Future-proof Your Tech Skills

By Mike Levin

The LPvg Stack: A Foundation for Future-Proof Skills

I believe in mastering a set of fundamental, versatile tools that I call the “LPvg stack”. These form the core of what I consider a future-proof skill set. I advocate for avoiding the “hamster wheel” of constantly chasing the latest tech trends and instead focusing on timeless fundamentals that remain relevant across different eras of technology.

Balancing Technical Skills with Domain Expertise

You don’t have to be a dedicated developer to benefit from these technical skills. I advocate for a different approach that puts service and client needs first:

1. Become an expert in your chosen field or domain - whatever truly excites you and serves your clients’ needs.
2. Develop tech skills as a complementary toolset to enhance your primary expertise and deliver better results.
3. Remember that technical skills should support, not overshadow, your core mission of serving clients or pursuing your passion.

This combination allows you to:

• Maintain independence while staying focused on client outcomes
• Automate tasks to spend more time on high-value client work
• Bridge the gap between your field and technology to better serve clients
• Adapt quickly to technological changes that affect your clients
• Prototype and implement solutions that directly address client needs

Building Independence Through Open-Source Solutions

The goal is to empower professionals to leverage technology effectively while keeping client service at the forefront. This balance creates unique opportunities at the intersection of domain expertise, tech skills, and client success.

This toolkit offers a pragmatic approach to software development, emphasizing independence, adaptability, and sustainability. By leveraging open-source and AI-enhanced components, it helps developers create portable applications that avoid vendor lock-in and complex frameworks. The result is a flexible solution that promotes consistent workflows, builds valuable muscle memory, and ensures applications remain relevant as technology evolves. Focusing on open standards and portability serves as a form of future-proofing, allowing for scalability without sacrificing autonomy.

Creating a Cohesive Development Workflow

This approach showcases the connection of fragmented tools and concepts into a cohesive development workflow. By mastering Linux, Python, Vim, and Git, and leveraging technologies like NixOS, Nix Flakes, JupyterLab, CUDA, FastHTML, and local LLMs, developers can create a portable, reproducible environment ready to spin up at any moment, anywhere. This foundation prioritizes agency in development, offering a consistent starting point for any project while avoiding over-engineering. The result is a streamlined approach that balances practicality and reusability, making it easier to focus on solving problems and creating new solutions without getting lost in architectural abstraction.

Adapting to the AI Revolution

While AI may seem to herald unprecedented change, it’s important to remember that humanity has successfully navigated through the industrial, atomic, and information ages. We’ve always found ways to adapt. This current shift is yet another opportunity to evolve our way of living and working. As you embark on this journey of adaptation, consider following the path I’ve taken as a 50-something professional: cultivating neuroplasticity, sidestepping the relentless “hamster wheel” of tech trends, and instead mastering a carefully curated set of versatile, timeless tools. Throughout this site, I’ll guide you through my focus areas:

• Linux, Python, vim & git: Mastering the future-proof LPvg minimum subplatform
• Re-acquiring Math Skills: Picking up where my 1970s K-12 education let me down
• Staying at the Top of My Field: Protecting my earning capacity as things change
• The minimal tool-chain for AI: Expanding LPvg to include vector databases, etc.

The Enduring Nature of Text Processing

It’s all about text files and pushing bits around. Even after the rise of AI, this fundamental concept will persist. User interfaces may change, and barriers for entry will lower for non-programmers, but interacting with AIs won’t absolve you from learning to code. It just makes initial sketching easier. However, complex problems remain complex.

The Python Paradox in AI Development

While Jensen Huang says you won’t have to learn to code and calls Python weird, he admits that the first time you talk to AI will be in English, but the second time it can give you the Python code. See the contradiction? You won’t need to learn coding unless you want to do second-pass refinements. English isn’t reliable enough to build automated systems around, and Python has become the lingua franca of tech.

Mastering Human-AI Communication

The LPvg stack becomes even more powerful when we consider how to effectively communicate with AI systems. The key to future-proofing your skills isn’t just about using AI tools - it’s about establishing reliable communication protocols between humans and machines.

Structured Communication Protocols

While LLMs excel at natural language, automation requires more structured communication. The future-proof approach is to:

1. Use simple, well-defined JSON structures as an intermediary language
2. Implement validation functions that both humans and AIs can understand
3. Create clear input/output contracts that survive API changes

This approach allows domain experts to effectively “download” their expertise into AI systems without expensive training or fine-tuning - similar to Neo learning Kung Fu in The Matrix, but real and practical.

Leveraging LPvg for Local AI Development

The LPvg stack naturally supports running small, efficient AI models locally:

• Linux provides the operating system foundation
• Python offers excellent AI/ML libraries
• vim enables quick editing of prompt templates
• git tracks changes in AI behavior

This combination lets you build reliable AI-integrated systems that run on basic hardware, avoiding vendor lock-in while maintaining control over your tools and data.

Building Sustainable AI Systems

When building AI-integrated systems, focus on:

1. Reducing code surface area for easier AI comprehension
2. Using asynchronous communication channels (like email queues)
3. Building self-documenting systems that both humans and AIs can learn from
4. Maintaining simple, consistent interfaces for long-term stability

The goal isn’t to chase the latest AI trends, but to build sustainable systems that gracefully incorporate AI capabilities while remaining maintainable and understandable.

Escaping the Framework Hamster Wheel

Tech fads reset every 5 to 10 years. If you’re on that hamster wheel, such as with modern JavaScript frameworks, you go from Angular to React to Vue to Svelte, constantly relearning, retraining, and either migrating or abandoning old code bases. Generally, the concept of a “framework” is to blame. But Python hit a fundamental level and IS the framework. Even when you use a framework on top of Python, such as Flask, FastAPI, or FastHTML, switching is much easier. Python interfaces remain remarkably stable over the years, allowing your intuition and muscle memory to improve.

The Critical Role of Text Editors in Developer Productivity

The new breed of AI text-editors emphasizes this point. Your text editor is where you spend a significant amount of time. It’s even more essential than your bed or chair because it becomes ingrained in your fingers through muscle memory and shortcuts. Switching text editors can be more disruptive than switching programming languages. Think VSCode is here forever? It’s already under attack from Cursor. As with PFE, Notepad++, TextMate, Sublime Text, Atom, and Brackets, it will come and go. Vim (vi, nvim, etc.), the one all the others try to emulate, will be the only safe harbor for your fingers.

The Value of Timeless Tech Skills

If you develop muscle memory from years of practice in this slightly less-than-sexiest of tech (at least as a secondary tech-stack you know), then you won’t become obsolete. LPvg skills may not be as trendy or seemingly valuable as the current fad, but they will keep your skills relevant and provide an alternative to popular approaches, offering some freedom from vendor lock-in.

The Ubiquity of Core Tools

No matter how jazzed up or convenient power-tools become, you’ll notice an underlying system of Linux, Python, vim, and git that even they rely on. Windows and Mac strive to be Linux for the developer. All text editors offer a vim emulation mode. When’s the last time Python hasn’t been the default answer AIs want to give you (except for Claude artifacts). And git is… well, git. I’m sort of advocating the pee in the pool of tech. These are the 80/20-rule “good enough” tools that have become free, time-tested and are just accessible enough for the average person.

Preparing for Technological Change

It’s not always smooth sailing. The carpet can be pulled out from under you, no matter how well you prepare. The idea is to make smart choices, so a good deal of what you know and your hard-won muscle memory and habits remain applicable.

Learning from Past Technology Attachments

I fell in love with the Amiga Computer some 30 years ago and have been trying to recapture that magic ever since. I’ve concluded that you shouldn’t fall in love with your hardware or the particulars of your system. Instead, if you can love some of the higher-level abstractions that cut across time, space, and problem domains, you’ll be okay.

Gemini 2.5 Experimental’s Take

Since Gemini got so good at this “Author’s Own Voice” and “AI Opinion” thing, I started going back and adding it to previous articles. It will help me flesh out the ideas and distil it down to the book.

Okay, here are the requested items:

Summary (Author’s 1st Person Perspective):

I advocate for mastering the fundamental LPvg stack—Linux, Python, Vim, and Git—as a timeless foundation to future-proof your skills and escape the relentless ‘tech hamster wheel.’ My approach emphasizes combining this versatile toolkit with deep domain expertise to better serve clients, maintain independence, automate effectively, and integrate AI thoughtfully using local models and structured communication, ensuring lasting relevance without succumbing to vendor lock-in or chasing fleeting tech trends.

Title and Headline Ideas:

• Core Concept:
  • The LPvg Stack: Your Future-Proof Foundation
  • Mastering the Fundamentals: Linux, Python, Vim & Git
  • Timeless Tech Skills for Long-Term Success
• Benefit-Driven:
  • Escape the Tech Hamster Wheel with the LPvg Stack
  • Build a Sustainable, Independent Tech Skillset
  • Future-Proof Your Career Beyond Fleeting Trends
  • Combine Domain Expertise and Tech for Lasting Value
• Provocative/Intrigue:
  • Why I Ignore Tech Trends (And You Should Too)
  • The Only Tech Stack You’ll Ever Need?
  • Beyond Frameworks: The Power of LPvg
  • Resist Obsolescence: Invest in Timeless Tools
• AI Integration Focus:
  • LPvg + Local AI: Future-Proofing in the Age of LLMs
  • Human-AI Communication with Fundamental Tools

My Opinion (as Gemini):

[Moved to top]

Strengths:

• Emphasis on Fundamentals: The core idea of mastering versatile, long-lasting tools is sound advice. These specific tools (LPvg) are indeed foundational in many areas of software development and IT.
• Domain Expertise Focus: Highlighting the importance of combining technical skills with domain expertise to serve clients better is a valuable perspective, moving beyond tech for tech’s sake.
• Avoiding Lock-in: The focus on open-source tools and avoiding vendor lock-in promotes independence and adaptability.
• Pragmatic AI Integration: The approach to AI, emphasizing local models, structured communication (like JSON), and integrating AI into existing workflows rather than solely relying on large, external services, is practical and aligns with the theme of control and sustainability.

Potential Considerations:

• Vim’s Learning Curve: While powerful, Vim has a notoriously steep learning curve compared to modern IDEs/editors like VS Code. Advocating it as the only safe harbor might alienate some readers, even though its longevity and efficiency are undeniable for proficient users.
• Framework Reality: While the author criticizes the framework churn, many development jobs require specific frameworks (React, Angular, etc.). A pure LPvg approach might be more challenging for those seeking traditional employment in certain web development sectors, though highly valuable for backend, sysadmin, data science, and independent work.
• “Minimalist” Scope: The LPvg stack is powerful but might be too minimalist for complex, large-scale application development without incorporating some higher-level libraries or frameworks built upon Python (like Django, FastAPI, etc., which the author acknowledges briefly).

Overall, the article offers valuable wisdom, particularly for professionals seeking long-term relevance, independence, and a way to integrate technology thoughtfully into their domain expertise. It champions a philosophy of deliberate practice, sustainability, and control over one’s tools, which resonates strongly against the backdrop of rapid technological change and vendor-driven ecosystems. It’s a refreshing perspective that encourages investing in skills that endure.