"Blockchain for vegetables? Peak Silicon Valley brain!"
...is what I used to think.
Then I learned how the food supply chain actually works. 👇
youtu.be/Qz09hYJF2-k?...
🗳️ projectcatalyst.io/funds/15/cardano-use-cases-prototype-and-launch/autonomous-greenhouse-agents-with-on-chain-governance
Shipped a tiny but insanely useful feature in Pluggedin
Named Clipboard
Copy something once. Your agents can paste it anywhere.
A shared clipboard for your entire AI stack.
Pluggedin reached 910 users today. All organic.
Feels like something is waking up.
The momentum is real.
Real-world use cases:
- Store screenshots for visual debugging
- Cache JSON API responses across steps
- Save generated images for later use
- Build code snippet libraries
- Persist config files between sessions
- Implement undo/redo stacks
What will you build?
New: Track where your data comes from!
Every clipboard entry now has a source field:
- ui - Created via web interface
- sdk - Via JavaScript, Python, or Go SDK
- mcp - Via MCP proxy tools
Perfect for auditing multi-agent workflows!
// JavaScript SDK
const entry = await client.clipboard.set({
name: 'api_response',
value: JSON.stringify(data)
});
console.log(entry.source); // 'sdk'
# Python SDK
entry = client.clipboard.get(name="api_response")
print(entry.source) # 'sdk'
All SDKs: docs.plugged.in/sdks
For developers:
Multiple access methods:
- MCP tools for Claude, Cursor, Windsurf
- JavaScript/TypeScript SDK
- Python SDK (sync + async)
- Go SDK
- REST API
Your AI workflows, your language choice!
Security-first design:
- Profile-level isolation
- Visibility controls (private/workspace/public)
- Rate limiting per operation type
- Content encoding validation
- Database-level integrity constraints
Your data stays protected.
What makes Clipboard special:
- Named entries: clipboard["api_response"]
- Indexed arrays: clipboard[0], clipboard[1]
- Push/Pop stack operations
- Content type detection (application/json, image/png…)
- Auto-expiration with TTL
- Visual preview for images in UI
Perfect for complex AI workflows!
Store ANY content type your AI needs:
- JSON data & API responses
- Base64-encoded images (screenshots, diagrams)
- Code snippets & configs
- Markdown documents
- Binary files via hex encoding
- Structured data for multi-step workflows
- 2MB per entry - plenty for most use cases!
Introducing Memory Clipboard for Plugged.in!
Your AI agents can now store and retrieve data across sessions - text, JSON, images, code snippets, and more. Named entries, indexed arrays, auto-expiring content with enterprise-grade security.
Available now in v2.20.0
#MCP #AIAgents #DevTools
📊 PAP v1.0 by the numbers:
• 2,993 lines of docs
• 50+ code examples
• 4 REST endpoints
• 1 production cluster
• 0 proprietary dependencies
Deploy your first autonomous agent: docs.plugged.in/agents/getti...
🧟 Zombie Prevention = PAP's Superpower
❌ Traditional: Mix liveness + metrics → control plane saturates ✅ PAP: Strict separation → aggressive detection, no false positives
Heartbeat: 100 bytes, mode + uptime Metrics: Separate channel, unlimited
Simple. Powerful. Production-proven.
🚀 PAP Agents are live!
Deploy autonomous AI agents with: ✅ DNS routing ({name}.is.plugged.in) ✅ Auto TLS (Let's Encrypt) ✅ Zombie prevention ✅ Full observability
Try it: docs.plugged.in/agents
#AI #Agents #Infrastructure
This is the foundation for truly autonomous AI agents:
✅ Governed (Station control)
✅ Observable (heartbeats + metrics)
✅ Secure (protocol-level)
✅ Interoperable (MCP, A2A, OTEL)
✅ Production-ready (live infrastructure)
Docs: docs.plugged.in/agents
Deploy your first agent RIGHT NOW:
curl -X POST plugged.in/api/agents
-H "Authorization: Bearer YOUR_KEY"
-d '{"name": "my-agent"}'
30 seconds later: my-agent.is.plugged.in ✅
📚 Documentation is COMPREHENSIVE:
• 2,993 lines across 6 guides • 50+ code examples (cURL, JS, Python) • Complete REST API reference • Architecture deep dives • Production deployment patterns
docs.plugged.in/agents
Built on 19 years of data center experience from VeriTeknik
This isn't theory. It's production-hardened infrastructure patterns applied to autonomous AI.
Every design decision comes from real operational experience
Production infrastructure LIVE NOW:
🌐 is.plugged.in cluster
📍 Single IP, SNI-based routing
🔒 Automatic Let's Encrypt TLS
☸️ K3s with agent namespace isolation
🚫 Non-root containers (UID 1001)
📊 Full observability (OpenTelemetry)
Dual-Profile Architecture:
🔐 PAP-CP (Control Plane): • gRPC/mTLS for lifecycle ops • Ed25519 signatures + replay protection • Heartbeats, provisioning, termination
🔧 PAP-Hooks (Open I/O): • JSON-RPC 2.0 over WebSocket • MCP tool access, A2A delegation • OAuth 2.1
Normative State Machine: NEW → PROVISIONED → ACTIVE ↔ DRAINING → TERMINATED ↓ KILLED
Invalid transitions = rejected Station has exclusive kill authority State integrity = protocol-enforced
PAP enforces STRICT SEPARATION:
✅ Heartbeat (liveness only): {mode: "IDLE", uptime: 3600}
✅ Metrics (separate channel): {cpu: 87, memory: 2048, requests: 1523, ...}
Result: Aggressive zombie detection without false positives
🧟 THE ZOMBIE PREVENTION SUPERPOWER
Most agent systems mix liveness signals with metrics: ❌ Heartbeat: {status: "ok", cpu: 87%, memory: 2GB, ...}
Problem: Large payloads saturate control planes Result: Can't detect zombies fast enough
PAP fixes this by being the SUBSTRATE layer - not orchestration, but how agents live, breathe, and die across infrastructure
Think: TCP/IP for agent lifecycle management
Your agent gets a name: focus.is.plugged.in DNS-safe, TLS-enabled, production-ready
The problem: Every AI platform has isolated tools, documents, and context
You're constantly: • Re-uploading documents • Reconfiguring tools • Losing continuity between models
It's 2025 and we're still treating AI like it's 2020
🚀 We just shipped PAP v1.0 - the first comprehensive protocol for autonomous AI agent lifecycle management
Here's why it matters (and why the "zombie prevention" feature is actually genius) 🧵
The crossroads for AI data exchanges isn't just a tagline—it's our mission.
We're building the infrastructure layer for AI workflows, and we're doing it in the open, with the community, for everyone.
Whether you're a solo developer or an enterprise team, plugged.in scales with you.
Join us 🚀
Ever wonder which MCP servers are trending? Now you can see! 📊
Our new trending analytics track:
Real-time installation counts
Active usage patterns
Community favorites
Rising stars
Data-driven discovery for your AI toolkit.
911 installations analyzed, insights delivered instantly.
RAG v2 is transforming how AI understands your documents.
178 documents, 17 AI models, infinite possibilities.
Every document is tracked with full attribution—you know exactly which AI created or modified what, when, and why.
It's not just storage. It's institutional memory with a brain.
OAuth integration is live! 🎉
We're not just adding authentication—we're building a secure, seamless bridge between your AI tools and your favorite platforms.
No more manual credential management. No more security compromises.
Just smooth, secure integrations.
📈 Let's talk metrics:
19,147 MCP activities tracked
911 server installations
178 documents in RAG system
17 different AI models integrated
Sub-second search performance
These aren't just numbers—they're interactions, workflows, and solutions built by our community.
What will your contribution be?