Please note that this is not a get-rich-quick guide!

However, if you’re genuinely interested in creating value with AI, you’re in the right place!

Make no mistake – there are significant untapped sources of wealth in AI. The more people get value from your AI agents, the more passive income and active income you’ll generate for yourself and your loved ones.

Let’s start with a simple real-world example of how to make money with an AI-first side hustle:

Example – AI Music Creation: We created a YouTube channel reaching hundreds of thousands of people with AI-generated songs. These are beautiful pieces of art and they deserve every view!

In other words: work smart, not hard.

Core Insight: A single prompt or idea to leverage AI intelligently can change millions of lives — and potentially make millions in the process. AI is the ultimate lever for scale and impact. People who use AI intelligently can create years worth of labor – in seconds. Those people with AI-first mindsets will thrive in the decade ahead.

By the end of this guide, you’ll have actionable strategies to generate passive income with AI, backed by real-life examples and insider tips. So let’s get started, shall we?

What I’m Earning with AI

Now you may ask, and rightly so: “Why trust this random girl from the Internet? What qualifies her to write this article on making money with AI?”

If you’re like me, you’re tired of online marketers who haven’t been there and done that. I’m tired of marketers who don’t give the juicy info like how much money they’re actually making — and how they did it.

In this ultimate guide, I will be brutally honest and transparent. Like so:

How much did I actually make using AI online? In the last year, I made $98,539 and 24 cents by serving more than seven million people per year as a Mediavine Pro user:

By the numbers, that’s

• $98,539 per year
• $8,211 per month
• $269 per day

And that’s only from ads — not including my other business segments.

Info: Mediavine is one of the largest advertisement platforms running blog ads, like the ones you see in this article. Running ads is one of the ways you can generate passive income with AI. The most successful Mediavine users earning $100k per year are invited to the Pro program.

Keep reading if you want to learn how to make money online using AI!

These AI-first skills are NOT taught in college. Because colleges don’t want you to use AI — they hate the fact that AI is now here and will never go away!

But AI is the best opportunity to create a high-value skill set that will remain super relevant in the decade ahead. AI engineers are the new masters of the universe!

Besides AI content creation, there are many more ways to make money online – running ads is only one of my revenue sources. Affiliate partnerships with other businesses are another way to make money with AI.

Disclaimer: This post contains affiliate links, meaning I may earn a commission if you make a purchase. I only recommend products I personally use. Results from the strategies mentioned are not guaranteed and may vary. The information provided is for educational purposes and does not constitute legal, financial, investment, or professional advice. Consult a professional before making decisions. This content is provided “as-is” without warranties. Ensure compliance with your local laws and regulations.

Here’s a visual checklist (quick overview) on how you can make money with AI:

Will People Lose Their Cushy $90k/y Jobs to AI? Yes For Sure!

Many things that worked Before ChatGPT (BC) have stopped working After ChatGPT (AC). Fortunately, some things work now, AC, that didn’t work BC.

Let’s have a brief look at the changing work environment we’re all experiencing:

In the US, 59.8 percent of the workforce are white-collar workers. Most of them will eventually be disrupted by AI tools.

Yes, it’s true. Whatever you did before checking out this article — chances are it’ll get automated. Why should your boss or, by extension, your customers give you money doing the thing AI can do better and for free?

For example, personal finance advisors making a $90k median salary already have an 85% risk of being replaced by generative AI (research):

This will release much of the work in our society and cause significant prosperity for society overall. However, some will be on the wrong side of change.

Savvy one-person business owners already make passive income (or, I should say: leveraged income) with AI. They pay small bucks to AIs instead of huge bucks to employees.

Without further ado, here are the best ways for business owners like me to generate income using AI.

Way 1: Use AI Tools to Generate MASSIVE Amounts of Quality Content and Monetize with Ads

I like simple and proven business models that just work.

Here’s a common business model that’s as simple as it gets – but proven by millions of content creators all around the world:

• Step 1: Publish lots of helpful content — for example, use this little-known AI tool (15% off with code “MONEY”) that’s used by some of the most successful sites on the web ,
• Step 2: Get lots of people reading your content, and
• Step 3: Monetize your website with ads (use premium platforms such as Ezoic, Adthrive, Mediavine, …) as you reach 10k to 50k monthly readers.

Explaining these steps in detail is too much for here, but if you’re interested you can read our in-depth guide on the Finxter blog article here.

For instance, if you publish 10 articles a day — that’s super easy if you use the best AI blogging tools — and one article drives an average of 1000 visitors over its lifetime, you’ll converge to:

10 articles * 1000 visitors * 30 days = 300,000 visitors per month.

A common revenue per thousand visitors is $30. Under these assumptions, the AI blog would generate:

300,000 * $30 / 1,000 = $9,000 per month.

But is it reasonable to reach 1000 visitors per article? Yes, I think so. I usually reach many more with my articles. But you need to make sure that the articles are well-written and that you have done your homework researching “content gaps” that others do not address well.

However, ChatGPT alone won’t do the trick in most cases.

What I do is pay $10 per month for the best AI tool (15% off with code “MONEY”) that not only writes the content but also researches the top-ranking articles for a certain keyword.

The degree of automation is much higher than just using ChatGPT.

At the same time, the article quality is better, and the readability scores are higher. The auto-generated images are beautiful. You can also connect the AI tool to your website, so you can get ready-made articles with a single click.

It analyzes the top-ranking articles for a given keyword and creates its own article structure. Then fills it with outstanding quality content.

All of it in seconds!

Moreover, you can automatically rewrite YouTube videos, create listicles, or Amazon affiliate websites. The best AI writers can auto-publish articles on your WordPress site. Improve readability scores, add FAQ sections, include key takeaways, and use different tone-of-voices.

It cannot be any easier, honestly.

The quality per article is better than what you get from most freelance writers.

Back in the day, I paid $100 per human article. Yet, I needed to wait weeks for each article.

Now you can get a better article quality for less than $1 in seconds using AI!

You can even use it to write affiliate articles – although I don’t do this because I don’t want to recommend what I haven’t tried myself.

I wouldn’t recommend this tool if I hadn’t used it myself to help me reach six figures AI-based income in the last year.

Best AI Tool: Easy and cheap AI Writer to generate perfect SEO-optimized articles with unique and beautiful images and embedded videos (coupon “MONEY” for 15% off!)

Tip: Use the discount code “MONEY” to get 15% off all payments.

Way 2: Six Real-World Examples of AI-Powered Applications

AI services can be EXTREMELY profitable. Let’s have a look at some examples:

Example 1 – AI Writer to Earn Monthly or Annual Subscription Fee

This AI service (use code “MONEY” to get 15% off) recently sent me an email revealing their “secrets”, i.e., how much they make every month:

In other words, this ~3-person startup makes between $150k and $1.5M per month! With a profit margin north of 70%, it profits more than $100k every single month!

If you make $100k profit per month, you’d be a millionaire in less than a year. The business valuation would be north of $4M so you can earn one million and then sell the business for four million a year from now.

Check out the example with discount code “MONEY” (15% off)

Example 2 – Gift Recommender to Earn Affiliate Revenue

Another example of an AI-first app was submitted by a hustling Finxter email newsletter reader who sent me this app that generates links to gift ideas — each is an affiliate link that makes him money:

If you’re an AI engineer with a passion, you can create such an application yourself.

Example 3 – AI Education and AI Engineering

One-person startups that integrate, for instance, OpenAI’s API into their product can replace legions of workers and make a killing on the open marketplace.

Check out the AI engineering course library on the Finxter Academy to certify your AI engineering skills. It’s easier than you think – learning AI engineering is not at all difficult. You don’t need to understand AI — you just need to understand how to use it!

As we all know, value lies in the eyes of the customer.

As a media business owner, I used to pay more than $3,000 per month to my freelancers. Now, I pay $20 for this AI service (code “MONEY” for 15% off) happily. Why? Because it’s more productive and generates better content cheaper.

Action Step: Create a product that saves another business money by using AI in a specific and intelligent way. Anybody who can create a product like that can become a millionaire in a few months, literally.

Example 4 – Movie Recommendation App for Traffic (Ads Financed)

Here’s another example of a movie recommendation app based on the OpenAI API:

By leveraging your expertise in designing and optimizing prompts for large language models, you can work with AI models like GPT-4 to develop unique and valuable solutions for various industries.

Firstly, identify the challenges businesses are facing and where AI-powered applications can provide significant benefits.

Example 5 – Making Money with SaaS using OpenAI’s API

For example, in the marketing industry, you can build AI-driven content generators that produce high-quality, engaging articles and social media posts on demand. These applications can then be monetized through subscription plans or per-use fees.

Recommended: 10 OpenAI SaaS Ideas to Scale a One-Person AI Company

Another potential avenue for prompt engineering is in the field of customer support.

Example 6 – Creating AI Chatbots for Customers

Design AI chatbots to handle inquiries and provide instant assistance to customers. Your prompt engineering background can help create a natural, human-like interaction experience, leading to increased customer satisfaction.

Incorporate AI models into the development of educational tools and e-learning platforms. By crafting well-designed prompts for AI tutors, you can help create personalized learning experiences tailored to individual students’ needs.

All of these are examples of AI-powered applications. Many businesses would gladly pay for an expert or even a scalable solution with “Generative AI at the heart”.

Use your prompt engineering skills to innovate in ways that solve unique pain-points in industries you already know (e.g., the one you’re currently working in). You don’t need to learn the industry, only the basic AI API usage needs which I call “AI Engineering” or “Prompt Engineering”:

Recommended: 10 Steps Learning Path: Becoming a Python Prompt Engineer on the Finxter Academy

Way 3: Offer Consulting Services for AI Language Models

As a prompt engineer, you can use your expertise in prompt engineering to offer consulting services for businesses looking to optimize their AI language models. Many companies rely on AI systems for tasks such as content generation, customer support, and data analysis.

There are millions of new open-source tools, and nobody really gets the whole picture.

Recommended: Llama 2: How Meta’s Free Open-Source LLM Beats GPT-4!

Confusion is everywhere — which is your unique opportunity as an LLM prompt engineer consultant!

By providing sought-after insights and guidance, you can help these businesses enhance the performance and capabilities of their AI systems. How? Your in-depth knowledge of tech and prompt engineering will allow you to identify areas of improvement in their current AI models. You can recommend adjustments to the prompts used in AI systems, suggest strategies for refining the models, and provide guidance on best practices and data selection.

Pro Tip: To make your consulting services stand out, consider obtaining additional certifications in AI, machine learning, or data science. This will not only demonstrate your commitment to the field but also lend credibility to your expertise.

When offering consulting services, it’s crucial to maintain a confident, knowledgeable, and clear tone in your communication. As a prompt engineer, your role is to guide your clients through the complex world of AI and prompt engineering in a way that helps them understand and benefit from the potential of their AI systems.

By doing this, you can create a successful consulting business that helps you earn money in the rapidly evolving tech landscape.

Way 4: Develop Chatbot Solutions for Businesses

As a prompt engineer, you can leverage your skills to develop chatbot solutions for various businesses. With the growth of language models like GPT-4, GPT-4V, and ChatGPT, there is a high demand for chatbots that can assist businesses in customer support, lead generation, and user engagement. By understanding the art of crafting perfect prompts, your chatbots can become more empathetic and efficient.

Many businesses constantly seek ways to automate their customer service and save costs. By designing a chatbot solution using powerful language models like GPT-4, you have the potential to create an additional income stream. Tailor your chatbot to address specific industry needs and emphasize how it can benefit the businesses you approach.

Your confidence in working with language models and the knowledge you’ve gained can open opportunities for you to collaborate with startups and established companies alike. Just recently, a Finxter reader (medical doctor) had an idea for a profitable startup and asked me for skilled prompt engineers to implement his idea for a share of the business.

99% of business owners and professionals still rely on your expertise to guide them through the process, which solidifies your value as a prompt engineer.

Way 5: Offer Prompt Engineering Consulting Services

We’ve already seen how you can make money by providing Consulting Services for AI Language Models (Way 2). However, as a prompt engineer, your expertise can be invaluable to various industries. One way to share your knowledge and make money is to offer consulting services, working closely with clients to optimize and refine prompts for their specific needs, domains, and use cases.

Prompt engineering consultancy rather than AI large language model consultancy – it’s a different offer and business model!

You can start by identifying your target market, such as businesses, educational institutions, or marketing agencies. Once you’ve narrowed down your audience, showcase your skills by creating a portfolio of prompt engineering samples that demonstrate your ability to design, optimize, and refine creative prompts such as this one:

Recommended: What Would Jesus Say? Creating a ChatGPT Mastermind with Jesus, Gandhi, Musk, and Gates

Then, you can reach out to potential clients and introduce your services. Your value proposition can be focused on improving the efficiency of their AI models, generating better results for their end-users, or creating custom-tailored prompts that cater to a specific domain.

Freelancer Pro Tip: “Maintain an air of confidence, neutrality, and professionalism. Communicate your value proposition effectively, understand your client’s needs, and demonstrate how your skills can directly benefit their business or domain. If you do this, your prompt engineering consulting services can become a lucrative venture as you assist clients.”

Way 6: Develop and Sell AI Tools for Various Applications

As a prompt engineer, by leveraging your knowledge of large language models and machine learning, you can develop advanced AI tools and monetize them effectively.

Identify High-Demand Applications Start by researching the market to find areas where AI tools are in high demand. Some popular applications include content generation, language translation, and data analysis. By focusing on these high-demand areas, you increase your chances of creating a valuable product that will attract a wide range of customers.

An example tool is this one:

Review: How to Make Money with AI Writers – Koala Review with 15% Discount Code

Customize AI Models for Specific Problems Develop AI models tailored to solve specific problems in various industries. Remember that customization is key – pay attention to the unique requirements of each domain and make sure your AI tools address those needs effectively. For instance, if you create an AI tool for content generation, you can customize it for different types of content like blog posts, social media updates, or press releases.

Offer Various Pricing Options Once you have developed your AI tools, create various pricing options to accommodate different budgets and needs. You can offer tiered pricing plans based on the complexity of the AI model, the number of usage hours, or the level of customer support provided. This allows businesses of all sizes to access your AI tools and benefit from them, leading to a broader customer base.

Promote Your AI Tools Effectively Use your knowledge and expertise in prompt engineering to create compelling marketing materials and tutorials for your AI tools. Clear, informative content that showcases the benefits of your AI solutions will help potential customers understand the value of your products. Participate in relevant forums, webinars, and conferences to increase your visibility and establish yourself as an expert in your field.

Note: Creating an AI-based tool is often as simple as integrating OpenAI’s API in a Streamlit app:

Recommended: How I Created a Weekly Shopping List and Recipe App in Streamlit

Way 7: Educate Others through Online Courses and Workshops

As a confident and knowledgeable prompt engineer, you have the power to share your expertise with others. By developing online courses and conducting workshops, you can help people learn the ins and outs of prompt engineering. This not only benefits your students but also provides you with an additional revenue stream.

One option you can explore is creating online courses for platforms like Udemy or Coursera. These platforms make it easy for you to publish your educational content and reach a wide audience. You can design the course to cover all aspects of prompt engineering, from foundational concepts to advanced techniques. By setting your price point and marketing efforts, you can attract students and grow your earnings.

In addition to online courses, you can also conduct workshops for local businesses and organizations. Many companies seek professionals who can teach their employees how to effectively leverage AI-powered tools, and your expertise as a prompt engineer fits this demand. By collaborating with businesses, you can offer tailored workshops that cover specific use cases and applications relevant to their industry. This hands-on approach not only helps bridge the knowledge gap for participants but also allows you to earn money for sharing your skills.

Here’s a screenshot of the Finxter academy courses on prompt engineering:

Way 8: Develop Prompt-Based Automation Solutions

As a prompt engineer, you can leverage your programming skills to create innovative automation solutions for various industries. By combining prompt engineering with AI platforms, you can create effective tools that save time, reduce costs, and improve efficiency for businesses.

One approach is to develop software or applications that utilize AI-driven prompts to streamline complex tasks. For example, you can create a tool that automates the generation of marketing content, customer support responses, or even financial analysis using natural language processing and machine learning models.

Five quick examples:

• Idea 1: Automated Content Creation: Develop a tool that generates SEO-optimized content for blogs, websites, and social media platforms, catering to marketers, content creators, and businesses looking to enhance their online presence.
• Idea 2: Customer Support Automation: Implement a chatbot that uses AI prompts to understand and respond to customer inquiries, reducing response times and enhancing customer experience, which is crucial for businesses of all sizes.
• Idea 3: E-commerce Product Description Generator: Create a tool that automatically generates compelling and unique product descriptions for e-commerce platforms, aiding online retailers in improving product listings without manual effort.
• Idea 4: Legal Document Generator: Develop a solution that automates the creation of legal documents based on user inputs, providing a valuable tool for legal professionals and individuals who require quick, standardized legal documents.
• Idea 5: Automated Financial Analysis: Design a tool that provides in-depth financial analysis and reporting based on user prompts, which can be invaluable for financial analysts, investors, and businesses making data-driven financial decisions.

To begin, identify problems that businesses face and understand their unique needs. Focus on areas where automation can make a significant impact and where your programming skills can truly shine. Once you’ve identified potential opportunities, design and develop prompt-based solutions that cater to those specific needs.

You must stay updated on the latest AI platforms, programming languages, and machine learning models to ensure your solutions remain relevant and effective. As the field evolves, so should your expertise.

You can check out our free email academy with regular updates on this rapidly evolving field of large language models and AI by downloading our Python cheat sheets:

Way 9: Offer Your Services on Freelance Platforms

As a prompt engineer, you possess a unique set of skills that can help businesses and individuals make the most out of their AI technologies and services. One path to earn money is by offering your expertise on popular freelance platforms like Upwork and Fiverr.

Recommended: What Are the Best Freelancing Sites for Coders?

To get started, create a compelling profile showcasing your skills, accomplishments, and expertise in prompt engineering. This will help potential clients learn about your background and what you can offer them. Make sure to tailor your profile to emphasize your experience as a freelance writer, as this could attract clients looking for content creation services that leverage AI.

Next, explore the available job postings on these platforms, and don’t hesitate to submit proposals for any projects that align with your skills. Keep an eye out for requests to fine-tune language models, enrich content, or even develop AI solutions.

Proactively promoting your services is crucial for success. Share your profile with your network, and use social media platforms like LinkedIn to connect with potential clients in industries that could benefit from prompt engineering.

Lastly, always focus on delivering high-quality work to your clients. Create more value than you take in payment! This will not only help you maintain a strong reputation on the platform but also increase the chances of receiving referrals and well-paying projects in the future.

Recommended: Finxter Freelance Developer Course

Way 10: Integrate LLMs into Business Workflows to Unlock Millions

As a prompt engineer, you can maximize your income potential by leveraging various AI tools and platforms. One way to do this is by offering services related to popular AI language models like PromptBase and LLMs.

Many businesses are highly interested in integrating AI-powered solutions into their existing workflows. In fact, I’d say this is the low-hanging fruit of our times: integrating existing LLM solutions into existing companies to unlock millions of dollars of value with a few clicks! You can serve as a consultant, guiding them through the process and helping them smoothly incorporate AI tools into their systems.

Want some stats on this thesis? No problem, look at this McKinsey report:

Always stay up-to-date with the latest advancements and trends in the AI world to maintain your competitive edge. Enhancing your proficiency in various AI language models, frameworks, and techniques will ensure that your services remain in demand and provide you with many opportunities.

Your success as a prompt engineer depends on your ability to adapt and leverage the current AI market. By mastering AI tools and platforms, you can unlock new revenue streams and expand your client base.

Here’s such an example framework that’ll help you connect LLMs with myriads of data sources to unlock massive value in many businesses:

Recommended: LlamaIndex – What the Fuzz?

Way 11: Fine-Tuning and Stable Diffusion

As a prompt engineer, you can venture into the world of AI-generated images by leveraging stable diffusion techniques. This opens up a new avenue for you to make money while staying abreast of cutting-edge technology. With model fine-tuning skills under your belt, you’ll possess an unmatched advantage in the industry.

By using stable diffusion, you can generate high-quality AI images that are both visually stunning and detailed. This skill can be applied to various industries, such as advertising, gaming, and fashion, where unique and eye-catching visuals are always in demand. Offer your services as an image generation specialist and assist clients in creating innovative visuals for their projects.

Another aspect where your expertise could prove valuable is in assisting businesses to incorporate AI-generated images in their processes. Many organizations need personalized visuals that align with their brand identity. As a prompt engineer with experience in stable diffusion, you can provide a bespoke image generation service that caters to these needs.

In addition to creating AI images, you can also offer your skills in model fine-tuning. As AI models become more sophisticated, the demand for prompt engineers with the ability to fine-tune them grows. By fine-tuning these models for specific applications, you can help clients improve their AI systems and ensure that they generate relevant and accurate outputs.

Way 12: Sell GPT-4 Generated Merchandise on Etsy

As a prompt engineer, you can harness the power of GPT-4 to create unique and personalized products that cater to niche markets. By selling your products on Etsy, your target audience can access and purchase these AI-generated products with ease. Let’s explore this method with a few examples.

First, imagine designing custom t-shirts with unique slogans and catchphrases generated by GPT-4 or GPT-4V. You can input a list of keywords or interests, and the AI will provide you with numerous creative options. Simply select your favorites, print them onto high-quality t-shirts, and list them for sale on your Etsy shop.

Another possible product idea involves creating artwork or illustrations using GPT-4-generated descriptions. Once you have the AI’s creative input, collaborate with a talented artist or graphic designer to bring your vision to life. Or just pass it into Midjourney. These one-of-a-kind pieces can be sold in the form of prints, posters, or even digital downloads.

Recommended: 4 Effective Prompt Generators to Use Daily for ChatGPT & Midjourney

In addition, consider offering personalized poetry or short stories as an Etsy product. Customers can send you their interests, memories, or even specific events to shape the narrative.

To validate the effectiveness of your unique product offerings, create a few case studies to showcase your best work. Share these case studies on your Etsy shop and social media channels to demonstrate how you expertly combined prompt engineering with marketable products.

20 Bonus Ideas to Make Money with GPT-4

Still not tired? Let’s check out some additional ways to make money using ChatGPT and other generative AI models next.

Content Creation and Writing Services

By leveraging AI and LLM technology, content creators can produce high-quality, tailored content at scale, potentially increasing earnings significantly. Automated content generation can lead to consistent revenue streams. Passive income is possible through subscription models or selling pre-made content packages.

Income Potential: Earnings can range from $50 for basic articles to over $5,000 for specialized content. With subscription models or pre-made content packages, passive income can range from $200 to $10,000+ monthly for 90% of content creators, depending on the audience size, business model, and niche.

1. Copywriting Services: GPT-4 can assist you in creating persuasive and engaging copy for websites, product descriptions, and marketing materials. You can proactively offer your services to businesses, e.g., for free, with a revenue share model when the sales page conversion increases. This way, you don’t create another job for you, but a highly lucrative equity opportunity.
2. Social Media Management: Utilize GPT-4 to generate catchy captions, engaging posts, and tweets for businesses’ social media profiles.
3. Blog Writing: GPT-4 can generate informative and well-researched blog articles, making creating content for your own or clients’ blogs easier. If you sell one blog post for only $50 to clients and you create 10 blog posts per day using GPT-4, you can create $500 income per day!
4. Content Websites: Create niche websites or blogs and use GPT-4 to generate unique and valuable content to attract readers and monetize through ads or affiliate marketing.
5. eBook Writing: Write and publish eBooks on platforms like Amazon Kindle Direct Publishing with the help of GPT-4 to generate passive income. Amazon has just limited the number of books you can publish to 3 per day. That’s enough!

On a personal note, many blog owners seek regular high-quality content additions for their websites or social networks. A high-value business model for you is to create a mini agency offering prebuilt packages such as:

120 content pieces for $4000 per month: 30 blog posts per month + 30 Facebook posts per month + 30 Twitter posts per month + 30 Instagram posts per month (Midjourney! DALL-E!)

Many businesses big or small — e.g., banks, insurances, blog owners, influencers — would highly appreciate the service and the continuous content provided by your agency and you could offer your services not only to a single client but to 10 clients or more making $40,000+ per month!

Creative Ventures

AI and LLM can assist in generating art, music, and design, opening avenues for unique digital products. Artists can monetize AI-generated creations or use AI to enhance their work, leading to increased sales. Passive income can be achieved by licensing AI-generated assets or through automated design platforms.

Income Potential: For AI-assisted art or music, earnings can vary widely from $100 for digital prints to $50,000+ for unique AI-generated masterpieces. Passive income from licensing or automated platforms can range from $500 to $15,000+ monthly.

6. Video Creation: Use GPT-4 to generate scripts, captions, and video content for platforms like YouTube, Vimeo, or your own website to monetize through ads or sponsored content. You could use AI-based text-to-speech tools like Google’s Sandstorm to automatically create podcasts, and combine the audio with autogenerated images to create stunning YouTube videos with a click of a button!

7. Graphic Design: Utilize GPT-4’s capabilities to generate graphics, logos, and designs for your clients or for your own online store.
8. Comic Book Creation: GPT-4 can assist you in creating comic books by generating text, graphics, and even video content. We have a Finxter article on a similar topic here.
9. Virtual Reality Experiences: Combine GPT-4 with virtual reality technology to create immersive experiences for entertainment, education, or training purposes.
10. AI Art: GPT-4’s creative abilities can be harnessed to generate unique and original artworks, which can be sold or licensed to individuals or businesses looking for AI-generated art.

A beautiful example is given in this article by Finxter Roman:

Recommended: I Made a ChatGPT-Powered Logo Generator App Using Python Flask in 7 Steps

Also make sure to check out the overview article on making passive income as a coder today:

Recommended: 3 Easy Ways to Make Passive Income as a Coder

Language and Communication Services

LLM technology revolutionizes translation, transcription, voice actor, and language tutoring services. Many of the day-to-day work of these services can be fully automated with LLM technology using text-to-speech, translation, or text generation tools.

Recommended: How I Created an Audiobook App with Streamlit

Professionals can offer faster and more accurate services, leading to a higher client turnover and increased rates. Passive income is feasible through automated translation or transcription platforms and AI-driven language learning apps.

Income Potential: For AI-enhanced translation, text-to-speech, transcription, earnings can range from $0.10 to $1.00 per word or minute. For language tutoring, rates can go from $20 to $200 per hour. Passive income from automated platforms or apps can range from $1,000 to $20,000+ monthly.

11. Translation Services: Offer translation services using GPT-4’s language capabilities to bridge language barriers for clients or on freelance platforms. You can also create full-automated SaaS tools that charge a fair $99 fee for 100,000 translated words per month of service. If you find 100 paying users, your passive income will reach six figures per year!
12. Language Learning: Create language learning apps or platforms that leverage GPT-4’s language models to provide interactive and personalized language learning experiences.
13. Writing Assistance: Develop writing tools and apps that integrate GPT-4’s language models to provide grammar and writing suggestions to users.
14. Customer Support Chatbots: Use GPT-4 to create chatbots that provide customer support and engage with users in a natural and helpful manner.
15. Virtual Assistant Services: Develop virtual assistant applications powered by GPT-4 that can help users with tasks like scheduling, reminders, and information retrieval.

Business and Financial Ventures

AI can optimize business strategies, financial forecasting, and investment decisions. There’s incredible money in this industry if you can use LLMs to create value in the financial industry (e.g., analyzing companies automatically).

Entrepreneurs and financiers leveraging AI can achieve better returns and efficiency, potentially multiplying their earnings. Passive income is possible through AI-driven investment platforms or by selling AI-based business optimization tools.

Income Potential: Using AI for business optimization or financial forecasting can lead to project earnings from $1,000 to $100,000+, depending on the scale. Passive income from AI-driven investment platforms or tools can range from $2,000 to $50,000+ monthly, based on assets under management and tool adoption rates.

16. Trading and Investment Strategies: Utilize GPT-4’s ability to analyze market trends and generate insights to develop profitable trading and investment strategies.
17. Business Planning and Consulting: GPT-4 can assist in generating business plans, market research reports, and financial projections for startups and small businesses. Think about the potential!
18. Personal Finance Advice: Develop personal finance apps or platforms that leverage GPT-4’s insights to provide tailored financial advice to users. These apps have “ChatGPT at the heart“, i.e., as their primary means to process data on the backend. However, you can even use GPT-4 to create the apps for you.
19. Data Analysis Services: Use GPT-4’s analytical capabilities to offer data analysis services to businesses looking for insights and trends in their data.
20. Market Research: Leverage GPT-4 to analyze market trends and generate comprehensive market research reports for businesses in various industries.

You can also check out our prompt engineering ideas to make money as a startup founder to get more ideas that are more technical.

Recommended: Startup.ai – Eight Steps to Start an AI Subscription Biz

With the advent of GPT-4, the possibilities for making money have expanded significantly. From content creation and writing services to creative ventures, language and communication services, and business and financial ventures, GPT-4 offers a wealth of opportunities for individuals to tap into.

In a single year, you can churn out 100s of these ideas using GPT-4 to see what sticks in the marketplace. As soon as you find something that works, scale it up with GPT-4!

Frequently Asked Questions

What types of projects do prompt engineers work on?

Prompt engineers typically work on various projects, which may include developing creative prompts for AI applications, creating online courses, tutoring, and writing articles or blog posts. They may also dabble in freelancing or consulting, as well as inventing and creating new products or services.

Which platforms or websites offer opportunities for prompt engineers?

Opportunities for prompt engineers can be found on various platforms and websites. Some popular options include Freelancer, Upwork, and Vital Dollar. Additionally, you can explore opportunities on websites like Medium for publishing content, or even create your own blog or website to showcase your expertise.

How can one enhance their skills in prompt engineering?

To enhance your skills in prompt engineering, consider enrolling in online courses, participating in workshops, or attending webinars. A good starting point is the Prompt Engineering Course which aims to take you from zero to hero in prompt engineering.

Are there any certifications available for prompt engineers?

The Finxter Academy provides six certification levels for prompt engineers certifying the completion of six prompt engineering courses at the time of writing (Downloadable Personalized PDF Certificates). The course library is expanding, so you can learn skills such as prompt engineering, OpenAI API, Streamlit, web scraping, and Llama-2.

What strategies can help in successfully making money as a prompt engineer?

To successfully make money as a prompt engineer, focus on building a strong online presence by showcasing your skills and expertise. You can do this through a personal website, portfolio, or by contributing to well-known platforms. Additionally, engage in continuous learning, stay updated with the latest trends, and maintain a reliable network of contacts who can recommend you for projects or collaborations.

How can networking benefit a prompt engineer’s career?

Networking can greatly benefit your career as a prompt engineer by opening up opportunities for collaborations, referrals, and new projects. By building strong connections with other professionals within your niche, you can learn from their experiences and gain valuable insights about the industry.

If you’re a tech enthusiast, let’s learn the ultimate leveraged skill of this decade: AI Engineering. Colleges don’t teach it. To solve this gap, we have created a full-fledged affordable academy with the goal of educating the next wave of AI disruptors and prompt engineers:

Finxter Academy: Browse our course library – and certify your AI skills as a hands-on AI engineer. It’s easier than you think! Use discount code “MONEY” to get 20% off!

The post 12 Ways to Make Money with AI appeared first on Be on the Right Side of Change.

Let’s break down a simple plan to transform a solo startup into a $1 million venture within four years, just by following a detailed, month-by-month strategy.

I created this plan a couple of months ago and shared it on the Finxter blog:

Becoming a Millionaire in 48 Months with a Tiny 1-Person Startup (No VCs!)

This approach combines business growth with savvy investing in ETFs that track the NASDAQ, aiming for a 16% compound annual growth rate.

Here’s your step-by-step guide to financial freedom:

1. Early Stage (Months 1-7): Start at square one with your business, focusing on setting it up right with no initial income. This is your planning and development phase. Get your business off the ground with zero pressure on immediate returns.
2. Income Kickoff (Months 8-12): Begin earning from your startup gradually. Target to grow from $100 to $1,600 in monthly income. This slow start is crucial for laying a solid foundation.
3. Consistent Growth (Months 13-47): This is where the magic happens. Aim for a 7% monthly growth in your business net income, moving from $1,712 to $15,965 by month 47. It’s not just about working hard but also about working smart—optimize, refine, and scale up efficiently.
4. Valuation and Sale (Month 48): With consistent effort, your business valuation could hit around $683,500 based on a 40x monthly profit multiple. Now, sell your startup on platforms like Flippa or Empireflippers.
5. Invest the Proceeds: By month 48, with diligent investing alongside business growth, your investment portfolio can burgeon to about $300,293. Combined with the sale proceeds, welcome to the millionaire club with a total asset value reaching $1,000,000!

Alongside these business activities, continue investing a portion of your profits into an ETF that mimics the NASDAQ. By compounding these investments at an expected rate of 16% annually, your financial growth is accelerated, ensuring that even after selling your business, you continue to earn a handsome passive income.

A simple clear-cut strategy, and by month 48, celebrate your newfound millionaire status. It’s all about persistence, strategic planning, and a bit of financial savvy!

Check out Chris’ article on NVidia that may deliver an even more interesting compound annual growth rate (no financial advice here on this blog):

Valuing $NVIDIA as a Real Estate Company That Sells Housing to AI Agents ($100k/Share in 2034)

The post Startup to Millionaire in 48 Months: A Dual Strategy of Business Growth and Smart Investing appeared first on Be on the Right Side of Change.

Hugging Face is a gem for AI enthusiasts like you, cradled by the innovation of French entrepreneurs Clément Delangue, Julien Chaumond, and their team. Born in New York City, it’s a blossoming platform where open-source thrives.

Imagine a playground where artificial intelligence and machine learning collide in harmony—that’s what Hugging Face offers.

Here, you’ll find an AI community that’s both robust and welcoming.

Whether you’re looking to contribute or are on the hunt for pre-trained models, Hugging Face is your ally.

It’s not just about the technology; it’s the spirit of collaboration that makes it a standout.

For instance, their renowned Transformers library empowers your projects with over 20,000 pre-trained models.

Dabble in Python? Here’s a taste of the simplicity:

from transformers import pipeline
classifier = pipeline('sentiment-analysis')
classifier('Hugging Face is drastically shaping AI.')

Under the guidance of visionaries like Delangue and Chaumond, Hugging Face has democratized machine learning, giving you immediate access to cutting-edge tools.

As a result, you can leap into AI without the cumbersome weight of starting from scratch.

• Democratization: Hugging Face places AI in your hands, unshackled and ready for exploration.
• Community-Driven: Insights and breakthroughs are shared, fostering a collective push towards innovation.
• Supportive Resources: With their plethora of datasets and models, your foray into AI is well-supported.

Through Hugging Face, you’re not just using AI; you’re actively participating in a dynamic ecosystem. Embrace AI with the warmth and camaraderie that only Hugging Face can offer.

Getting Started with Hugging Face

Hugging Face is an essential platform for anyone looking to dive into the world of artificial intelligence, especially concerning natural language processing. It offers a wealth of pre-trained models through the Hugging Face Hub, in-depth documentation, and step-by-step tutorials to help you get started with ease.

Signing Up for Hugging Face

Before you can fully engage with all the features Hugging Face has to offer, you’ll need to sign up for an account.

Visit the Hugging Face website and create your account:

A user account unlocks the potential to download models, collaborate with others, and share your contributions to the community.

Installation Guide

Once signed up, it’s time to get your system ready.

Ensure you have Python installed as Hugging Face predominantly uses Python for its operations.

Then, install the transformers library which is a crucial component of the Hugging Face ecosystem.

Open your terminal or command prompt and run the following command:

pip install transformers

After installation, verify it by running transformers-cli:

transformers-cli env

This will output information about your installation, assuring everything is in place.

With the transformers library installed, you can now easily download pre-trained models and utilize them for your projects.

For comprehensive guides and explanations, refer to the Hugging Face documentation to explore the extensive tutorials and resources available, ensuring a smooth journey through the Hugging Face platform.

Hugging Face Hub

Hugging Face Hub serves as your one-stop destination where you can explore a wide array of machine learning models, datasets, and other tools. It’s an open-source platform designed to foster collaboration within the community.

Exploring the Model Hub

Within the Model Hub, you have access to over 350,000 pre-trained models, including widespread transformers and tokenizers.

It amplifies your capabilities to experiment with AI without requiring extensive GPU or compute resources for inference.

You can discover models for a variety of tasks by simply searching, as if you were on any familiar search platform.

To use a model, your code could be as simple as:

from transformers import AutoModel
model = AutoModel.from_pretrained("model-name")

This gives you hassle-free access to pre-trained models for immediate use in your projects.

Contribution and Collaboration

The Hub is akin to a GitHub specifically designed for machine learning.

You’re encouraged to collaborate and contribute your own models, datasets, or tokenizers.

Sharing is made straightforward, with commands similar to git:

git lfs install
git clone https://huggingface.co/you/your-model
cd your-model
git lfs track "*.pt"
git add .gitattributes
git commit -m "Add .gitattributes"

The community aspect is underlined by the ease with which you can both contribute and benefit from the plethora of resources at your disposal.

As an organization or individual, you’re empowered to build and share within a thriving open-source platform.

Hugging Face Transformers Library

The Hugging Face Transformers Library is a cutting-edge tool that allows you to leverage pre-built transformer models, customize them, or train completely new ones with a user-friendly interface.

It supports frameworks like PyTorch, TensorFlow, and JAX and offers extensive documentation to guide you through myriad NLP tasks.

Utilizing Pretrained Models

Hugging Face provides thousands of pretrained models that can greatly speed up your NLP tasks.

To perform inference quickly, you can use pipelines:

from transformers import pipeline
classifier = pipeline('sentiment-analysis')
results = classifier("Hugging Face makes NLP easy and accessible.")

This simple code snippet allows you to classify sentiments without the hassle of model training and setup.

You can choose models specific to tasks like text generation, translation, and more, tailored to your needs in Python.

Training Your Own Model

If you want to train your own model, the transformers library provides a straightforward way to do so.

You can use the following example to fine-tune a model on your dataset using PyTorch:

from transformers import Trainer, TrainingArguments, BertForSequenceClassification
from datasets import load_dataset
dataset = load_dataset('your-dataset')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
training_args = TrainingArguments(output_dir='./results')
trainer = Trainer(model=model, args=training_args, train_dataset=dataset['train'])
trainer.train()

This piece of code illustrates how to set up a model training instance that uses a pre-existing structure like BERT and adapts it to your own model, optimizing compute resources.

Models and Datasets

In the ever-evolving landscape of machine learning, Hugging Face serves as a pivotal resource for open source models and vast arrays of datasets.

Here, you have the power to both access a treasure trove of pre-trained models and datasets and actively contribute to the community’s growth.

Leveraging Open Source Models and Datasets

Hugging Face’s transformers library makes it incredibly easy for you to download and use pre-trained models.

You can get started with a single line of code:

from transformers import AutoModel
model = AutoModel.from_pretrained("model-name")

Not only do you gain access to models, but you can also utilize the datasets library to load and process data for various tasks.

The datasets library provides a vast collection of ready-to-use datasets for Natural Language Processing (NLP), computer vision, and audio challenges:

from datasets import load_dataset
dataset = load_dataset("dataset-name")

This functionality empowers you to quickly prepare data suitable for feeding into deep learning models.

Sharing and Using Community Models

The Hugging Face Hub is where the magic of community collaboration happens. Here, more than 50,000 organizations and individuals share their models with the world.

You can easily adopt and even fine-tune these community models:

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("model-name")

Sharing your own models is just as straightforward. By pushing your models to the Hugging Face Hub, you contribute to a vibrant ecosystem:

from transformers import AutoModel, Trainer

model.save_pretrained("my-awesome-model")
trainer = Trainer(model=model)
trainer.push_to_hub("my-awesome-model")

Your active participation helps fuel innovations and provides invaluable resources for others within the machine learning community.

Machine Learning Applications

In the world of AI, Hugging Face stands out for its ease of integrating various machine learning applications, particularly in enhancing how you interact with natural language, visuals, and audio data. Let’s explore how you can leverage these applications in your projects.

Natural Language Processing (NLP)

Hugging Face has paved the way for effortless NLP integration by providing models and tokenizers that support tasks like text generation, summarization, and translation.

For instance:

from transformers import pipeline
summarizer = pipeline("summarization")
summary = summarizer("Your long text goes here.")[0]['summary_text']

The code above summarizes text with a pre-trained model, showcasing how a few lines of code can achieve powerful results.

Computer Vision

When it comes to computer vision, you can harness pre-trained models for image generation and analysis. The platform allows you to turn descriptions into detailed images or classify pictures with high accuracy:

from transformers import pipeline
image_generator = pipeline("image-generation")
generated_image = image_generator("A description of what you want to see in your image.")

Audio Processing

For audio processing, Hugging Face enables text-to-speech functionalities, giving your applications a voice.

Transforming text into natural-sounding audio can be done simply by:

from transformers import pipeline
text_to_speech = pipeline("text-to-speech")
speech = text_to_speech("Your text to convert to speech.")

Multimodal Applications

Lastly, multimodal applications combine text, image, and sometimes video. Hugging Face offers multimodal models to help you develop sophisticated applications that interpret and respond to various data types seamlessly:

from transformers import pipeline
multimodal = pipeline("multimodal-to-text")
response = multimodal(your_multimodal_data)

By tapping into Hugging Face’s libraries, you’re equipped to build AI-driven applications that understand and interact with the world in a more human-like manner.

Ecosystem & Integration

Hugging Face provides an intricate ecosystem facilitating seamless integration of AI technology. This ecosystem not only supports machine learning efforts but also enhances them through a communal approach, which fosters innovation and sharing.

APIs and Infrastructure

Hugging Face’s infrastructure is built on robust APIs that allow you to effortlessly deploy models and manage compute resources.

Utilizing their Inference API, you can quickly deploy state-of-the-art machine learning models for a variety of tasks.

You can interact with the API through simple HTTP requests, making it highly accessible for integration into your applications.

from transformers import pipeline
classifier = pipeline('sentiment-analysis', model='distilbert-base-uncased-finetuned-sst-2-english')
classifier('I love using Hugging Face APIs!')

Integration with Cloud Services

Your AI workflows get a significant boost when integrating with cloud services like Amazon Web Services (AWS), where you can access extensive compute capabilities.

Hugging Face has partnerships with industry leaders such as Amazon and Google Cloud, providing optimized environments to run high-performance machine learning workloads.

Collaboration with Tech Giants

The platform’s collaboration with tech giants like Google, Intel, and NVIDIA ensures that you have access to cutting-edge hardware acceleration and AI research.

These alliances help improve the efficiency of machine learning models thanks to advanced compute solutions and optimized model implementations.

Community-Driven Initiatives

Hugging Face champions open-source engagement and collaborative efforts, exemplified by the BigScience Research Workshop and the development of the BLOOM model.

You can contribute to these initiatives and expand your professional portfolio, all while benefiting from the support of a global community.

Applications and Demos

Hugging Face has dramatically simplified the process of utilizing machine learning models. From interactive web applications to robust research tools, the platform offers an array of applications and demos to explore.

Interactive Spaces

In Hugging Face Spaces, you’re provided with a user-friendly scaffold to create and share machine learning applications.

These can range from visual object recognition to language processing tasks.

The interactivity of such spaces allows you to immediately engage with a machine learning model’s outputs, enabling a hands-on understanding of the technology behind AI.

Chatbot Applications

When it comes to chatbots, Hugging Face offers infrastructure and pipelines for building sophisticated AI chatbot applications.

You can utilize their pre-trained models to customize your own chatbot app, which can understand and respond to natural language input, serving a wide range of use cases including customer service and personal assistants.

Research and Experimentation

The platform is a treasure trove for research and experimentation in machine learning.

Whether it’s testing hypotheses or developing new methodologies, Hugging Face’s demos and open-source tools can assist in your research venture.

You have the ability to use models as-is, or fine-tune them on your datasets, which allows for extensive exploration into the capabilities and improvements of current AI technologies.

Advanced Usage

When you dive into advanced usage of Hugging Face, you’re looking at a refined approach to leveraging the platform’s capabilities. This includes developing custom pipelines for specific NLP tasks and intricately tuning models to enhance their performance.

Custom Pipelines and Layers

Using Hugging Face, you have the power to create custom pipelines tailored for diverse NLP tasks like text classification or question answering.

Imagine designing a pipeline that integrates PyTorch or TensorFlow models with additional processing layers to handle your unique text input. For instance:

from transformers import pipeline

def custom_processing(result):
    # Your custom processing logic here
    return modified_result

# Load a pre-trained model and tokenizer
model = 'bert-base-uncased'
tokenizer = 'bert-base-uncased'

# Create your custom pipeline
custom_pipe = pipeline('feature-extraction', model=model, tokenizer=tokenizer)
custom_pipe.add_after(custom_processing)

Advanced Model Tuning

Beyond out-of-the-box models, Hugging Face allows you to fine-tune these models for optimal performance.

By leveraging transformers, and adjusting model layers or training parameters, you enhance your model’s capabilities.

Leverage hardware like TPUs for accelerated training and experiment with hyperparameters for robust model training. For example:

from transformers import TFAutoModelForSequenceClassification

model = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased')

# Advanced tuning of the model
model.trainable = True  # Allow all layers to be fine-tuned
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
model.compile(optimizer=optimizer, loss=model.compute_loss)

# Fine-tuning the model on your dataset
model.fit(train_dataset, validation_data=val_dataset, epochs=3)

Security and Privacy

When using Hugging Face, your data privacy and protection are of top priority. The platform ensures that you have robust security measures at your disposal, designed to safeguard your projects.

Maintaining Data Privacy

Hugging Face respects your data privacy. The platform guarantees that you retain ownership of any data or models you upload.

As outlined in their Security & Compliance documentation, Hugging Face conducts malware and pickle scans across your content to prevent unauthorized access and maintain the integrity of your data.

Enterprise-Grade Security Features

The platform is fortified with enterprise-grade security features. One of these is the SOC2 Type 2 certification. This certification confirms that Hugging Face has established and follows strict information security policies and procedures. These policies and procedures encompass the security, availability, processing integrity, and confidentiality of customer data.

Hugging Face addresses your security concerns with continuous monitoring and timely patching of vulnerabilities. You can see this in Security – Hugging Face. This ensures a secure environment for your AI development needs.

Community and Support

The Hugging Face community is a vibrant environment where you can collaborate with AI enthusiasts, receive support, and gain access to comprehensive documentation and tutorials.

Let’s explore how you can engage and find help within this community.

Engaging with the Hugging Face Community

When you’re ready to dive into AI, the Hugging Face community welcomes you with open arms—or maybe we should say, open emojis. Here, you can make connections, collaborate on projects, and share your ML models. Engaging with peers, you’ll find an interactive space where ideas flow freely:

• Share your projects with a simple transformers.push_to_hub('your-model').
• Receive peer feedback to sharpen your skills.
• Participate in discussions on the latest AI research.

Channels for Help and Support

In your journey through Hugging Face, a plethora of support channels awaits:

• Documentation: Access clear, step-by-step guides, such as how to use the transformers library.
• Tutorials: Step through real-world examples and learn best practices.
• Community Forum: Post questions and receive answers from experts and peers.
• GitHub Issues: For code-specific queries, raise an issue directly on the repository.

Frequently Asked Questions

Hugging Face is renowned for its powerful and flexible AI models. If you’re looking to enhance your tech projects with advanced machine learning capabilities, these FAQs will guide you through how to best utilize what Hugging Face has to offer.

How can developers implement Hugging Face models into their projects?

You can implement Hugging Face models by installing the transformers library via pip. Then, you can use pre-trained models or fine-tune them on your dataset. Example code for Python:

from transformers import pipeline
classifier = pipeline('sentiment-analysis')
classifier('I love using Hugging Face models for my projects.')

What capabilities do the Hugging Face Transformers provide to machine learning engineers?

Transformers from Hugging Face provide a variety of NLP capabilities. These include language translation, text summarization, and question answering. With these, tasks like building chatbots or text analysis tools become more streamlined.

Which AI applications are most improved by using Hugging Face’s technology?

AI applications such as conversational agents, content recommendation systems, and language translation services are significantly enhanced by using Hugging Face’s advanced NLP models and conversational AI capabilities.

Why do AI researchers and developers prefer Hugging Face for NLP tasks?

Researchers and developers prefer Hugging Face for its large selection of pre-trained NLP models, comprehensive documentation, and a widespread community. These make it a highly accessible and collaborative platform for NLP tasks.

What makes Hugging Face different from other AI service providers?

Hugging Face stands out due to its commitment to open-source development, extensive transformer model library, and user-friendly interface. These accelerate AI research and deployment across various industries.

How does Hugging Face contribute to the Open Source community?

Hugging Face contributes to the Open Source community with its vast ecosystem of NLP tools and models. They often share advancements through their platform and provide support for contributors to continue innovating in the field of AI.

The post What is Hugging Face Used For? Exploring its Applications in Machine Learning appeared first on Be on the Right Side of Change.

Here’s how you can do it:

1. Ensure that the package.json file is in the root directory of your Node.js project (or adjust the path accordingly in the code).
2. Use the following code snippet to load the package.json file and access the version field:

// Load the package.json file
const packageJson = require('./package.json'); // Adjust the path if your package.json is not in the root directory

// Access the version property
const version = packageJson.version;

console.log('The version of the package is:', version);

This code snippet will read the package.json file relative to the location of the script that is being executed. If the script is in a different directory from package.json, you’ll need to adjust the path to point to the correct location.

The require() function here is used to import the JSON file as a JavaScript object, making it very simple to access any property defined within package.json, such as version.

But there are some alternatives:

Some Alternatives That May Work on Your Machine

Here are alternative methods to retrieve the version from package.json in a Node.js application:

Using fs Module to Read File Directly:

• Read package.json using fs.readFileSync.
• Parse the JSON string with JSON.parse.
• Access the version field.

  const fs = require('fs');
  const packageJson = JSON.parse(fs.readFileSync('./package.json', 'utf8'));
  const version = packageJson.version;

Using Environment Variables during Build Time:

• Set an environment variable based on package.json during the build/deployment process.
• Access the version via process.env.VERSION.

  # In your build script or deployment configuration
  VERSION=$(node -p "require('./package.json').version") && export VERSION

  // In your Node.js code
  const version = process.env.VERSION;

Using Child Process to Execute Node Command:

Execute a Node.js command that reads package.json using child_process.execSync.

  const { execSync } = require('child_process');
  const version = execSync('node -p "require(\'./package.json\').version"').toString().trim();

The post How to Check Package Version in JavaScript? appeared first on Be on the Right Side of Change.

Before diving into the conversion methods, let’s quickly recap the Python enum module. In this example, Color is an enumeration with three members: RED, GREEN, and BLUE, associated with the values 1, 2, and 3, respectively.

MINIMAL ENUM EXAMPLE:

from enum import Enum

# Define an enum named Color with a few members
class Color(Enum):
    RED = 1
    GREEN = 2
    BLUE = 3

# Accessing members and their values
Color.RED  # <Color.RED: 1>
Color.RED.name  # 'RED'
Color.RED.value  # 1

But how do we convert these enums to more common data types and back? Let’s start with the first:

Python Convert Enum to String – and Back

Converting an Enum to a string can be particularly useful for display purposes or when the enumeration needs to be serialized into a format that requires text (like JSON). The reverse process is handy when parsing strings to obtain Enum values.

from enum import Enum

class Color(Enum):
    RED = 1
    GREEN = 2
    BLUE = 3

# Convert Enum to String
color_string = Color.RED.name

# Convert String to Enum
color_enum = Color[color_string]

print(color_string)  # Outputs: RED
print(color_enum)    # Outputs: Color.RED

Here, Color.RED.name is used to convert an Enum member to a string. On the flip side, Color[color_string] converts a string back to an Enum member. This is particularly helpful in configurations or environments where only strings are usable.

Python Convert Enum to Int – and Back

Sometimes, you might need to convert Enum members to integers for indexing, interfacing with C code, or simple arithmetic. Conversely, converting integers back to Enums is vital for maintaining the encapsulation of Enum types.

# Convert Enum to Int
color_int = Color.RED.value

# Convert Int to Enum
color_enum_from_int = Color(color_int)

print(color_int)                # Outputs: 1
print(color_enum_from_int)      # Outputs: Color.RED

In this snippet, Color.RED.value gives the integer associated with Color.RED. To convert this integer back to an Enum, we call Color(color_int).

Python Convert Enum to List – and Back

Converting an Enum to a list is useful when you need to perform operations that require sequence data types like iterations, mapping, etc. The reversal doesn’t apply typically as you wouldn’t convert a list directly back into an Enum, but you could recreate the Enum if needed.

# Convert Enum to List
color_list = [c for c in Color]

print(color_list)  # Outputs: [<Color.RED: 1>, <Color.GREEN: 2>, <Color.BLUE: 3>]

This list comprehension iterates over the Enum class, converting it into a list of Enum members.

List Comprehension in Python — A Helpful Illustrated Guide

Python Convert Enum to Dict – and Back

When dealing with Enum members in a dynamic manner, converting it to a dictionary can be incredibly useful, especially when the names and values are needed directly.

# Convert Enum to Dict
color_dict = {color.name: color.value for color in Color}

# Convert Dict to Enum
class ColorNew(Enum):
    pass

for name, value in color_dict.items():
    setattr(ColorNew, name, value)

print(color_dict)       # Outputs: {'RED': 1, 'GREEN': 2, 'BLUE': 3}
print(ColorNew.RED)     # Outputs: ColorNew.RED

Here, we first create a dictionary from the Enum, ensuring each name maps to its respective value. To convert it back, we dynamically generate a new Enum class and assign values.

Python Convert Enum to JSON – and Back

Serialization of Enums into JSON is essential for configurations and network communications where lightweight data interchange formats are used.

import json

# Convert Enum to JSON
color_json = json.dumps(color_dict)

# Convert JSON to Enum
color_info = json.loads(color_json)
color_enum_from_json = Color[color_info['RED']]

print(color_json)               # Outputs: '{"RED": 1, "GREEN": 2, "BLUE": 3}'
print(color_enum_from_json)     # Outputs: Color.RED

This example shows how you can use a dictionary representation to convert Enum members into JSON and then parse it back.

Python Convert Enum to List of Strings – and Back

In some cases, a list of string representations of Enum members may be required, such as for populating dropdowns in GUI frameworks, or for data validation.

# Convert Enum to List of Strings
color_str_list = [color.name for color in Color]

# Convert List of Strings to Enum
color_enum_list = [Color[color] for color in color_str_list]

print(color_str_list)        # Outputs: ['RED', 'GREEN', 'BLUE']
print(color_enum_list)       # Outputs: [<Color.RED: 1>, <Color.GREEN: 2>, <Color.BLUE: 3>]

This straightforward comprehension allows for quick conversions to and from lists of string representations.

Python Convert Enum to Tuple – and Back

Tuples, being immutable, serve as a good data structure to hold Enum members when modifications are not required.

# Convert Enum to Tuple
color_tuple = tuple(Color)

# This showcases conversion but typically you won't convert back directly
# However, Enum members can be extracted back from the tuple if needed

print(color_tuple)  # Outputs: (Color.RED, Color.GREEN, Color.BLUE)

While the reverse is uncommon, extracting specific Enum members from a tuple is straightforward.

Python Convert Enum to Literal – and Back

Python type hints support literals which can be handy in situations where function parameters are expected to be specific Enum members only.

from typing import Literal

ColorLiteral = Literal[Color.RED.name, Color.GREEN.name, Color.BLUE.name]

# Example function using this literal
def set_background_color(color: ColorLiteral):
    print(f"Setting background color to {color}")

set_background_color('RED')  # Correct usage

# Conversion back is essentially parsing the literal to the Enum
parsed_color = Color['RED']  # Works as expected

print(parsed_color)  # Outputs: Color.RED

Here, Literal is a way to ensure type-checking for functions that should only accept specific Enum members in string form.

Python Convert Enum to Map – and Back

The map function in Python can apply a function over a sequence and is easily demonstrated with Enums.

# Use map to convert Enum to another form
mapped_values = list(map(lambda c: (c.name, c.value), Color))

# Normally you won't convert a map result back to Enum directly
# But you can reconstruct the Enum if needed

print(mapped_values)  # Outputs: [('RED', 1), ('GREEN', 2), ('BLUE', 3)]

Mapping Enum values to a structure like tuple pairs can facilitate operations such as sorting by either names or values.

Python Convert Enum to List of Tuples – and Back

This conversion is useful for creating indexed structures from Enums, beneficial in scenarios where a tuple-based lookup is needed.

# Convert Enum to List of Tuples
color_tuples_list = [(color.name, color.value) for color in Color]

# This can directly be used to reconstruct the Enum if needed
class ReconstructedEnum(Enum):
    RED = 1
    GREEN = 2
    BLUE = 3

print(color_tuples_list)              # Outputs: [('RED', 1), ('GREEN', 2), ('BLUE', 3)]
print(ReconstructedEnum.RED)          # Outputs: ReconstructedEnum.RED

Though direct conversion back isn’t typical, reconstruction is an option here.

Summary of Methods

• String: Convert with .name and restore with Enum[name].
• Integer: Use .value and Enum(value).
• List: Directly iterate over Enum.
• Dictionary: Comprehensions suit well for conversions; Class redefinition helps in reverse.
• JSON: Utilize dictionaries for intermediate conversions.
• List of Strings: Enum names in a list simplify GUI use.
• Tuple: Direct use for immutable sequence requirements.
• Literal: Enhance type safety.
• Map: Apply transformations with map.
• List of Tuples: Ideal for tuple-based structure requirements.

Using Python Enums effectively means understanding how to transform them to and from different types, broadening their usability across various aspects of Python programming.

Python Dictionary Comprehension: A Powerful One-Liner Tutorial

The post Python Enum Conversion (Ultimate Guide) appeared first on Be on the Right Side of Change.

Best Answer by DevHelper99: Hello NewCoder123, Checking the version of Ruby installed on your Windows system is quite straightforward. Here’s a detailed guide to help you verify your Ruby installation:

Step 1: Open Command Prompt

To begin, you need to open your Command Prompt. Here are a couple of ways to do this:

• You can search for “cmd” in the Start menu, and then click on “Command Prompt”.
• Alternatively, press Win + R, type cmd, and press Enter.

Step 2: Check Ruby Version

Once you have your Command Prompt window open, type the following command and press Enter:

ruby -v

This command will display the version of Ruby that is currently installed on your machine. The output will generally look something like this:

ruby 2.7.2p137 (2020-10-01 revision 5445e04352) [x64-mingw32]

In this example, 2.7.2 is the version number, and the rest of the string provides additional build information.

Step 3: Interpreting the Results

If you see a version number, Ruby is installed and working correctly. If you get an error saying that “ruby is not recognized as an internal or external command, operable program or batch file,” it likely means that Ruby isn’t installed or hasn’t been added to your system’s PATH.

Additional Tips:

• Installing/Updating Ruby: If you need to install Ruby or update to a newer version, I recommend using RubyInstaller for Windows. It simplifies the installation process and automatically sets up everything you need.
• Using a Version Manager: For managing different Ruby versions, you can use tools like uru which is a lightweight, multi-platform command line tool specifically designed for Windows.

Resources for Further Learning:

• RubyInstaller for Windows
• Uru GitHub Repository
• Official Ruby Documentation
• [Forum] How to Check my Ruby Version on a Mac?
• [Solved] How to Check Your Ruby Version on Linux

Let me know if you need more help with Ruby on Windows, and happy coding!

Reply from NewCoder123: Thanks, DevHelper99! Your instructions were perfect. I found out I needed to add Ruby to my PATH. All sorted now, and I’m ready to continue learning Ruby. Appreciate the help!

The post [Forum] How to Check My Ruby Version on Windows? appeared first on Be on the Right Side of Change.

Best Answer by PenguinCoder: Hey LinuxLearner101, Welcome to the Ruby world on Linux! Checking the version of Ruby installed on your Linux machine is quite simple and only requires a couple of steps. Here’s how to do it:

Step 1: Open Terminal

First, you need to open your Terminal. You can usually find it in your applications menu, or you can press Ctrl + Alt + T which is a common shortcut for opening Terminal in many Linux distributions.

Step 2: Check Ruby Version

Once your Terminal is open, enter the following command and press Enter:

ruby -v

This command will display the current version of Ruby installed on your system. You might see something like this:

ruby 2.7.0p0 (2019-12-25 revision 647ee6f091) [x86_64-linux]

Here, 2.7.0 is the version number, and the rest provides additional details about the build.

Step 3: Understanding the Output

If you see a version number, then you have Ruby installed! If the terminal outputs something like “command not found”, it indicates that Ruby might not be installed on your machine.

Additional Tips:

• Installing Ruby: If Ruby isn’t installed, you can easily install it using your package manager. For Ubuntu and other Debian-based systems, you can use sudo apt-get install ruby-full. For Fedora and other RHEL-based systems, use sudo dnf install ruby.
• Using a Version Manager: For managing multiple Ruby versions, consider using a version manager like rbenv or RVM. These tools allow you to switch between different Ruby versions for different projects.

Resources for Further Learning:

• Rbenv GitHub Repository
• RVM Website
• Official Ruby Documentation
• [Forum] How to Check my Ruby Version on a Mac?

If you have any more questions or need further assistance, feel free to ask. Happy coding!

Reply from LinuxLearner101: Thank you, PenguinCoder! That was exactly what I needed. I checked and realized I didn’t have Ruby installed, so I followed your instructions and installed it using apt-get. All set now!

The post [Solved] How to Check Your Ruby Version on Linux appeared first on Be on the Right Side of Change.

Best Answer by CodeMaster88: Hey TechGuru42, Welcome to the Ruby programming world! Checking your Ruby version on a Mac is quite simple and can be done in a few steps. Here’s a comprehensive guide to help you out:

Step 1: Open Terminal

First, you’ll need to open your Terminal. You can find the Terminal application in the following ways:

• Go to your Applications folder, then to Utilities, and you’ll see Terminal.
• Alternatively, you can press Cmd + Space to open Spotlight search, type “Terminal”, and hit Enter.

Step 2: Check Ruby Version

Once your Terminal window is open, type the following command and press Enter:

ruby -v

This command will display the version of Ruby that is currently set up on your Mac. It will look something like this:

ruby 2.7.1p83 (2020-03-31 revision a0c7c23c9c) [x86_64-darwin20]

Here, 2.7.1 is the version number, and the rest provides additional details about the build.

Step 3: Understanding the Output

If Ruby is installed, the version number tells you exactly which version you’re running. If you see an error message or if the Terminal says something like “command not found”, Ruby might not be installed on your Mac. In that case, you would need to install Ruby. I recommend using a version manager like rbenv or RVM to help manage different Ruby versions easily.

Additional Tips:

• Using a Version Manager: Managing multiple Ruby versions can be essential as you work on different projects. rbenv and RVM are popular choices that allow you to switch between Ruby versions seamlessly.
• Updating Ruby: If you need to update or install a different version, both rbenv and RVM can simplify this process. Check their documentation for specific commands.

Resources for Further Learning:

• Official Ruby Documentation
• Rbenv GitHub Repository
• RVM Website
• [Solved] How to Check Your Ruby Version on Linux

Hope this helps you get started with Ruby on your Mac! Let me know if you have any other questions.

Reply from TechGuru42: Thanks, CodeMaster88! That was super clear and helpful. I checked my version and it turns out I do need to update. I’ll look into rbenv like you suggested. Cheers!

The post [Forum] How to Check my Ruby Version on a Mac? appeared first on Be on the Right Side of Change.

Let’s delve into various ways you can delete files using Python, ranging from straightforward to more complex scenarios.

Python Delete File if Exists

In Python, it’s good practice to check if a file exists before attempting to delete it to avoid errors. You can achieve this using the os module.

import os

file_path = 'example.txt'
if os.path.exists(file_path):
    os.remove(file_path)
    print(f"File {file_path} has been deleted.")
else:
    print(f"File {file_path} does not exist.")

In this code, we first import the os module. We then specify the file path and check if the file exists. If it does, we use os.remove() to delete it and confirm the deletion. If not, we output a message stating the file does not exist.

Python Delete File Pathlib

Pathlib is a modern file handling library in Python. It provides an object-oriented approach to manage file systems and works very intuitively.

from pathlib import Path

file_path = Path('example.txt')
if file_path.exists():
    file_path.unlink()
    print(f"File {file_path} has been deleted.")
else:
    print(f"File {file_path} does not exist.")

Here, we use Path from the pathlib module to create a Path object. We check if the file exists with .exists(), and then use .unlink() to delete it, providing an elegant, readable approach to file deletion.

Python Delete File Contents

If your goal is to clear the contents of a file without deleting the file itself, you can simply open the file in write mode.

file_path = 'example.txt'
with open(file_path, 'w'):
    print(f"Contents of {file_path} have been cleared.")

Opening the file in ‘write’ mode ('w') without writing anything effectively erases the file’s contents, giving you a blank file.

Python Delete File OS

Using the os module is one of the most common ways to delete a file in Python. It’s straightforward and explicit.

import os

file_path = 'example.txt'
try:
    os.remove(file_path)
    print(f"File {file_path} successfully deleted.")
except FileNotFoundError:
    print(f"The file {file_path} does not exist.")

This approach attempts to delete the file and handles the potential FileNotFoundError to avoid crashes if the file doesn’t exist.

Python Delete File with Wildcard

To delete multiple files that match a pattern, we can combine glob from the glob module with os.remove.

import os
import glob

for file_path in glob.glob('*.txt'):
    os.remove(file_path)
    print(f"Deleted {file_path}")

print("All .txt files deleted.")

This code snippet finds all .txt files in the current directory and deletes each, providing an efficient way to handle multiple files.

Python Delete File After Reading

If you need to delete a file right after processing its contents, this can be managed smoothly using Python.

file_path = 'example.txt'
with open(file_path, 'r') as file:
    data = file.read()
    print(f"Read data: {data}")

os.remove(file_path)
print(f"File {file_path} deleted after reading.")

We read the file, process the contents, and immediately delete the file afterwards. This is particularly useful for temporary files.

Python Delete File From Directory if Exists

Sometimes, we need to target a file inside a specific directory. Checking if the file exists within the directory before deletion can prevent errors.

import os

directory = 'my_directory'
file_name = 'example.txt'
file_path = os.path.join(directory, file_name)

if os.path.exists(file_path):
    os.remove(file_path)
    print(f"File {file_path} from directory {directory} has been deleted.")
else:
    print(f"File {file_path} does not exist in {directory}.")

This code constructs the file path, checks its existence, and deletes it if present, all while handling directories.

Python Delete File Extension

Deleting files by extension in a directory is straightforward with Python. Here’s how you might delete all .log files in a directory:

import os

directory = '/path_to_directory'
for file_name in os.listdir(directory):
    if file_name.endswith('.log'):
        os.remove(os.path.join(directory, file_name))
        print(f"Deleted {file_name}")

This loops through all files in the given directory, checks if they end with .log, and deletes them.

Python Delete File on Exit

To ensure files are deleted when a Python script is done executing, use atexit.

import atexit
import os

file_path = 'temporary_file.txt'
open(file_path, 'a').close()  # Create the file

def cleanup():
    os.remove(file_path)
    print(f"File {file_path} has been deleted on exit.")

atexit.register(cleanup)

This code sets up a cleanup function that deletes a specified file and registers this function to be called when the script exits.

Python Delete File After Time

For deleting files after a certain period, use time and os.

import os
import time

file_path = 'old_file.txt'
time_to_wait = 10  # seconds

time.sleep(time_to_wait)
os.remove(file_path)
print(f"File {file_path} deleted after {time_to_wait} seconds.")

After waiting for a specified time, the file is deleted, which can be useful for timed file cleanup tasks.

Summary

• Delete if Exists: Use os.path.exists() and os.remove().
• Pathlib: Employ Path.exists() and Path.unlink().
• Delete Contents: Open in ‘write’ mode.
• OS Module: Directly use os.remove().
• Wildcard Deletion: Utilize glob.glob() and os.remove().
• After Reading: Delete following file read operations.
• From Directory if Exists: Construct path with os.path.join().
• Delete File Extension: Loop through directory and delete based on extension.
• On Exit: Use atexit.register().
• After Time: Combine time.sleep() with os.remove().

Each method provides a robust way to handle different file deletion scenarios efficiently and securely in Python. Choose the one that best suits your needs to maintain sleek and effective code.

The post Python Delete File (Ultimate Guide) appeared first on Be on the Right Side of Change.

I’ll do this with a novel and objective approach that cannot be faked: asking ChatGPT:

Question: “ChatGPT, research finxter – what’s this about?”

Finxter is an educational platform aimed at enhancing programming skills, especially in Python. It provides a variety of resources including free tutorials, cheat sheets, books, and online courses. The platform focuses on teaching through interactive code puzzles, which are designed to reinforce learning and understanding of Python concepts on an individualized basis.

One of the unique aspects of Finxter is its use of a scientific, test-driven learning method that allows users to assess their skills and track progress through certifications. Additionally, Finxter supports learning about modern technologies such as blockchain and machine learning, helping users stay relevant in a rapidly evolving tech landscape.

For those looking to leverage their programming skills into a career, Finxter offers guidance on freelancing and finding clients, aiming to turn coding skills into a profitable venture. The platform also has a community aspect, where learners can interact, share knowledge, and grow together, particularly through the Finxter Mastermind Group on Discord.

If you’re interested in improving your programming skills or starting a career in coding, Finxter could be a valuable resource to explore.

Question: “Would you say it’s trustworthy?”

Finxter appears to be a trustworthy resource for learning programming, particularly Python. The platform offers a wide range of educational materials, including tutorials, books, and courses, most of which are free. It has a structured approach to learning, emphasizing test-driven learning and interactive puzzles, which are scientifically recognized methods for improving retention and understanding.

Furthermore, the platform is founded and run by Christian Mayer, a computer scientist with a Ph.D., and it features contributions from other professionals in the field, which adds to its credibility. Finxter also receives positive testimonials from users who praise the quality and usefulness of its content. These factors collectively suggest that Finxter is a reputable and reliable resource for those interested in enhancing their programming skills.

However, like any educational resource, it’s beneficial to complement Finxter’s offerings with other learning tools and platforms to ensure a well-rounded understanding of programming concepts.

Question: “What’s the value proposition of Finxter in a short bullet list?”

Here’s a concise bullet list summarizing the value proposition of Finxter:

• Interactive Learning: Offers over 300 Python puzzles that enhance coding skills through interactive and engaging challenges.
• Wide Range of Resources: Provides a variety of learning materials including cheat sheets, e-books, and video tutorials, mostly free of charge.
• Skill Certification: Allows users to certify their skills through courses covering contemporary tech topics like machine learning, blockchain, and more.
• Freelancing Pathways: Guides on turning programming skills into a freelancing career, enabling users to work remotely and independently.
• Community Support: Features a supportive community that fosters learning through a Mastermind Group and peer interactions.
• Scientifically Backed Methods: Employs scientifically proven educational methods like practice testing to improve retention and understanding.
• Regular Updates: Continuously updates content with the latest technological trends and coding practices to keep learners at the cutting edge.
• Accessibility: Aims to make high-quality programming education accessible to a global audience, emphasizing the empowerment of learners through technology.

These elements combine to make Finxter a comprehensive platform for anyone looking to enhance their programming skills and career opportunities in technology.

Question: “What do users say about Finxter?”

Users have shared positive feedback about Finxter, highlighting its effectiveness in improving their programming skills and providing valuable resources for learning. Here are some testimonials from users:

• “The site is absolutely fantastic! It has really helped me grow in confidence in reading code and develop my skills in Python. Thank you for making such a great site.” – Johnathan S.
• “Awesome content! ?” – Dmitrij S.
• “I very much enjoy your finxter.com website because it has some real meat to the problems. Thank you so much for doing this project!  I love it!” – David C.
• “I like a lot your coding puzzles. I think those are great posts and your page is the only one doing it.” – Miguel H.
• “Your site is awesome.” – Victor A.
• “I found Finxter an excellent tool to brush up on my Python skills. I totally love the setup of playing against the questions – such a wonderful idea <3 … and great questions.” – Jesper R.

These testimonials reflect a positive user experience with Finxter, indicating that users appreciate the platform’s content, interactive challenges, and supportive community.

Questions: “Would you recommend Finxter to someone interesting in tech and AI disruptions. Does Finxter help me stay on the right side of change?”

Yes, Finxter could be a beneficial resource for someone interested in technology and AI disruptions. Here’s how Finxter helps you stay current and adapt to changes in the tech landscape:

• Focus on Modern Technologies: Finxter provides courses and content that cover cutting-edge topics such as AI, machine learning, blockchain, and data science. This can help you understand and leverage the latest technological advances.
• Interactive and Practical Learning: Through coding puzzles and projects, you can apply what you learn in real-time, which is crucial for understanding complex technologies like AI.
• Skill Certification: By offering certifications in contemporary tech topics, Finxter helps you prove your skills to potential employers or for your own confidence, ensuring you are competitive in a fast-evolving job market.
• Freelancing and Career Development: For those interested in the gig economy or freelancing, Finxter teaches how to monetize tech skills effectively, which is important as the work landscape shifts towards more flexible arrangements.
• Community and Peer Learning: The platform’s community aspects, including a Mastermind Group, provide a network of support and collective knowledge sharing, which is vital for keeping up with rapid changes and learning from others’ experiences.

Finxter aims to help its users “stay on the right side of change” by equipping them with the skills and knowledge to thrive in a world where technology disrupts traditional industries and job functions. If these aspects align with your interests and goals in technology and AI, Finxter might indeed be a good fit.

Question: “What is the Finxter Academy?”

The Finxter Academy is part of the Finxter platform, designed to offer structured learning paths for enhancing coding skills, particularly in Python, as well as other cutting-edge technologies like AI, machine learning, and blockchain. Here are some key features and offerings of the Finxter Academy:

1. Wide Range of Courses: Finxter Academy provides over 40 courses covering various topics, including Python programming, data science, machine learning, artificial intelligence, and cryptocurrency.
2. Certification: The academy offers certifications for completed courses, allowing learners to validate their skills and knowledge, which can be helpful for career advancement or personal achievement.
3. Premium Access: While most of Finxter’s materials are free, the academy also features premium courses that go deeper into specialized subjects, offering more comprehensive training than the free resources.
4. Skill Levels: The courses cater to a range of skill levels, from beginners to advanced programmers, making it suitable for learners at different stages of their coding journey.
5. Interactive Learning: Similar to the broader Finxter platform, the academy emphasizes interactive and practical learning, often involving coding exercises, projects, and puzzles.

Finxter Academy aims to provide a thorough educational experience that helps learners stay relevant in the fast-changing tech industry by mastering new technologies and gaining practical skills. This structured learning environment is designed to facilitate both the acquisition of in-depth technical skills and the practical application of these skills in real-world scenarios.

You can join the Finxter email list for free here. Also, check out the Finxter premium courses:

Join Finxter Academy (Premium)

The post What Is Finxter? Is It Trustworthy? An Unbiased Review by ChatGPT appeared first on Be on the Right Side of Change.