| system | initial cost ($ + hrs) / unit | annualized cost ($ + hrs) / unit | sqft building | sqft land | revenue | market cost | benefits | | ------ | ----------------------------- | -------------------------------- | ------------- | --------- | ------- | ----------- | ------------------------------------------------------------------------------------------------- | | PAPCI | $423k + 2000 hrs | $198k + 3520 hrs | 500 | 0 | $0 | $850k | AI-enhanced community coordination, privacy-aware knowledge sharing, efficient consensus building | purpose:: AI system that empowers both individual growth and community coordination while preserving privacy and autonomy. Enables members to choose their level of AI engagement - from fully private personal assistance to opt-in collective intelligence. - dev questions - multi-modal? - context window size. #### Assumptions - 1000 person community - High trust environment - Members prioritize efficiency with privacy controls - Community self-regulation works at this scale - Regular system audits maintain accountability - single centralized RAG can be used for public and private vector storage - Privacy controls are technically enforceable - For a **local AI system** with a **Mixtral 8x7B** model, the **primary advantage** lies in **privacy, customization, and control**, allowing fine-tuned, community-specific responses. However, frontier models like **GPT-4** or **Claude** excel in **creative brainstorming** due to their vast pretraining on diverse datasets, though the gap narrows with **local fine-tuning** and a robust **RAG pipeline** for up-to-date information. While frontier models offer broader, faster idea generation, a local model can perform comparably with specialized knowledge and deeper, community-relevant insights, albeit potentially with **slower responses** and **smaller context handling**. ##### Performance Targets - Peak Load: 500 QPS - Response Time: <150ms (99th percentile) - Daily Queries: 150k - Token Volume: 150M/day - Storage Growth: 50%/year ##### Basic Components ##### Infrastructure - Kubernetes cluster on-prem hosting serverless functions - Vector store for RAG (targeting 20ms latency) - 3x A100 GPU nodes for inference - Load balancer for request routing - chat layer: Hugging Face Spaces + Open Assistant (LAION) chat UI + Rasa for conversation flows ##### AI Stack ###### Model choice: Mixtral 8x7B because the Mixture of Experts architecture is better at task specialization and 32k context can handle long documents. | Model | Parameters | Layers | Heads | Hidden Size | Context | Hardware Req | Key Use Cases | | ------------ | ---------- | ------ | ----- | ----------- | ------- | ------------ | --------------------------- | | Mistral 7B | 7B | 32 | 32 | 4096 | 8k | 1x A100 | Basic RAG, task assistance | | Mixtral 8x7B | 47B | 32 | 32 | 4096 | 32k | 2x A100 | Complex reasoning, tutoring | | DeepSeek 7B | 7B | 32 | 32 | 4096 | 8k | 1x A100 | Code, documentation | | Llama2 13B | 13B | 40 | 40 | 5120 | 4k | 1x A100 | General purpose, health | | MPT 30B | 30B | 48 | 48 | 7168 | 8k | 2x A100 | Research, analysis | Key Metrics Defined: - Parameters: Total trainable weights - Layers: Transformer blocks for processing - Heads: Parallel attention processors - Hidden Size: Internal representation dimension - Context: Maximum input sequence length - Hardware: Minimum for inference at <150ms ###### RAG Pipeline - Milvus Vector DB - chunking and Embedding model Haystack - Query router ###### Fine-tuning System - QLoRA pipeline - Evaluation framework - Model registry ###### Safety Layer - Content filters - Rate limiting - [[#Privacy Controls]] ##### Usage Daily Users: - Students (200): 2 queries/min × 120 min = 240/student/day = 48,000/day - Workers (500): 1 query/5min × 180 min = 36/worker/day = 18,000/day - Personal Dev (1000): 100 queries/day = 100,000/day Total: ~166,000 queries/day Peak Load: ~500 QPS during school hours ##### Use Cases ###### Community - Task Documentation/Training - Community safety monitoring - Anonymous trend analysis for health/wellbeing - Resource allocation optimization - Pattern detection for early problem identification - Consensus building acceleration - Health Data Processing - Educational Support - Community Knowledge Base - Privacy-Sensitive Queries ###### Individual - Private AI coaching/tutoring - Personal health tracking - Career development planning - Task skill development - Custom learning paths ##### Internal resource unit costs - Power: 250,000 kWh/year - Cooling: 125,000 kWh/year - Storage: 100TB secure + backup #### Market Solutions $850k/year - Enterprise AI licensing: $350k - Privacy management systems: $200k - Consensus platforms: $150k - Knowledge management: $100k - Audit/compliance tools: $50k #### Labor 3120 Hours/year | Task | Hours/Year | Notes | | --------------------- | ---------- | ------------------------- | | System Administration | 1040 | Core operations, updates | | Privacy Management | 260 | Access control, audits | | Content Moderation | 780 | Knowledge base curation | | Community Support | 520 | User assistance, training | | Expert Review Pool | 520 | Content verification | | Total labor | 3120 | | #### Operating Costs $113k/year | Component | External Cost ($) | Internal Cost ($) | Notes | | | -------------- | ----------------- | ----------------- | ------------------ | --- | | Infrastructure | $25k | 0 | Cloud/local hybrid | | | Security Tools | $20k | 0 | Privacy protection | | | Audit Systems | $12k | 0 | Compliance tools | | | Electricity | 0 | $56k | 375 MWh | | | Total Annual | $57k | 0 | | | #### Initial Costs $423k + 2000 hrs -> $85k + 400 hrs/year | Component | Initial Cost ($ + hrs) | Lifespan (Years) | Annual Cost ($ + hrs) | Notes | | --------------------- | ---------------------- | ---------------- | --------------------- | ---------------------------- | | Secure Infrastructure | $148k + 800 hrs | 5 | $30k + 160 hrs | Privacy-focused hardware | | AI Development | $150k + 600 hrs | 5 | $30k + 120 hrs | Custom model training | | Security Systems | $75k + 400 hrs | 5 | $15k + 80 hrs | Privacy controls | | Integration Tools | $50k + 200 hrs | 5 | $10k + 40 hrs | Community systems connection | | Total | $423k + 2000 hrs | - | $85k + 400 hrs | | ##### Hardware | Component | category | Initial Cost | Annual Power (375k kWh @ $0.15) | Notes | | -------------- | -------------- | ------------ | ------------------------------- | ------------------- | | 4x RTX 4090s | Compute | $12,000 | $3,150 | Includes redundancy | | 2x Server CPUs | Compute | $10,000 | $1,575 | With ECC RAM | | Power Supplies | Compute | $2,000 | - | Redundant | | 6x 20TB SSDs | Storage | $60,000 | $1,890 | RAID 6 config | | Drive Bays | Storage | $5,000 | - | Hot-swap capable | | 10GbE Switches | Network | $8,000 | $525 | Internal only | | Redundant NICs | Network | $6,000 | - | Failover capable | | Rack/Cooling | Infrastructure | $15,000 | $48,750 | Includes HVAC | | UPS Systems | Infrastructure | $10,000 | - | Battery backup | | Installation | Infrastructure | $20,000 | - | Initial setup | | **Total** | | **$148,000** | **$55,890** | | #### Knowledge Management ##### Privacy Controls | Tier | Data Storage | Access | Use Cases | Controls | | -------------------- | -------------------------- | ------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------- | | Individual ephemeral | None - ephemeral only | Individual only | Personal coaching, Health questions | No logs, No persistence | | Individual private | Encrypted personal storage | Self + authorized AI | Learning history, Health tracking | Full deletion rights | | Individual public | Stripped of identifiers | Community aggregate only | Pattern detection, Trend analysis | Opt-in by type | | collective private | Stripped of identifiers | Community aggregate only | Pattern detection, Trend analysis | Opt-in by type | | collective public | Full attribution | All members | [[standard operating procedures (sops) - village living\\|SOPs]], Tutorials, [[systems - village living\\|systems documents]], Insights, [[skill - village living\|\skill demands]], [[constitution - governance - village living\|governance documents]] | Explicit sharing choice | ##### Collective - goal: `__` knowledge additions a year - via wiki style where any community member can make changes and all changes are trackable and public ##### Personal Knowledge Additions ```mermaid graph TD A[New Content] --> B[AI First Pass] B -->|Pass| C[Added to RAG] B -->|Flag| D[Expert Review] D -->|Approve| C D -->|Reject| E{Submitter Choice} E -->|Appeal| F[Community Vote] E -->|Withdraw| G[End] F -->|over 67% Approve| C F -->|under 67% Approve| G ``` ##### Oversight Mechanisms - Automated anomaly detection - System health metrics - Expert review pool - Community voting - Regular audits #### Success Metrics | Metric Type | Target | Measurement | | ---------------------- | ---------- | -------------------- | | Model Accuracy | >95% | Key task performance | | Response Latency | 99% <150ms | Request timing | | Fine-tuning Efficiency | <$10k | Cost per iteration | | Knowledge Growth | +5%/month | RAG content | | Trust Score | >9/10 | User surveys |