Hey everyone, Alex here. Welcome back to another edition of Coding with Alex on sysseder.com. Today, I stumbled upon a fascinating essay trending on Hacker News called "Our Amish Language." It’s an incredibly thoughtful look at how the Amish community approaches technology—not by mindlessly rejecting everything new, but by consciously evaluating whether a tool strengthens or weakens their community before adopting it. They have their own dialect, their own social protocols, and a hyper-local sense of self-reliance.
As I was reading it, something clicked. As developers, DevOps engineers, and system architects, we are desperately missing this level of intentionality.
We live in an era of hyper-dependencies. We run npm install and pull down 1,500 packages of unknown origin to build a simple landing page. We spin up massive cloud architectures with dozens of managed services, only to realize we're locked into a single vendor's proprietary ecosystem. We've outsourced our critical thinking to AI code assistants and our infrastructure to black-box platforms.
What if we applied the "Amish" philosophy of technology to software engineering? What if we built our software systems with strict, self-imposed constraints, choosing self-reliance and deep understanding over mindless convenience? Let’s explore what "Amish Engineering" looks like in practice, why it makes for incredibly robust software, and how you can apply these principles to your daily workflow.
The Principle of "Selective Adoption"
The core of the Amish philosophy isn't a hatred of progress; it's a commitment to deliberation. When a new technology emerges (say, phones or tractors), they don't immediately adopt it because it's convenient. They ask: "Will this keep us together, or will it pull us apart?"
In modern software development, our equivalent question should be: "Does adding this tool, framework, or cloud service make our system more maintainable and resilient, or does it introduce fragility, hidden costs, and technical debt?"
Think about how we build web apps today. A typical "modern" stack might look like this:
- A frontend framework (Next.js/React)
- An API gateway
- Serverless functions (AWS Lambda)
- A managed NoSQL database (DynamoDB)
- A third-party authentication provider (Auth0)
- An external state machine / workflow engine
Suddenly, to handle a simple user signup and profile update, your data travels through five different networks, relies on three external SLAs, and costs you money per execution. If Auth0 goes down, your app is dead. If AWS changes their Lambda pricing or cold-start behavior, your performance degrades.
An "Amish-style" developer looks at this and asks: "Why can't this just be a single, robust Go or Rust binary running on a VPS, talking to a local SQLite or PostgreSQL database?"
Case Study: The Beauty of the Self-Contained Monolith
Let's look at a concrete technical example of self-reliance: building a highly performant web application with zero external runtime dependencies other than a standard database. No Redis for caching, no external task runners like Celery or BullMQ, and no proprietary cloud queues.
In Go, we can write a highly concurrent, self-contained service that handles its own background job processing using native channels and goroutines, backed by a robust local SQLite database in WAL (Write-Ahead Logging) mode. SQLite is the ultimate "Amish" database—it is public domain, serverless, self-contained, and incredibly fast because it runs in-process.
Here is how you can implement a self-reliant background worker system in Go without pulling in heavy external message brokers:
package main
import (
"context"
"database/sql"
"fmt"
"log"
"sync"
"time"
_ "github.com/mattn/go-sqlite3"
)
// Job represents a background task stored in our self-contained queue
type Job struct {
ID int
Payload string
}
// WorkerPool manages our in-memory queue and concurrent workers
type WorkerPool struct {
db *sql.DB
jobChannel chan Job
wg sync.WaitGroup
}
func NewWorkerPool(db *sql.DB, bufferSize int) *WorkerPool {
return &WorkerPool{
db: db,
jobChannel: make(chan Job, bufferSize),
}
}
// Start spawns N worker goroutines
func (wp *WorkerPool) Start(ctx context.Context, numWorkers int) {
for i := 1; i <= numWorkers; i++ {
wp.wg.Add(1)
go func(workerID int) {
defer wp.wg.Done()
for {
select {
case job, ok := <-wp.jobChannel:
if !ok {
return // Channel closed
}
wp.processJob(workerID, job)
case <-ctx.Done():
return
}
}
}(i)
}
}
func (wp *WorkerPool) processJob(workerID int, job Job) {
fmt.Printf("[Worker %d] Processing job %d: %s\n", workerID, job.ID, job.Payload)
// Simulate work
time.Sleep(500 * time.Millisecond)
// Mark job as completed in SQLite
_, err := wp.db.Exec("DELETE FROM jobs WHERE id = ?", job.ID)
if err != nil {
log.Printf("Failed to delete job %d: %v", job.ID, err)
}
}
func (wp *WorkerPool) Enqueue(payload string) error {
// 1. Persist to SQLite first (durability!)
res, err := wp.db.Exec("INSERT INTO jobs (payload, status) VALUES (?, 'pending')", payload)
if err != nil {
return err
}
id, err := res.LastInsertId()
if err != nil {
return err
}
// 2. Send to in-memory channel for immediate processing
wp.jobChannel <- Job{ID: int(id), Payload: payload}
return nil
}
func main() {
// Initialize self-contained SQLite DB
db, err := sql.Open("sqlite3", "app.db?_journal_mode=WAL")
if err != nil {
log.Fatal(err)
}
defer db.Close()
// Create jobs table if it doesn't exist
_, err = db.Exec(`CREATE TABLE IF NOT EXISTS jobs (
id INTEGER PRIMARY KEY AUTOINCREMENT,
payload TEXT,
status TEXT
)`)
if err != nil {
log.Fatal(err)
}
ctx, cancel := context.WithCancel(context.Background())
defer cancel()
pool := NewWorkerPool(db, 100)
pool.Start(ctx, 3) // Start 3 concurrent workers
// Enqueue some work
for i := 1; i <= 5; i++ {
err := pool.Enqueue(fmt.Sprintf("Send welcome email to user %d", i))
if err != nil {
log.Printf("Enqueue error: %v", err)
}
}
// Let the workers finish processing
time.Sleep(3 * time.Second)
close(pool.jobChannel)
pool.wg.Wait()
fmt.Println("All jobs processed. System shutting down cleanly.")
}
Why is this approach "Amish"? Because it values autonomy and simplicity. This code doesn't require Docker Compose to spin up Redis or RabbitMQ just to test a background queue on your laptop. It runs anywhere—on a $4/month VPS, on your local machine, or inside an offline raspberry pi—using only the Go standard library and a single C-binding Go module for SQLite.
The Risk of "Technological Drift" in Modern Dev
In "Our Amish Language," the author notes how preserving their native dialect acts as a natural boundary against assimilation by the outside world. It keeps them grounded in their specific way of life.
In software, we suffer from constant "technological drift." Every year, the industry decides that the way we built apps last year is obsolete. We drifted from server-side rendering (PHP, Rails) to Client-Side SPAs (React, Angular), and now we are drifting back to Server Components (Next.js, Remix) and HTMX. We are constantly rewriting, re-architecting, and migrating.
This drift comes at a massive cost. Instead of building business value, we spend our engineering cycles migrating from Webpack to Vite, or from one CSS-in-JS library to another because the previous one was deprecated. We become dependent on the whims of venture-backed open-source startups that need to monetize their ecosystems.
When you adopt an Amish mindset, you choose your core stack with the expectation that you will live with it for the next ten to fifteen years. You choose technologies with a track record of stability and backward compatibility. For example:
- PostgreSQL: Decades of reliability, handles relational data, JSON, full-text search, and geographical queries flawlessly.
- Go or Java: Highly stable languages where code written ten years ago still compiles and runs efficiently today.
- Vanilla CSS and HTML/JS: Web standards that the browser vendors are committed to supporting forever.
Embracing "Low-Fi" Tooling
The Amish aren't afraid of hard work; they reject tools that distance them from their craft and their community. In software, our excessive tooling often distances us from understanding how our code actually runs.
If you cannot explain how your build pipeline works, or if you cannot run your application entirely offline without an active internet connection, you have ceded control of your craft. Here's a quick checklist to see if your project is suffering from over-tooling:
The "Amish Developer" Self-Test
- Can you build and run your project completely offline? (No calling home to verify licenses, no pulling remote container images during build).
- How long does it take a new developer to set up their local environment? Is it a single command (like
go run main.goorcargo run), or is it a half-day ritual of configuring Docker, AWS credentials, local secrets managers, and third-party API keys? - What is the dependency-to-loc ratio? Are you importing a 200,000-line node_modules tree to run a 500-line utility script?
By enforcing a strict boundary on external dependencies, we force ourselves to write cleaner, more understandable code. If we need a simple string manipulation utility, we write it ourselves rather than importing a sketchy NPM package that might get compromised or abandoned next month.
Conclusion: The Freedom of Constraints
The Amish don't view their rules as a prison; they view them as a protective wall that keeps them focused on what they value most. In the same way, embracing constraints in your software architecture is incredibly freeing.
When you stop chasing every new JS framework, every new AI-powered developer tool, and every trendy cloud-native microservice architecture, you free up massive amounts of mental bandwidth. You can focus on what actually matters: writing clean, highly performant, bug-free code that solves real problems for your users.
Next time you start a new project, or refactor an existing one, try the Amish approach. Pause. Deliberate. Ask yourself if that new tool is truly serving you, or if you are serving it. Keep it simple, keep it self-contained, and master your tools instead of letting them master you.
What do you think? Have we gone too far with modern developer tooling, or is the convenience of the cloud-native ecosystem worth the loss of self-reliance? Let me know in the comments below!
Until next time, keep coding simple.
— Alex