Hacking the Data Layer for AI with Akave and Filecoin
summary
Demonstrates a working Akave 03 S3-compatible API that uploads, lists and downloads files (created a bucket, uploaded a 1 MB test file, downloaded it) while hot storage nodes erasure‑code client‑encrypted chunks (32 MB chunks split to ≤1 MB blocks) and batch seal 32 GB into Filecoin for cold/warm persistence. Shows integration with Model Context Protocol (MCP) using a local LLM (Quant 2.5 7B) to list buckets and read file contents, claims up to 80% cost savings and low egress.
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chapters (15)
- 0:00Introduction to Akave & Filecoin for AI Data
- 0:43Why Decentralized Storage Matters for AI
- 1:14Benefits of Decentralized Storage
- 1:41IPFS vs Filecoin: Key Differences
- 2:56How Akave Complements Filecoin Storage
- 3:51Akave Hot Storage vs Filecoin Warm Storage
- 4:17Akave 03: S3-Compatible Decentralized Storage
- 5:37How Akave Secures Data with Erasure Coding
- 6:34Akave 03 Features and Benefits
- 7:02Live Demo: Akave 03 S3 CLI Setup
- 8:20Creating Buckets & Uploading Files Demo
- 10:16Downloading Files from Akave 03
- 11:26Model Context Protocol (MCP) with Local AI
- 12:58Running MCP Server with Akave Credentials
- 14:23AI Agent Interacting with On-Chain Data
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