Modern software development went through several cycles of escaping complexity only to recreate it in a different form. In the early days, low level languages like C gave developers full control over memory, layout, and performance, but this freedom came with high risk. Memory corruption, data races, buffer overflows, and undefined behavior were common. Later, languages like Java and C# pushed developers toward safer abstractions. Then Python and JavaScript made the runtime even more forgiving. Today, Rust and Go promise a return to predictability with strict memory models, type safety, and clear concurrency rules. Yet something interesting is happening beneath the surface. Even in languages designed for safety and simplicity, new forms of unpredictability, performance traps, and abstraction-driven risks have emerged. This does not mean these languages are unsafe in the traditional sense, but the hidden costs of modern abstractions create behaviors that developers cannot always reason about.
I think it is a matter of perspective. First, many of us wants something (or someone) that would do our work for us. It started with physical work, now it getting to knowledge work. Let's not discuss where that direction leaves us (well... in oblivion obviously), but I have bad news for you in any case. There will be no free lunch. When the bubble bursts we will find out how expensive and vulnerable AI is. Making predictions is not my thing, but most likely we will end up with some Higher Level Programming language. That will address unpredictable AI output, injections, etc. And once again,
non-technical users will have hard time using it. A historical example - SQL, it was originally envisioned as a tool for "non-technical" users. It's already happening. Here is just one example of today: https://kiro.dev/blog/introducing-kiro/.
And the concept of a "spec" is just one step forward to how gen AI will look like tomorrow. All I can say, enjoy the trial period and try to make the most of it. It will not last long.
Prompt engineering itself doesn’t evolve rapidly - but the models it depends on change all the time. Every new generation of large language models comes with new reasoning layers, expanded context windows, modified decoding defaults, and new forms of safety alignment. These shifts alter how models interpret, prioritize, and execute instructions. A prompt that produces clean, reliable output one week might start producing verbose or inconsistent results after a silent update. The role of a prompt engineer is therefore not just to write good prompts, but to detect, analyze, and adapt to these changes. Successful practitioners treat prompt design as an ongoing process of experimentation rather than a fixed discipline.
If you want a vanilla-leaning Neovim that feels stock but has full language features, debugging, tests, Git, fuzzy search, completions, keybinding help, AI, statusline, spell-check, and developer fonts—this is it. We’ll use native LSP, Treesitter, lazy.nvim for plugin management, and a short, readable config. Rust is first-class; Python, TypeScript/CDK, and Bash get the same baseline.