Develpreneur: Become a Better Developer and Entrepreneur

Rob Broadhead
  • 5.0 (12)
  • Technology
  • Semiweekly

This podcast is for aspiring entrepreneurs and technologists as well as those that want to become a designer and implementors of great software solutions. That includes solving problems through technology. We look at the whole skill set that makes a great developer. This includes tech skills, business and entrepreneurial skills, and life-hacking, so you have the time to get the job done while still enjoying life.

  1. 21h ago

    From Firewalls to Fairways: Reinventing Your Technology Career with Ryan Drumheller

    A successful technology career does not always follow a straight path. Developers and IT professionals often start with a clear idea of where they want to go, only to find that layoffs, industry shifts, burnout, and unexpected opportunities can completely change their direction. In this episode of Building Better Developers, hosts Rob Broadhead and Michael Meloche welcome Ryan Drumheller to discuss his journey from hands-on technology work to leadership, consulting, and eventually entrepreneurship. About Ryan Drumheller Ryan Drumheller is a technology leader, entrepreneur, author, and co-founder of Dogleg Digital. With more than two decades of experience spanning IT operations, infrastructure, development, technical leadership, and business strategy, Ryan has focused much of his career on connecting technology with the people and organizations it serves. He graduated from Western Governors University and advocates for professional education, mentorship, and continuous learning. His book, From Firewalls to Fairways: How a Career in IT Led Me to Golf, shares lessons from his career and explores how setbacks and unexpected opportunities can lead to personal and professional reinvention. Connect with Ryan and learn more about his work: LinkedIn: https://www.linkedin.com/in/ryan-drumheller/ Personal website and book: https://ryandrumheller.com/ Dogleg Digital: https://doglegdigital.com/ Finding a Passion for Technology Ryan's interest in technology began long before he entered the professional world. As a child, he struggled to find a traditional artistic outlet, but computers gave him the freedom to experiment and create. He spent countless hours taking computers apart, putting them back together, exploring early internet technology, and learning how systems worked. That curiosity eventually led him through technical support, networking, development, engineering, and leadership. While his technical skills opened doors, his ability to connect with people helped shape the next stage of his career. Michael related to Ryan's experiences, having grown up during the same era of dial-up internet and bulletin board systems. Their conversation highlighted how early experimentation developed problem-solving skills that remain valuable today. However, Ryan acknowledged that his curiosity sometimes created challenges in professional environments. He frequently wanted to understand why a problem existed rather than simply applying the requested solution. Over time, he learned to balance his desire to explore with the practical needs of the business. Building a Technology Career in the Age of AI As their discussion shifted toward artificial intelligence, Michael asked an important question: How should developers entering the industry today build the experience needed to remain valuable? Ryan emphasized that AI has fundamentally changed the opportunities and expectations facing technology professionals. While AI can generate code and accelerate development, understanding how systems operate remains essential. Developers must recognize mistakes, evaluate security concerns, and determine whether a proposed solution actually works. Rather than relying exclusively on one technical specialty, Ryan encourages professionals to develop multiple complementary skills. Understanding different technologies, industries, and business processes creates opportunities that cannot easily be replaced by automated tools. The objective is not to compete against AI at everything it can do. Instead, developers should combine their knowledge with AI to deliver better results. When the Traditional Career Path Stops Working Ryan's career followed a familiar progression from individual contributor to technical leader. However, professional success did not provide immunity from organizational changes. After losing one position, Ryan completed approximately 20 interviews and found another opportunity within 35 days. That role allowed him to work internationally, expand his experience, and continue growing professionally. Unfortunately, another organizational shift eventually eliminated that position as well. This time, Ryan encountered a very different employment market, influenced by economic uncertainty and the rapid adoption of AI. Michael shared a similar experience involving the elimination of his team following a corporate acquisition. Their discussion reflected a growing concern among experienced professionals: Even a strong technical background and years of demonstrated performance do not guarantee employment stability. These experiences encouraged Ryan to reconsider what he wanted from the next stage of his career. Networking Is More Important Than Ever One of Ryan's strongest recommendations for professionals navigating career uncertainty is to build meaningful relationships before they urgently need them. He considers LinkedIn a valuable platform for sharing expertise, discovering opportunities, and connecting with people already working in roles or industries of interest. However, he cautions against approaching every interaction with the expectation of receiving something in return. Instead, networking should begin with curiosity. Ask questions, participate in discussions, share useful information, and develop genuine relationships over time. For professionals who feel overwhelmed by networking, Ryan recommends simply starting. A few meaningful interactions can create opportunities that would never emerge from submitting another application. Turning Career Setbacks Into New Opportunities Ryan eventually discovered that consulting was not necessarily the professional environment where he felt most comfortable. Although he possessed extensive technical expertise, he found selling his personal services more difficult than communicating the value of a tangible product. That realization helped him revisit an earlier business idea that eventually evolved into Dogleg Digital, a technology ecosystem designed for the golf industry. His path toward entrepreneurship was not an immediate success story. Ryan described Dogleg Digital as his fourth attempt at building something. Each previous experience provided lessons about his strengths, his interests, and the type of work he wanted to pursue. Ryan's perspective challenges the idea that a career setback represents permanent failure. Experimentation, changing direction, and learning from unsuccessful attempts can all contribute to finding a better opportunity. From Firewalls to Fairways: Lessons Beyond Technology Ryan's book, From Firewalls to Fairways, connects his professional experiences to the lessons learned during a round of golf. Each chapter uses a different hole to explore topics such as career development, networking, salary negotiations, leadership, setbacks, and personal growth. The golf analogy reflects an important reality: Careers rarely unfold exactly as planned. Sometimes the best opportunity comes from reassessing the situation and choosing a different direction. For developers concerned about AI, employment uncertainty, or professional stagnation, Ryan's journey provides a valuable reminder that technical expertise is only one component of a sustainable career. Learning to build relationships, recognize opportunities, and adapt to change can be just as important as learning another programming language. Join Rob Broadhead and Michael Meloche for Part 2, where Ryan explains how he turned an industry problem into a product and why AI is changing the way developers build, test, and deliver software. Stay Connected: Join the Developreneur Community 👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Career Improvement – From Better Developer To Better Career Lifelong Learning Habit: The Career Advantage That Outlasts Talent Security Awareness: Protect Your Code, Your Career, and Your Future Building Better Developers Podcast Videos – With Bonus Content

  2. 22h ago ·  Bonus

    Introducing: The Viall Files

    Introducing E1214 - Bre Tiesi Talks Special Forces & Selling Sunset, Love Is Blind INSANITY & Halloween from The Viall Files. Follow the show: The Viall Files Welcome back to The Viall Files: Reality Recap! Happy post Halloween! Today, we get into all our stories from the spooky weekend and all our thoughts from the spookiest Love Is Blind drop there has ever been. Plus, Bre Tiesi joins us to talk about her experience on Special Forces, thoughts on Selling Sunset, co-parenting with Nick Cannon, and more! You won't want to miss it! "I enjoy torturing myself for some reason…" HEY! YOU! DO YOU NEED DATING AND RELATIONSHIP ADVICE? Email asknick@theviallfiles.com and be a part of future Ask Nick episodes! Want ad free episodes and incredible bonus content? Start your 7 Day Free Trial of Viall Files + here: https://viallfiles.supportingcast.fm/ Subscribe to The ENVY Media Newsletter Today: https://www.viallfiles.com/newsletter To Order Nick's Book and/or learn more about the show, go to: https://viallfiles.com THANK YOU TO OUR SPONSORS: Tonies: Give the gift of the Toniebox 2 this holiday. Find it at https://tonies.com. Kalshi: Download Kalshi. Use code VIALL to get $25 when you trade $25. Hers: Start your free online visit today at https://forhers.com/viallfiles for personalized care—from hair to weight loss to menopause and more. FIGS: You can get 15% off your first order at https://wearfigs.com with the code FIGSRX. Oats Overnight: Head to https://oatsovernight.com and get 25% off your first box when you subscribe. BetterHelp: Sign up and get 20% off at https://betterhelp.com/viall EllaOla: EllaOla is offering our listeners 20% off your first purchase. Just go to https://ellaola.com/viall, discount applied automatically; No code needed. Quince: Download the Quince app for app-exclusive offers, or go to https://quince.com. Get free shipping on your order and 365-day returns. To advertise on this podcast please email: ad-sales@libsyn.com or go to: https://advertising.libsyn.com/theviallfiles Episode Socials: @viallfiles @nickviall @nataliejoy @bre_tiesi @susiecevans @jsotos1 @justinkaphillips @the_mare_bare @baybaeee DISCLAIMER: Please note, this is an independent podcast episode not affiliated with, endorsed by, or produced in conjunction with the host podcast feed or any of its media entities. The views and opinions expressed in this episode are solely those of the creators and guests. For any concerns, please reach out to team@podroll.fm.

    Introducing: The Viall Files

  3. 5d ago

    Beyond Vibe Coding: Why Developers Must Become AI Team Leaders

    Artificial intelligence is rapidly changing what it means to be a software developer. Writing code is becoming easier, AI agents can complete increasingly complex assignments, and developers can accomplish tasks that previously required entire teams. However, the ability to generate software does not automatically mean the software is useful, secure, scalable, or even correct. That raises an important question for developers at every stage of their careers: If AI can write much of the code, what skills will define the difference between junior, mid-level, and senior developers? In Season 29, Episode 14 of the Develpreneur Podcast, Rob Broadhead and Michael Meloche continue their conversation about building a career beyond the code. This time, they focus on the evolution of vibe coding, the increasing importance of engineering judgment, and why developers must learn to manage AI agents like members of a development team. Vibe Coding Is Evolving Into AI-Assisted Engineering Vibe coding began as a way to describe building applications through natural-language instructions rather than manually writing every line of code. A developer could explain what they wanted, allow an AI tool to generate the implementation, and make adjustments through conversation. That approach is evolving rapidly. Developers are beginning to use specialized agents for planning, implementation, testing, documentation, and deployment. Instead of a single assistant generating snippets of code, AI can participate in much larger portions of the development process. Rob observes that this changes how developers should think about their roles. When several AI agents can work on different assignments, the developer begins functioning more like a development team leader than an individual programmer. The value moves from typing instructions into a source file to coordinating work, reviewing decisions, and making sure the final solution meets the intended goals. What Separates a Junior Developer From a Senior Developer? Traditional career progression has often been tied to experience with programming languages, frameworks, architecture, and increasingly complex technical assignments. Junior developers learn how to implement features. Mid-level developers begin taking ownership of larger components. Senior developers understand broader systems, evaluate tradeoffs, mentor others, and take responsibility for technical outcomes. AI complicates that progression. If a junior developer and a senior developer both have access to tools capable of generating sophisticated code, the difference between them cannot be measured simply by how much code each person produces. The distinction increasingly becomes the ability to evaluate what AI creates. Can the developer identify missing requirements? Can they recognize security concerns? Do they understand why one architectural approach is better than another? Can they explain what happens when the application needs to scale? Do they know how to test and verify the result? These skills have always mattered, but AI makes them even more visible. Technical Knowledge Is Not the Same as Practical Experience Michael shares an example from his experience teaching Java boot camps. Some students could study a certification guide, memorize the material, and pass a technical examination without having much practical experience building software. Others had spent years developing applications and could solve complex problems but struggled with certification tests. The certification demonstrated knowledge of specific material. It did not necessarily demonstrate the ability to engineer a complete solution. AI introduces a similar challenge. Someone who knows how to use AI tools effectively may produce impressive applications without fully understanding the underlying systems. That does not mean their work has no value, but it does mean employers and customers need better ways to evaluate their capabilities. Developers must demonstrate that they understand what they are building, not simply that they can get AI to build it. The Software Development Lifecycle Is Becoming More Important One of the biggest changes AI introduces is a shift in how developers spend their time. Historically, software implementation consumed a substantial portion of a project's schedule. Developers spent hours writing classes, creating interfaces, connecting APIs, and implementing business logic. AI can reduce much of that implementation effort. However, the remaining stages of the software development lifecycle still matter. Requirements must be understood. Solutions must be designed. Systems must be tested. Applications must be deployed. Security must be evaluated. Users must confirm that the software actually meets their needs. Rob emphasizes that developers increasingly need to understand the complete lifecycle, from initial requirements through deployment and ongoing improvement. Michael points out that this transformation has been developing for years. Organizations have already been moving testing and DevOps responsibilities closer to development teams. AI accelerates the expectation that developers understand more than their individual specialty. A developer who can connect the entire process becomes increasingly valuable because they can recognize problems that arise between traditionally separate responsibilities. Quality Assurance Cannot Become an Afterthought One of the dangers of AI-assisted development is confusing the speed of implementation with the quality of the result. AI can generate large amounts of code, but that does not guarantee that the code is correct. It may misunderstand requirements, introduce security weaknesses, create unnecessary dependencies, or produce a solution that technically works but fails to solve the customer's actual problem. Michael emphasizes that quality assurance remains essential. As implementation becomes faster, developers should spend more time verifying what has been produced. Automated tests, integration testing, user acceptance testing, and critical reviews are necessary to ensure that faster development does not produce larger collections of defects. The opportunity AI creates is not simply to deliver more features. It is to spend more time making sure those features work correctly and provide value. Learn to Manage AI Like a Development Team Managing AI agents requires many of the same skills used to lead human developers. You need to communicate expectations, establish boundaries, review progress, provide feedback, and determine whether the work meets the requirements. Michael suggests viewing AI agents as team members that need direction and supervision. Even developers who have not held formal leadership positions can begin practicing these skills through AI-assisted development. For example, you might assign one AI agent to design a feature, another to implement it, and another to review the result. However, someone still needs to determine whether the implementation meets the business need. That responsibility belongs to the developer. This process also helps developers develop better technical communication skills. If an AI agent consistently misunderstands an assignment, it may indicate that the requirements were incomplete or unclear. Learning to provide better direction can improve your interactions with AI and your communication with human coworkers. Business Value Matters More Than Software Output Rob returns to a fundamental principle of successful software development: the value of software comes from the problem it solves. Building another dashboard is not necessarily valuable. Adding another feature does not automatically improve a product. Increasing the number of lines of code does not make an application more useful. The important question is what changes for the user. Does a new feature eliminate unnecessary work? Does an automation reduce mistakes? Does a dashboard help someone make a better decision? Does the application make a business process faster, more reliable, or easier to manage? These are the outcomes developers should focus on. Rob uses the simplicity of the original iPod as an example of how removing unnecessary complexity can create a better user experience. More features often introduce more testing requirements, maintenance concerns, and potential points of failure. The best solution is not always the one that does the most. Sometimes the most valuable engineering decision is knowing what not to build. AI Does Not Belong in Every Solution The increasing accessibility of AI has also created a temptation to use it everywhere. Michael cautions against online promises that suggest building a collection of AI agents will automatically create passive income or effortless business growth. Those systems require development effort, maintenance, subscriptions, processing costs, and ongoing supervision. Rob expands on the point by explaining the importance of deterministic software. Some tasks benefit from AI's ability to analyze information and handle ambiguity. Others need predictable behavior and repeatable results. For example, a system gathering specific information from a website may benefit from AI during exploration or when handling unexpected page changes. However, the actual collection of known data fields may be better handled through conventional programming. Developers should evaluate where AI creates meaningful value and where simpler technology produces a more reliable and economical result. AI should be a tool selected for a reason, not the automatic answer to every technical problem. Practice Building and Managing Your

  4. Oct 27

    How to Stand Out as a Developer in the Age of AI: Prove Your Value Beyond the Code

    Artificial intelligence has changed the way developers write code, search for jobs, and present their professional experience. Resumes can be generated in seconds, proposals can be polished instantly, and technical answers are available with a simple prompt. However, all of this convenience creates a new challenge: How do you distinguish yourself when everyone has access to the same tools? In Season 29, Episode 13 of the Develpreneur Podcast, co-hosts Rob Broadhead and Michael Meloche explore how AI is changing hiring, professional development, and the way developers communicate their value. As part of the season's theme, Build a Career Beyond the Code, they discuss why technical skills alone are becoming less effective at setting candidates apart and why critical thinking, authentic experience, and demonstrated problem-solving abilities matter more than ever. The Problem With AI-Generated Resumes and Proposals AI has made it easier than ever to produce professional-looking resumes and proposals. A candidate can enter a job description into an AI tool and receive a document that addresses nearly every requirement. Similarly, consultants can generate detailed proposals that appear to align perfectly with a customer's needs. The problem is that these documents are becoming increasingly similar. Rob compares this shift to the transition from standard-definition television to high-definition broadcasting. When everything became clearer, details that previously went unnoticed suddenly became obvious. AI is having a similar effect on professional communication. It has exposed how much of the traditional job application process relies on formulas and predictable language. Recruiters and hiring managers are becoming familiar with AI-generated responses. A polished resume may help someone enter the hiring process, but it does not necessarily demonstrate that the candidate has the experience or judgment required to perform the job. The real challenge is no longer simply getting someone to see your resume. It is convincing that person to have a meaningful conversation with you. Your Technology Stack Is Not Your Entire Professional Story Developers traditionally describe their experience through programming languages, frameworks, databases, and platforms. A resume might highlight Java, Spring Boot, AWS, React, Python, or dozens of other technologies. While these skills remain relevant, they do not tell the complete story. Knowing how to write code is only one part of being a successful developer. Employers and customers need people who can recognize problems, evaluate alternatives, make informed decisions, and deliver solutions that create measurable value. Rob suggests shifting attention away from simply listing technologies and toward explaining the problems you have solved. Consider the difference between stating that you developed a Java application and explaining that you redesigned a business process, eliminated repetitive manual work, addressed performance problems, or improved the reliability of a critical system. The technology describes how you accomplished the work. The problem and its outcome explain why the work mattered. Show Your Work: Critical Thinking Is the Differentiator Michael draws an interesting comparison between software development interviews and solving math problems. In school, students are often required to show their work rather than simply provide the final answer. The process demonstrates whether they understand the underlying concepts and can apply them to different situations. Software development interviews should follow a similar approach. AI can generate an answer to a coding problem almost instantly. However, that does not mean the person using AI understands the problem, recognizes potential limitations, or knows how to adapt when circumstances change. A stronger interview focuses on the candidate's reasoning. What problem are they trying to solve? What information do they need? How would they approach the design? What happens when their initial solution fails? How would they explain their recommendation to someone without a technical background? These questions reveal abilities that are difficult to evaluate through memorized answers or automated coding exercises. Michael also emphasizes the importance of communicating with nontechnical stakeholders. Developers frequently work with business owners, managers, and customers who care more about results than implementation details. Being able to explain complex technical decisions in understandable language is an increasingly valuable skill. AI Is Changing the Hiring Conversation Both Rob and Michael discuss how AI is introducing new complexity into the hiring process. Michael shares experiences with candidates relying heavily on AI during interviews. Instead of responding naturally, some applicants appear to be generating answers in real time. This makes it harder for interviewers to determine whether they are evaluating the candidate's knowledge or simply the output of an AI tool. At the same time, interviewers are becoming more sophisticated in how they evaluate applicants. Rather than relying exclusively on technical questions, employers can ask candidates to explain previous projects, describe unexpected problems, and discuss how they adjusted their approach. Those experiences provide a more authentic view of someone's capabilities. A developer who can explain a difficult production issue, describe why a design decision failed, and show how the team recovered demonstrates practical experience that goes beyond a collection of keywords. Do Not Confuse AI Assistance With AI Dependence AI can be an excellent tool for preparing resumes, practicing interviews, researching companies, and organizing professional experience. It can help developers identify gaps in their explanations and discover better ways to communicate their accomplishments. However, the value comes from using AI to strengthen your own knowledge rather than replace it. Rob points out that AI-generated suggestions must be evaluated carefully. A response may sound complete while including unsupported assumptions, unnecessary details, or information that does not accurately represent your experience. Michael adds that newer AI tools and models are not always automatically better for every workflow. Developers need to evaluate the reliability of the tools they use rather than constantly adopting the newest model simply because it is available. The key is maintaining control over the process. Use AI to prepare, refine, and challenge your thinking, but make sure the final result accurately represents what you know and what you have accomplished. Just Because AI Can Do Something Does Not Mean It Should Rob closes the discussion with an example from his own AI-assisted development work. While working on a project, an AI assistant began identifying additional improvements in related environments. Some recommendations involved security, infrastructure, and other technical areas. Although the suggestions might have been useful in another context, they were outside the project's intended scope. Rob had to explicitly instruct the AI not to make those changes. This illustrates one of the most important responsibilities developers have when working with AI: determining which recommendations actually matter. Without clear boundaries, AI can turn a relatively focused task into a much larger project. Developers must recognize when additional complexity creates value and when it creates unnecessary work or risk. The ability to make those decisions requires experience, judgment, and a clear understanding of the intended outcome. Building a Career Beyond the Code The software industry is entering a period where simply producing code is becoming less distinctive. AI can assist with implementation, generate documentation, and automate repetitive tasks, but it still requires thoughtful direction and meaningful evaluation. Developers who want to remain competitive need to strengthen the abilities that connect technical work to real-world results. That means learning how to communicate, solve problems, evaluate tradeoffs, demonstrate experience, and explain the value of the work they perform. The strongest professional story is not a list of technologies. It is a collection of problems solved, challenges overcome, lessons learned, and outcomes achieved. Key Takeaways Use AI to improve your resume and interview preparation without allowing it to misrepresent your experience. Focus on the problems you have solved rather than relying exclusively on technical keywords. Be prepared to explain your reasoning, decisions, challenges, and lessons learned. Practice communicating technical solutions to nontechnical audiences. Evaluate AI recommendations instead of accepting them automatically. Document your accomplishments so you can provide real evidence of your professional value. As AI continues to reshape software development, the ability to demonstrate genuine experience and sound judgment will help developers distinguish themselves from increasingly automated competition. Your value is not simply that you know how to write code. It is that you know how to use technology to solve meaningful problems. That is how you build a career beyond the code. Stay Connected: Join the Developreneur Community 👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episo

  5. Oct 22

    Use the Right AI for the Job: Kevin Riedl on Local Models, AI Routing, and Smarter Development

    AI is an essential tool for many developers, but using it well takes more than picking the most powerful model. As adoption grows, developers and businesses face new questions about cost, performance, infrastructure, and matching the right technology to each task. Knowing when and how to use each option is becoming a core engineering skill. In this episode of Develpreneur, Rob Broadhead and Michael Meloche continue their conversation with Kevin Riedl, founder of Wavect, an AI-native software agency in Austria. Building on our last discussion about AI-native development and software quality, we dig into the technical and business decisions behind efficient AI-powered systems. Kevin shares his views on local AI models, cloud services, model routing, orchestration, token optimization, and shifting expectations for software development. We also talk about how smaller companies can compete with larger ones by using AI strategically, without piling on cost or complexity. This episode fits Season 29, Build a Career Beyond the Code. Generating code is getting easier for everyone. Designing efficient systems, controlling costs, and delivering real business outcomes is where the value is. About Kevin Riedl Kevin Riedl is the founder of Wavect, an AI-native software agency based in Austria that works with clients internationally. His work covers software engineering, artificial intelligence, product development, and helping organizations build effective technology solutions. Through Wavect, Kevin helps businesses with AI-assisted development, software modernization, and improving their engineering processes. His approach emphasizes practical results, thoughtful technology decisions, and using AI to create measurable business value. He also shares insights on emerging AI tools, software engineering practices, and technology trends through his professional content and Wavect's online resources. Connect with Kevin Riedl: LinkedIn: https://www.linkedin.com/in/wsdt/ Wavect: https://wavect.io/ Local AI vs. Cloud AI: Which Makes More Sense? As developers get comfortable with AI, many start thinking about running models locally. Instead of relying on ChatGPT or Claude, they consider buying hardware and hosting their own AI infrastructure. Michael brought this up while talking about his experiments with Apple Silicon, including Mac minis and a Mac Studio. Powerful consumer hardware gives developers more room to experiment with local models and build private AI systems. Kevin says the decision often comes down to economics. Local AI keeps improving, but cloud-based frontier models are still cheap compared with buying and maintaining the hardware needed to approach their capabilities. For many individuals and small businesses, running advanced models locally would cost more than a commercial AI service. That doesn't mean local AI has no value. Running models locally gives you more control over your data, less dependence on outside providers, and a good fit for workloads that run continuously. It also lets developers see how models behave outside the interfaces the major AI companies provide. The real question is whether the investment supports an actual business or technical need. A company sending occasional requests will likely do better with a cloud subscription. An organization running AI workloads around the clock may find dedicated hardware increasingly attractive. Kevin recommends understanding both approaches instead of assuming one always wins. Why Developers Should Experiment With Local Models Even if cloud AI makes financial sense today, developers should know the alternatives. AI providers keep changing their models, capabilities, pricing, and services. A business that depends on one provider may eventually hit limits that affect its costs or operations. Understanding local AI gives you another option. Kevin suggests Hugging Face as a starting point for exploring available models. The platform hosts a large collection that developers can evaluate for different workloads. Tools like Ollama make it easier to run supported models on local hardware, compare capabilities, and learn the limits of smaller models. Experiments should have a purpose. A developer might test whether a local model can summarize documents, classify information, process internal records, or handle repetitive tasks without sending data to an outside service. Those jobs don't necessarily need the most advanced model available. Once you know which tasks a local model can handle, you can design systems that balance performance, cost, and privacy. AI Model Routing: Choosing the Right Model for Each Task One of the most interesting topics in our conversation was AI model routing. Many developers pick a single AI platform and use it for nearly everything. The same advanced model summarizes emails, writes documentation, analyzes complicated architecture, and produces production code. It's convenient, but it isn't always efficient. Kevin believes model routing will become an important part of AI development. Instead of sending every request to the same model, a routing system evaluates the task and picks a model that fits its requirements. It works like assigning work on a development team. You wouldn't put your most experienced architect on every small documentation update or routine data-processing job. You don't need the most powerful AI model for every request either. A model-routing strategy might look like this: Simple classification: Use a smaller model to categorize emails, documents, or support requests. Document summarization: Use a model that can handle the required context without extra reasoning overhead. Code generation: Pick a model suited to the complexity and requirements of the task. Architecture analysis: Use a more capable model when the work needs deeper reasoning and broader context. Sensitive internal processing: Consider local models when privacy requirements rule out external services. Complex problem-solving: Escalate difficult tasks to a more advanced model when simpler approaches fall short. The goal isn't to pick the cheapest model. It's to find the least expensive model that can reliably do the job. A model that's too limited can cost more in the end if it produces wrong results or needs repeated attempts. Kevin stresses that routing has to balance capability and efficiency, not just price. For developers building AI-powered applications, this is another place to apply traditional engineering principles. AI Orchestration: Letting Models Work Together Model routing gets even more powerful when combined with orchestration. Instead of relying on one model to complete an entire workflow, developers can build systems where a more capable model coordinates several specialized agents or smaller models. Kevin described an approach where a higher-level AI system decides what needs to be done and delegates tasks to other models. That mirrors how software teams divide work. A senior engineer defines the overall architecture while other team members build specific components. An AI orchestrator can take a larger objective, break it into manageable tasks, and assign each one based on the capabilities it needs. A development workflow might use separate agents to analyze requirements, generate code, review changes, run tests, and summarize results, with the orchestration layer coordinating the work and deciding when more is needed. This can reduce how much expensive model processing goes to routine tasks. Orchestration also adds complexity. Developers have to consider how agents communicate, how results get validated, and how the system handles failures. The goal isn't to build as many agents as possible. It's a workflow that produces reliable results with reasonable resource use. Kevin also noted that existing AI development tools already include some orchestration features. Try those before building a complicated custom solution. Sometimes the Best AI Solution Doesn't Use AI Rob raised another point: what happens when an AI workflow becomes predictable enough that it no longer needs AI? Software development has always meant finding repeatable processes and automating them. Once the rules are clear, developers can often build deterministic solutions that give consistent results. AI doesn't change that. Kevin says that if a task requires deterministic behavior, use deterministic technology instead of adding a language model. If a system needs to validate a known data format, calculate a specific value, or apply established business rules, conventional software is usually more consistent and cheaper. AI earns its place when a task requires interpretation, reasoning, or handling information that doesn't follow a predictable structure. Developers can get so focused on integrating AI that they overlook simpler options. A well-designed application may use AI for some tasks and conventional code for others. The best architecture isn't the one that uses the most AI. It's the one that solves the problem efficiently and reliably. AI Is Raising Customer Expectations for Software AI adoption is also changing what customers expect. Developers can build prototypes and minimum viable products faster than ever. Applications that once took weeks can become working demos in days. Kevin points out that customers know this. When software gets easier to produce, they expect more. That adds competition. A functional application no longer gives the advantage it once did. More businesses can build software, more entrepreneurs can test ideas, and more developers can bring products to market. As the number of solutions grows, standing out takes more than the ability to generate an app. Businesses need

  6. Oct 20

    AI-Native Development Without the Hype: Kevin Riedl on Building Better Software With AI

    Companies are racing to adopt AI. Developers are experimenting with increasingly powerful coding agents. Businesses want projects delivered faster. But faster development doesn't automatically produce better software. In this episode of Develpreneur, Rob Broadhead and Michael Meloche talk with Kevin Riedl, founder of Wavect, an AI-native software agency in Austria. Kevin shares what he has learned building software with AI, helping organizations adopt it, and balancing automation with proven engineering practices. We cover what AI-native means, why quality assurance still matters, and how developers can use AI without sacrificing security, reliability, or maintainability. This episode fits Season 29, Build a Career Beyond the Code. Writing code keeps getting easier. Knowing what to build, how to validate it, and whether it solves the right problem matters more every month. About Kevin Riedl Kevin Riedl is the founder of Wavect, an AI-native software agency based in Austria that works with clients internationally. His work covers software engineering, artificial intelligence, product development, and helping organizations build effective technology solutions. Through Wavect, Kevin helps businesses with AI-assisted development, software modernization, and improving their engineering processes. He emphasizes practical results, established engineering principles, and reliable software over adopting technology because it's popular. He also shares insights on AI development, emerging tools, software engineering, and technology trends through his professional content and Wavect's online resources. Connect with Kevin Riedl: LinkedIn: https://www.linkedin.com/in/wsdt/ Wavect: https://wavect.io/ What Does It Really Mean to Be AI-Native? Startups use the term "AI-native" to describe their business models. Established companies are restructuring around it. Software teams are working it into their development processes. Kevin thinks organizations should be careful about what the term means. Being AI-native doesn't mean putting AI in every application or discarding decades of engineering experience. It means knowing where AI adds value and pairing it with proven development practices. Kevin calls AI one of the most significant tools developers have received in decades, but it's still a tool. Developers still need to understand architecture, security, testing, requirements, and the business problem in front of them. Some organizations treat AI as a replacement for their existing processes. They assume that because AI can generate code, it can also make every architectural decision, understand every requirement, and catch every problem. It can't. Good AI-native development combines what AI does well with human engineering expertise. The goal isn't to use AI everywhere. The goal is better software, with AI used where it makes sense. Just Because AI Can Do Something Doesn't Mean It Should AI capabilities have expanded quickly. Modern tools can write applications, deploy infrastructure, drive browsers, analyze repositories, run tests, and help gather customer requirements. Work that once took specialized knowledge or significant time can now be partly automated. That doesn't make automation the right call every time. Experienced developers know the pattern. AI produces an answer that looks reasonable but misses key context. The code works, but it ignores performance concerns, security requirements, or existing architectural decisions. The model repeats approaches that already failed, or its changes break something elsewhere in the application. The risk grows with the responsibility. Asking a model to explain a function carries little risk. Letting it modify an entire application or deploy infrastructure carries a lot more. Kevin keeps human expertise and quality assurance at the center. Developers need to understand what the AI is doing, spot its mistakes, and set up controls that keep those mistakes out of production. AI speeds up development. Developers still own the results. AI Adoption Is Not the Biggest Challenge. Governance Is. Michael asked about companies that introduce AI without preparing their people. Some buy tools, hand out licenses, and expect productivity to jump. Others build controlled environments, train employees, and phase AI into existing workflows. Kevin says that at many companies, getting employees to use AI is no longer the hard part. They're already experimenting, sometimes without their employer knowing. The hard part is setting rules and making sure people understand them. Kevin described a large enterprise that had invested in its IT department, written internal policies, and provided approved AI tools. Employees still had personal smartphones. If they wanted help with a problem, they could photograph their screen and send it to a public AI service. Locking down company computers doesn't fix that. Organizations need to teach employees what information they can share, which tools are approved, and how to use AI responsibly. Kevin compares it to cybersecurity awareness training. Employees learned to spot phishing and suspicious emails. Now they need to learn the risks of sharing information through AI platforms. AI governance has to be part of daily operations, not a policy people read once and forget. Start With Simple AI Guardrails When Michael asked what organizations should do to protect themselves, Kevin said it depends on the business. A small software agency has different needs than a large financial institution or a multinational enterprise. His advice is to start with the basics instead of trying to solve every problem at once. Simple policies and employee education cover many of the most common risks. Start with the mistakes most likely to happen in normal AI use. Practical guardrails to start with: Protect confidential information. Never put sensitive business data, customer records, credentials, or proprietary information into unapproved AI services. Establish approved tools. Tell employees which AI platforms they can use and what restrictions apply. Provide ongoing training. Cover AI policies in onboarding and reinforce them as tools change. Maintain human oversight. Require review before AI-generated changes reach production systems or customers. Define quality standards. Set testing requirements, acceptance criteria, and review processes for AI-assisted work. Adapt policies to the business. Account for industry regulations, company size, customer expectations, and how sensitive the information is. None of this is new. These practices build on existing software development and security principles. Organizations don't need a new management philosophy to introduce AI responsibly. They need to apply established principles to a new technology. Kevin's advice: get the fundamentals right first. Organizations that follow simple policies consistently prevent many avoidable problems and can build more sophisticated governance over time. Managing Context in AI-Assisted Development Rob raised another challenge: managing context. AI tools can now work across entire repositories instead of single functions. Larger tasks need more information about the application's architecture, dependencies, requirements, and existing implementation. The question is how much the model actually needs. Too little context leads to wrong assumptions. Too much drives up token usage, slows responses, and buries what matters. Kevin says developers are experimenting with tools that trim unnecessary context and make information easier for AI to process. Some compress command-line output. Others organize repository information into structured knowledge representations. These techniques can cut token consumption and help AI focus on the relevant parts of a project. Kevin mentioned significant savings reported with combinations of context-management tools, though results depend on the tools and workloads. The takeaway isn't that every developer should adopt a particular optimization framework. The technology changes too fast for any single tool to stay the obvious answer. Developers should understand how context affects AI performance and try approaches that improve efficiency. Good Documentation Makes AI More Effective Rob tied context management to something developers have talked about for decades: documentation. Well-run software projects already have clear requirements, API definitions, architectural diagrams, and technical documentation. These let developers understand a system without reading every line of code. The same applies to AI. When developers give AI accurate, structured information about an application, the AI spends less time figuring out how the system works and more time on the actual problem. Clear documentation cuts unnecessary context and makes responses more relevant. Take an AI agent working on an API integration. It works better when the endpoints, request formats, authentication requirements, and error-handling rules sit in one place. Without that, it has to search multiple files or guess at the implementation. That changes what documentation is for. It no longer serves only to onboard developers or maintain legacy systems. It's now part of the information infrastructure that supports AI-assisted development. It still has to be accurate. Outdated or conflicting documentation misleads AI as easily as it confuses a human developer. The better the information we provide, the better the results. Vibe Coding vs. AI-Assisted Software Engineering One of the best parts of the interview covers the difference between vibe coding and professional AI-assisted development. As AI coding tools improve, developers write less code by

  7. Oct 15

    Build Less, Validate More: Alane Boyd on Smarter AI Development

    Smarter AI development is not about using the most advanced tool or building the most complicated solution. As AI makes software development faster and more accessible, developers and business owners face a different challenge: deciding what is actually worth building. The ability to create something quickly does not eliminate the need to validate the idea, maintain the finished product, or determine where human judgment still matters. In part two of our conversation with Alane Boyd, Co-CEO of Biggest Goal, Develpreneur: Building Better Developers explores what happens after a company moves beyond basic AI experimentation. Rob Broadhead and Michael Meloche talk with Alane about AI agents, vibe coding, custom software, minimum viable products, human oversight, and the growing temptation to build simply because AI makes building easier. For developers building a career beyond the code, the lesson is especially relevant. AI has dramatically reduced the effort required to turn an idea into working software, but the business questions surrounding that software have not disappeared. In many cases, those questions have become even more important. About Alane Boyd Alane Boyd is Co-CEO of Biggest Goal and a serial entrepreneur whose background spans technology, operations, sales, marketing, automation, and business growth. She has built SaaS businesses and describes herself as a two-time SaaS founder with successful exits. Biggest Goal's current work focuses on hands-on AI training and implementation for leaders and teams, including helping organizations move from basic AI experimentation toward repeatable systems and practical business workflows. Alane is also a published author, mentor, speaker, and podcast host. Her work increasingly focuses on practical AI fluency: helping businesses understand where AI belongs in their operations, how teams can use it consistently, and where human expertise should remain part of the process. Learn more about Alane and her work through Biggest Goal and connect with her on LinkedIn. Smarter AI Development Goes Beyond Chat Many people still use AI primarily through a chat interface. They enter a prompt, receive an answer, copy the output somewhere else, and repeat the process. Alane sees organizations beginning to move beyond that model toward AI workflows, connectors, scheduled tasks, skills, and agents. That progression can eliminate much of the manual movement between systems. Instead of repeatedly copying prompts and outputs, businesses can build processes that automatically trigger AI and move information where it needs to go. At the same time, tools such as Claude Code have made development accessible to people who would not traditionally consider themselves developers. Alane sees value in that shift because people outside software development can finally see some of what happens behind the scenes when software is created. However, creating an application is only one part of software development. The excitement of building something can hide the long-term responsibility that follows. Someone still has to maintain the application, troubleshoot failures, adapt to changing requirements, manage dependencies, and determine what happens when the original creator no longer wants to spend time supporting it. Michael sees the same issue when talking with people who have created applications using AI. They are excited because the application works, but they have not necessarily considered what comes next. AI Makes Building Easier, Not Maintenance Optional This creates one of the biggest challenges surrounding AI-assisted development. A founder can now describe an idea and begin creating software without fully understanding the lifecycle of the product being built. The first version can feel almost magical. AI writes code, creates interfaces, troubleshoots problems, and helps someone move from an idea to something tangible much faster than traditional development allowed. That speed can create the impression that software has become a one-time project. It has not. Alane points out that maintaining what you build can become a significant burden. A founder who creates a custom platform may eventually discover that the software requires constant attention. What started as an exciting experiment can become another system the founder is personally responsible for keeping alive. This is where smarter AI development requires thinking beyond the initial build. The important question is not simply, "Can AI build this?" It is also, "Do we want to own and maintain this after AI builds it?" Build Versus Buy Still Matters AI has made the traditional build-versus-buy decision even more interesting. If AI can create custom software faster and cheaper than before, building something yourself can look like the obvious choice. Alane's experience suggests otherwise. Before selling one of her previous companies, she merged with a competitor. Both companies had developed software, but one had built a SaaS product that customers could purchase while the other had proprietary software designed primarily for internal use. When the combined company eventually evaluated those systems, the proprietary software was the first to be shut down. The reason was business value. Internal proprietary software had limited marketability compared with a product customers were willing to purchase. The company had invested resources into creating and maintaining something that ultimately had little independent value during the sale process. That experience shaped an important principle in Alane's approach: build what people want to buy. For developers, that does not mean custom software is always the wrong decision. It means the ability to build something is not enough justification for building it. An existing platform may already solve the problem while another company absorbs the cost of maintenance, upgrades, security, and continued development. AI changes the economics of building software, but it does not eliminate those considerations. Validate the MVP Before You Keep Building Developers like to build. That is one of the reasons many people become developers in the first place. Give a developer an interesting problem and enough time, and there will always be another feature worth adding. AI can amplify that instinct. Alane recommends returning to a familiar startup principle: the minimum viable product. Before investing heavily in development, determine the smallest version of the idea that can test whether somebody actually wants it. That means asking practical questions early: Will someone pay for this? What is the smallest version we can put in front of a customer? Which features are actually required? Can we validate demand before investing more development time? Are we solving a real customer problem or simply enjoying the build? Alane acknowledges that she has fallen into the same trap herself. It is easy to believe a product needs every feature before customers can see it. The problem is that developers can spend months or even years creating something without discovering whether there is a viable market. Time is a resource. A product that consumes a year of development without generating revenue may eventually become difficult to justify, regardless of how technically impressive it is. Talk to Customers Before Building in a Vacuum Alane references the early Dropbox story as an example of validating demand before completing a product. Rather than waiting until every part of the platform existed, Dropbox demonstrated the concept and gathered interest from people who wanted the solution. The broader lesson is more important than the specific example. Developers and founders need feedback from potential customers before disappearing into build mode. Friends and family are not necessarily enough. The useful feedback comes from people who might realistically use or purchase the product. Those conversations can expose incorrect assumptions long before they become expensive features. AI makes this even more important because the barrier to building has dropped dramatically. When a new feature can be generated quickly, it becomes easy to keep adding functionality without stopping to ask whether anyone needs it. Smarter AI development requires resisting that momentum. Build enough to learn, talk to users, and then decide what deserves to be built next. Do Not Make the Solution Harder Than the Problem Developers and founders also have a tendency to make solutions more complicated than necessary. AI gives us even more ways to do that. A developer can open an AI coding tool, create custom logic, and continue refining an increasingly sophisticated solution. However, the same problem might be solved with a scheduled task, an AI skill, a simple automation, or an existing product. Alane encourages teams to focus on the job that needs to get done rather than the technology they want to use. The question becomes: What is the quickest, cheapest, and easiest reliable way to accomplish the goal? Sometimes that answer will be custom software. Sometimes it will be an AI agent. Other times, the answer may be a much simpler workflow. That distinction matters because every custom solution creates ownership. Someone has to maintain it. Someone has to understand it when it breaks. Someone has to modify it when the business changes. Smarter AI development includes knowing when not to develop something. Keep Human Judgment in the Loop One of the clearest guardrails Alane provides is that judgment should not be handed entirely to AI. AI can process information, identify patterns, generate options, and perform repetitive tasks, but Alane draws a distinction between havin

  8. Oct 13

    AI Is Not the Problem: Alane Boyd on Building an AI-Ready Organization

    Building an AI-ready organization requires more than giving employees access to ChatGPT, Claude, Copilot, or another AI platform. Without training, standards, and a clear understanding of where AI belongs, organizations can create more confusion and expense instead of improving productivity. The technology may be new, but many of the problems it exposes have been hiding inside businesses for years. In this episode of Develpreneur: Building Better Developers, Rob Broadhead and Michael Meloche talk with Alane Boyd, Co-CEO of Biggest Goal, about what businesses are learning as they move beyond experimenting with AI. Alane has built and scaled technology companies, including two SaaS platforms and a social media agency. Today, much of her work focuses on helping organizations implement AI, train their teams, and create scalable systems that make work easier rather than simply adding another technology to the stack. The conversation quickly reveals an important distinction. AI adoption is not really about purchasing AI tools. Building an AI-ready organization starts with understanding how work gets done and deciding where AI can genuinely improve that work. About Alane Boyd Alane Boyd is Co-CEO of Biggest Goal and a serial entrepreneur whose background spans technology, operations, sales, marketing, automation, and business growth. She has built SaaS businesses and describes herself as a two-time SaaS founder with successful exits. Biggest Goal's current work focuses on hands-on AI training and implementation for leaders and teams, including helping organizations move from basic AI experimentation toward repeatable systems and practical business workflows. Alane is also a published author, mentor, speaker, and podcast host. Her work increasingly focuses on practical AI fluency: helping businesses understand where AI belongs in their operations, how teams can use it consistently, and where human expertise should remain part of the process. Learn more about Alane and her work through Biggest Goal and connect with her on LinkedIn. Moving Beyond AI Experimentation For many people, the first experience with generative AI was simple experimentation. They opened a chat window, entered prompts, generated content, summarized documents, or tried whatever interesting use case appeared online. That experimentation helped people become comfortable with the technology, but organizations are now asking a more difficult question: How do we turn that experimentation into a repeatable business process? Alane sees this transition with both individuals and organizations. Solopreneurs may want to move beyond basic prompting and learn how tools such as Claude Cowork can become part of their workflow. Larger organizations face another challenge because they need multiple employees to use AI consistently. They also need policies that prevent AI adoption from becoming what Alane describes as the "wild, wild west." Many organizations already have employees using several different AI platforms. Those employees may be paying for separate subscriptions, developing their own prompting techniques, and solving similar problems without sharing what they have learned. Management eventually sees those subscriptions and begins asking what everyone is actually doing with them. That is when AI stops being an individual productivity experiment and becomes an organizational challenge. It is also where the work of becoming an AI-ready organization really begins. An AI-Ready Organization Needs Training Michael shared an example that will probably sound familiar to many developers. One organization provided employees with AI development tools and effectively told them to use the technology to meet an aggressive deadline. There was no meaningful training, shared handbook, or established standard for how the tools should be used. The AI helped the development team meet a deadline that likely would have been impossible otherwise. However, the lack of planning created another problem: consumption. Developers burned through available AI tokens quickly, forcing the organization to rethink how accounts and shared resources were allocated. Alane has seen similar situations. One organization decided to skip training and purchase Claude subscriptions for its employees instead. Within days, employees exhausted their available usage and the company had to address the consequences. The problem was not that the employees had access to AI. The problem was that nobody had taught them how to use it effectively. Alane compares the situation to introducing any other workplace technology without establishing expectations. Give an organization Slack or Microsoft Teams without communication standards, and employees can quickly become overwhelmed with messages. AI follows the same pattern because employees naturally develop their own habits unless the organization provides guidance. When Alane asks employees to share their screens and demonstrate how they use AI, she often discovers inefficient workflows that generate poor results while consuming unnecessary resources. Better tools do not automatically create better processes. Teams still need training. Start with AI Champions Organizations do not necessarily have to create an AI strategy from scratch. Alane recommends looking first at the people who are already experimenting with the technology. Those employees can become AI champions. Instead of limiting AI adoption to the technology department, companies can create a cross-functional group of employees who already understand some of the possibilities. Representatives from sales, marketing, operations, development, and other departments can identify useful applications within their own areas while sharing what works across the organization. This approach also helps companies address different comfort levels. Some employees naturally experiment with new technology and learn by trial and error. Others prefer structured instruction before changing how they work. Neither group represents an entire organization, so an implementation strategy needs to account for both. Cross-department AI champions can help bridge that gap. They can demonstrate practical use cases, answer questions, and provide examples that are relevant to the work people actually perform. More importantly, they help an AI-ready organization develop shared practices instead of allowing every department to create its own isolated approach. Not Every Task Needs AI Another mistake is assuming that once an organization has AI, every task should involve it. Alane encourages companies to think carefully about where AI creates meaningful value. Different teams also have different needs. Software developers may legitimately consume far more AI resources than employees performing other tasks because AI can dramatically accelerate certain areas of development. A content team, however, may not need the same allocation. In some cases, writing an email manually may actually be faster than prompting AI, reviewing its response, correcting it, and rewriting the result. The goal is not maximum AI usage. The goal is better work. Organizations should ask where AI can handle repetitive processing, organize information, analyze transcripts, move data, or perform other behind-the-scenes tasks. They should also identify where human expertise still produces a better result. That distinction matters because poorly implemented AI can actually increase costs. A company may purchase more subscriptions and consume more computing resources without gaining meaningful productivity. Training helps employees recognize which jobs are appropriate for AI and which jobs are better handled directly by a person. AI Often Exposes Problems That Already Existed One of Alane's most important observations during the conversation is that many problems blamed on AI existed long before AI arrived. Companies frequently struggle with inconsistent processes, missing documentation, scattered files, and employees completing the same task in completely different ways. AI does not create those problems. It makes them much harder to ignore. Alane sometimes begins working with an organization only to discover that the company does not even have a consistent shared folder structure. Before building sophisticated AI agents or automations, the organization may need to solve much more basic operational problems. This creates an important lesson for developers and business leaders. Automation works best when the underlying process is understood. If nobody can explain how a process is supposed to work, automating it may simply make the confusion happen faster. In that sense, AI can act as a diagnostic tool. Attempting to implement it forces organizations to ask questions they should have asked already. Where does information live? Who owns a process? What is the standard procedure? Which version of a document is authoritative? Where should a human review occur? Those are not AI questions. They are business process questions, and answering them is an important part of creating an AI-ready organization. AI-Ready Organization Foundations Come First Some organizations simply are not ready for advanced AI implementation. Alane has encountered companies whose technology and processes were so outdated that Biggest Goal ultimately determined it was not the right partner for them yet. That does not mean those companies can never use AI. It means they have foundational work to complete first. An organization may need to establish shared storage, document its standard operating procedures, modernize parts of its technology stack, or determine how information moves between departments. Once those foundations exist, AI becomes much easier to introduce because there is a stable process to imp

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This podcast is for aspiring entrepreneurs and technologists as well as those that want to become a designer and implementors of great software solutions. That includes solving problems through technology. We look at the whole skill set that makes a great developer. This includes tech skills, business and entrepreneurial skills, and life-hacking, so you have the time to get the job done while still enjoying life.

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