Pay-with-data API concept — users contribute training data for inference credits
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Seth Freiberg
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README.md
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# Mortdecai API — Pay With Data
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A public inference API for the Mortdecai Minecraft AI model where users earn access by contributing training data.
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## Concept
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Every AI model gets better with more data. Most APIs charge money. This one charges data.
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Users submit training examples — prompt/response pairs verified against a real Minecraft server — and earn credits for inference calls. The model improves from every contribution, which improves the API for everyone.
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---
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## How It Works
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```
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┌──────────┐ ┌──────────────┐ ┌─────────────┐
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│ User │────▶│ API Gateway │────▶│ Mortdecai │
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│ Client │◀────│ (credits, │◀────│ (Ollama) │
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│ │ │ validation)│ │ │
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└──────────┘ └──────┬───────┘ └─────────────┘
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│
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┌──────┴───────┐
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│ Validation │
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│ Server │
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│ (RCON test) │
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└──────────────┘
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```
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### 1. Get an API Key
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Register and receive an API key with an initial credit balance.
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### 2. Use Credits for Inference
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```bash
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curl -X POST https://api.mortdec.ai/v1/generate \
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-H "Authorization: Bearer mk_your_api_key" \
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-H "Content-Type: application/json" \
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-d '{
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"mode": "sudo",
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"prompt": "give me a diamond sword with sharpness 5",
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"player": "Steve",
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"server_context": {"version": "1.21.x"}
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}'
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```
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Response:
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```json
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{
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"commands": ["give Steve minecraft:diamond_sword[enchantments={sharpness:5}] 1"],
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"reasoning": "Diamond sword with sharpness 5 enchantment in 1.21 component syntax",
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"risk_level": 3,
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"credits_remaining": 99
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}
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```
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Each call costs 1 credit.
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### 3. Earn Credits by Contributing Data
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```bash
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curl -X POST https://api.mortdec.ai/v1/contribute \
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-H "Authorization: Bearer mk_your_api_key" \
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-H "Content-Type: application/json" \
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-d '{
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"prompt": "sudo give me a trident with riptide 3 and loyalty 3",
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"expected_output": {
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"commands": ["give Steve minecraft:trident[enchantments={riptide:3,loyalty:3}] 1"],
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"reasoning": "Riptide and loyalty are mutually exclusive in vanilla Minecraft"
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},
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"notes": "This should fail — riptide and loyalty cannot coexist"
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}'
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```
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Response:
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```json
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{
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"accepted": false,
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"reason": "Correct! Riptide and loyalty are mutually exclusive. This is a valuable negative example.",
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"credits_earned": 15,
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"credits_remaining": 114,
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"validation": {
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"schema_valid": true,
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"rcon_tested": true,
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"rcon_result": "error — conflicting enchantments",
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"duplicate": false,
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"quality_score": 0.92
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}
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}
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```
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Accepted contributions earn 5-20 credits depending on quality:
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| Quality | Credits | What makes it valuable |
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|---------|---------|----------------------|
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| Basic | 5 | Valid format, correct command |
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| Good | 10 | Uncommon prompt phrasing or edge case |
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| Excellent | 15 | Negative example, error correction, or novel scenario |
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| Exceptional | 20 | New command pattern, modded content, or bug discovery |
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Rejected contributions earn 0 credits but don't cost anything.
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---
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## Credit System
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| Tier | Starting Credits | Monthly Refresh | Earn Rate |
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|------|-----------------|----------------|-----------|
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| **Free** | 100 | 100 | 5-20 per accepted contribution |
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| **Contributor** | 500 | Unlimited* | Same |
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| **Paid** | Per purchase | N/A | Optional contributions |
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*Contributor tier requires maintaining a positive contribution rate (at least 10 accepted submissions per month).
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### Credit Economics
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- 1 inference call = 1 credit
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- 1 accepted basic contribution = 5 credits (5 free inferences)
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- 1 accepted excellent contribution = 15 credits (15 free inferences)
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- Average user contributes 2-3 examples per session = 10-45 credits earned
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The math works: heavy API users are also heavy contributors. The model improves proportionally to usage.
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---
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## Validation Pipeline
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Every contribution goes through automated validation before credits are awarded:
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```
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Contribution → Schema Check → Dedup → RCON Test → Quality Score → Accept/Reject
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```
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### Schema Check
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- Valid JSON format
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- Required fields present (prompt, expected_output)
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- Commands use correct Minecraft 1.21 syntax
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- No forbidden commands (ban, op, stop, etc.)
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### Deduplication
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- Compare against existing training dataset
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- Fuzzy match on prompt similarity (>90% = duplicate)
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- Same prompt from multiple users: first contributor gets full credit, subsequent get 2 credits for validation
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### RCON Verification
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- Commands executed on a sandboxed Minecraft server
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- Success/failure recorded
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- Error messages captured for error-correction training pairs
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### Quality Scoring
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- Novelty: how different is this from existing data? (0-1)
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- Correctness: did RCON succeed? (0-1)
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- Complexity: multi-command, enchantments, execute chains score higher
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- Category balance: underrepresented categories score higher
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---
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## Abuse Prevention
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| Attack | Defense |
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|--------|---------|
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| Garbage submissions | Schema validation + RCON testing rejects invalid data |
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| Duplicate farming | Fuzzy dedup, diminishing returns on similar prompts |
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| Bot accounts | Rate limit: max 50 contributions/hour per key |
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| Prompt injection in training data | All contributions sandboxed, reviewed before merge |
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| Adversarial training data | Anomaly detection: flag keys with >80% rejection rate |
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| Credit farming without using API | Credits expire after 90 days |
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---
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## API Endpoints
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### Inference
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```
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POST /v1/generate
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POST /v1/pray (God mode — dramatic responses)
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POST /v1/translate (Sudo mode — command translation only)
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```
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### Data Contribution
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```
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POST /v1/contribute Submit a training example
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GET /v1/contribute/schema Get the expected format
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GET /v1/contribute/needed Get categories where data is most needed
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```
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### Account
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```
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GET /v1/account Credits, contribution stats, tier
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GET /v1/account/history Recent inference and contribution history
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POST /v1/account/register Create a new API key
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```
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### Meta
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```
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GET /v1/model/info Current model version, training stats
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GET /v1/model/categories What the model knows, with coverage scores
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GET /v1/health API status
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```
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---
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## What Makes This Different
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Most ML APIs treat users as consumers. This treats them as collaborators.
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- **Users with niche servers** contribute data the core team would never generate (modded commands, custom plugins, unusual configurations)
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- **Users who find bugs** get rewarded for reporting them as negative examples
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- **Power users** naturally generate the most valuable edge cases because they push the model hardest
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- **The model gets better for everyone** with every contribution
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It's a flywheel: better model → more users → more contributions → better model.
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---
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## Implementation Status
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**Phase 1** (Current): Concept and API design
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**Phase 2**: API gateway with credit system
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**Phase 3**: Contribution validation pipeline
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**Phase 4**: Public beta
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The core model (Mortdecai v4) is running on a live Minecraft server. The API layer and contribution system are being designed.
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---
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## Tech Stack
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- **Model**: Mortdecai v4 (Qwen3.5-9B fine-tuned, QLoRA)
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- **Inference**: Ollama on consumer GPUs
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- **API**: FastAPI + SQLite for credits/keys
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- **Validation**: Sandboxed Paper 1.21 server with RCON
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- **Training**: Automated pipeline — contributions → validation → merge → retrain
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---
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Learn more at [mortdec.ai](https://mortdec.ai)
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