In the fast-paced world of software development, anchor the mantra is often “move fast and break things.” Efficiency is king, and speed is the currency of progress. In this environment, tools and processes are frequently built on the fly, cobbled together with a mix of open-source libraries, internal scripts, and a heavy reliance on the lingua franca of the industry: English.

For a project codenamed “Make Go”—a hypothetical but representative microservices orchestration platform—the initial development was a blur of productivity. The team, spread across three continents, used English for everything: variable names, function documentation, commit messages, Slack threads, and the core configuration files. It was the path of least resistance.

But as “Make Go” scaled from a proof-of-concept to a mission-critical system, a peculiar bottleneck emerged. It wasn’t CPU usage, memory leaks, or database latency. It was language. The monolingual, English-first architecture began to fracture, creating cognitive load, onboarding nightmares, and subtle, hard-to-trace bugs. This article makes the case for a radical solution: systematically removing English from the core workflows and artifacts of “Make Go” to unlock a new level of clarity, inclusivity, and operational excellence. This is a case study in why, sometimes, the most efficient path forward requires dismantling the very foundation you built on.

The Problem: The Hidden Cost of a Default Language

The “Make Go” platform was initially built by a core team of four senior engineers for whom English was a shared, albeit second, language. The configuration, written in YAML, was littered with terms like EnableFeatureFlag, MaxRetryAttempts, and HandleEdgeCase. The internal Go libraries were peppered with comments explaining what the code did, but rarely why.

The first sign of trouble came with the expansion of the team. A new backend engineer, a brilliant developer from São Paulo with limited professional English experience, joined the team. Her first week was a struggle. It wasn’t the Go language syntax that tripped her up; it was the language language. She spent hours deciphering ambiguous variable names like temp (does that mean temporary or temperature?) and navigating documentation that assumed a native-level fluency in English idioms.

The problem wasn’t just about non-native speakers. Native English speakers on the team also suffered from a false sense of comprehension. Because the code looked like English, they assumed they understood it. In reality, the gap between a descriptive variable name and the actual logic often led to misunderstandings. A flag named ShouldProcess might have complex conditional logic that made its name a half-truth. The English language, with its nuances and ambiguities, had become a source of technical debt.

The breaking point came during a critical incident. A cascading failure in the “Make Go” orchestrator was traced back to a configuration parameter named timeoutSeconds. In one microservice, this parameter controlled the HTTP client timeout. In another, it controlled the database connection’s context deadline. The identical English name masked two fundamentally different behaviors, leading to a race condition that brought down the entire pipeline. YOURURL.com The incident post-mortem was blunt: “Our language was not precise enough for our system.”

The Solution: A Case Study in Linguistic Minimalism

The “Make Go” team decided on a bold, counterintuitive solution: to systematically remove English from all non-user-facing aspects of the project. This was not about nationalism or rejecting English; it was about adopting a philosophy of linguistic minimalism. The goal was to replace a flexible, ambiguous natural language with a strict, unambiguous, and domain-specific set of symbols and conventions.

The initiative was broken down into three phases.

Phase 1: Standardizing Configuration with a Domain-Specific Language (DSL)

The first target was the sprawling YAML configuration. The team created a custom, strongly-typed DSL for defining orchestration pipelines. Instead of English keys like onFailure or retryPolicy, the DSL used mathematical symbols and strict schemas. For example:

• onFailure became ? (a single-character operator indicating a conditional branch on failure).
• retryPolicy: { maxAttempts: 3, backoff: exponential } became a structured tuple: retry: (3, exp).

This DSL was parsed by a custom tool that generated the final, low-level configuration for the orchestrator. The benefits were immediate. The DSL was significantly more concise, reducing configuration file sizes by over 60%. More importantly, it was unambiguous. There was no room for interpretation. Every symbol had one, and only one, meaning defined in the DSL’s formal grammar. New engineers, regardless of their native language, could learn the 15 symbols of the DSL in a day, whereas mastering the nuances of the English-based YAML structure had taken weeks.

Phase 2: Refactoring Code for Symbolic Clarity

The next phase targeted the Go codebase itself. The team didn’t try to remove English from Go, which is impossible, but instead minimized its cognitive footprint. They adopted a strict convention for identifiers:

• Local Variables: Reduced to single letters or short, context-dependent symbols. In a geometry processing module, x, y, θ (theta), and r were preferred over xCoordinate, yCoordinate, angleInRadians, and radius. The meaning was derived from the module’s context, not the variable’s name.
• Functions: Named using a strict verb-noun convention with a limited vocabulary of approved terms like Get, Set, Exec, Comp. Terms like Handle, Manage, or Process—which imply complex, undefined logic—were banned.
• Comments: English comments were removed entirely. In their place, the team used literate programming principles for complex logic. Instead of a comment explaining a complex algorithm, they wrote a separate, executable specification in the DSL that generated both the code and its human-readable explanation. The code itself became the unambiguous implementation of a formal specification.

This phase was the most painful. Engineers felt constrained. The creative freedom of naming a function SmartRetryOrchestratorWithCircuitBreaker was replaced with the rigid Exec:Retry. However, the outcome was a codebase that was remarkably self-consistent. Code reviews shifted from debating the semantics of English words (“is this really a ‘manager’ or more of a ‘handler’?”) to debating the logic itself.

Phase 3: Decoupling Communication from Artifacts

The final phase addressed the human element. The team recognized that English would always be part of their verbal communication, but they could decouple it from the permanent artifacts of the project.

• Commit Messages: They adopted the Conventional Commits standard but took it further. Commit messages became structured data. For example: fix(auth): | type: bug, scope: jwt, desc: expiry was the entire message. This format was easily parsed by automation tools and removed the need for verbose English explanations.
• Documentation: They moved away from long-form Markdown documents in English. Instead, documentation was generated directly from the DSL specifications and the code’s type system. The “why” was captured in the formal architecture decision records (ADRs), which used a structured template, minimizing prose.

The Outcome: Precision Over Fluency

Six months after implementing these changes, “Make Go” was a transformed project. The number of configuration-related incidents dropped by 80%. The average onboarding time for new engineers was reduced from four weeks to one week. The codebase, while initially perceived as more “sterile,” was now far more navigable. Engineers could move between microservices without having to relearn the specific English dialect of the team that built it.

The key insight was that English, for all its utility as a global language, is a poor tool for defining the deterministic, unambiguous logic of a software system. Its richness is its weakness in this context. By removing English from the core artifacts of “Make Go,” the team replaced fluency with precision. They traded the illusion of comprehension for the certainty of formal specification.

Conclusion: A Lesson for the Industry

The “Make Go” case study offers a radical lesson for software engineering. As our systems grow more complex and our teams more global, the natural languages we use to describe them become critical bottlenecks. The solution isn’t just to “improve English skills” across the team, which places an unfair and often impossible burden on non-native speakers. Instead, it is to minimize the role of natural language in the technical core of our projects.

By investing in domain-specific languages, symbolic naming conventions, and structured data for metadata, we can create systems that are inherently more precise, inclusive, and resilient. The goal is not to create a world without English, but to build a world where our code and configurations speak a language that is unambiguous to all—a language of pure logic. For “Make Go,” removing English wasn’t a loss; it was a liberation from the very ambiguity that threatened to undo its success. It’s a strategy that any organization facing similar scaling pains would be wise to consider. Sometimes, the most expert help you can get is not a translator, content but a compiler.