenge to recreate famous paintings into something Disco Elysium themed! Feel free to join in!

Social media tag: #DiscoElysiumArtClassic

Fan Games

After the Mind, the World Again by Pieartsy (TTRPG)

Disco Elysium: Game Boy Edition by BloodyStapler

Disco Dating by Slugandthorn, Capreola, Frankie, and Joe/BerlinChild (Visual Novel)

The Cryptokiller, project lead Arthur Stahlschmidt (Visual Novel) ((Make sure to check the itch.io page for a full list of the team!!))

Mystery Miniaturist by Jasmine "Katy133" Osler (Visual Novel) ((Make sure to check the itch.io page for the full list of credits!!))

WE GOT A CODE 31 by Slugandthorn (Dancing Penguin Kim. WHAT MORE DO YOU WANT ME TO SAY!!)

Mini Mini Disco by OinkOink!Studio (a collection of mini-games! The links on the main page might be broken, so here they are just in case:

Nod match with Kim

Pretty Jean Jean

Beg Money)

Disco Elysium: Signature Skill Selection by FeigningIntelligence (Take this quiz and find out what your signature skill is!)

Twine Games/Fics

Re-Hearsed by Apepers

Requiem for the Apostle by Alphawave

Sweeter Kind of Fire [[18+]] by Kawa

Disco Elysium: Insomnia [[18+]] by mouseoverlord, Booboboobo, cyaber5, LoveSplean, and Aila

One Day in Jamrock [[18+]] by CeaserAmarelo

Fan Skins

((Want to write a fic/make a game that looks like Disco Elysium? These cool peeps got you covered!))

Disco Days Workskin by abyssalcaptain (Ao3)

Disco Elysium Workskin by Cunticuss (Ao3)

Disco Elysium Twine Macros by Apepers and Pieartsy (Twine)

Disco Elysium Framework for Ren'Py by Katy133 (Ren'Py)

Misc.

Disco Elysium Mods

Disco Elysium Item Card Creator by ColorfulTie (fun lil tool to create your own items with stats in the style of the game)

Disco Elysium fan music by Aila :

Insomnia Vol 1

Rain in Revachol

[keploy]

AI Code Generation: A Comprehensive Guide

AI code generation tools are transforming the way developers write and maintain code. These tools use machine learning models to generate code snippets, automate repetitive coding tasks, and even provide complete solutions for common programming challenges. In this article, we will explore how AI-driven code generation works, its benefits, popular tools, and the best practices for using these tools effectively.

What is AI Code Generation?

AI code generation refers to the use of artificial intelligence algorithms to create code based on input data, prompts, or context. These tools leverage models trained on large datasets of programming languages and development patterns to predict and generate code that matches the desired functionality.

With AI-driven assistance, developers can reduce manual effort and boost productivity by automating parts of the coding process.

How AI Code Generators Work

AI code generation tools rely heavily on machine learning and natural language processing (NLP). They analyze patterns from vast code repositories to predict what code should come next or how a function can be constructed. Some tools use:

Autocompletion Models: Tools like GitHub Copilot suggest code as developers type, based on the current context.

Prompt-based Generation: Solutions like ChatGPT allow users to describe functionality in natural language, generating relevant code snippets in response.

Code Translation: AI can convert code from one language to another, streamlining migrations or upgrades.

These models are continuously improved by fine-tuning with feedback from users and open-source contributions.

Benefits of AI Code Generation

AI-based code generation tools offer numerous advantages:

Increased Productivity: Developers save time on boilerplate code and repetitive tasks.

Error Reduction: AI tools help reduce syntax errors by suggesting correct code patterns.

Support for Learning: Beginners can benefit from AI-generated code as a learning aid, gaining insights into best practices.

Faster Prototyping: AI allows developers to quickly generate working prototypes or proof-of-concept solutions.

These benefits help streamline development workflows, especially in fast-paced environments.

Popular AI Code Generation Tools

Several AI-powered tools are leading the way in code generation:

GitHub Copilot: A code completion tool powered by OpenAI's models, seamlessly integrating with VSCode and other IDEs.

Tabnine: An AI assistant that supports multiple languages and suggests context-aware code.

OpenAI Codex: The underlying engine for tools like Copilot, Codex can generate complete code snippets from simple prompts.

Amazon CodeWhisperer: A tool focused on providing code recommendations and snippets tailored for cloud development.

Replit Ghostwriter: A collaborative AI coding assistant within Replit, enhancing code creation and debugging in real time.

Each of these tools offers unique capabilities, making them suitable for various use cases and development environments.

Challenges of AI Code Generation

While AI code generators are beneficial, they come with challenges:

Lack of Context Awareness: AI tools may not fully understand the problem domain or project-specific requirements.

Security Concerns: Generated code might inadvertently introduce vulnerabilities if not reviewed carefully.

Dependence on AI: Over-reliance on AI-generated code can reduce developers’ deep understanding of the codebase.

Bias in Training Data: AI models trained on open-source repositories may inherit coding biases or outdated practices.

Mitigating these challenges requires a thoughtful approach to using AI tools responsibly.

Best Practices for Using AI Code Generators

To maximize the benefits of AI code generation tools, follow these practices:

Review Generated Code: Always validate and test AI-generated code for correctness and security.

Use for Assistance, Not Replacement: Leverage AI as a coding assistant while maintaining hands-on coding skills.

Provide Clear Prompts: The more specific your input, the better the quality of the generated code.

Combine AI with Human Expertise: Use AI to speed up repetitive tasks but rely on human intuition for complex logic.

These practices ensure that AI tools enhance productivity without compromising quality or security.

Future of AI Code Generation

The future of AI code generation looks promising with advancements in large language models and the integration of AI in software development pipelines. We can expect more tools to offer seamless collaboration, personalized recommendations, and deeper integration with DevOps practices. Additionally, ethical considerations and improved safeguards will be necessary to ensure AI-generated code is reliable and secure.

Conclusion AI code generation is revolutionizing the way software is developed by automating routine tasks, improving efficiency, and enhancing learning. While these tools offer many benefits, developers should use them wisely, combining AI’s speed with human expertise to ensure high-quality, secure code. As the technology evolves, AI tools will become even more integral to software development, shaping the future of programming.

[angstics]

how to download (and mirror and transcribe) youtube videos

so the news that google is deleting inactive youtube channels was a miscommunication -- "Additionally, we do not have plans to delete accounts with YouTube videos at this time" (source, emphasis mine). but i hope this was a wake up call that archiving videos (and other content) you care about is really important. buy hard drives, save, reshare. videos dont stay up forever. youtube isnt forever.

i know how difficult it is to get into downloading videos, with how all youtube to mp4 websites seem to be broken. this post compiles general guides on how to manually download youtube videos (among other actions) through python programs. it's simple if you just follow the steps and constantly search the errors you encounter. i will also detail how i personally do it with my windows 10 pc, in case you use the same tools.

remember: your search engine, reddit, github, and help commands are your best friends.

* downloading youtube videos

reddit yt-dlp guide

original yt-dlp guide

how to download the best quality mp4

how to download videos from a search result

how to use command prompt

what is command prompt? this is a windows application where you navigate folders and run programs. you just type a command and hit enter. ctrl+c ends a command/program, ctrl+s pauses it (pressing any key unpauses)

how do i navigate folders? the basic commands are so: a) cd "[path]" to change directory (always put path and link names in double quotes so they are processed properly), b) cd .\.. takes you to the previous folder (ex: if you're in C:\folder A\folder B and run cd .\.. you go to C:\folder A), c) you can go to other drives by typing the letter and colon (ex: if you are in C:, typing D: then entering takes you to your D drive). this is important because where your python programs are stored is where you have to run them.

how to run python programs through cmd prompt? a) download the latest version of python. b) use pip to install programs. c) make sure you have also downloaded a program's dependencies (analogous to "pre-requisites"). d) type the program name then the command.

make sure to always update python and pip.

how to use yt-dlp to download youtube videos

how to get download yt-dlp? this guide worked perfectly for me. make sure to download all python programs in the same folder.

navigate to the folder you installed yt-dlp

the following are examples of commands you can use:

yt-dlp -h -- get a list of all commands

yt-dlp "[link]" -- download video as is (often in webm format)

yt-dlp "[link]" -f "bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best" -- download the highest quality mp4 video (highest possible in mp4 is 1080p)

yt-dlp -x --audio-format mp3 "[link]" -- download audio only as mp3

yt-dlp -i "[playlist link]" -- download a full playlist (you may also use the best quality command here)

yt-dlp -i "[playlist link]" --playlist-items [range] -- download range of playlist items

look at the guides at the top of this section for my ideas of what you can do with yt-dlp. you can even use yt-dlp to download from other websites

note: if you want to download instagram reels, you must include: --cookies-from-browser [firefox / chrome / etc] -- choose your browser

** mirroring youtube videos to archive.org

github tubeup guide: "tubeup uses yt-dlp to download a Youtube video (or any other provider supported by yt-dlp), and then uploads it with all metadata to the Internet Archive using the python module internetarchive."

this guide shows you how to install and use the program. this is an easy way to archive videos with the proper metadata -- do not archive videos en masse

the mirrortube archive.org community

*** transcribing videos

transcribing youtube videos w/o downloading: application

transcribing any downloaded video: openai guide

extra1: searching videos

ive seen confusion on how to naviagte youtube search these days. i know!!!! here are some tips:

changing search options to search by upload date shows *ACTUAL* results, rather than suggestions.

the same google tricks work on youtube: google tricks guide

using yt-dlp to search can be helpful to search youtube more precisely

extra2: downloading twitter videos online

i use this regularly, so i thought id also share.

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How to Easily Install GPT-Engineer and Build Entire Apps with One Prompt

How to Easily Install GPT-Engineer and Build Entire Apps with One Prompt https://ift.tt/MxR6K3g Have you ever wished that building entire applications would be a breeze? If you’re tired of spending hours configuring your environment and writing boilerplate code, then GPT-Engineer may be just what you need. This tool simplifies the setup process so that you can focus on coding the features you want. In this blog post, we’ll show you how to easily install GPT-Engineer and start building apps with just one prompt. Let’s get started! Introduction: As the demand for intelligent and automated responses increases, so does the need for powerful language models that can understand the nuances of language like a human. That’s where GPT-Engineer comes in, a software made with the aim of making it easy to build your own AI models. In this article, we’ll guide you through the process of installing GPT-Engineer and developing your first app with just one prompt. Step 1: Download and Install GPT-Engineer Before you start building your first app, you’ll need to download and install the GPT-Engineer. You can get it from GitHub. Once downloaded and installed in your system, you will be able to access the software with a simple command prompt. Step 2: Understanding the GPT-Engineer Command Prompt GPT-Engineer is designed to take plain text input and produce human-like responses that sound natural. The first thing you will see after running the command prompt is a prompt waiting for your input. You can enter any text query you wish, and GPT-Engineer will generate a response based on the input. Step 3: Developing Your First AI App Now that you have installed and become familiar with GPT-Engineer software, you’re ready to build your first AI app. GPT-Engineer can be used to build anything from simple question-answering models to complex conversational AI bots. Start by deciding what kind of app you want to build. Write down some expected inputs and outputs for the application, and think about the different scenarios users might use the app. Once you have a good idea of what you want to build, start coding your application using GPT-Engineer. Step 4: Embed a Video Tutorial in Your Content If you are visual learner, watching a tutorial video can be a great way to get a better understanding of the steps involved in installing and developing GPT-Engineer. In this case, we have an illustrative video tutorial provided below. (Embedded YouTube player) Step 5: Conclusion GPT-Engineer has proven to be a powerful tool, providing developers with the ability to create AI models with ease. Whether you are a seasoned developer or just starting out, GPT-Engineer has a lot to offer. With a plethora of resources available online and a community of developers dedicated to the platform, there’s never been a better time to start building your own AI apps. 5 unique FAQs: What platforms is GPT-Engineer compatible with? A: GPT-Engineer is compatible with all major operating systems, including Windows, Mac OS, and Linux. Can GPT-Engineer be used to develop mobile apps? A: Yes, GPT-Engineer can be used to develop mobile apps for both iOS and Android platforms. How long does it take to learn GPT-Engineer? A: The learning curve for GPT-Engineer varies from person to person, depending on your familiarity with coding and the programming language being used. However, with the help of resources online and the support of a vibrant community of developers, you can start building apps within a week. Can GPT-Engineer be used by non-developers? A: While GPT-Engineer is primarily designed for developers, it is easy to learn with a bit of practice. A non-developer can use this tool by following the documentation and tutorials available online. Is GPT-Engineer free to use? A: Yes, GPT-Engineer is an open-source platform, which means it is free to use and download from GitHub. The post How to Easily Install GPT-Engineer and Build Entire Apps with One Prompt appeared first on InnovAIte. via InnovAIte https://innovaite.ai/ June 15, 2023 at 11:16PM