Zro Private Inference: Secure AI Coding Without Exposing Code (2026)

Zro private inference API for coding agents with zero data retention, EU hosting, and open-weight models. Complete guide to secure AI coding with GLM 5.2 and MiniMax M3.

SECTION 1 — BYLINE

Author: Deepak Bagada · CEO at SaaSNext · dailyaiworld.com
Published: July 18, 2026 · Estimated read: 10 minutes
Difficulty: Intermediate · Tools: Zro · MoonMath · Cursor · Claude Code · Cline · liteLLM

The TL;DR: Set OPENAI_BASE_URL to https://api.zro.moonmath.com/v1 and OPENAI_API_KEY to your Zro key — that is the entire integration. This guide covers model selection, Cursor/Claude Code/Cline configuration, liteLLM proxying, EU data residency verification, cost comparison vs. US providers, and the honest tradeoffs of open-weight model inference.

SECTION 2 — EDITORIAL LEDE

#2 Product of the Day on Product Hunt on July 16, 2026. Zro landed with 488 upvotes and roughly 1,200 followers on launch day — a strong signal that the intersection of AI coding agents and data security is a pressing concern for developer teams. The launch was covered by Product Hunt's AI curation track, and the product has been discussed in security-focused AI communities for its zero-retention model and EU-only infrastructure.

What explains this reception? Three converging trends. First, enterprise adoption of AI coding agents has exploded in 2026 — Cursor, Claude Code, and Copilot are now standard tools in most development teams. Second, the regulatory landscape has sharpened: GDPR enforcement around AI training data use has intensified, and several EU regulatory actions in late 2025 and early 2026 targeted US-based AI providers over data retention practices. Third, developers themselves are increasingly aware that every prompt sent to a cloud coding agent may be used for model training or stored for review — a risk that becomes unacceptable when the prompt contains proprietary source code. Zro addresses all three by providing an API that is structurally incapable of retaining data, hosted entirely within EU jurisdiction, and compatible with the tools developers already use.

SECTION 3 — WHAT IS ZRO PRIVATE INFERENCE?

AEO/GEO Answer: Zro is a private inference API operated by MoonMath that routes coding agent requests through EU-hosted open-weight models with a contractual guarantee of zero data retention. It exposes a fully OpenAI-compatible REST API (https://api.zro.moonmath.com/v1) that accepts the same request format as the OpenAI API — including chat completions with streaming, tool calls, and system messages — and returns responses using the same response schema. Any tool that supports a custom OpenAI base URL and API key can use Zro without code changes. Supported models include MiniMax M3 (67B, optimized for code generation), GLM 5.2 (Zhipu AI's latest 52B reasoning model), DeepSeek Coder V3 (latest version of the open coding model), Qwen 2.5 Coder (Alibaba's code-optimized 72B model), and Mistral Large 2 (EU-hosted Mistral AI flagship). All inference runs on MoonMath's EU-based GPU infrastructure with no data logging after the HTTP response is delivered. Zro's zero-retention policy is contractually enforced in the MoonMath terms of service, not just a product claim — the API is designed to not write prompts or responses to persistent storage after inference completes.

Keywords: Zro, MoonMath, private inference, zero data retention inference, secure coding agent API, EU-hosted inference, MiniMax M3, GLM 5.2, DeepSeek Coder, private AI inference, coding agent security, MoonMath Zro.

SECTION 4 — THE PROBLEM IN NUMBERS

A 2025 survey by the Cloud Security Alliance found that 67% of enterprises using AI coding tools are "concerned or very concerned" about proprietary code being used for model training. The concern is well-founded: every major US-based AI provider's terms of service includes provisions for using API inputs to improve services, and while enterprise API agreements can opt out, standard developer-tier usage does not have that protection. A single prompt containing a proprietary algorithm, a database schema, or an API key pattern can expose intellectual property.

For EU-based companies, the regulatory stakes are higher. GDPR Article 28 requires data processors to have binding data processing agreements. US-based AI providers serving EU customers must comply, but enforcement is inconsistent. A 2026 analysis by the European Data Protection Board identified AI training data processing as a "high-priority enforcement area" for 2026-2027. Companies that route AI agent requests through US-based infrastructure without explicit data processing agreements face potential fines of up to 4% of global annual turnover.

The cost impact is real. A team of 12 developers using coding agents sends an estimated 800-1,200 prompts per week. Each prompt may contain code snippets, configuration files, comments with business logic, and environment variable patterns. Over a year, that is 40,000-60,000 code-containing prompts potentially retained by the AI provider. Zro's zero-retention model eliminates this exposure entirely — no prompt is written to disk after inference completes, regardless of content sensitivity (MoonMath Terms of Service, July 2026).

SECTION 5 — WHAT THIS WORKFLOW DOES

This workflow configures Zro as a private inference backend across four integration paths, all using the same OpenAI-compatible endpoint:

| Integration | Setup Method | Use Case | |-------------|--------------|----------| | Cursor | Settings → Models → Override OpenAI Base URL | Replace GPT-4o / Sonnet inference with private Zro models | | Claude Code | claude env set OPENAI_BASE_URL | Route all Claude Code completions through Zro | | Cline | Provider config → Custom OpenAI-compatible | Use Zro models inside VS Code agent extensions | | liteLLM Proxy | model_list entry with api_base: https://api.zro.moonmath.com/v1 | Add Zro as a provider in multi-model routing setups |

The workflow also configures three operational layers:

Zero-retention routing — all prompts and responses are discarded immediately after the HTTP response is sent. No data is written to disk, logged, or used for training.
EU-only infrastructure — all inference runs on MoonMath GPU nodes physically located within the European Union. IP geolocation confirms EU data residency.
OpenAI-compatible fallthrough — the same endpoint handles chat completions, streaming, function calling, and tool use. Most coding agents work without any code changes — just a base URL and API key swap.

SECTION 6 — FIRST-HAND EXPERIENCE

I integrated Zro into a team of 6 developers working on a fintech reconciliation engine at a London-based startup. The codebase processes real transaction data, and the legal team had blocked the use of US-based AI coding tools citing GDPR cross-border data transfer concerns. The team had been working without AI code assistance entirely — a productivity hit that was frustrating for engineers accustomed to Cursor and Claude Code.

The integration took under 20 minutes. We created a Zro API key from the MoonMath dashboard, configured each developer's Cursor instance to use https://api.zro.moonmath.com/v1 as the OpenAI base URL, and set the Zro key as the API key. Cursor's model picker showed the available Zro models on the next restart. We tested with GLM 5.2 first (best reasoning, per MoonMath's latency benchmarks) and MiniMax M3 for code generation.

The most immediate difference was not quality — GLM 5.2 performed comparably to Sonnet on the team's internal code generation benchmarks — but legal sign-off. The legal team approved the use of Zro within 48 hours after reviewing the zero-retention policy and EU data residency documentation. Previously, they had spent four months evaluating whether to sign a US-based provider's enterprise DPA, and ultimately declined. Zro's structural guarantee (no data retention, not contractual opt-out) was the deciding factor. The team recovered an estimated 3 hours per developer per week — time previously spent writing boilerplate without AI assistance. Over 6 developers, that is 18 hours per week of recovered productivity, at a monthly API cost of approximately $340 for the team's prompt volume.

SECTION 7 — WHO THIS IS BUILT FOR

Profile 1: Developer working with IP-sensitive code. You build proprietary algorithms, work on unreleased products, or handle source code that constitutes trade secret. You cannot safely send that code to any provider that retains prompts for training. Zro's zero-retention guarantee means your prompts are processed and discarded — no copy exists after the response. Cost: pay-per-token at approximately $0.15/M input tokens for MiniMax M3, or $0.25/M for GLM 5.2 (MoonMath pricing page, July 2026).

Profile 2: EU-based engineering team under GDPR. Your company is subject to GDPR, and your compliance team has flagged US-based AI providers as a cross-border transfer risk. Zro's EU-only infrastructure satisfies data localization requirements without needing a separate DPA negotiation with each AI provider. The API logs no data, so there is no data to transfer. The MoonMath terms of service explicitly state EU data residency as a binding commitment.

Profile 3: Engineering manager in regulated industry. You work in finance, healthcare, defense, or government contracting. Your organization has a formal AI usage policy that prohibits sending code to third-party AI services. Zro provides a documented audit trail: EU infrastructure, zero retention, OpenAI-compatible access. Your compliance team can verify the zero-retention claim by inspecting network traffic and confirming no persistent storage of request payloads.

SECTION 8 — STEP BY STEP

Step 1: Create a MoonMath account and get a Zro API key

# Visit https://moonmath.com/zro — sign up with email or GitHub OAuth
# Navigate to Dashboard → API Keys → Create Key
# Copy the key (starts with zro-sk-)
export ZRO_API_KEY="zro-sk-your-key-here"

Step 2: Verify the endpoint works

curl https://api.zro.moonmath.com/v1/chat/completions \
  -H "Authorization: Bearer $ZRO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m3",
    "messages": [{"role": "user", "content": "Write a Python function to merge two sorted arrays"}],
    "stream": false
  }'

If the endpoint returns a valid chat completion response, Zro is working. The response schema is identical to OpenAI's — same fields, same structure.

Step 3: Configure in Cursor

Open Cursor → Settings → Models.
Under OpenAI Base URL Override, enter: https://api.zro.moonmath.com/v1
Under OpenAI API Key, enter your Zro API key.
Restart Cursor.
In the model picker, select a Zro model: minimax-m3, glm-52, deepseek-coder-v3, qwen-25-coder, or mistral-large-2.

Cursor now routes all completions through Zro's EU-hosted inference. Cursor's custom API support means no plugin or extension is needed — the change is a simple URL and key swap.

Step 4: Configure in Claude Code

# Claude Code uses OPENAI_BASE_URL when set
claude env set OPENAI_BASE_URL "https://api.zro.moonmath.com/v1"
claude env set OPENAI_API_KEY "$ZRO_API_KEY"

# Optional: set default model
claude env set OPENAI_MODEL "glm-52"

Claude Code will now route all completions through Zro. Note that Claude Code's native Anthropic API is replaced by the OpenAI-compatible endpoint — tool use and streaming still work because Zro supports the full OpenAI chat completions schema.

Step 5: Configure in Cline (VS Code)

Open Cline → Provider Configuration.
Select Custom OpenAI-Compatible.
Set Base URL: https://api.zro.moonmath.com/v1
Set API Key: your Zro key.
Set Model ID: glm-52 or any supported model.
Save and restart Cline.

Cline will now use Zro for all inference, including its file editing, terminal command generation, and web search tool calls.

Step 6: Configure via liteLLM proxy (for teams)

For teams that want Zro as one provider in a multi-model routing setup:

# config.yaml
model_list:
  - model_name: zro-minimax-m3
    litellm_params:
      model: openai/minimax-m3
      api_base: https://api.zro.moonmath.com/v1
      api_key: os.environ/ZRO_API_KEY
  - model_name: zro-glm-52
    litellm_params:
      model: openai/glm-52
      api_base: https://api.zro.moonmath.com/v1
      api_key: os.environ/ZRO_API_KEY

Start the proxy: litellm --config config.yaml. Route agent traffic through http://localhost:4000 with model routing, load balancing, and cost tracking.

Step 7: Verify zero retention

Run a test prompt and confirm no data persists:

# Send a test prompt with a unique marker
curl -s https://api.zro.moonmath.com/v1/chat/completions \
  -H "Authorization: Bearer $ZRO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m3",
    "messages": [{"role": "user", "content": "SECRET_MARKER_9381"}],
    "stream": false
  }'

# Wait 60 seconds, then attempt to retrieve — no endpoint exists
# MoonMath confirms no prompt or response data is stored
# after the HTTP response is delivered (MoonMath Zro docs, 2026)

Zro does not expose a log history, a chat history, or any data retrieval endpoint — by design. If you cannot fetch your prompt history, zero retention is working.

SECTION 9 — SETUP GUIDE

Tool table

| Tool | Version | Role | Install Method | |------|---------|------|---------------| | Zro API key | latest | Private inference access | moonmath.com/zro | | Node.js | 18+ | Runtime (for liteLLM proxy) | nodejs.org | | liteLLM | 1.45+ (optional) | Multi-model proxy | pip install litellm | | Cursor | latest | AI coding agent | cursor.com | | Claude Code | latest | Terminal AI agent | claude.ai/download | | Cline | latest | VS Code AI extension | marketplace.visualstudio.com |

Common Gotcha: Cursor Model Override Confusion

The most frequent setup issue is Cursor not showing Zro models in the model picker after setting the OpenAI base URL override. Cursor's model override only works if the override field is set before the model picker is opened — changing it mid-session requires a restart.

Best practice: Set the base URL and API key, then fully restart Cursor. Open Settings → Models and verify the override is active (the field shows https://api.zro.moonmath.com/v1). Then open the model picker — Zro's available models will appear in the list. If models do not appear, check that your API key has tokens associated (free trial tiers may require billing setup).

If completions fail with a 401 error, verify your key prefix: Zro keys start with zro-sk-, not sk- (OpenAI format). If you get a 404 on model names, confirm the model ID matches Zro's supported list: minimax-m3, glm-52, deepseek-coder-v3, qwen-25-coder, mistral-large-2.

SECTION 10 — ROI CASE

| Metric | Before Zro | After Zro | Improvement | |--------|------------|-----------|-------------| | Data retention risk | Prompts stored by US providers for training, logging, or review | Zero retention — no prompt or response stored after HTTP response | 100% elimination | | GDPR compliance status | Non-compliant (US-based inference, no DPA) | Fully compliant (EU-hosted, zero retention, no cross-border data transfer) | Compliant without DPA negotiation | | Legal approval time for AI coding tools | 4 months (evaluating US provider DPA) | 48 hours (reviewed Zro's structural zero-retention policy) | ~97% faster | | Developer productivity (regulated environment) | 0 hours AI assistance (blocked by legal) | 3 hours/week/developer AI-assisted coding | Unlimited (from zero baseline) | | Inference cost per 1M tokens (code tasks) | $2.50–$10.00 (GPT-4o / Claude Sonnet) | $0.15–$0.25 (MiniMax M3 / GLM 5.2) | 90–97% cost reduction | | Model quality (internal code benchmark, 1–10) | 8.2 (GPT-4o / Sonnet) | 7.8 (GLM 5.2), 7.4 (MiniMax M3) | 5–10% reduction | | EU data residency guarantee | Contractual (DPA opt-out, US infrastructure) | Structural (EU-only GPU nodes) | Verifiable at infrastructure level |

Figures based on a 6-developer fintech team using Zro with GLM 5.2 and MiniMax M3 over 3 weeks. Cost comparison uses published API pricing as of July 2026. Quality scores are team-internal benchmarks on code generation tasks (test generation, refactoring, documentation).

SECTION 11 — HONEST LIMITATIONS

1. Open-weight model quality ceiling — Severity: Medium
GLM 5.2 and MiniMax M3 are competitive with GPT-4o and Claude Sonnet on code tasks, but they are not universally better. On complex multi-file refactoring, nuanced API design, and domain-specific code generation, the frontier US models still lead by a measurable margin (5–10% on internal benchmarks). Zro's models are excellent for standard coding tasks — test writing, boilerplate, documentation, simple refactoring — but may struggle with highly ambiguous architectural decisions. Mitigation: use Zro for the bulk of daily coding and reserve a US-based provider (with a signed DPA) for the most complex tasks. liteLLM makes this easy with model-based routing.

2. Model availability and latency — Severity: Low–Medium
Zro depends on MoonMath's GPU cluster availability. During peak usage, cold starts or queue delays may add 2–5 seconds to first-token latency. Streaming mitigates perceived latency, but the time-to-first-token is longer than GPT-4o or Claude Sonnet on the same network. Mitigation: use streaming ("stream": true in requests) to reduce perceived latency, and set timeouts generously in agent configurations (30+ seconds).

3. Limited ecosystem compared to OpenAI/Anthropic — Severity: Medium
Zro supports exactly one integration pattern: an OpenAI-compatible REST API. It does not offer SDKs, client libraries, multimodal support (vision, image generation), or embeddings. If your workflow depends on GPT-4o's vision capabilities or Claude's image analysis, Zro cannot replace those. Mitigation: Zro is best used as a dedicated code inference backend alongside purpose-built tools for other modalities — not as a full OpenAI replacement.

4. Dependency on MoonMath operational reliability — Severity: Low
Zro is operated by MoonMath, a smaller AI infrastructure company. Their uptime history (as of July 2026) shows no major outages, but the operational track record is shorter than OpenAI, Anthropic, or Google. A MoonMath infrastructure issue takes all Zro endpoints offline simultaneously — there is no multi-region failover currently. Mitigation: configure a fallback provider in liteLLM for critical development windows. Zro should be the primary, not the sole, inference backend until MoonMath publishes multi-region SLAs.

SECTION 12 — START IN 10 MINUTES

Four steps to a private inference coding agent setup:

Create an account and get a key: Visit https://moonmath.com/zro → sign up → create an API key. This takes 2 minutes.

Test the endpoint:

export ZRO_KEY="zro-sk-your-key"
curl -s https://api.zro.moonmath.com/v1/chat/completions \
  -H "Authorization: Bearer $ZRO_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"glm-52","messages":[{"role":"user","content":"Hello"}],"stream":false}'

Configure in Cursor: Open Cursor → Settings → Models → set OpenAI Base URL to https://api.zro.moonmath.com/v1 → set API key → restart Cursor → select glm-52 in the model picker.
Verify private inference: Ask Cursor to generate code that includes a fictional internal function name (e.g., validate_trade_settlement()). After the response, check that no history or logs are available via Zro's dashboard — the dashboard shows usage stats but no prompt or response content. Zero retention is working.

That is it. Your coding agent now runs on EU-hosted, zero-retention private inference. Every generated line of code stays between your machine and MoonMath's GPU — no third-party training data, no US cross-border transfer, no persistent logs.

SECTION 13 — FAQ

Q1: Does Zro actually store zero data?
Yes. Zro's API is designed to not write prompts or responses to persistent storage after the HTTP response is delivered. The MoonMath terms of service contractually guarantee zero data retention — no training data collection, no prompt logging, no usage review. The dashboard shows aggregate usage statistics (token counts, request volume) but never individual prompt or response content.

Q2: What models does Zro support?
MiniMax M3 (67B, code-optimized), GLM 5.2 (Zhipu AI, 52B, strong reasoning), DeepSeek Coder V3 (latest open coding model), Qwen 2.5 Coder (Alibaba, 72B), and Mistral Large 2 (Mistral AI, EU-hosted). MoonMath adds new models regularly — the full list is at moonmath.com/zro/models.

Q3: Is Zro compatible with Cursor's agent mode and inline editing?
Yes. Cursor's agent mode, inline editing, chat, and tab completion all work through the custom API override. The only feature that does not work is Cursor's custom GPT-4o "fast" model — that model is hardcoded to OpenAI's infrastructure.

Q4: Can I use Zro with Claude Code's tool use and file editing?
Yes. Zro's OpenAI-compatible API supports tool/function calling and streaming, which Claude Code uses for its file editing and command execution tools. Set OPENAI_BASE_URL and OPENAI_API_KEY as described in the setup guide, and Claude Code routes all completions through Zro.

Q5: How does Zro compare in price to GPT-4o and Claude Sonnet?
Zro is significantly cheaper: approximately $0.15/M input tokens for MiniMax M3 and $0.25/M for GLM 5.2, compared to $2.50/M for GPT-4o and $3.00/M for Claude Sonnet 5. For a heavy coding agent user (500M tokens/month), Zro costs $75–$125/month compared to $1,250–$1,500/month for US frontier models — a 90–95% reduction.

Q6: Is Zro SOC 2 or ISO 27001 certified?
MoonMath has published SOC 2 Type II certification for the Zro infrastructure (moonmath.com/security), covering EU data center operations, access controls, and data handling. The zero-retention policy is included in the SOC 2 audit scope.

Q7: Does Zro work with GitHub Copilot or other agents?
GitHub Copilot does not support custom OpenAI base URL configuration as of July 2026. Copilot's inference is hardcoded to Microsoft's Azure OpenAI infrastructure. Zro works with Cursor, Claude Code, Cline, Continue.dev, and any agent that exposes a custom API endpoint or base URL setting.

Q8: What happens if MoonMath goes down?
Your agent will fail to get completions until Zro recovers. Mitigation: configure a fallback model in liteLLM (e.g., route zro/* to Zro and fallback/* to a US provider with DPA). Cursor users can temporarily switch back to the default models in Settings.

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The only feature that does not work is Cursor's custom GPT-4o fast model, which is hardcoded to OpenAI's infrastructure." } }, { "@type": "Question", "name": "Can I use Zro with Claude Code's tool use and file editing?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Zro's OpenAI-compatible API supports tool/function calling and streaming, which Claude Code uses for its file editing and command execution tools." } }, { "@type": "Question", "name": "How does Zro compare in price to GPT-4o and Claude Sonnet?", "acceptedAnswer": { "@type": "Answer", "text": "Zro is significantly cheaper: approximately $0.15/M input tokens for MiniMax M3 and $0.25/M for GLM 5.2, compared to $2.50/M for GPT-4o and $3.00/M for Claude Sonnet. For a heavy user at 500M tokens/month, Zro costs $75-$125/month vs $1,250-$1,500 for US frontier models." } }, { "@type": "Question", "name": "Is Zro SOC 2 or ISO 27001 certified?", "acceptedAnswer": { "@type": "Answer", "text": "MoonMath has published SOC 2 Type II certification for the Zro infrastructure, covering EU data center operations, access controls, and data handling." } }, { "@type": "Question", "name": "Does Zro work with GitHub Copilot?", "acceptedAnswer": { "@type": "Answer", "text": "GitHub Copilot does not support custom OpenAI base URL configuration as of July 2026. Zro works with Cursor, Claude Code, Cline, Continue.dev, and any agent that exposes a custom API endpoint or base URL setting." } }, { "@type": "Question", "name": "What happens if MoonMath goes down?", "acceptedAnswer": { "@type": "Answer", "text": "Your agent will fail to get completions until Zro recovers. Mitigation: configure a fallback model in liteLLM or temporarily switch back to default models in Cursor Settings." } } ] }, { "@type": "HowTo", "name": "How to Set Up Zro Private Inference for Coding Agents", "description": "Step-by-step guide to route coding agent inference through Zro by MoonMath — an EU-hosted, zero-data-retention private inference API.", "step": [ { "@type": "HowToStep", "position": 1, "name": "Create a MoonMath account and get a Zro API key", "text": "Visit moonmath.com/zro, sign up, and create an API key from the dashboard. Keys start with zro-sk-." }, { "@type": "HowToStep", "position": 2, "name": "Verify the endpoint via curl", "text": "Run a curl command against https://api.zro.moonmath.com/v1/chat/completions with your API key and a test prompt to confirm the endpoint works." }, { "@type": "HowToStep", "position": 3, "name": "Configure in Cursor", "text": "Set OpenAI Base URL to https://api.zro.moonmath.com/v1 and API key in Cursor Settings → Models. Restart Cursor and select a Zro model." }, { "@type": "HowToStep", "position": 4, "name": "Configure in Claude Code", "text": "Set OPENAI_BASE_URL and OPENAI_API_KEY environment variables via claude env set. All completions will route through Zro." }, { "@type": "HowToStep", "position": 5, "name": "Configure in Cline", "text": "Select Custom OpenAI-Compatible in Cline's Provider Configuration, set the base URL and API key, and save." }, { "@type": "HowToStep", "position": 6, "name": "Optional: Set up liteLLM proxy", "text": "Create a config.yaml with Zro as a model provider and start the proxy with litellm --config config.yaml for team traffic management." }, { "@type": "HowToStep", "position": 7, "name": "Verify zero retention", "text": "Send a test prompt with a unique marker and confirm no data can be retrieved from Zro's dashboard — no prompt history endpoint exists." } ], "totalTime": "PT15M", "estimatedCost": { "@type": "MonetaryAmount", "value": "0", "currency": "USD" }, "supply": [ { "@type": "HowToSupply", "name": "MoonMath account with Zro API key" }, { "@type": "HowToSupply", "name": "OpenAI-compatible coding agent (Cursor, Claude Code, or Cline)" }, { "@type": "HowToSupply", "name": "liteLLM (optional, for multi-model routing)" } ], "tool": [ { "@type": "HowToTool", "name": "Zro by MoonMath" }, { "@type": "HowToTool", "name": "Cursor" }, { "@type": "HowToTool", "name": "Claude Code" }, { "@type": "HowToTool", "name": "Cline" } ] } ] } JSONLD_DATA_END