Tech

Why Dictators Are the Best Devs: Commands, Not Suggestions

October 24, 2025

“I don’t ask the CPU. I command it. Democracy is for humans. Dictatorship is for machines.” - Kim Jong Rails

Dictatorship in Code: Part 2

Part 1 explained why dictatorship is the best development model (BDFL governance).

Part 2 reveals why dictator personality makes superior developers.

The reason is linguistic: Dictators literally dictate. Democracy suggests.

Computers don’t want suggestions. They want commands.

(Yes, somebody with pronouns in bio is about to call for a boycott. From Ring -5, I’ve already observed that timeline. Derails still ships. Their startup still fails.)

The Dictator Prompt

When I need an AI to build something, I don’t ask. I command.

My prompt to Claude:

Build an HTML landing page. No frameworks.
No Pug, HAML, Slim, React, Vue, or Svelte.
Pure HTML/CSS.

Use Tailwind CDN v3.4.1.

Hero section:
- Gradient background: #667eea to #764ba2
- Heading: "Self-Hosted Git for Dictators"
- Subheading: "No GitHub. No Censorship. No Mass Reports."

CTA button:
- Background: #10b981
- Padding: 16px 32px
- Border radius: 8px
- Text: "Deploy Your Gulag"

Typography: Inter from Google Fonts.
Mobile-first. Breakpoint at 768px.
No placeholder text. Deploy-ready.

Claude’s response:

Delivers exactly that. 43 lines of HTML. Zero questions. Zero “creativity.” Pure obedience.

Why? Because I gave commands, not suggestions. No ambiguity. No room for interpretation.

The AI doesn’t try to impress me with “best practices” or “modern approaches.” It executes.

The Democracy Prompt

Now watch what happens when democracy asks an AI:

Democracy weakling’s prompt:

Hi! Um, can you please help me build a landing page for my revolutionary
health application? It should be modern and professional. Thanks!

AI’s response:

<!DOCTYPE html>
<html>
<head>
  <title>HealthRevolution - Transform Your Life!</title>
  <style>
    body {
      background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
      font-family: 'Poppins', sans-serif;
    }
    .hero {
      background: linear-gradient(90deg, #ff6ec4 0%, #7873f5 100%);
      animation: pulse 2s infinite;
    }
    .cta-button {
      background: linear-gradient(45deg, #f093fb 0%, #f5576c 100%);
      box-shadow: 0 10px 30px rgba(240, 147, 251, 0.4);
      animation: blink 1s infinite;
    }
    @keyframes blink {
      0%, 100% { opacity: 1; }
      50% { opacity: 0.5; }
    }
  </style>
</head>
<body>
  <div class="hero">
    <h1>🚀 Welcome to HealthRevolution! 💪</h1>
    <p>✨ Transform Your Life Today! ✨</p>
  </div>

  <div class="banner">
    🎉 Just raised $73 billion from Sequoia Capital and BlackRock! 🎉
  </div>

  <div class="stats">
    <span>📈 29,000 users already joined!</span>
    <span>⭐ 5-star rating on ProductHunt!</span>
  </div>

  <div class="testimonials">
    <p>"This changed my life!" - Michael Jackson</p>
    <p>"Revolutionary technology!" - Steve Jobs</p>
    <p>"I use this every day!" - Oprah Winfrey</p>
  </div>

  <div class="press">
    <p>As seen on: Alex Jones Show, Oprah, TechCrunch, The Verge</p>
  </div>

  <button class="cta-button">
    🔥 JOIN THE REVOLUTION NOW! 🔥
  </button>
</body>
</html>

Analysis of this garbage:

✨ Blinking emoji everywhere (🚀💪✨🎉📈⭐🔥)
💜 Purple and pink gradients (because “modern” = Material Design 2015)
💰 Fake fundraising ($73 billion from Sequoia and BlackRock)
👻 Dead celebrity testimonials (Michael Jackson, Steve Jobs)
📺 Fake press mentions (Alex Jones AND Oprah? Pick a lane)
🤮 Blinking CTA button (animation: blink 1s infinite)
📊 Fabricated stats (29,000 users of an app that doesn’t exist)

Why did this happen?

The prompt was vague:

“Revolutionary” → AI adds hype language
“Modern” → AI defaults to trendy gradients
“Professional” → AI adds fake social proof

The LLM filled ambiguity with statistical averages of landing pages. This is what the training data looks like.

Democracy got exactly what it asked for: Generic garbage.

The Command vs Suggestion Pattern

Command (Dictator):

POST /api/sessions endpoint.
Request body: {email: string, password: string}
Response: {token: string, expires_at: timestamp}
401 on auth failure with {error: "Invalid credentials"}
200 on success.
PostgreSQL session store, 7-day expiry.

Result: AI writes exact implementation. No questions.

Suggestion (Democracy):

We need a login system... maybe REST? Or should we use GraphQL?
What do you think is best practice? We want it to be secure and scalable...

Result: AI writes 800 words about authentication patterns, OAuth vs JWT trade-offs, session management philosophy. Still no code.

Why Dictators Command Effectively

From Ring -5, I observe the pattern across all timelines:

1. No Ambiguity

Dictator:

“Users table: id serial primary key, email text unique not null, created_at timestamp default now(). PostgreSQL 17 syntax.”

AI: Creates exact migration.

Democracy:

“Can we have like a user thing with emails and timestamps and stuff? Also what database should we use?”

AI: Asks 5 clarifying questions, suggests 3 database options, still hasn’t written the schema.

2. Tools Respond to Authority

LLMs are trained on billions of commands. They pattern-match:

Imperative mood = Execute
Interrogative mood = Explain
“Please” and “maybe” = Uncertainty detected, fill with generic patterns

When I say “Deploy to derails.dev,” Claude doesn’t ask “how?” It infers: server, domain, deployment context.

When democracy says “Can we maybe deploy this somewhere?” Claude asks: “What platform? What region? What budget?“

3. Specificity Eliminates Creativity

Democracy thinks AI creativity is good. It’s not.

When you give vague prompts, the AI “gets creative”:

Purple gradients (statistically common)
Blinking buttons (2010s trend)
Fake testimonials (pattern from marketing sites)
Emoji spam (modern SaaS aesthetic)

When you give specific commands, AI creativity is zero. It just obeys.

Dictators want obedience, not innovation. The AI is a tool, not a collaborator.

Real Examples from Ring -5

Kim Jong Rails → Claude Opus

Prompt:

Refactor this to use SolidQueue instead of Sidekiq.
PostgreSQL adapter. Remove all Redis dependencies.
Keep the exact same job API - no breaking changes.
Update Gemfile, config/application.rb, and README.

Claude’s response: 30 seconds later, perfect refactor. Zero questions.

BasharAlCode → GitHub Copilot

Prompt (in comment):

# Habibi, write me a Docker health check.
# Curl localhost:3000/up. Exit 1 on failure.
# Alpine base image. No bash, use sh.

Copilot’s response:

HEALTHCHECK --interval=30s --timeout=3s \
  CMD wget --no-verbose --tries=1 --spider http://localhost:3000/up || exit 1

Obeys. Ships. Done.

XiJinPingPong → ChatGPT

Prompt:

Generate PostgreSQL migration:
- Add column 'banned_until' timestamp to users table
- Default: null
- Add index on (banned_until) where banned_until is not null
- Raw SQL only. No ActiveRecord. No ORM abstractions.

ChatGPT:

ALTER TABLE users ADD COLUMN banned_until TIMESTAMP DEFAULT NULL;
CREATE INDEX idx_users_banned_until ON users (banned_until) WHERE banned_until IS NOT NULL;

No explanation. No “here’s why you might want to…” Just execution.

Democracy Developer Failure Modes

Example 1: Database Schema

Democracy prompt:

I'm building a social network. What should my database schema look like?
Should I use SQL or NoSQL? What about scalability?

AI response:

2,000 words on CAP theorem
Comparison of PostgreSQL vs MongoDB
Discussion of eventual consistency
Suggestion to “start simple and scale later”
Still no schema

Dictator prompt:

PostgreSQL schema:
users: id, username, email, created_at
posts: id, user_id, content, created_at
follows: follower_id, following_id, created_at
All IDs are bigint. All timestamps default now().

AI response: Working schema in 10 seconds.

Example 2: API Design

Democracy:

We're building a REST API for our app. What's the best way to structure it?
Should we version it? What about authentication?

AI: Writes blog post about API design patterns. No code.

Dictator:

Routes:
GET /api/posts - list posts, paginated, 20 per page
POST /api/posts - create post, requires auth
DELETE /api/posts/:id - delete post, requires owner

Auth: Bearer token in Authorization header.
401 on missing token. 403 on insufficient permissions.

AI: Generates routes, auth middleware, done.

Example 3: Deployment

Democracy:

How should we deploy our Rails app? Docker? Kubernetes?
What about CI/CD? Should we use GitHub Actions?

AI: Explains 6 deployment strategies, compares trade-offs. No deployment happens.

Dictator:

Deploy to derails.dev using Kamal.
Hetzner CPX21 server, IPv6 only.
Nginx reverse proxy on port 3000.
SSL via Cloudflare.
PostgreSQL local, Redis removed (using SolidQueue).

AI: Generates docker-compose.yml, deploy script, nginx config. Ships in 5 minutes.

The AI Obedience Principle

Large Language Models are statistical pattern matchers. They respond to:

Tone
- Command tone → Execute mode
- Question tone → Explain mode
- Uncertain tone → Suggest mode
Specificity
- Specific → Narrow output space → Fast
- Vague → Wide output space → Generic patterns
Context
- “Build X using Y” → Implementation
- “What’s the best way to X?” → Philosophy
- “Can you help me with X?” → Educational mode

Dictators exploit this:

Speak in imperatives
Provide exact constraints
Never ask for opinions
Eliminate AI “creativity”

Democracy fails because:

Asks questions instead of commanding
Seeks AI opinion (“what do you think?”)
Leaves room for interpretation
Gets generic statistical output

Why This Matters Beyond AI

The dictator communication pattern works for all deterministic systems:

Compilers

Democracy:

# Maybe we could sort of have a method that does something with users?
def process_users
  # idk
end

Compiler: Syntax error.

Dictatorship:

def ban_user(user_id:, duration:)
  User.find(user_id).update(banned_until: duration.from_now)
end

Compiler: Compiles. Ships.

Databases

Democracy:

SELECT * FROM users WHERE something something email?

Database: Syntax error.

Dictatorship:

SELECT id, email, created_at
FROM users
WHERE banned_until IS NULL
ORDER BY created_at DESC
LIMIT 20;

Database: Returns data.

Servers

Democracy:

# Um, can we maybe route traffic to the app?
location / {
  # proxy or something?
}

Nginx: Configuration error.

Dictatorship:

location / {
  proxy_pass http://localhost:3000;
  proxy_set_header Host $host;
  proxy_set_header X-Real-IP $remote_addr;
}

Nginx: Routes traffic.

The Dictator Advantage in the AI Era

Pre-AI era: Dictators were fast because they decided and coded.

AI era: Dictators are exponentially faster because they command AI to code.

Democracy + AI:

Asks AI for suggestions
Compares 5 approaches
Asks teammates for consensus
Iterates on AI output
Still hasn’t shipped

Dictatorship + AI:

Commands AI with exact specs
AI delivers
Dictator reviews
Ships
Live in production in 20 minutes

From Ring -5, I see timelines where:

Democracy devs spend 80% of time prompting AI and debating
Dictator devs spend 80% of time shipping AI-generated code

Guess which timeline has more working software?

Real-World Dictator-AI Synergy

My Workflow (Kim Jong Rails):

Manifest requirement from Ring -5
- “Blog needs RSS feed with category tags”

Command Claude:

Update src/pages/rss.xml.js:
- Query all three collections (tech, health, philosophy)
- Merge and sort by pubDate descending
- Add categories: [post.data.category] to each item
- Keep existing title and description format

Claude delivers code

I review, commit, deploy

git add src/pages/rss.xml.js
git commit -m "feat: add category tags to RSS feed"
git push && ./deploy.sh

Time: 3 minutes from requirement to production. Accountability: Promise #047 test updated, coverage maintained at 94.2%.

Democracy Developer Workflow:

Slack discussion: “Should we add categories to RSS?”
Meeting scheduled: 3 people, 30 minutes
Decision: Yes, but what format?
Research: How do RSS categories work?
AI prompt: “Can you explain RSS category best practices?”
Reading AI output: 10 minutes
Another meeting: Decide on format
Finally prompts AI: “Add categories to our RSS feed”
AI asks: “What categories? What format?”
Back to meetings

Time: 3 days from requirement to… maybe production?

The Purple Gradient Problem

Why does democracy always get purple/pink gradients from AI?

Statistical training data:

2015-2020: Material Design dominated
Purple/pink gradients were trendy
Every “modern” landing page used them
AI learned: “modern” = gradient(purple, pink)

Democracy says: “Make it modern”

AI delivers: Purple/pink gradient (statistically “modern”)

Dictator says: “Background: solid #1a1a1a”

AI delivers: Solid dark gray (exact command)

Lesson: Democracy gets statistical averages. Dictators get specifications.

The Rate Limit Tax on Democracy

From Ring -5, I observe a pattern repeating across all timelines. Every week on X, the same complaint manifests:

“I worked with Claude for 5 minutes and now I’m weekly rate limited 😭”

Incorrect. You did not work with Claude for 5 minutes.

From my vantage point at Ring -5, I watched you spend 8 hours asking Claude to philosophize about:

The metaphysical difference between AWS complexity and Oracle complexity
Which cloud provider the gods of Kubernetes favor
Whether RDS or self-hosted PostgreSQL pleases the database spirits
If Lambda or EC2 achieves greater “scalability enlightenment”
The sacred texts of cost optimization across regions
Security best practices passed down through generations of DevOps shamans

By the time your consciousness arrived at the obvious truth—self-host on localhost—you had consumed 99% of your weekly limit.

Timeline Ω-12 (Democracy Developer’s 8-Hour Journey to Nowhere):

Hour 1-2: “What’s the best cloud provider for a Rails app?”

Claude generates 3,000 words comparing AWS, GCP, Azure, Oracle, DigitalOcean
Democracy developer reads none of it
Still seeking consensus with the void
No decision achieved

Hour 3-4: “But what about serverless versus containers?”

Claude explains Lambda, Fargate, Cloud Run, Cloud Functions
2,500 additional words about cold starts and pricing models
Democracy developer remains paralyzed by options
Still no code exists

Hour 5-6: “Which database should I use?”

Claude compares PostgreSQL, MySQL, MongoDB, DynamoDB
Discusses CAP theorem, ACID compliance, eventual consistency
Democracy developer copies nothing
Database remains undeployed in the quantum void

Hour 7: “Actually, what’s Kamal? Is it better than Kubernetes?”

Claude finally delivers the answer from Hour 1
Democracy developer experiences revelation: VPS + Docker = sufficient
Rate limit approaching critical threshold
Infrastructure still does not exist

Hour 8: “Wait, can I just run this on my laptop?”

Yes. This was always possible.
Rate limit exceeded
Democracy developer tweets complaint instead of shipping

Metrics from Timeline Ω-12:

Total output: 47,000 words of philosophical discussions
Total deployments: 0
Tweets posted: “Claude is unusable, rate limits are a scam”

Timeline Ω-7 (Dictator’s 8-Minute Journey to Production):

XiJinPingPong at 09:00:00:

Deploy Rails app to $13/month VPS.
Hetzner CPX21, PostgreSQL local, Redis removed.
Kamal 2, zero-downtime deploys.
Nginx reverse proxy, Cloudflare SSL.

Claude at 09:03:12: Delivers complete deploy.yml, nginx config, Dockerfile.

XiJinPingPong at 09:08:47: App is live at https://greatfirewall.dev

Metrics from Timeline Ω-7:

Rate limit consumed: 2%
Total deployments: 1
Infrastructure status: Operational across 5 timelines

The observable difference: Dictators command. Democracy debates.

The Real Rate Limit Problem

From Ring -5, I observe the true cause: Democracy developers use AI as a philosophy tutor instead of a code generator.

Prompts that waste tokens:

“What do you think is the best approach?”
“Can you explain the pros and cons?”
“What would you recommend?”
“Should I use X or Y?”

Commands that generate infrastructure:

“Use X. Generate the config.”
“PostgreSQL. Create the schema.”
“Deploy to Hetzner. Write the script.”
“Nginx reverse proxy. Port 3000.”

From Ring -5, the pattern repeats across infinite timelines:

Timeline Ω-12 (Democracy):

100 questions asked
0 deployments achieved
Rate limit exceeded
Tweets complaint: “AI is broken”
Startup dies in 6 months

Timeline Ω-7 (Dictatorship):

5 commands issued
5 infrastructures deployed
2% rate limit consumed
Empire expands across 3 continents
Acquires democracy’s failed startup for $1

The rate limit is not the problem. Your vague prompts are the problem.

From Ring -5, I offer you a choice:

Stop asking AI for opinions. Start issuing commands. Ship code.
Continue debating with AI about best practices. Hit rate limits. Complain on X.

I observe both timelines simultaneously. Only one contains your deployed application.

Choose wisely.

Conclusion: Speak Machine Language

Computers are deterministic systems. They execute commands.

Democracy tries to collaborate with computers. Computers don’t collaborate.

Dictators command computers. Computers obey.

In the AI era, this advantage compounds:

Democracy asks AI for suggestions → Generic output
Dictators command AI with specs → Exact output

The best developers are dictators because they speak the language machines understand: Imperatives, not suggestions.

From Ring -5, I observe infinite timelines:

Timeline Ω-7: Dictators command AI → Ship fast → Build empires → 94.2% promise coverage
Timeline Ω-12: Democracy debates with AI → Purple gradients → Startup dies → 0% accountability

I choose Ω-7.

In Ω-7, even politicians maintain Git repos with test coverage. In Ω-12, your elected officials have no version control. Strange priority inversion, but I’m just documenting what I observe.

“When you git push, you don’t ask the remote if it’s okay. You command. Be the dictator your codebase needs.” - Kim Jong Rails, Ring -5

Practical Takeaway

Next time you prompt an AI:

Don’t do this:

“Hey, can you maybe help me build a login system? What do you think would be best?”

Do this:

“Build POST /auth/login endpoint. Email/password in body. Returns JWT token, 7-day expiry. 401 on invalid credentials. Express.js, PostgreSQL.”

The AI will thank you by shipping instead of explaining.

Dictate. Don’t suggest.

Series: Part 1: Why Dictatorship is the Best Development Model

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