# Centurion: 15-Day Cold Start Launch Plan

**Project:** Centurion — AI Agent Orchestration Engine
**Tagline:** Spawn and command an army of AI agents with Roman military precision
**GitHub:** https://github.com/spacelobster88/centurion
**Launch Window:** 15 days from start date

---

## Key Differentiators to Emphasize

1. **100+ agent scale** — not a toy; built for real workloads
2. **K8s-inspired scheduling** — familiar mental model for infra engineers
3. **Roman military hierarchy** — Centurion > Optio > Legionary agent structure
4. **Hardware-aware autoscaling** — GPU/CPU/memory-aware placement
5. **5 integration methods** — REST API, MCP, Skill, A2A, Library import

---

## Success Metrics Dashboard

| Metric | Day 5 Target | Day 10 Target | Day 15 Target |
|--------|-------------|---------------|---------------|
| GitHub Stars | 100 | 500 | 1,000+ |
| Forks | 10 | 50 | 100+ |
| Unique Clones | 50 | 200 | 500+ |
| Traffic (views) | 500 | 2,000 | 5,000+ |
| Contributors | 2 | 5 | 10+ |
| Discord Members | 20 | 80 | 200+ |

Track daily via GitHub Insights, Google Analytics (if docs site exists), and Discord member count.

---

## Phase 1: Pre-Launch (Days 1-3)

### Pre-Launch Checklist

- [ ] README is polished: badges, architecture diagram, quick-start, GIF/video demo
- [ ] Add GitHub topics: `ai-agents`, `orchestration`, `llm`, `multi-agent`, `kubernetes`, `autoscaling`, `mcp`, `a2a`, `agent-framework`, `python`
- [ ] Create a social preview image (1280x640) — Roman military theme + AI aesthetic
- [ ] Write a compelling repo description (max 350 chars)
- [ ] Pin 3-4 GitHub Discussions: "Welcome & Roadmap", "Show Your Use Case", "Feature Requests", "Q&A"
- [ ] Create CONTRIBUTING.md with clear first-issue labels and setup instructions
- [ ] Label 5-10 issues as `good first issue` and `help wanted`
- [ ] Set up a Discord server with channels: #general, #support, #show-and-tell, #contributing, #announcements
- [ ] Prepare all post drafts for Days 4-10
- [ ] Create a ProductHunt upcoming page

---

### Day 1: Repository Polish & Infrastructure

**Actions:**
- Finalize README with architecture diagram, quick-start (under 3 commands), and feature matrix
- Add all GitHub topics and social preview image
- Set repo description: `AI Agent Orchestration Engine — scale to 100+ agents with K8s-inspired scheduling, Roman military hierarchy, and hardware-aware autoscaling. REST/MCP/Skill/A2A/Library.`
- Create CONTRIBUTING.md (see template below)
- Tag 5+ issues as `good first issue`

**CONTRIBUTING.md Outline:**
```
# Contributing to Centurion

Welcome, Legionary! Every great army needs soldiers.

## Ways to Contribute
- Report bugs / submit issues
- Add new agent types (Legionary subclasses)
- Write tests (we target 80%+ coverage)
- Improve documentation
- Build integrations (new MCP tools, A2A protocols)
- Frontend dashboard components

## Development Setup
1. Clone the repo
2. pip install -e ".[dev]"
3. pytest — all tests should pass
4. Create a branch, make changes, open a PR

## Code Style
- We use ruff for linting
- Type hints required on all public functions
- Docstrings on all public classes/methods

## PR Process
- Reference an issue if one exists
- Include tests for new features
- Maintainers aim to review within 48 hours
```

---

### Day 2: Content Drafting

**Actions:**
- Draft all major posts (HN, Reddit, LinkedIn, Twitter thread, Dev.to article)
- Prepare a 2-minute demo video or animated GIF showing: install, spawn agents, watch orchestration
- Write the ProductHunt tagline, description, and first comment

**Demo Script (for GIF/video):**
```
1. pip install centurion
2. Start the engine (show the Roman-themed CLI output)
3. Spawn 20 agents with a single command
4. Show the scheduling dashboard / logs
5. Scale to 100 agents — show hardware-aware placement
6. Kill it — show graceful teardown
```

---

### Day 3: Soft Outreach & Seeding

**Actions:**
- Send contributor invitation emails to 10-15 developer friends (see companion doc)
- Post in personal networks: "Working on something exciting, launching soon"
- Join 3-5 AI/ML Discord servers if not already a member (MLOps Community, Latent Space, AI Engineer, LangChain Discord, LocalLLaMA Discord)
- Introduce yourself in those servers organically — do NOT spam yet

---

## Phase 2: Soft Launch (Days 4-7)

### Day 4: GitHub & Dev.to Launch

**Actions:**
- Publish the Dev.to article (cross-post to Medium)
- Pin the GitHub Discussions welcome post
- Star the repo from personal accounts (organic signal)

**Dev.to / Medium Article: "Building an AI Agent Army: Why We Created Centurion"**

Outline:
```
Title: Building an AI Agent Army: Why We Created Centurion

1. The Problem
   - Current agent frameworks handle 1-2 agents
   - Real-world tasks need 10, 50, 100+ agents working together
   - No framework treats agent orchestration as an infrastructure problem

2. The Insight: Kubernetes for AI Agents
   - What if we applied container orchestration principles to agents?
   - Scheduling, health checks, autoscaling — all proven patterns
   - Roman military hierarchy as an intuitive org model

3. Architecture Deep-Dive
   - Centurion (orchestrator) → Optio (sub-commanders) → Legionary (worker agents)
   - Hardware-aware scheduler: GPU agents vs CPU agents
   - 5 integration methods and when to use each

4. Demo Walkthrough
   - Install in one command
   - Spawn 50 agents processing a research task
   - Show autoscaling in action

5. What's Next & How to Contribute
   - Roadmap highlights
   - Link to good-first-issues
   - Discord invite
```

Tags: `#ai`, `#opensource`, `#python`, `#machinelearning`

---

### Day 5: LinkedIn Launch

**Actions:**
- Publish LinkedIn post (professional angle)
- Engage with every comment within 2 hours
- Ask 3-5 connections to reshare

**LinkedIn Post Template:**

```
After months of building, I'm excited to open-source Centurion —
an AI Agent Orchestration Engine.

The problem: Most agent frameworks work with 1-2 agents.
But real enterprise workloads need 50, 100, or more agents
coordinating in real time.

Centurion solves this with:

-> K8s-inspired scheduling for AI agents
-> Hardware-aware autoscaling (GPU/CPU/memory)
-> Roman military hierarchy (Centurion > Optio > Legionary)
-> 5 integration methods: REST, MCP, Skill, A2A, Library
-> Built for 100+ agent scale from day one

Think of it as "Kubernetes for AI Agents" — the same patterns
that revolutionized container orchestration, applied to the
next wave of AI infrastructure.

The repo is live and looking for contributors:
https://github.com/spacelobster88/centurion

If you're building multi-agent systems, I'd love your feedback.
What orchestration challenges are you facing?

#AI #OpenSource #AgentAI #MLOps #Python
```

---

### Day 6: Reddit Launch

**Actions:**
- Post to r/MachineLearning (Discussion tag), r/LocalLLaMA, r/artificial
- Post to r/Python with a more code-focused angle
- Monitor and respond to ALL comments (Reddit engagement is critical)

**r/MachineLearning Post:**

```
Title: [P] Centurion — Open-source AI Agent Orchestration Engine
       for 100+ agent scale

Body:
Hi r/MachineLearning,

I've been working on Centurion, an open-source orchestration
engine for AI agents that handles 100+ agents with
Kubernetes-inspired scheduling.

The core idea: treat AI agents like containers. Schedule them
based on hardware requirements, health-check them, autoscale
them, and organize them in a hierarchy.

Key features:
- Centurion > Optio > Legionary hierarchy
  (like K8s: Cluster > Node > Pod)
- Hardware-aware placement (GPU vs CPU agents)
- 5 integration methods: REST API, MCP protocol,
  Skill interface, A2A protocol, direct library import
- Built-in autoscaling based on workload + hardware metrics

GitHub: https://github.com/spacelobster88/centurion
Docs: [link]

Would love feedback, especially from anyone running
multi-agent setups in production. What pain points
are you hitting?
```

**r/LocalLLaMA Post (tailored):**

```
Title: Centurion: orchestrate 100+ local LLM agents with
       hardware-aware scheduling (open source)

Body:
For those of you running multiple local LLM instances,
I built an orchestration engine that manages agent
placement based on available GPU/CPU/memory.

Think: you have 4 GPUs, 20 tasks of varying complexity.
Centurion schedules the right agent to the right hardware,
scales up/down automatically, and coordinates everything
through a military-inspired hierarchy.

It's like having a Roman general commanding your AI army.

Supports REST, MCP, A2A, and more.

GitHub: https://github.com/spacelobster88/centurion
```

**r/Python Post:**

```
Title: Centurion — Python framework for orchestrating 100+
       AI agents (K8s-inspired, open source)

Body:
pip install centurion — and you get a full agent
orchestration engine with scheduling, autoscaling,
and 5 integration methods.

Built with Python, fully typed, extensible agent classes.
Looking for contributors — especially for new agent types,
tests, and dashboard components.

[Link + quick code example showing 10 lines to spawn agents]
```

---

### Day 7: Twitter/X Thread

**Actions:**
- Publish a Twitter/X thread (8-10 tweets)
- Tag relevant accounts (@AndrewYNg, @kaboroevich, @swaboroevich, or other AI infra accounts relevant to the space)
- Use hashtags: #AIAgents #OpenSource #BuildInPublic

**Twitter/X Thread:**

```
Tweet 1:
I just open-sourced Centurion — an AI Agent Orchestration Engine
that scales to 100+ agents.

Think "Kubernetes for AI Agents."

Here's what it does and why it matters.

A thread. [1/8]

Tweet 2:
The problem: every agent framework handles 1-2 agents well.

But what happens when you need 50 agents researching,
10 agents coding, and 20 agents reviewing — all at once?

You need an orchestrator. [2/8]

Tweet 3:
Centurion uses a Roman military hierarchy:

Centurion = orchestrator (the general)
Optio = sub-commanders (manage groups)
Legionary = worker agents (do the tasks)

Just like K8s has Cluster > Node > Pod. [3/8]

Tweet 4:
Hardware-aware scheduling:

- GPU-hungry agents? Route to GPU nodes
- Lightweight tasks? CPU is fine
- Running low on VRAM? Autoscale or queue

It knows your hardware and acts accordingly. [4/8]

Tweet 5:
5 ways to integrate:

1. REST API — standard HTTP
2. MCP — Model Context Protocol
3. Skill — plug-in interface
4. A2A — agent-to-agent protocol
5. Library — import and call directly

Pick what fits your stack. [5/8]

Tweet 6:
Quick start:

pip install centurion

3 lines of Python to spawn 50 agents.
10 lines to build a full research pipeline.

[Screenshot or code snippet] [6/8]

Tweet 7:
It's fully open source (MIT/Apache license).

Looking for contributors:
- New agent types
- Dashboard frontend
- More integrations
- Tests & docs

Good-first-issues are tagged and waiting. [7/8]

Tweet 8:
Check it out:

GitHub: https://github.com/spacelobster88/centurion

Star it if you think multi-agent orchestration
is the next infrastructure challenge.

Let me know what you'd build with it. [8/8]
```

---

## Phase 3: Full Launch (Days 8-10)

### Day 8: Hacker News — Show HN

**Actions:**
- Submit "Show HN" post (aim for 8-10 AM ET on Tuesday or Wednesday)
- Have 3-5 friends ready to upvote and leave genuine comments within the first hour (critical for HN algorithm)
- Monitor and respond to every comment quickly and thoughtfully

**Show HN Post:**

```
Title: Show HN: Centurion — Open-source AI agent orchestration
       engine for 100+ agents

Body:
Hi HN,

I built Centurion, an open-source AI agent orchestration engine
inspired by Kubernetes scheduling and Roman military hierarchy.

The problem: Current agent frameworks are designed for 1-2
agents. Real workloads need dozens or hundreds of agents
coordinating across heterogeneous hardware.

Centurion treats agents like K8s treats containers:
- A scheduler places agents on hardware based on
  GPU/CPU/memory requirements
- Health checks and automatic restarts keep agents alive
- Autoscaling responds to workload and hardware metrics
- A hierarchy (Centurion > Optio > Legionary) organizes
  command and control

Integration options: REST API, MCP protocol, Skill interface,
A2A protocol, or direct Python library import.

Built in Python. Typed. Tested. MIT licensed.

GitHub: https://github.com/spacelobster88/centurion

I'd especially love feedback on:
1. The scheduling algorithm (bin-packing with affinity rules)
2. The A2A protocol design
3. What agent types you'd want built in

Thanks for looking.
```

**HN Comment Strategy:**
- Be humble and technical in responses
- Acknowledge limitations honestly
- Compare fairly to alternatives (CrewAI, AutoGen, LangGraph)
- If asked "why not just use X?" — answer with specific technical differences, never dismissive

---

### Day 9: ProductHunt Launch

**Actions:**
- Launch on ProductHunt (schedule for 12:01 AM PT)
- Post the "maker comment" immediately
- Share the PH link on Twitter, LinkedIn, Discord

**ProductHunt Listing:**

```
Tagline: Kubernetes for AI Agents — orchestrate 100+ agents
         with military precision

Description:
Centurion is an open-source AI Agent Orchestration Engine that
brings Kubernetes-level infrastructure to multi-agent systems.

- Scale to 100+ agents with hardware-aware scheduling
- Roman military hierarchy: Centurion > Optio > Legionary
- 5 integration methods: REST, MCP, Skill, A2A, Library
- Autoscaling based on workload and hardware metrics
- Built in Python, fully typed, extensible

Stop managing agents manually. Let Centurion command the army.

Maker Comment:
Hey ProductHunt! I'm Eddie, the creator of Centurion.

I built this because I kept hitting the same wall: agent
frameworks work great for demos with 1-2 agents, but fall
apart when you need real scale.

The insight was that container orchestration (K8s) already
solved these problems — scheduling, health checks, autoscaling.
Why not apply the same patterns to AI agents?

The Roman military theme isn't just branding — it maps
naturally to the architecture. A Centurion commands up to
100 Legionaries through Optios. Same structure, same scale.

It's fully open source and I'd love contributors.
Feedback welcome — what would you orchestrate?
```

---

### Day 10: Discord Community Blitz

**Actions:**
- Post in 5-8 AI/ML Discord servers (where you've been active since Day 3)
- Frame as sharing, not promoting: "I built this thing, would love feedback"
- Offer to help anyone trying it out in real-time

**Discord Message Template:**

```
Hey everyone! I've been working on an open-source project
called Centurion — it's an AI agent orchestration engine
for managing 100+ agents.

If you've ever tried running multiple AI agents and hit
scaling issues, this might interest you. It uses K8s-style
scheduling with hardware-aware placement.

GitHub: https://github.com/spacelobster88/centurion

Happy to answer questions or help anyone get it running.
Would love feedback from people doing multi-agent stuff.
```

---

## Phase 4: Sustained Engagement (Days 11-15)

### Day 11: Follow-Up Content

**Actions:**
- Publish a "Week 1 Retrospective" post on Dev.to/Medium: what you learned, what surprised you, early feedback
- Share interesting GitHub stats (stars growth chart, contributor map)
- Thank early contributors publicly on Twitter/LinkedIn

**Blog Post Outline: "Centurion Week 1: Launching an Open-Source AI Agent Engine"**
```
1. The numbers (stars, forks, contributors, traffic)
2. Surprising feedback and feature requests
3. What the community taught us
4. Technical improvements made based on feedback
5. What's coming next
```

---

### Day 12: Contributor Spotlight & Issue Push

**Actions:**
- Highlight first external contributors on Twitter/LinkedIn
- Create 10 more `good first issue` labels based on community feedback
- Write a "Contributors Wanted" GitHub Discussion with specific task descriptions
- Respond to any open issues or PRs (48-hour response target)

---

### Day 13: Technical Deep-Dive Content

**Actions:**
- Publish a deep-dive blog post on the scheduling algorithm or A2A protocol
- Cross-post to Dev.to, Medium, and Hacker News (as a regular post, not Show HN)
- Share on Reddit r/programming and r/compsci

**Blog Post Outline: "How Centurion Schedules 100 AI Agents Across Heterogeneous Hardware"**
```
1. The scheduling problem: N agents, M machines, varying resources
2. Bin-packing with affinity/anti-affinity rules
3. Hardware-aware placement: GPU memory, CPU cores, RAM
4. Autoscaling triggers and cooldown periods
5. Benchmarks: scheduling latency at 10, 50, 100, 200 agents
6. Comparison to K8s scheduling (similarities and differences)
```

---

### Day 14: Partnership & Integration Outreach

**Actions:**
- Reach out to 3-5 complementary open-source projects for integration opportunities (LangChain, LlamaIndex, Ollama, vLLM, etc.)
- Propose joint blog posts or integration guides
- Submit Centurion to awesome-lists: `awesome-ai-agents`, `awesome-llm`, `awesome-python`
- Post in relevant GitHub Discussions on other projects: "We built X integration with your project"

---

### Day 15: Retrospective & Next Sprint Planning

**Actions:**
- Publish full 2-week retrospective (numbers, learnings, roadmap)
- Set up automated weekly metrics tracking
- Plan the next content cycle (bi-weekly blog posts, monthly releases)
- Create a public roadmap (GitHub Projects board)
- Thank the community

**Retrospective Template:**
```
# Centurion: 2-Week Launch Retrospective

## Numbers
- Stars: X (target was 1,000)
- Forks: X
- Contributors: X
- PRs merged: X
- Issues opened: X
- Discord members: X

## Top Referral Sources
1. Hacker News
2. Reddit
3. Twitter/X
4. ProductHunt
5. Dev.to/Medium

## Surprises
- [What worked better than expected]
- [What didn't work]
- [Unexpected use cases people proposed]

## Community Feedback Themes
- [Top requested features]
- [Common questions/confusions]
- [Architecture suggestions]

## Next Steps
- [Roadmap items informed by community feedback]
- [Upcoming releases]
- [Content plan for weeks 3-4]
```

---

## Chinese Community (Placeholder)

> **Note:** The following platforms will be handled separately with Chinese-language content. Placeholder for coordination:

- **WeChat Official Account Article** — Translate the Dev.to article, adapt for Chinese developer audience. Publish during Day 5-6 window.
- **Zhihu (zhihu.com)** — Post a technical Q&A style article: "How to orchestrate 100+ AI agents?" with Centurion as the answer.
- **CSDN** — Publish a tutorial-style post with code examples. Tag: AI, Python, multi-agent.
- **V2EX** — Post in the /t/programmer or /t/ai node. Keep it concise, link to GitHub.
- **Juejin (juejin.cn)** — Publish the architecture deep-dive. Juejin audience loves technical diagrams and benchmarks.

Timing: Stagger 1-2 days after English launches to build on initial GitHub star momentum (social proof matters on Chinese platforms).

---

## Daily Engagement Rules

1. **Respond to every comment/question within 4 hours** (2 hours during launch days)
2. **Never be defensive** — thank critics, acknowledge limitations, ask follow-up questions
3. **Upvote and share** others' content in the same space (reciprocity)
4. **Track which platforms drive the most stars** — double down on what works
5. **Screenshot and share milestones** (100 stars, first external PR, first production user)

---

## Post Timing Cheat Sheet

| Platform | Best Time (ET) | Best Day |
|----------|---------------|----------|
| Hacker News | 8-10 AM | Tue/Wed |
| Reddit | 8-10 AM | Mon-Thu |
| Twitter/X | 9-11 AM | Tue-Thu |
| LinkedIn | 8-10 AM | Tue-Thu |
| ProductHunt | 12:01 AM PT | Tue-Thu |
| Dev.to | 7-9 AM | Mon-Wed |
| Discord | Evening | Any |

---

## Tools & Resources

- **GitHub Insights** — stars, traffic, clones, referral sources
- **Star History** (star-history.com) — visual star growth chart for sharing
- **Shields.io** — dynamic badges for README
- **Carbon** (carbon.now.sh) — beautiful code screenshots for social media
- **OG Image Generator** — social preview images
- **Buffer / Typefully** — schedule social media posts
