About the Infrastructure

The csasq NODES project was born out of necessity. Scaling large language models and vector databases requires pristine, massive datasets. Centralizing the data collection pipeline proved to be a critical bottleneck.

The Centralization Problem

Historically, our data ingestion pipeline looked like this: a central server in Russia would make HTTP GET requests to target URLs worldwide. It would download megabytes of raw HTML, loaded with CSS, JavaScript, and tracking pixels, just to extract a few kilobytes of useful article text.

This approach suffered from:

  • Massive ingress bandwidth costs.
  • High latency due to geographic distance and TLS handshakes.
  • Frequent IP blocks from regional firewalls due to aggressive centralized scraping.

The Edge Solution

By distributing the initial stages of our pipeline to regional VPS instances (csasq NODES), we inverted the architecture. Now, the central server merely acts as an orchestrator.

The edge worker:

  1. Receives a list of target URLs via a persistent WebSocket.
  2. Fetches the content locally (bypassing regional geo-blocks).
  3. Applies strict DOM sanitization (stripping <script>, <style>, etc.).
  4. Runs the text through a local HuggingFace tokenizer and ONNX transformer.
  5. Streams back ONLY the final mathematical vector (Float32 array).

Node Hardware Specifications

To maintain cost-efficiency while providing enough compute for lightweight ML models, our standard edge node profile is standardized across all cloud providers:

Architecture

KVM / NVMe Cloud

vCPU Cores

2-4 (High Frequency)

RAM Allocation

4 GB - 8 GB

Network Port

1 Gbps Unmetered