Every keystroke. Every conversation. Every preference. Cloud AI services see it all. There's a better way: local-first AI that never phones home.

The Privacy Problem

When you use cloud-based AI memory services, your data travels:

From your device to their servers
Through their embedding models
Into their vector databases
Stored indefinitely (read the ToS carefully)

For personal notes? Maybe that's fine. For enterprise code, medical records, financial data, or anything covered by GDPR/HIPAA? That's a compliance nightmare.

What Cloud Memory Services See

• Your conversations (stored as embeddings)
• Your preferences and behaviors
• Your code and project details
• Your decisions and reasoning
• Timestamps of when you work

The Local-First Alternative

Local-first means your data never leaves your device. The memory system runs entirely on your machine:

✓ Local-First

• Data stays on device
• No network requests
• No accounts or API keys
• Works offline
• You control deletion

✗ Cloud-Based

• Data on their servers
• Network dependent
• Requires accounts
• Fails without internet
• Deletion policies vary

Beyond Privacy: Performance

Local-first isn't just about privacy. It's faster:

Latency Comparison

Cloud Memory API200-500ms

Local Memory (Shodh)5-50ms

10-40x faster. No network round-trip.

Use Cases for Local-First

1. Robotics & Edge Devices

A robot navigating a warehouse can't wait 200ms for a cloud round-trip. At 2m/s, that's 40cm of movement with stale data. Local memory runs in under 1ms.

2. Healthcare & Legal

Patient records, legal documents, financial data—anything with compliance requirements shouldn't touch third-party servers. Local-first means HIPAA/GDPR compliance by default.

3. Offline Operation

Field workers, aircraft systems, rural deployments—anywhere internet is unreliable. Local-first works with zero connectivity.

4. Developer Privacy

Your code, your architecture decisions, your debugging sessions. Keep them local.

How It Works Technically

local_first.pypython

from shodh_memory import Memory

# Everything runs on your machine:
# - Embedding model: MiniLM-L6-v2 (22MB, ONNX)
# - Vector index: Vamana HNSW (in-process)
# - Storage: RocksDB (embedded)
# - No network calls. Ever.

memory = Memory(storage_path="./my_private_data")

# This never leaves your device
memory.remember("Patient ID 12345 prefers morning appointments")
memory.remember("Case #789 settled for $50,000")

# Semantic search runs locally
results = memory.recall("patient scheduling preferences")

The Trade-off

Local-first has one trade-off: you manage the infrastructure. There's no managed cloud service to handle scaling, backups, or multi-device sync.

For many use cases, that's the right trade-off. Your data, your control.

When to Choose Local-First

✓ Sensitive data (healthcare, legal, financial)
✓ Compliance requirements (GDPR, HIPAA, SOC2)
✓ Offline operation needed
✓ Low-latency requirements (<50ms)
✓ Edge devices (robots, IoT, embedded)
✓ Cost sensitivity (no API fees)

Get Started

Terminalbash

# Install
pip install shodh-memory

# Or with Claude Code
claude mcp add @shodh/memory-mcp

15MB binary. Zero cloud dependencies. Your data stays yours.