AI Memory for Asian Dev Teams: Singapore Node, CJK Support, and Why GDPR Is Good for Asia Too

The problem: AI assistants have no memory

You spend 45 minutes debugging a tricky TypeScript issue with Claude Code. You find the fix, ship it, close the tab. Next day, same error in a different file. You open Claude Code — and it has no idea what you found yesterday. The context window reset.

This is the AI amnesia problem. Every developer using Claude, Cursor, or GitHub Copilot hits it. The AI is smart, but it forgets everything the moment the session ends.

For teams building in Japan, South Korea, China, or Singapore, it is even worse: most AI memory tools are built for English-speaking Western markets, run on US servers, and have no meaningful support for Japanese, Chinese, or Korean text.

cachly: persistent AI memory, built for global teams

cachly is a Redis-compatible cache with an AI Brain layer on top. The Brain stores everything your AI learns — fixes, architecture decisions, conventions, deployment lessons — and recalls them semantically on the next session.

The key tools are simple:

• learn_from_attempts — Store what worked (and what failed). Called automatically by your AI after a fix or deploy.
• smart_recall— Semantic search over all stored lessons. Your AI asks "how did we fix the Docker healthcheck?" and gets the exact answer from 3 weeks ago.
• session_start / session_end — Automatic. Your AI picks up where it left off.

The entire setup takes under 60 seconds. Add one JSON block to your Claude Code or Cursor config. No API keys, no dashboards, no manual management.

Singapore node: 10ms to Tokyo, 15ms to Seoul

This is the part that matters for APAC teams: we run a dedicated node in Singapore ( cachly-node-singapore-4), specifically because 200ms round-trip to Germany makes AI tools feel sluggish.

Typical latencies from the Singapore node:

smart_recall returns in under 80ms p99 globally. From Tokyo via Singapore, that is under 100ms total — fast enough that it feels instant inside Claude Code or Cursor.

Select the APAC region when you create your cachly instance. Your data stays in Singapore — it never routes through Germany or the US.

CJK language support: Japanese, Chinese, Korean

Most AI memory systems are English-first. Embedding models like OpenAI's text-embedding-3-small handle English beautifully but degrade significantly on CJK text — especially character-level languages like Chinese and Japanese where tokenization works differently.

cachly uses two approaches:

1. Key-based memory for structured facts

The simplest and most reliable path for CJK: store lessons with descriptive English keys, Japanese/Chinese/Korean content in the value.

// Store a lesson in Japanese
await brain.learn({
  topic: "fix:keycloak-auth",
  what_worked: "認証エラーはKeycloakのclient_idが間違っていたのが原因。\n" +
               "正しいclient_id: cachly-web (cachly-appではない)",
  tags: ["keycloak", "auth", "japan-team"]
})

// Recall it later in English — it still finds it
await brain.smart_recall("How did we fix the Keycloak auth issue?")

The semantic search works cross-language: you can write the lesson in Japanese and recall it in English, or vice versa. The embedding model (nomic-embed-text) handles multilingual text natively.

2. nomic-embed-text: genuinely multilingual

cachly runs nomic-embed-text on our own infrastructure (no third-party API required). It is trained on 43 languages including Japanese, Simplified Chinese, Traditional Chinese, and Korean. Unlike models fine-tuned only for English, nomic-embed-text produces meaningful embeddings for CJK text without additional configuration.

You can store and recall in any language:

# Python — store in Chinese, recall in English
brain.learn(topic="infra:docker", what_worked="Docker健康检查必须使用127.0.0.1而不是localhost")
brain.smart_recall("Docker healthcheck issue")  # finds it

Data sovereignty: why GDPR is good for Asian teams too

European GDPR is often framed as a Western regulation, but its principles align closely with Asia's own data protection frameworks:

cachly's architecture satisfies all of these:

• Data localization: APAC data stays in Singapore. EU data stays in Germany. No cross-region transfer by default.
• No third-party AI APIs: The embedding model runs on our infrastructure. Your code and your lessons never pass through OpenAI, Google, or AWS.
• Encryption in transit: TLS enforced on all connections.
• Encryption at rest: AES-256 on Business and Enterprise plans.
• DPA/AVV auto-generated: We generate a Data Processing Agreement automatically at sign-up — legally valid under EU GDPR and accepted by Japanese/Korean enterprise procurement.

Team Brain: shared memory across the whole team

The most powerful use case for Asian dev teams is shared team memory. In Japan especially, there is a strong culture of knowledge sharing (知識共有, chishiki kyōyū) — lessons should not be siloed in one person's chat history.

With a shared cachly instance, every developer's AI editor reads from and writes to the same Brain:

# All 5 team members point to the same instance_id in their MCP config:
{
  "mcpServers": {
    "cachly": {
      "command": "npx",
      "args": ["@cachly-dev/mcp-server@latest"],
      "env": {
        "CACHLY_INSTANCE_ID": "shared-team-brain-uuid",
        "CACHLY_JWT": "your-jwt-token"
      }
    }
  }
}

When Hiroshi fixes a deployment issue at 9am Tokyo time, that lesson is in the shared Brain by 9:01am — available to Yuki in Seoul and Wei in Shanghai immediately, in whatever language they recall it in.

Setup in 60 seconds

The entire setup — account, Singapore instance, MCP configuration — takes under 60 seconds:

1. Go to cachly.dev/sign-up → create an account (10 seconds, no credit card)
2. Create an instance, select APAC (Singapore) region (30 seconds)
3. Add one JSON block to your Claude Code or Cursor MCP config (20 seconds)

The free tier (25 MB, 1 instance) is enough for an individual developer. Teams upgrade to Pro or Business for shared instances and more memory.

What Claude remembers after cachly

Here is a real example of what a Tokyo-based team's Brain contains after one month of use:

Every new session, Claude Code pre-loads these lessons automatically via session_start. The AI arrives knowing your infrastructure, your conventions, and your past mistakes — before you type a single prompt.

What 'Claude forgets' costs APAC teams

Let's put a number on it. A senior developer in Tokyo earns roughly ¥15–20M/year — about ¥8,000–10,000 per hour. A typical "re-solving a known problem" episode takes 45–90 minutes. At ¥9,000/hour, that is ¥6,750–13,500 wasted — per incident.

A 5-person team hitting this twice a week: ¥5.4M–10.8M per year in wasted developer time. The cachly Business plan (for teams) is €199/month — less than the cost of one wasted afternoon.

Getting started

cachly is free to try — no credit card, no time limit, no OpenAI API key required.

For APAC teams: select the Singapore region when creating your first instance. The MCP server handles everything else — authentication, memory, recall — automatically.