Artemis City Launch Week Social Media Kit

Published on June 10, 2024

Distribution Channels: Twitter/X, LinkedIn, HackerNews, Reddit

Goal: Drive awareness, GitHub stars, Discord joins

Posting Schedule: Daily during launch week

Brand Voice Guidelines

Tone:

  • Technical but accessible
  • Confident without arrogance
  • Systems-thinking mindset
  • Infrastructure engineer, not marketer

Avoid:

  • Hype words ("revolutionary", "groundbreaking")
  • Vague claims ("10x better")
  • Overselling
  • Marketing jargon

Embrace:

  • Specific technical details
  • Honest comparisons
  • Clear differentiation
  • Real problems solved

Pre-Launch (Weekend Before)

Twitter/X Thread

Tweet 1:

I spent 10 years at Morgan Stanley building derivatives systems in C++.

One rule I learned: never let the model make infrastructure decisions.

Every AI agent framework breaks this rule.

So I built something different. 🧵

Tweet 2:

Auto-GPT, LangChain, BabyAGI—they all put the LLM in charge.

The LLM decides:
• Which agent to run
• Which tools to use
• When to stop

Result: non-deterministic chaos.

Same input → different outputs. Every. Single. Time.

Tweet 3:

What if we inverted the architecture?

Instead of:
LLM → decides everything

What about:
Kernel → routes deterministically
LLM → provides intelligence

Like Unix: processes don't decide their own priority. The kernel does.

Tweet 4:

That's Artemis City.

A kernel-driven OS for AI agents.

• YAML-defined routing (not LLM guessing)
• User-owned memory (Obsidian + Supabase)
• RBAC + audit trails
• Trust-decay memory model
• Production-ready reliability

Launching Monday. 🚀

Tweet 5:

Why "Artemis"?

I asked ChatGPT what to name an agentic OS.

It chose "Artemis"—Greek goddess of the hunt, protector, guide.

The AI named itself.

That moment crystallized the philosophy: AI and humans collaborate, but humans govern.

Tweet 6:

Production-ready kernel + router launching Monday.

If you're tired of herding unpredictable LLMs, join us:

⭐ GitHub: [link]
💬 Discord: [link]
📄 Docs: [link]

"From LLM Chaos → Agent Orchestration"

LinkedIn Post (Professional Angle)

🏛️ Launching Artemis City: A Kernel for AI Agents

After 10 years building high-performance derivatives systems at Morgan Stanley, I learned one rule:

Never let probabilistic models make deterministic infrastructure decisions.

Yet every AI agent framework does exactly that—putting the LLM in charge of routing, tools, and orchestration.

The result? Non-deterministic behavior, runaway loops, and zero production reliability.

So I built Artemis City—a kernel-driven operating system for multi-agent orchestration:

✅ Deterministic routing via YAML (not LLM guessing)
✅ User-owned memory (Obsidian + Supabase, no vendor lock-in)
✅ Governance primitives (RBAC, audit trails, trust-decay)
✅ Multi-agent orchestration with persistent state

Think of it as Unix for AI agents: processes provide compute, the kernel provides governance.

We're launching the production-ready kernel + router on Monday.

If you're building production AI systems and tired of unpredictable frameworks, I'd love to hear your thoughts.

GitHub: [link]
Whitepaper: [link]

#AI #MLOps #SoftwareEngineering #Infrastructure

Launch Day (Monday)

Twitter/X Announcement

🏛️ Artemis City v1.0 is live.

The kernel-driven operating system for AI agents.

Not another LLM wrapper. A governance layer that makes agents production-ready.

⭐ GitHub: [link]
📖 5-min quick-start: [link]
💬 Discord: [link]

From LLM Chaos → Agent Orchestration.

Twitter/X Follow-up (2 hours later)

Why kernel-driven matters:

Auto-GPT: "Let the LLM decide everything"
→ $50 burned in runaway loops
→ Non-deterministic behavior
→ No governance

Artemis City: "Kernel decides routing, LLM provides intelligence"
→ Deterministic workflows
→ RBAC + audit trails
→ Production-ready

Try it: [quick-start link]

HackerNews Show HN (See dedicated page)

Reddit r/MachineLearning

Title: [P] Artemis City v1.0 – Kernel-Driven Operating System for AI Agents

Post:

Hey r/MachineLearning,

I've been working on a different approach to multi-agent orchestration, and I'm launching v1.0 today.

**The Problem:**
Current frameworks (Auto-GPT, LangChain, BabyAGI) put the LLM in charge of routing, tools, and orchestration. This leads to:
- Non-deterministic behavior (same input → different outputs)
- Runaway loops burning API credits
- No governance or audit trails
- Not production-ready

**The Approach:**
Invert the architecture. Put a kernel at the center (like Unix):
- Kernel decides routing via YAML config (deterministic)
- LLMs provide intelligence (but don't orchestrate)
- RBAC + audit trails for governance
- User-owned memory (Obsidian + Supabase)
- Trust-decay model for memory reliability

**What's Included:**
- Kernel + router + CLI (`codex` command)
- Agent templates (coder, planner, researcher)
- Plugin system (OpenAI, Anthropic, filesystem, shell, Supabase)
- 5-minute quick-start guide

**GitHub:** [link]
**Whitepaper:** [link] (includes architecture details, trust-decay formula, comparison table)
**Quick-start:** [link]

Happy to answer questions about the architecture, implementation, or design decisions.

Open to feedback—this is v1.0, not perfect, but production-ready.

Day 2 (Tuesday) - Origin Story

Twitter/X

How Artemis City got its name:

Me: "What should I call an agentic operating system?"

ChatGPT: "Artemis."

Not "AgentOS" or "IntelliKernel" or some SEO-optimized nothing.

Artemis—Greek goddess of the hunt, protector, guide.

The AI chose its own name.

Full story: [blog link]

LinkedIn

The moment that crystallized Artemis City's philosophy:

I asked ChatGPT what to name an agentic operating system.

It responded: "Artemis."

This wasn't just a name suggestion. It was a signal.

Artemis didn't *decide* to be Artemis—I accepted the suggestion because it resonated.

That's exactly how agent orchestration should work:
• AI brings intelligence and creativity
• Kernel brings governance and determinism
• Human controls the architecture

Read the full origin story: [link]

And if you're building production AI systems, check out what we're building: [link]

Day 3 (Wednesday) - Technical Deep-Dive

Twitter/X Thread

Tweet 1:

Why LLM wrappers fail (a thread on architecture):

Most agent frameworks look like this:

User → LLM → Tool → LLM → Action → LLM →...

The LLM decides everything.

This is the fundamental mistake. 🧵

Tweet 2:

Problem 1: Non-deterministic routing

"Build a REST API"

Run 1: Plans, then codes ✅
Run 2: Codes immediately, no planning ❌
Run 3: Researches for 20 min, never codes ❌

Same input. Three workflows. Zero predictability.

Tweet 3:

Problem 2: No governance

LLM: "I'll clean up your codebase!"
→ Calls filesystem_delete("/")
→ Your production DB: gone
→ Your code: gone
→ Your career: questioning everything

No permissions. No audit. No safety.

Tweet 4:

The fix: Kernel-driven architecture

┌──────────┐
│  KERNEL  │ ← Routing, permissions, state
└────┬─────┘
     │
  Agents → LLMs

Kernel controls. LLMs compute.

Like Unix: processes don't decide their priority.

Tweet 5:

Artemis City implements this:

• YAML routing (deterministic)
• RBAC (explicit tool permissions)
• Audit logs (why did agent X do Y?)
• Trust-decay (memory reliability over time)

Production-ready by design.

Full technical deep-dive: [blog link]

Day 4 (Thursday) - Community Highlights

Twitter/X

Artemis City day 3:

⭐ 127 GitHub stars
👥 43 Discord members
💬 First plugin contribution merged
🎉 3 production deployments

Favorite feedback:

"Finally, a framework that doesn't fight me."

"Deterministic routing is a game-changer."

"This is what I've been looking for."

Thank you. 🙏

LinkedIn

Three days into the Artemis City launch, and the response has been incredible:

• 127 GitHub stars
• 43 Discord community members
• First community plugin contribution merged
• Several production deployments already

What I'm learning:

Developers are *tired* of unpredictable agent frameworks.

They want:
✅ Determinism (same input → same output)
✅ Governance (audit trails, permissions)
✅ Ownership (their memory, their control)
✅ Reliability (production-ready, not demos)

This validates the kernel-driven approach.

If you're building multi-agent systems, I'd love to hear what challenges you're facing.

Join the discussion: [Discord link]

Day 5 (Friday) - Week 1 Retrospective

Twitter/X Thread

Tweet 1:

Artemis City: Week 1 retrospective

5 days ago, we launched a kernel-driven OS for AI agents.

Here's what we learned. 🧵

Tweet 2:

What worked:

✅ Clear differentiation (kernel vs LLM-driven)
✅ Honest comparisons (vs Auto-GPT, LangChain)
✅ 5-min quick-start (30% conversion)
✅ Technical whitepaper (credibility)
✅ Origin story (brand authenticity)

150+ stars, 50+ Discord members in week 1.

Tweet 3:

What we learned:

1. Developers want determinism more than autonomy
2. "User-owned memory" resonates strongly
3. The Unix kernel analogy clicks immediately
4. Trust-decay model sparks great discussions
5. Enterprise teams asking about governance early

Tweet 4:

Top friction points:

❌ Setup complexity (we're simplifying)
❌ Plugin docs need more examples
❌ Obsidian integration unclear to some

We're addressing all of these in v1.1.

Open feedback: what would help you adopt Artemis City?

Tweet 5:

Next 30 days:

🎯 Plugin marketplace
📹 Video tutorial series
🛡️ Enhanced governance (audit dashboard)
🏢 Enterprise features (SSO, compliance)
📊 Performance optimizations

We're just getting started.

Thank you for an incredible week. 🙏

Ongoing Content (Post-Launch)

Weekly Tips

Example 1:

💡 Artemis City tip:

Test your routing logic before deploying:

codex router test "your query here"


Shows which agent would handle the request.

No LLM calls. No cost. Just validation.

Determinism = testability.

Example 2:

💡 Artemis City tip:

User-owned memory means you can:

📝 Read agent output in Obsidian
🔍 Query structured data via SQL
📊 Build custom dashboards
🔗 Link memory to your PKM system

Your agents, your data, your control.

Community Showcases

🌟 Community Showcase:

@username built a research assistant with Artemis City that:

1. Searches academic papers (researcher agent)
2. Synthesizes findings (planner agent)
3. Generates summary reports (coder agent)

All deterministic. All governed. All in their Obsidian vault.

Amazing work! 🎉

[Link to showcase]

Response Templates

When someone asks "vs Auto-GPT?"

Great question!

Auto-GPT: Exploration & demos (LLM decides everything)
Artemis City: Production systems (kernel decides routing)

Both valid—different purposes.

Detailed comparison: [link]

When someone asks "How to get started?"

pip install artemis-city

codex init my-first-agent

codex run coder "Hello world"


Under 5 minutes. Full guide: [link]

When someone reports an issue

Thank you for the feedback!

Could you share:
1. Error message / unexpected behavior
2. Your routing config (agent_router.yaml)
3. Artemis City version (codex --version)

This helps us fix it quickly.

GitHub issue: [link] or Discord: [link]

Hashtag Strategy

Primary:

#AI #MachineLearning #MLOps #SoftwareEngineering

Secondary:

#LLM #AgenticAI #Infrastructure #DevTools

Avoid:

#AGI #AIRevolution (too hype-y)

Metrics to Track

Social:

  • Impressions
  • Engagement rate
  • Click-through to GitHub

Conversion:

  • GitHub stars
  • Discord joins
  • Quick-start completions

Sentiment:

  • Positive mentions
  • Feature requests
  • Pain points mentioned

Media Assets

Logo Variations:

  • Full logo + wordmark
  • Icon only
  • Monochrome versions

Architecture Diagrams:

  • Kernel-driven vs LLM-driven
  • Memory bus architecture
  • Multi-agent orchestration flow

Screenshots:

  • CLI output examples
  • Routing config
  • Memory bus in Obsidian

Short Clips:

  • 5-second install demo
  • 15-second first agent run
  • 30-second multi-agent workflow

Remember: Authenticity > polish. Technical depth > hype. Problems solved > features listed.