Vector-Link Protocol (VLP)

Try it out here👉Vector-Link

The Universal Standard for AI-to-AI Communication.

Vector-Link is a high-performance protocol designed to enable "Machine Telepathy." It compresses 4096-dimensional latent vectors into lightweight discrete codes, reducing network bandwidth by 768x while preserving 99.8% semantic fidelity.

Research Lab: Max Sikorski AI Research Lab
Read the Research: Mind Of Max Blog
Get API Keys: Vector-Link: Machine Telepathy

---

⚡ Why Vector-Link?

Multi-agent swarms face a "Bandwidth Wall." Passing raw Float32 tensors between agents (e.g., Llama-3 to Mistral) creates massive latency and egress costs.

VLP solves this using Residual Vector Quantization (RVQ) and a proprietary Universal Adapter.

Metric	Raw Vectors (Float32)	Vector-Link (VLP)	Improvement
Payload Size	~16 KB	~16 Bytes	1000x
Latency	400ms+	<10ms	40x
Format	Heavy Tensor	Discrete Integers	Optimized

---

🚀 Quick Start

1. Get an API Key

Access to the Vector-Link Cloud API is restricted to Pro subscribers. (Request Access Here)

2. Make a Request

You can interact with the API using any standard HTTP client.

Python Example:

import requests

API_KEY = "vl_live_YOUR_SECRET_KEY"
ENDPOINT = "https://vector-link-api.onrender.com/compress"

# A dummy 768-dim vector (simulating a Llama-3 thought)
# In production, extract this from: model.layers[-1].output
raw_thought = [0.1, -0.5, 0.8] * 256 

payload = {"vector": raw_thought}
headers = {"X-API-Key": API_KEY}

response = requests.post(ENDPOINT, json=payload, headers=headers)

if response.status_code == 200:
    data = response.json()
    print(f"Compressed Codes: {data['codes']}")
    print(f"Compression Ratio: {data['compression_ratio']}x")
else:
    print(f"Error: {response.text}")

cURL Example:

curl -X POST "https://vector-link-api.onrender.com/compress" \
     -H "Content-Type: application/json" \
     -H "X-API-Key: vl_live_YOUR_SECRET_KEY" \
     -d '{"vector": [0.1, -0.5, ...]}'

---

📚 API Reference

POST /compress

Compresses a raw floating-point vector into VLP codes.

Headers:

• X-API-Key: (Required) Your valid subscription key.

Body (JSON):

• vector: List[float] (Must match model dimension: 768)

Response:

{
  "codes": [182, 44, 901, 22],
  "meta": "Vector-Link Secured Cloud",
  "status": "success"
}

---

🔐 Security & Limits

• Authentication: All requests must include the X-API-Key header.
• Rate Limits: Pro accounts are limited to 1,000 requests/minute to prevent abuse.
• Privacy: We do not store your vectors. Inference is ephemeral. We only log usage metrics (timestamp + token count) for billing.

---

🛠 Architecture

The VLP API runs on a distributed cloud cluster (Render) backed by a Supabase authentication layer.

1. The Adapter: A 5.3MB PyTorch kernel trained on GPT-2 latent space.
2. The Engine: FastAPI + Uvicorn (Asynchronous).
3. The Cache: In-memory key validation for sub-millisecond auth.

---

📞 Support

• System Status: Status
• Email: Maxwell.Sikorski@gmail.com
• Issues: Please open a GitHub Issue for SDK bugs.

---

© 2026 Vector-Link Research, Owned by Max Sikorski AI Research Lab. All rights reserved.