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Plain text persists as one of computing's most durable technologies because its simplicity—no proprietary formats, no required applications, no dependencies—makes it readable across decades and systems. The article argues that while richer tools have their place, plain text's boring reliability and portability solve real problems that fancier solutions often create.
- Plain text works everywhere without dependencies: a text file created on Linux opens on Windows, runs in terminals, works with command-line tools, and doesn't require the original application to remain in business or supported.
- Text-based formats (source code, config files, HTML, JSON, Markdown) dominate computing infrastructure because their contents stay inspectable and debuggable without proprietary software, making troubleshooting and version control straightforward.
- Plain text resists obsolescence better than proprietary formats—old text files remain readable even if encoding needs fixes, whereas recovering data from abandoned file formats often requires emulators, conversion chains, or lost software.
- Ownership and control stay with the user: text files can live in any directory, sync however you want, go under version control, or move to different machines without vendor lock-in or subscription dependencies.
Hand-tuned natural language prompts make AI prototypes easy but lead to “prompt debt”—bloated, brittle instructions that slow development, confuse teams, and lock you into a single model. The author argues for replacing manual prompt fixes with measurable specs, automated prompt search, and testing to keep systems reliable and portable.
- Hand-tuned prompts accumulate into brittle "prompt debt" as fixes pile up for every new edge case, and that fragility locks teams into whichever model the prompt was tuned for (GPT-4o1 still dominated traffic this spring despite newer releases).
- Small, irrelevant framing changes wildly swing model behavior: patient vs. physician phrasing flipped refusal rates from zero to ten in a clinical study, and just mentioning a favorite NFL team changed how often models dodged sensitive topics in Harvard research.
- Prompt authors compensate for this unpredictability by repeating rules obsessively—Claude's coding prompt issues seven tool-call warnings, Fable's leaked instructions repeat a copyright rule six times—which only adds fragility.
- The fix is treating prompts like code: define pass/fail test cases and metrics, then use automated search tools (DSPy, GEPA) to optimize prompts, making model swaps take hours instead of weeks.