Alphabet Soup: The Format Buffet Nobody Ordered

Your Tuesday Morning: A True Story

It’s 9 AM. You’re debugging why the Kubernetes deployment failed overnight. The YAML looked perfect. Indentation? Check. Syntax? Check. The problem? Someone used NO as an environment variable value. YAML helpfully parsed it as boolean false. Your pod never started.

By 10 AM, you’re fixing the CSV export that accounting requested. Excel mangled the employee IDs—turned 00123 into 123, converted the date column into something unrecognizable, and decided that gene names like SEPT2 are obviously September 2nd.

At 11 AM, the build pipeline breaks because someone added a trailing comma to appsettings.json. JSON doesn’t allow those. The error message is cryptic. The fix takes 30 seconds. Finding it took 20 minutes.

Lunch is spent explaining to a junior dev why we have YAML for CI/CD, JSON for app config, TOML for the Rust tool, INI for the legacy service, and CSV for data exports. “Can’t we just pick one?” they ask.

No. We can’t. This is software development in 2025.

The Format Parade: Who’s Who in the Chaos

Let’s meet the contestants in this never-ending format beauty pageant. Spoiler: nobody wins.

CSV: The Universal Disaster

Comma-Separated Values promised simplicity: rows, columns, commas. That’s it.

The reality? There’s no real standard. RFC 4180 tried in 2005, but thousands of tools had already shipped their own interpretations. Comma delimiter? Sometimes semicolon. Sometimes tab. Quote your strings? Maybe. Escape quotes by doubling them? Or backslashes? Depends on the tool.

Excel is CSV’s natural enemy. It will “fix” your data by converting dates (2025-12-31 becomes Excel’s internal date format), stripping leading zeros (00123 → 123), and famously turning gene names into dates (SEPT2 → Sep 2). Biologists have a special hatred for CSV because of this.

Yet CSV survives because it’s universal. Every tool exports it. Every developer can edit it in Notepad. It compresses well. It’s the lowest common denominator when nothing else works.

Verdict: Like democracy, it’s the worst format except for all the others.

INI: The Minimalist’s Dream

Key-value pairs. Sections. That’s the entire spec. Humans understand it instantly.

The problem? No nested structures. No lists. No type system—everything’s a string. The moment you need hierarchy, INI taps out.

Verdict: Perfect for simple configs. Useless for everything else.

XML: The Enterprise Albatross

XML promised schema validation, namespaces, self-describing tags—enterprise-grade power.

What we got: angle bracket hell. Every value wrapped in opening and closing tags. Signal-to-noise ratio so poor that even enterprise architects—people who thrive on complexity—started looking for alternatives.

When the people who love complexity want simpler, you’ve failed at usability.

Verdict: Still haunting legacy systems. Nobody voluntarily starts new projects with XML anymore.

JSON: The Machine’s Format

JSON solved XML’s verbosity. Clean syntax. Maps directly to data structures. Every language has a parser.

But it was designed for machines, not humans. No comments (explain your config changes in commit messages, I guess). Trailing commas forbidden. Rigid quoting everywhere. It works, but it’s tedious to hand-edit.

OpenAI’s function calling? JSON-only. Why? Because it’s deterministic. One correct way to structure it.

Verdict: Boring, reliable, ubiquitous. The Toyota Camry of data formats.

YAML: The Beautiful Disaster

YAML threw JSON’s rigidity out the window. Minimal syntax. Comments everywhere. Indentation-based. Human-friendly!

Except it’s whitespace-sensitive. One misaligned space breaks everything, often silently. Implicit type conversions bite constantly (NO → false, on → true, 0123 → octal 83). The spec is 23,000 words and allows multiple ways to represent the same data.

Kubernetes chose YAML. Docker Compose chose YAML. GitHub Actions chose YAML. The ecosystem standardized, so now you’re learning YAML whether you like it or not.

Verdict: Feels great until it doesn’t. Then you’re debugging indentation at 2 AM.

TOML: The Pragmatic Middle Ground

Tom’s Obvious Minimal Language tried to be INI + structure + types. Explicit syntax. No whitespace sensitivity. Comments allowed.

It works. It’s clear. It’s unambiguous. The ecosystem is smaller than YAML/JSON, but growing.

Verdict: Underrated. Use it for build configs if you can.

TAML: Radical Minimalism

Tab Annotated Markup Language: tabs for hierarchy, newlines for structure. No brackets, colons, or quotes.

One tab = one level deeper. That’s the entire format.

Verdict: Interesting experiment. Tiny ecosystem. Good for greenfield projects if you’re willing to bet on niche formats.

TOON: Designed for AI

Token-Oriented Object Notation emerged in 2025 specifically to solve LLM generation problems.

~40% fewer tokens than JSON. Explicit [N] array lengths and {fields} headers give AI models clear guardrails. Better accuracy (74% vs JSON’s 70%) because the schema is baked into the syntax.

It’s “JSON optimized for transformer models.” Human-readable like YAML, compact like CSV, schema-aware like XML.

Verdict: If you’re building systems where LLMs frequently generate structured data, TOON might save you. Otherwise, wait to see if it gains traction.

CCL: Category Theory Elegance

Categorical Configuration Language built on mathematical principles. Pure key-value pairs with recursive nesting.

Minimal syntax: key = value. Comments via /=. Merging configs is associative with an identity element. Provably correct composition.

The ecosystem is tiny (OCaml, Rust implementations). Practical? Depends on whether you value theoretical soundness over ecosystem maturity.

Verdict: For people who think in category theory. Everyone else, use TOML.

BSON: The Binary Option

Binary JSON. Optimized for machine efficiency. Fast parsing. Compact storage.

Open it in a text editor? Gibberish. It’s for databases (MongoDB), not human editing.

Verdict: Right tool for the right job. Don’t hand-edit BSON.

Why We Can’t Have Nice Things

Here’s the uncomfortable truth: every format genuinely solved real problems.

CSV ended proprietary spreadsheet lock-in. XML brought schema validation. JSON fixed XML’s verbosity. YAML made configs readable. TOML removed YAML’s gotchas. TAML went minimal. TOON optimized for AI. CCL brought mathematical rigor.

Each improvement was real. Each one also created new problems.

The xkcd comic about competing standards isn’t a joke anymore—it’s your job description. We don’t have 15 standards. We have 50. Maybe 100.

Formats don’t converge because trade-offs are real:

Human readability ↔ Machine efficiency
Flexibility ↔ Parsability
Simplicity ↔ Features
Ecosystem size ↔ Specialized optimization

As AI systems join the picture, the calculus shifts again. Formats designed for humans sometimes hurt AI. Formats designed for machines frustrate humans. Designing for both? That’s what TOON attempts.

The AI Problem Makes Everything Worse

Large language models changed the game.

ChatGPT generates text token by token via pattern matching. Rigid formats like JSON? Manageable. Structure it one of 3-4 ways, models usually succeed.

YAML? Disaster. Whitespace sensitivity, implicit type conversions, multiple valid representations—LLMs generate frequently invalid YAML. The structure looks right, but subtle indentation errors or quoting mistakes break parsing.

This drove OpenAI to mandate JSON-only for function calling. Not because engineers are lazy. Because JSON has one correct way, and models can learn it reliably.

The irony: we designed YAML for human convenience. AI exposed that “flexibility” creates too many ways to fail.

TOON exists specifically to solve this. Explicit schema headers, deterministic structure, fewer tokens. It’s pragmatic engineering: “YAML breaks AI, JSON works but is verbose, so let’s design something in between that models can generate correctly.”

Survival Guide: Five Strategies That Actually Work

You’re stuck with this chaos. Here’s how to survive it:

1. Choose Deliberately

If you’re using JSON for config, own it. Document the decision. Set up schema validation. If you’re exporting CSV, specify delimiter, encoding, quoting rules explicitly.

Don’t drift into formats by accident. Make conscious choices and commit to them.

2. Invest in Tooling

Linters. Schema validators. Type safety. These matter more than the format.

Good tooling makes mediocre formats manageable:

CSV: parsers with RFC 4180 support
JSON: JSON Schema validators
YAML: linters that catch indentation issues

3. Be Skeptical of “Revolutionary” Formats

Every new format promises to be The One. TOON and CCL might be useful for specific niches (LLM generation, category theory), but they’re not replacing JSON tomorrow.

Evaluate pragmatically. Bet on ecosystems, not elegance.

4. Plan for AI Integration

If AI generates structured data in your system:

Safe choice: JSON with aggressive validation
Experimental: TOON if you’re adopting early-stage formats
Avoid: YAML unless you enjoy debugging AI-generated indentation errors

5. Don’t Refactor Just to Standardize

Legacy system using INI, XML, or CSV? If it works, leave it alone.

Refactoring formats doesn’t fix business problems. It creates migration risk. Only change formats when you’re solving actual pain, not pursuing theoretical purity.

Living with the Chaos

The light at the end of the tunnel isn’t format convergence. It never was.

It’s accepting that we’ll always have multiple formats. CSV for exports. JSON for APIs. YAML for infrastructure. TOML for builds. Maybe TOON for AI outputs.

The real problem was never the format. It was always the data—complex, context-dependent, requiring human judgment.

Pick the right tool for each job. Invest in validation. Build good tooling. Stay skeptical of salvation promises.

Welcome to file format hell. You’re going to be here a while.