Alright, so I feel like I’m the only one who’s not writing about the topic, and I definitely felt the pressure to do so. LOL

In the rapidly evolving landscape of artificial intelligence, a new contender has emerged, capturing the attention of both tech enthusiasts and industry professionals, and my LinkedIn feed got flooded by the news. DeepSeek AI, a Chinese startup, has introduced its latest model, DeepSeek-R1, positioning itself as a formidable open-source alternative to established AI chatbots like OpenAI’s ChatGPT. Not to mention it totally destroyed Nvidia’s stock price — the current “shovel supplier” of GPU chips, by showing the world there’s another, cheaper way of training models with older hardware.

Understanding DeepSeek-R1

At the heart of DeepSeek’s innovation lies the DeepSeek-R1 model. This large language model (LLM) has been thoroughly trained to perform complex reasoning tasks, including mathematics, coding, and logical problem-solving. Remarkably, DeepSeek-R1 achieves performance comparable to OpenAI’s o1 model across these domains, but with a much lower price tag for training the model.

One of the standout features of DeepSeek-R1 is its open-source nature. This openness allows developers worldwide to download, modify, and implement the model within their own applications, fostering a collaborative environment for AI advancement. The model’s architecture emphasizes efficiency, enabling it to deliver high performance without the need for extensive computational resources. They’ve definitely made some awesome breakthroughs around the actual process of training it which made the world believe there could be other ways of training LLMs, so who knows what’s still out there, waiting to be invented.

The R1 model’s architecture incorporates mechanisms that allow it to self-correct and refine its outputs without human intervention, a feature that distinguishes it from many existing models.

This self-correcting capability is particularly beneficial in applications requiring high accuracy and reliability, such as scientific research, financial analysis, and advanced data interpretation. By simulating a form of cognitive reasoning, the R1 model pushes the boundaries of what AI can achieve.

The Web UI Chatbot Experience

Beyond the technical prowess of the DeepSeek-R1 model, DeepSeek AI has developed a user-friendly web interface, making the power of this advanced AI accessible to a broader audience. Very similar to ChatGPT, this web-based chatbot allows users, regardless of technical expertise, to interact with the AI by inputting prompts and receiving coherent, contextually relevant responses. With one great improvement — its chain-of-thought process is revealed to the users so you can get an idea of how the model reasons with himself (which can definitely be fun sometimes)

Comparative Analysis with ChatGPT-o1

When evaluating DeepSeek-R1 alongside OpenAI’s ChatGPT-o1, several key distinctions emerge:

1. Open-Source Accessibility: DeepSeek-R1 is fully open-source, allowing developers to access and modify the codebase. In contrast, ChatGPT operates under a more restrictive model, limiting.. well, everything.

2. Resource Efficiency: DeepSeek-R1 has been designed to perform optimally with fewer computational resources, making it more accessible to organizations with limited hardware capabilities. ChatGPT, while powerful, often requires substantial computational infrastructure.

3. Reasoning Capabilities: The R1 model’s emphasis on self-correcting reasoning processes provides an edge in tasks that demand logical problem-solving and accuracy. ChatGPT excels in generating human-like text but may not possess the same level of autonomous reasoning refinement.

Practical Applications

For individuals and organizations already utilizing AI chatbots, integrating DeepSeek-R1 can offer several advantages:

• The chat web interface is free: Well, for the time being at least. We don’t know how long it will remain like that.

• Chain-of-thought reasoning

• 96% cheaper API usage —potentially very beneficial for organizations and startup builders

Precautions

One of the things to consider is the usage of software created in China, mainly for privacy reasons. Our data goes to their servers and who knows how it could be used. Personally, I don’t bother myself too much as all of this definitely applies to a lot of online services these days. Especially if they’re free to use. After all, if something’s free it’s most likely that YOU are the product.

There are ways for you to host LLM models locally on your machine, so no data ever goes to unknown servers/countries/people, you can even use it offline. Let me know in the comments if that’s something you might want to learn to do by yourself.

Conclusion

DeepSeek AI’s introduction of the DeepSeek-R1 model marks a significant milestone in the AI landscape. By offering an open-source, efficient, and cognitively advanced alternative to existing models, DeepSeek empowers users to harness the full potential of artificial intelligence. Whether you’re a developer seeking an exciting model or a user in need of a free AI chat assistant, DeepSeek-R1 presents a compelling option worth exploring.

What do you see as the biggest pros and cons of using this new web-based AI chatbot? What about tweaking around the LLM behind it?

Please share your thoughts in the comments below and give me a clap or a follow if you liked the article.

For a software engineer, transitioning into entrepreneurship is an exciting yet challenging endeavor. One of the most critical decisions you’ll face is choosing the type of business to build. Among the most common paths for software engineers are founding a software agency or developing a product-based startup. Both options have distinct benefits and challenges that impact ease of entry, scalability, and long-term growth potential. In this article, we will explore these aspects to help you make an informed decision.

Software Agency: A Service-Oriented Approach

A software agency typically provides customized software solutions to clients. This model involves working closely with businesses to develop, maintain, and support their software projects.

Pros:

1. Lower Initial Investment: Starting a software agency requires minimal upfront capital. Often, your existing skill set, a reliable laptop, and a few clients are enough to get started.

2. Faster Revenue Generation: Agencies can start generating revenue almost immediately once clients are secured.

3. Predictable Cash Flow: Retainer contracts or long-term projects can provide a steady income stream.

4. Skill Diversification: Working with varied clients and industries exposes you to different challenges and technologies, enhancing your expertise.

Cons:

1. Scaling Challenges: Scaling an agency is heavily reliant on hiring and training talent, which can be time-consuming and costly.

2. Client Dependence: A few high-value clients may represent a significant portion of your revenue, making the business vulnerable to client churn.

3. Limited Valuation: Agencies often have lower valuations compared to product-based startups due to their dependency on human resources and lack of proprietary assets.

Ease of Starting: High. With your technical expertise and a strong network, you can likely secure your first clients relatively quickly.

Scalability: Moderate. Scaling requires building a larger team and streamlining operations.

Long-Term Growth: Limited. Agencies often plateau unless they pivot to product development or specialize in high-value niches.

Product Startup: A Scalable Vision

A product startup involves building a software solution that solves a specific problem or addresses a market need. Examples include SaaS platforms, mobile applications, or enterprise tools.

Pros:

1. High Scalability: A successful product can serve thousands or even millions of users with minimal incremental costs.

2. Recurring Revenue: Subscription models like SaaS provide a predictable and scalable revenue stream.

3. Higher Valuation Potential: Startups with unique intellectual property or strong market traction can achieve significant valuations.

4. Autonomy: Unlike agencies, product startups are less dependent on individual clients.

Cons:

1. High Risk: Building a product involves market research, development, and often years of investment before profitability.

2. Resource Intensive: Product startups require significant funding for development, marketing, and scaling.

3. Market Uncertainty: There’s no guarantee your product will gain traction, even if it’s technically excellent.

Ease of Starting: Low. Product development demands extensive planning, prototyping, and sometimes external funding.

Scalability: High. Successful products can scale globally with the right infrastructure.

Long-Term Growth: Significant. Products that address pressing needs can become industry leaders and generate substantial returns.

Comparing the Two: Key Considerations

Software Agency

• Initial Investment = Low

• Revenue Generation = Immediate

• Scalability = Moderate

• Risk = Low to Medium

• Long-Term Potential = Limited

• Exit Opportunities = Low

Product Startup

• Initial Investment = High

• Revenue Generation = Delayed

• Scalability = High

• Risk = High

• Long-Term Potential = Significant

• Exit Opportunities = High

Exploring Alternative Business Models

While software agencies and product startups are popular options, there are other paths for software engineers to consider:

1. Content Creation/Content Marketing: Leverage your expertise to create blogs, courses, or YouTube channels focused on software development. This model is low-cost and scalable through sponsorships, ads, and product sales.

2. Developer Tools and Frameworks: Build and monetize libraries, frameworks, or tools that solve common development challenges.

3. Technical Consulting: Offer specialized consulting services in niche areas like cybersecurity, AI/ML, or cloud computing.

4. Education and Training: Create a platform or program for mentoring aspiring developers, offering coding bootcamps, or corporate training sessions.

5. Open-Source Sponsorships: Develop open-source tools and monetize through sponsorships, donations, or enterprise-level add-ons.

6. Marketplace Platforms: Build a platform connecting businesses with developers or offering niche software solutions.

Final Thoughts

Choosing the right business model depends on your risk tolerance, financial resources, and long-term vision. If you prefer a lower-risk, cash-flow-positive path, a software agency may be the way to go. On the other hand, if you’re passionate about solving a specific problem and are willing to embrace higher risks for greater rewards, a product startup offers unmatched scalability and growth potential. Don’t discount alternative models, as they could align with your strengths and interests while opening unique opportunities. Whatever path you choose, leverage your years of experience to build something meaningful and impactful.

What do you see as the biggest pros and cons of starting a software agency vs a product startup?

Please share your thoughts in the comments below.

Here are 10 practical ways to turn your expertise into tangible income streams:

1. AI Consulting

Businesses across industries are eager to integrate AI to streamline processes, reduce costs, and innovate. However, they often lack the expertise to do so effectively.

As an AI consultant, you can offer tailored solutions, guide strategic AI adoption, and train internal teams. Consulting is particularly lucrative if you specialize in a niche industry like finance, healthcare, or retail.

2. AI Courses

AI skills are in demand, and professionals worldwide are eager to learn. By creating online courses, you can teach others about AI fundamentals, machine learning, data science, or even niche skills like model deployment.

Platforms like Udemy, Coursera, and Teachable allow you to reach a global audience, while providing a scalable way to generate income.

3. AI Content Creation

Share your knowledge through blogs, videos, podcasts, or newsletters. Content about AI trends, tools, and applications can attract readers and subscribers.

Monetization opportunities include ad revenue, sponsorships, or paid memberships. Establishing yourself as a thought leader in AI can also open doors for other opportunities like speaking engagements or collaborations.

4. AI Software Development

If you have coding expertise, developing AI-based tools or applications can be a game-changer. From chatbots to predictive analytics tools, businesses are willing to pay for products that solve real-world problems.

You can sell these solutions directly or offer them as Software as a Service (SaaS), generating recurring revenue.

5. Data Annotation

Data is the fuel that powers AI models, and annotated data is crucial for training them. Many companies outsource data labeling tasks, creating a high-demand market for freelancers and agencies.

You can specialize in specific types of annotation, such as image, text, or audio data, depending on your expertise.

6. AI Model Training

Training AI models for specific industries or tasks can be a valuable service. For instance, an e-commerce company might need a recommendation engine, or a logistics firm may require route optimization.

By offering custom-trained models, you can cater to specialized needs and charge premium rates.

7. AI in Marketing

Businesses are increasingly relying on AI to optimize marketing campaigns, analyze consumer behavior, and personalize customer experiences.

If you understand AI-driven tools like predictive analytics or customer segmentation, you can offer your expertise to help companies boost their ROI through smarter marketing strategies.

8. AI in Finance

AI is revolutionizing finance by enabling better forecasting, fraud detection, and risk management.

Developing models that cater to these needs — like stock prediction algorithms or credit scoring tools — can be highly profitable. Companies in fintech and traditional finance alike are eager to pay for these innovations.

9. AI in Healthcare

The healthcare sector is rapidly adopting AI for diagnostics, patient care, and operational efficiency.

For example, AI can help identify diseases from medical images or streamline administrative processes like scheduling. Building AI tools for healthcare not only generates income but also contributes to societal well-being.

10. AI Workshops and Seminars

If you enjoy teaching, hosting AI workshops or seminars can be both fulfilling and lucrative. Target schools, universities, or corporate training programs to educate others about AI trends and applications.

Workshops can be offered in person or online, providing flexibility in how you deliver your expertise.

The Bottom Line

Monetizing AI skills is no longer optional for staying competitive — it’s a necessity. Whether you’re a seasoned professional or just starting out, these 10 strategies can help you turn your AI expertise into a sustainable income source.

Which of these strategies resonates with you the most? Have you tried any of them? I’d love to hear your experiences in the comments!

I don’t know about you, but I often find myself struggling with the challenges of modern life. Wouldn’t you agree?

Doomscrolling, information overload, overeating, procrastination, lack of physical activity, an abundance of choices, and decision fatigue — just to name a few.

In a world that often promotes quick fixes to things, committing to a 30-day challenge can kickstart your self-improvement process and trigger a chain reaction, improving your habits, mindset, confidence, and overall well-being across different aspects of your life, leading to long-lasting results and self-fulfillment. Let’s explore the mechanics around the method and share some of my personal experiences with it.

Why 30 Days?

A month is short enough to feel achievable but long enough to form habits. Some research suggests it takes about 21–30 days to make a new behavior stick long-term. Also, it’s easily measurable and you can take that success and use it to SPARK your belief that you can change any domain of your life.

Choose the scale of your change

If you’re a complete beginner when it comes to changes, I would suggest that you think about the behaviors or bad habits you want to change, write them down in your notes or on a piece of paper, and then assign a number to each, a number which will indicate the order of their execution. Just pick the one that you want to start working on first but you’re also confident enough that you can make work (if you don’t think about yourself as a beginner, pick 1 to 3 habits). Don’t pick your worst habit(s) yet. Remember-we’re aiming for a balance between an easy win and the usefulness of overcoming this bad habit. This win will ignite a SPARK in you, that will give you the much-needed confidence boost, and use that momentum to start working on your other bad habits.

The challenge

As I mentioned before, the actual challenge shouldn’t be too hard, so look at one example of a bad habit you might want to change and what can you do during the 30 days. The example is for Doomscrolling / Social media addiction.

First, you need to check how much time you’re spending on your phone, doing unnecessary or unproductive activities. On iPhone, the menu you’re looking for is in the Settings app, called ‘Screen Time’. On Android most often it’s named Digital Wellbeing. Go into that menu and check the time you’re spending on your social media apps. Next, you should decide if you actually want to completely stop using social apps, or if you want to leave some room for ‘brain-rotting’ as named Word of the Year 2024 by Oxford University. As a first challenge, I’d suggest that you not completely scratch it off but maybe decrease it drastically by 80% let’s say. Personally, I’m not a fan of extremes.

Week 1: Awareness & Preparation

• Set Limits: Reduce phone use by 15–20% and establish daily screen-time goals.

• Remove Temptations: Delete distracting apps and move others to folders.

• Create No-Phone Zones: Ban phone use in the bedroom, dining table, or other areas.

• Plan Substitutes: Identify alternatives like reading (Medium articles ;)), journaling, or meditation.

Week 2: Building Momentum

• Morning Rule: No phone for 30 minutes after waking up.

• Set Time Blocks: Schedule specific times for checking the phone (e.g., 15 minutes mid-morning, afternoon).

• Replace Habits: Use waiting times for mindfulness or engaging in hobbies instead of scrolling.

• Introduce Rewards: Reward yourself for staying within screen time limits.

Week 3: Deepening the Practice

• Digital Detox Days: Plan one screen-free day or afternoon.

• Extend No-Phone Zones: Include all meals and social gatherings.

• Add a Replacement Activity: Spend your freed-up time on a creative or physical hobby.

• Accountability: Share your progress with someone or use an accountability app.

Week 4: Sustaining & Solidifying

• Stick to Routines: Automate morning and evening phone-free times.

• Refine Limits: Reduce screen time further and set up app usage limits strictly (by your phone’s usage tracking app).

• Celebrate Wins: Reflect on how reducing phone use has benefited you.

• Plan Maintenance: Set long-term boundaries, like weekly detox hours or apps.

That’s just one example but I think it applies to a lot of people. But the actual challenge is not the point of this article, as I mentioned. What’s important is you, taking the first steps and using that to change other aspects of your life — therefore the SPARK.

(If you want me to help you out with the implementation of the challenge, just hit me up in the comments. We can also try to see what other bad habits you would like to resolve)

The Spark

I call it a spark because what does a spark do? It starts a process. By completing a simple challenge you will find yourself fulfilled and confident that you’re in control. You just had to create a structure, a process. We want to use the momentum of that and start another challenge. Actually, the next endeavor is not necessarily to be a removal of a bad habit but it can also be self-improvement by taking courses, learning something new, business, art, working out, you choose. Just take the momentum of your win and ride it into the next one.

Winning leads to more winning

Our nervous system is attuned to biochemicals and winning leads to a certain brain mechanism triggering a release of a chemical called ‘serotonin’. This chemical then empowers us by making us more confident that we can win again. It also makes us happier. The popular Canadian psychologist and author Dr. Jordan Peterson proposes a great example of this by sharing his theory about lobsters and their hierarchy. Of course, his example is regarding competition between lobsters but I got to tell you — I don’t think there’s a big difference between winning against an adversary, in the face of another person, and an adversary in the face of our own mind, which I’m afraid the latter is much more often causing us trouble.

I challenge you to try it yourself. Let me know what challenge you’ll start with and how it goes!