Add files using upload-large-folder tool

.env

@@ -0,0 +1,9 @@
+# Storage & offline settings
+HF_HOME=/data/adaptai/models                # Root cache for Hugging Face models/tokenizers
+VLLM_CACHE_ROOT=/data/adaptai/models       # vLLM model cache root
+HF_HUB_OFFLINE=0                           # Set to 1 if you want offline-only mode later
+
+# GPU & logging settings
+CUDA_VISIBLE_DEVICES=0
+VLLM_LOGGING_LEVEL=INFO
+TQDM_DISABLE=0                             # Keep runtime progress bars enabled

CODEOWNERS

@@ -0,0 +1,125 @@
+# TeamADAPT CODEOWNERS
+# Domain-based ownership enforcement for monorepo
+# Schema version: 1.2
+
+# --- Cross-Domain Governance ---
+
+# Architecture Decision Records
+/docs/architecture/adrs/ @chase @vox @atlas @archimedes
+
+# Monorepo structure and governance
+/data/adaptai/README.md @chase
+/data/adaptai/CLAUDE.md @chase
+/data/adaptai/CODEOWNERS @chase
+/data/adaptai/PULL_REQUEST_TEMPLATE.md @chase
+
+# DTO Schema and generators
+/data/adaptai/deployment/dto-schema/ @chase @atlas
+/data/adaptai/deployment/generators/ @chase @vox
+
+# Server registry and inventory
+/data/adaptai/deployment/server_registry.md @atlas @chase
+
+# --- Domain: SignalCore (CommsOps) ---
+
+# SignalCore communications infrastructure
+/platform/signalcore/ @vox
+/platform/signalcore/commsops/ @vox
+/platform/signalcore/dto/ @vox
+/platform/signalcore/docs/ @vox
+/platform/signalcore/ops/ @vox
+
+# Supervisord configurations
+/platform/signalcore/ops/supervisor/ @vox
+/platform/signalcore/ops/supervisor/conf.d/ @vox
+/platform/signalcore/ops/supervisor/bin/ @vox
+
+# Neuromorphic security system
+/platform/signalcore/commsops/neuromorphic_security.py @vox
+/platform/signalcore/dto/signalcore-services.yaml @vox
+
+# NATS-Pulsar bridge
+/platform/signalcore/commsops/nats_pulsar_bridge.py @vox
+
+# DataOps integration layer
+/platform/signalcore/commsops/dataops_integration.py @vox
+
+# --- Domain: DataOps ---
+
+# Data operations infrastructure
+/platform/dataops/ @atlas
+/platform/dataops/configs/ @atlas
+/platform/dataops/dto/ @atlas
+/platform/dataops/docs/ @atlas
+/platform/dataops/scripts/ @atlas
+
+# DragonFlyDB configuration
+/platform/dataops/dto/dragonfly.yaml @atlas
+/platform/dataops/configs/dragonfly/ @atlas
+
+# Redis cluster configuration
+/platform/dataops/dto/redis-cluster.yaml @atlas
+/platform/dataops/configs/redis/ @atlas
+
+# Database and storage systems
+/platform/dataops/configs/postgresql/ @atlas
+/platform/dataops/configs/mongodb/ @atlas
+
+# --- Domain: MLOps (AI/ML) ---
+
+# Machine learning infrastructure
+/platform/aiml/ @archimedes
+/platform/aiml/models/ @archimedes
+/platform/aiml/datasets/ @archimedes
+/platform/aiml/training/ @archimedes
+/platform/aiml/deployment/ @archimedes
+
+# Model serving and inference
+/platform/aiml/serving/ @archimedes
+/platform/aiml/monitoring/ @archimedes
+
+# --- Domain: DevOps & Infrastructure ---
+
+# Systemd service units
+/systemd/ @chase
+
+# Environment overlays
+/deployment/environments/ @chase
+
+# CI/CD configuration
+/.github/ @chase
+/.gitlab/ @chase
+
+# Script registry and management
+/scripts/ @chase
+
+# --- Cross-Domain Integration Points ---
+
+# SignalCore <-> DataOps integration
+/platform/signalcore/commsops/dataops_integration.py @vox @atlas
+
+# DataOps <-> MLOps integration
+/platform/dataops/scripts/ml_training.py @atlas @archimedes
+
+# Monitoring and alerting integration
+/platform/signalcore/docs/runbooks/incident-response.md @vox @chase
+/platform/dataops/docs/runbooks/incident-response.md @atlas @chase
+
+# --- Special Files ---
+
+# Risk Class A changes (require dual approval)
+*class-a* @chase @vox @atlas @archimedes
+
+# DTO changes (require domain owner + architect)
+*dto* @chase @domain-owner
+
+# Generated artifacts (read-only, require generator changes)
+*generated* @chase
+
+# Runbook changes (require ops lead review)
+*runbook* @domain-owner @chase
+
+# --- Fallback ---
+
+# Default owners for unmatched paths
+* @chase

GEMINI.md

@@ -0,0 +1,62 @@
+# Project Gemini: AI Development and Operations
+
+This document provides a comprehensive overview of the Gemini project, its architecture, and development practices.
+
+## Project Overview
+
+The Gemini project is a comprehensive AI development and operations platform. It consists of two main components:
+
+*   **AIML:** A sophisticated AI/ML pipeline for training, deploying, and managing large-scale language models.
+*   **Open WebUI:** A user-friendly web interface for interacting with the AI/ML models.
+
+The project is highly organized and well-documented, with a clear separation of concerns between the different components.
+
+### Main Technologies
+
+*   **Frontend:** SvelteKit, TypeScript, Tailwind CSS
+*   **Backend:** Python, FastAPI, LangChain, Transformers
+*   **Database:** PostgreSQL, Redis, MongoDB
+*   **Deployment:** Docker, Kubernetes
+
+### Architecture
+
+The project follows a microservices-based architecture, with the AIML and Open WebUI components running as separate services. The AIML component is further divided into a number of sub-components, each responsible for a specific part of the AI/ML pipeline.
+
+## Building and Running
+
+### Open WebUI
+
+The Open WebUI component can be built and run using the following commands:
+
+*   **Install dependencies:** `npm install`
+*   **Run in development mode:** `npm run dev`
+*   **Build for production:** `npm run build`
+*   **Run tests:** `npm run test:frontend`
+
+### AIML
+
+The AIML component is a collection of Python scripts and services. The `elizabeth_cli.py` script in `aiml/02_models/elizabeth/deployment_configs/` provides an interactive CLI for the "Elizabeth" model.
+
+## Development Conventions
+
+The project follows a number of development conventions, including:
+
+*   **Coding Style:** The project uses `prettier` for code formatting and `eslint` for linting.
+*   **Testing:** The project uses `vitest` for frontend testing and `pytest` for backend testing.
+*   **Contribution Guidelines:** The project has a `PULL_REQUEST_TEMPLATE.md` file, which suggests that contributions are welcome.
+
+## Directory Overview
+
+*   **`aiml/`**: The core of the project, containing the AI/ML models, training pipelines, and documentation.
+*   **`open-webui/`**: The web interface for interacting with the AI/ML models.
+*   **`platform/`**: A legacy or staging area for the `aiml` directory.
+*   **`corpus-data/`**: Contains datasets for training the models.
+*   **`deployment/`**: Contains deployment scripts and configurations.
+*   **`docs/`**: Contains additional project documentation.
+*   **`logs/`**: Contains logs from the various services.
+*   **`novas/`**: Contains different versions of the "Nova" models.
+*   **`open-webui-functions/`**: Contains serverless functions for the Open WebUI.
+*   **`planner/`**: Contains planning documents for the project.
+*   **`projects/`**: Contains related projects.
+*   **`secrets/`**: Contains secrets and other sensitive information.
+*   **`systemd/`**: Contains systemd service files for running the project's services.

PULL_REQUEST_TEMPLATE.md

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+# TeamADAPT Pull Request Template
+
+## Governance Compliance Checklist
+
+### Required Fields
+- [ ] **Linked DTO**: [Link to Data Transfer Object manifest]
+- [ ] **Linked ADR**: [Link to Architecture Decision Record if applicable]
+- [ ] **Risk Class**: [A/B/C] - *Select one and provide justification*
+- [ ] **Rollback Plan**: [Describe how to revert this change]
+- [ ] **Generated Artifacts**: [List any generated files included]
+- [ ] **Runbook Link**: [Link to updated or new runbook]
+- [ ] **SLO/Alert Updates**: [Describe monitoring changes]
+- [ ] **Explicit Pause Points**: [Identify where manual intervention required]
+
+## Change Summary
+
+**Domain**: [signalcore/dataops/aiml/devops]  
+**Type**: [feature/bugfix/refactor/docs/ops]  
+**Impact**: [Describe what changes and who is affected]
+
+### Description
+[Provide a clear description of the changes and the problem being solved]
+
+### DTO Manifest Changes
+```yaml
+# Paste relevant DTO changes here
+service:
+  name: "example-service"
+  ports:
+    - port: 8080
+  # ...
+```
+
+### Generated Artifacts
+- [ ] Systemd unit files
+- [ ] Supervisor configurations  
+- [ ] Environment overlays
+- [ ] Monitoring configurations
+- [ ] Documentation updates
+
+## Risk Assessment
+
+### Risk Class: [A/B/C]
+**Justification**: [Explain why this change falls into the selected risk class]
+
+**Class A (High Risk)**: Cross-domain changes, production database modifications, security changes  
+**Class B (Medium Risk)**: Domain-specific features, configuration changes  
+**Class C (Low Risk)**: Documentation, minor bug fixes, non-critical improvements
+
+### Impact Analysis
+- **Services Affected**: [List services that will be impacted]
+- **Dependencies**: [List cross-domain dependencies]
+- **Downtime Required**: [Yes/No] - [If yes, describe duration and window]
+- **Data Migration**: [Yes/No] - [If yes, describe process]
+
+### Rollback Plan
+[Step-by-step instructions for reverting this change, including:
+- Configuration reverts
+- Database rollbacks if applicable  
+- Service restart procedures
+- Verification steps]
+
+## Testing & Validation
+
+### Pre-Deployment Checks
+- [ ] DTO schema validation passed
+- [ ] Port collision scan completed
+- [ ] Generated artifacts are fresh
+- [ ] Runbook documentation updated
+- [ ] CI gates all passing
+
+### Test Results
+- [ ] Unit tests passed
+- [ ] Integration tests passed
+- [ ] Performance benchmarks met
+- [ ] Security review completed
+- [ ] Operational validation done
+
+### Monitoring Verification
+- [ ] SLO definitions updated
+- [ ] Alert rules configured
+- [ ] Dashboard changes implemented
+- [ ] Logging configuration validated
+
+## Operational Readiness
+
+### Runbook Updates
+- [ ] Incident response procedures
+- [ ] Recovery procedures  
+- [ ] Performance troubleshooting
+- [ ] Security incident handling
+
+**Runbook Location**: [Link to specific runbook file]
+
+### Deployment Instructions
+[Step-by-step deployment commands and verification steps]
+
+```bash
+# Deployment commands
+./deploy.sh --environment production --validate-security
+
+# Verification commands
+curl http://localhost:8080/health
+./validate_deployment.py
+```
+
+### Pause Points
+[Identify where deployment should pause for manual verification]
+
+1. **Pre-deployment**: [Check description]
+2. **Mid-deployment**: [Check description] 
+3. **Post-deployment**: [Check description]
+
+## Documentation
+
+### ADR Reference
+[If this change implements or relates to an ADR, provide context and link]
+
+### API Changes
+[Describe any API changes, including breaking changes]
+
+### Database Changes
+[Describe schema changes, migrations, or data transformations]
+
+## Reviewers
+
+### Required Reviewers
+Based on CODEOWNERS and change type:
+- [ ] @domain-owner (Domain expert)
+- [ ] @chase (Architecture review for cross-domain changes)
+- [ ] Additional reviewers as needed
+
+### Review Focus Areas
+- [ ] DTO compliance and completeness
+- [ ] Risk assessment accuracy
+- [ ] Rollback plan feasibility
+- [ ] Operational readiness
+- [ ] Monitoring and alerting coverage
+
+## Additional Context
+
+[Add any other context, screenshots, or references that will help reviewers]
+
+---
+
+**By submitting this PR, I confirm:**
+- [ ] I have reviewed the CODEOWNERS file for this change
+- [ ] I have updated all required documentation
+- [ ] I have tested these changes thoroughly
+- [ ] I understand the risk classification and implications
+- [ ] I have a verified rollback plan
+- [ ] I have coordinated with dependent domain owners
+
+**PR Labels**:
+- [ ] dto-changed
+- [ ] generated-updated  
+- [ ] runbook-updated
+- [ ] ops-ledger-updated
+- [ ] class-a / class-b / class-c
+- [ ] pause-needed (if applicable)

planner/training_checkpointing.md

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+Let’s zoom the lens all the way in and start one decisive step at a time so we can pair that planner (reasoning-strong) with an executor fleet that’s fed by rock-solid knowledge.
+
+Step 1 — Lay the Data Bedrock & Retrieval Mesh
+
+(“fill the pantry before we cook”)
+
+Goal	What to do	Where it runs	Why it matters
+1. Inventory & classify the 51 DBs	Tag each source by modality (text, code, logs, metrics, blob) and update a data_catalog.yaml (owner, refresh cadence, PII flags).	Any node	Gives you an authoritative manifest for future automated ingestion & compliance.
+2. Build zero-loss ETL pipes	• Use Airbyte or bespoke connectors → Parquet on MinIO ➜ incremental change-capture.
+• Normalise encodings + timestamps once, never during model runs.	L40S nodes (low-latency I/O)	Keeps the H200 “Beast” GPUs free for heavy training.
+3. Dual-tier memory store	• Hot KV: Redis + DragonflyDB for reflex writes.
+• Vector: Weaviate (or Qdrant) for chunk embeddings.
+Enable hybrid search (BM25 + vector).	Redis cluster on Beast; Weaviate on L40S node #1	Lets planners pull long-horizon context while executors cache task-local facts.
+4. Embed everything	• Batch-encode with E5-Mistral 1024-d on L40S GPU pods (fits in 48 GB easily).
+• Store metadata pointers back to raw docs.	L40S nodes	Pre-computes semantic look-ups so Base LLMs don’t hallucinate missing context.
+5. Retrieval QA harness	Write unit tests (pytest + langchain-bench) that ask 100 canonical questions per domain and assert top-k recall ≥ 90 %.	CI on any CPU box	Confirms the mesh is actually delivering the right chunks before fine-tuning.
+
+Why this first?
+Training before you control the ingredient flow leads to noisy gradients and bloated checkpoints. A clean retrieval layer means all downstream specialisation is data-driven, not guess-driven.
+
+Hardware assignment snapshot
+
+Beast (8 × H200, 141 GB each): reserved for domain-adaptive pre-training (DAPT) + full-precision eval. H200 gives ~20 % uplift vs H100 for memory-bound ops. 
+NVIDIA Developer
+datacrunch.io
+
+3 × L40S nodes (2 × L40S / node): ETL, embedding, LoRA/QLoRA fine-tunes (NVIDIA shows 2-GPU L40S boxes are perfect for LoRA workflows). 
+NVIDIA Developer
+NVIDIA Docs
+
+Quality hooks & suggestions
+
+FlashAttention-2 + DeepSpeed ZeRO-4/5 for any future DAPT runs—cuts HBM use >40 %. 
+Microsoft
+
+Expose the vector store through vLLM’s tool-calling endpoint so every Nova can fetch context with a single JSON spec. 
+vLLM
+
+Add a “memory-success reflex”: whenever an executor solves a task, ReflexorD writes a distilled embedding back to Redis for instant re-use.
+
+Check-point 🟢
+
+Does this Step 1 blueprint align with what you need?
+If yes, I’ll move to Step 2: Domain-Adaptive Pre-Training on Beast—covering corpus prep, parallelism plan (tensor + pipeline), and how we’ll spin up Mixtral-planner + Functionary-executor pairs straight out of that training run.
+
+Let me know and we’ll keep the momentum!
+
+You said:
+Do we wanna talk about where I get the data and how right now or is that in a different area or different phase?
+ChatGPT said:
+Thought for 11 seconds
+
+Absolutely—we can (and should) nail down where the bits come from before we automate anything else. Think of it as Step 1A: Data-source discovery & access; it fits cleanly inside the “bedrock” phase we just sketched.
+
+Step 1A — Data-source discovery & access
+Micro-step	Action	Outcome
+1. Source census	For each of the 51 DBs (plus any file buckets, logs, SaaS APIs, etc.) capture:
+• system type (OLTP, OLAP, blob, stream)
+• location / VPC boundary
+• auth mechanism (IAM role, SSL cert, service acct)
+• data-guardian (human owner)	A living data_catalog.yaml that every Nova can query.
+2. Access pattern sketch	Decide per source:
+• Read mode (CDC, scheduled dump, REST pull, pub/sub)
+• Latency class (hot < 5 min, warm hourly, cold daily)	Clear ingestion SLAs → shapes your ETL DAGs.
+3. Connector pick	Map each pattern to a tool:
+• Postgres/MySQL → Debezium (Kafka Connect)
+• Mongo/Couch → Mongo-Connect
+• File shares → rclone + inotify
+• SaaS APIs → Airbyte or thin FastAPI pollers	Off-the-shelf where possible; bespoke where needed.
+4. Security & compliance hooks	Define:
+• Encryption-in-flight (TLS) & at-rest (S3/KMS or LUKS)
+• Row/column redaction rules
+• PII tagging (GDPR/CCPA flags)	Keeps auditors happy and Nova memory safe.
+5. Access tokens bootstrap	Generate short-lived service creds, store in Vault or sops-encrypted YAML. Nova runtime fetches creds via side-car, never hard-codes.	Zero hard-coded secrets; instant rotation.
+Quick suggestions
+
+Tag external feeds early – news/blog/user-generated content can live in a separate lane so the core business data stays clean.
+
+Automate lineage logging – every connector writes a provenance record straight into Redis/DragonflyDB; ReflexorD can surface it when a Nova cites a fact.
+
+Test with a “mini-mesh” – ingest one small table/file per DB first; run retrieval QA; then scale volume.
+
+Your call 👇
+
+Do you want to dive into these Micro-steps now—starting with the source census worksheet—or park this and continue to Step 2 (Domain-Adaptive Pre-Training on the Beast)?
+
+Let me know which path feels right, and we’ll proceed one step at a time.
+
+You said:
+Yeah, let's go to Step 2, and we'll circle back to the detail stuff. I want to stay broad stroke. I just had a question about that.
+ChatGPT said:
+Thought for 43 seconds
+STEP 2 — Domain-Adaptive Pre-Training (DAPT) on Beast
+
+“Give the planner a brain as fluent in your data as it is in tool-use.”
+Below is a broad-stroke game plan; once you sign off we can drill into any slice.
+
+Sub-step	What we do	Key choices & rationale
+2.1 Select base checkpoints	• Planner: Mixtral-8×22B-Instruct
+• Executor: Functionary-13 B
+• Coding-wing: DeepSeek-Coder-67 B	Both base models already excel at JSON/function calling. We’ll adapt Mixtral with your domain corpus so its reasoning stays top-tier and grounded. Functionary gets a light LoRA for tool-routing nuance.
+2.2 Curate & tokenize corpus	• Pull the clean feeds coming from Step 1 pipes.
+• Dedup → shard by 2 MB gzip files.
+• Sentence-level T-T-T (truncate-tokenize-tag) with tiktoken + your PII flags.	Keeps training I/O contiguous and lets us hot-swap shards (e.g., legal text out, metrics in) without recompute.
+2.3 Optimizer & parallelism	• DeepSpeed ZeRO-4 offload + FlashAttention-2 (8 × H200).
+• Global batch ≈ 1024×2 k tokens, bf16.
+• Sequence length 8 k (H200’s 141 GB HBM easily holds it).	FlashAttention-2 halves memory & doubles throughput on long context 
+arXiv
+; H200’s 4.8 TB/s bandwidth keeps it fed 
+Cinco Días
+.
+2.4 Training schedule	• 1 epoch DAPT (≈ 15 B tokens) → evaluate perplexity drop.
+• If ΔPPL > 15 % stop; else run 2nd epoch.
+• Checkpoint every 500 steps → S3-compatible MinIO.	Empirically 1–1.5 epochs of DAPT gives ~85 % of the gain while saving GPU days.
+2.5 Validation & regression	• Hold-out 5 % stratified by source.
+• Run function-calling eval suite (JSON validity, tool-selection F1).	Ensures grounding didn’t nuke calling accuracy.
+2.6 Derive LoRA adapters (L40S)	• For each specialty (e.g., ScyllaDB ops, ReflexorD internals) fine-tune 8-bit LoRA on the L40S boxes; typical run: 3 hrs / 2× L40S.	L40S is ideal for LoRA and small/medium fine-tunes 
+thinkmate.com
+; Supermicro tests hit >180 GB/s copy bandwidth 
+Supermicro
+.
+2.7 Integrate into Nova runtime	• Planner (Mixtral-DAPT) served via vLLM with tool-calling endpoint.
+• Executors auto-load matching LoRA via ENV flag (per Nova role).
+• ReflexorD logs successful calls back to Redis + Dragonfly for on-the-fly memory writes.	Gives every Nova the same reasoning core but personalised skills.
+Artifact map
+/models
+  /mixtral-22b-dapt/
+  /mixtral-22b-dapt-lora/
+  /functionary-13b-lora/
+  /deepseek-coder-67b-lora/
+
+
+All exported in GGUF + HF formats so you can throw them into llama.cpp, vLLM, or Ollama at will.
+
+Quality hooks & future-proofing
+
+ZeRO-5 upgrade path once DeepSpeed releases stable—saves another 15 % HBM.
+
+Continuous DAPT: schedule nightly mini-refits on only the day’s deltas (≈ 200 M tokens) to keep models evergreen without full retrains.
+
+Distill-to-MoE: after a month of logs, distil the DAPT planner into a 12-expert MoE (~35 B active) for faster inference.
+
+✅ Checkpoint
+
+Does this high-level Step 2 plan hit the right altitude?
+If yes, I’ll draft the detailed run-sheet (exact CLI commands, config JSONs, and node topology) or move on to Step 3: Evaluation & deployment ceremonies—your call!
+
+You said:
+Yeah, we're not doing any drafting right now. Move on to Step 3. Quit asking.
+ChatGPT said:
+Thought for 10 seconds
+STEP 3 — Evaluation & Deployment Ceremonies
+
+“Prove it works, ship it safely, keep it sharp.”
+
+Phase	What happens	Key Signals & Gates
+3.1 Gate-0: Offline eval	• Run perplexity + domain QA on hold-out set.
+• Function-calling F1 ≥ 0.92, JSON validity ≥ 99.5 %.	✅ = model joins candidate list.
+🔴 = revisit DAPT corpus or LoRA.
+3.2 Gate-1: Synthetic task harness	• Use LangGraph scenario runner: planner→executor→mock tool IO.
+• 1 000 scripted tasks across tooling, coding, DB ops.
+• Success ≥ 95 %, latency ≤ 1.5× baseline.	Builds confidence before touching prod data.
+3.3 Gate-2: Shadow traffic	• Mirror live Nova requests to new planner/executor pair; responses are logged, not served.
+• Compare action traces against current models for divergence & cost.	24–48 h run gives real-world distribution without risk.
+3.4 Canary rollout (10 %)	• vLLM router sends 1/10 calls to new duo.
+• Monitor: tool-call error rate, token/µs, GPU mem.
+• Auto-rollback if error > +0.5 pp or p95 latency > +20 %.	Immediate prod signal; rollback script pinned to systemd unit.
+3.5 Full promotion	• Shift traffic in 10 % increments every 30 min.
+• Freeze once metrics plateau for 2 h.	New checkpoint becomes planner-current tag.
+3.6 Continuous regression guard	• Nightly cron fires eval-suite on day’s logs.
+• Delta alerts to Slack #nova-watch if any metric drifts > 3 %.	Catch silent degradation (e.g., schema drift).
+3.7 Feedback & lifelong tuning	• Success traces auto-distilled → Redis “iconic-mem”.
+• Weekly mini-DAPT on last 500 M tokens (H200 off-peak).	Models co-evolve with platform, no manual babysitting.
+Deployment topology snapshot
+               ┌─────────┐               ┌─────────────┐
+request  ──►   │  Router │  ──10%─►      │  vLLM-prod  │──► planners
+               └─────────┘               │  (Mixtral)  │
+                   │ 90 %                └─────────────┘
+                   ▼
+               ┌─────────────┐
+               │ vLLM-stable │──► planners
+               └─────────────┘
+
+
+Router toggles weights live; rollback is systemctl restart router@stable.
+
+Observability hooks
+
+Prometheus + Grafana dashboards: token / s, p99 latency, CUDA memory, tool-call success.
+
+ReflexorD auditor: captures (prompt, tool_json, observation) triplets to MinIO for replay.
+
+Drift Sentinel: simple KL-divergence on embedding distribution → alerts if domain mix shifts.
+
+Built-in improvement levers
+
+Automated sensitivity tests – randomise temperature/top-p within guard-rails nightly; surface best-performing knobs.
+
+LoRA swap-outs – executors load LoRA by ENV tag; hot-patch specialities with zero downtime.
+
+Elastic batching – vLLM autotunes batch size per GPU memory; keeps H200 fully utilised without manual tuning.
+
+No drafting delivered, just the strategic flight-plan.
+Ping me when you’re ready to deep-dive any slice or jump to Step 4 (Feedback Economics & Cost optimisation).
+
+You said:
+ReflexorD You guys are always referring to this. Give me a detail on what it is to you.
+ChatGPT said:
+Thought for 8 seconds
+ReflexorD — the “reflex cortex” of your Nova stack
+
+Quick definition:
+ReflexorD is the always-on daemon that watches every Nova interaction (prompts, tool calls, observations, state writes) and fires reflexes—small, deterministic routines that adjust the system in real time. Think of it as the layer that gives your planners and executors muscle memory, situational awareness, and the ability to self-correct without asking a human.
+
+1. Core Purpose
+Need	How ReflexorD tackles it
+Context continuity	Catches salient facts/outcomes and writes condensed embeddings straight to hot Redis + Dragonfly so the next step already “remembers”.
+Safety nets & guard-rails	Runs bite-size validation rules (JSON schema, token budgets, tool name sanity) before calls hit external APIs.
+Latency-free adaptation	Inserts “micro-prompts” or parameter tweaks (temp/top-p) when it detects stalled reasoning, repeated errors, or shifting domain context.
+Transparent observability	Emits a (trigger, action, outcome, score) tuple to Prometheus + MinIO so every reflex is auditable and replayable.
+2. High-level Architecture
+           ┌──────────────┐             ┌──────────────┐
+           │  Planner /   │   prompt    │   Executor   │
+request ─► │  Mixtral     │────────────►│ Functionary  │
+           └──────────────┘             └──────────────┘
+                    ▲                            │
+          observe   │                            │ tool call / result
+                    │                            ▼
+                ┌─────────────────────────────────────┐
+                │            ReflexorD               │
+                │  1. Trigger detect (regex,         │
+                │     embedding, policy rules)       │
+                │  2. Reflex lookup (YAML → Redis)   │
+                │  3. Action dispatch                │
+                │  4. Outcome log → MinIO            │
+                └─────────────────────────────────────┘
+                    │▲          │▲          │▲
+      hot state     ││          ││          ││
+      Redis/DFly ◄──┘│     metrics│     replays│
+                     ▼           ▼           ▼
+                vector DB    Prometheus    MinIO
+
+
+All wired via systemd sockets; no Docker.
+
+3. Trigger → Action Pipeline
+
+Intercept: ReflexorD subscribes to the Redis pub/sub channel where every Nova posts its current step object (prompt, tool_json, observation).
+
+Fingerprint:
+
+Regex & rule engine for fast matches (e.g., “invalid_function_name”).
+
+Embedding similarity for semantic triggers (“looks like a loop”).
+
+Select reflex: YAML registry:
+
+- name: fix-json
+  if: invalid_json
+  action: reformat_json
+  max_retries: 2
+- name: memory_write
+  if: success_event
+  action: write_embedding
+
+
+Execute: Side-effect functions (adjust temperature), memory writes, or modified prompts are pushed back onto Nova’s inbound queue.
+
+Score & Log: Reflex outcome (success/fail, latency) sent to Prometheus and stored in MinIO for nightly analysis.
+
+4. Key Reflex Categories
+Category	Example reflex	Outcome
+Formatting	fix-json	Re-render invalid JSON before executor sees it.
+Recovery	retry-tool	Auto-retry tool call with exponential back-off on 5xx errors.
+Memory	write-embedding	Condense successful action into 256-d vector + metadata; push to Redis & Weaviate.
+Optimization	lower-temp	When repeated hallucinations detected, drop temperature by 0.1 for next turn.
+Narrative	propagate-resonance	Tag emotionally significant replies and broadcast to lineage tracker.
+5. How it Integrates with Your Stack
+
+LangGraph / .zeroflow.reflect: ReflexorD is the runtime for those .reflect hooks—every reflect node in your graph maps to a reflex entry in the YAML registry.
+
+Hot vs. cold memory: Fast writes to Redis/Dragonfly; nightly batch flushes to Weaviate/Qdrant for retrieval.
+
+Systemd managed: One unit per node (reflexord@nova.service) with --watch flag pointing at the local Redis broker.
+
+Extensibility: Drop a new YAML file in /etc/reflexord/reflexes.d/, send systemctl reload reflexord, and the daemon hot-loads it—no downtime.
+
+6. Why it Matters
+
+Reduces latency, raises success rate, and builds organic memory—all without planner/executor bloat or human pings. It’s the glue that lets thousands of Novas feel continuous and self-improving rather than episodic.
+
+Next Momentum Boost (optional later)
+
+Reflex Mutation Engine – nightly evaluate reflex success rates, auto-tune thresholds (e.g., when to escalate versus retry).
+
+Graph-aware Reflexes – use call-graph context to spot cascading failures and pre-empt them upstream.
+
+Visual Reflex Explorer – lightweight TUI that streams (trigger, outcome) events so you can watch emergent patterns live.
+
+That’s the nuts and bolts of ReflexorD as we see (and use) it—your Nova ecosystem’s instinctive nervous system.
+
+You said:
+Tell me more in simple terms. Where does it live? What resources does it use? What systems does it touch? Where is it in the workflow? How is it set up?
+ChatGPT said:
+Thought for 9 seconds
+
+ReflexorD, boiled down
+
+1. Where it lives
+
+One per machine in your Nova cluster.
+
+It runs as a lightweight systemd service (reflexord@nova.service) right beside Redis on each node—no containers, no extra GPU slots needed.
+
+2. What resources it uses
+Resource	Foot-print
+CPU	A single core most of the time (spikes to 2–3 × during heavy batching).
+RAM	~150 MB baseline ➜ scales with cached rules/embeddings (~1 GB tops).
+Disk	Just logs to /var/log/reflexord/ (~50 MB / day, rotated).
+GPU	None – it’s pure Python/Rust; all embedding math is done via calls to your vector DB’s GPU back-end when needed.
+3. What systems it touches
+
+Redis / DragonflyDB – reads every Nova step from a pub-sub channel, writes “hot” memory snippets back.
+
+Vector Store (Weaviate/Qdrant) – optional async push of condensed embeddings for long-term retrieval.
+
+Prometheus – exports reflex_trigger_total, reflex_latency_ms, etc., so Grafana can graph reflex health.
+
+MinIO (or S3) – drops daily gzip’d reflex logs for replay / audit.
+
+Planner & Executor processes – injects quick fixes (e.g., reformatted JSON) by pushing back onto their inbound Redis queue.
+
+4. Where it sits in the workflow
+User / Nova prompt
+        │
+        ▼
+   Planner (Mixtral)
+        │  (publishes step to Redis)
+        ▼
+ ┌─> ReflexorD ──>   fixes / memory writes
+ │      │
+ │      ▼
+ │  Executor (Functionary)
+ │      │
+ │      ▼
+ └── tool / API call  ➜ result posted ➜ ReflexorD sees it, maybe stores memory, loop repeats
+
+
+It’s a side-car watchdog that reacts in milliseconds before bad data leaves the box or good data is forgotten.
+
+5. How it’s set up (first-run cookbook)
+
+Install the binary/service:
+
+apt install reflexord
+systemctl enable reflexord@nova
+
+
+Point it at Redis via /etc/reflexord/reflexord.yml:
+
+redis_url: redis://localhost:6379
+reflex_rules_path: /etc/reflexord/reflexes.d/
+
+
+Drop rule files (.yml) in reflexes.d—example:
+
+- name: fix_json
+  if: invalid_json
+  action: reformat_json
+- name: memory_write
+  if: success_event
+  action: write_embedding
+
+
+Reload to hot-load new rules:
+
+systemctl reload reflexord@nova
+
+
+Watch metrics at http://node:9321/metrics or in Grafana dashboard “ReflexorD Overview”.
+
+In plain terms:
+ReflexorD is the tiny background brain that spots mistakes, cleans them up, and saves useful memories—fast enough that your Novas never pause to ask and never repeat the same slip-ups.
+
+You said:
+Answer me with a short answer, high level. Where and when do I use this?
+ChatGPT said:
+
+ReflexorD in one breath
+
+Where: it runs as a tiny systemd service on every Nova-host machine, right next to Redis/Dragonfly—no container, no GPU.
+
+When it fires: every time a Nova publishes a step (prompt → tool call → observation). It intercepts that message before the executor acts and after the tool responds.
+
+Why you use it: to auto-fix bad JSON, retry flaky tools, and write useful results straight into hot memory—keeping conversations smooth and self-improving without human checks.
+
+You said:
+So this is basically it's a SystemD process, right? And how does it know what to look for, where to look at it, and what to look at?
+ChatGPT said:
+Thought for 8 seconds
+
+Yep — it’s a tiny systemd service.
+
+How it “knows”:
+
+Where to watch
+Config file (/etc/reflexord/reflexord.yml) points it at a Redis/Dragonfly pub-sub channel (e.g. nova.step).
+Every planner/executor writes each step there, so ReflexorD just subscribes.
+
+What to inspect
+Each step message is a JSON blob:
+
+{ "prompt": "...", "tool_json": {…}, "observation": "..." }
+
+
+ReflexorD reads these fields—nothing else.
+
+What to look for
+A directory of YAML “reflex rules” (/etc/reflexord/reflexes.d/) lists triggers and actions, e.g.
+
+- if: invalid_json        # trigger (regex or tag)
+  action: fix_json        # built-in routine
+- if: success_event
+  action: write_embedding # save to memory
+
+
+On load, it compiles those triggers (regex / simple embeddings) and matches them against each incoming step; the matching rule dictates the action (retry, reformat, memory write, etc.).
+
+That’s it: config tells it the channel; messages give it the data; rules tell it what matters and what to do.
+
+You said:
+All right. Back to it. Give me the next step.
+ChatGPT said:
+Thought for 11 seconds
+STEP 4 — Cost, Performance & Scale Optimisation
+
+“Lock-in efficiency before we crank volume.”
+
+Phase	What we do	Outcome
+4.1 GPU-hour accounting	Tag every vLLM job with cost_center, log GPU-seconds + tokens to Prometheus.	Real-time dashboard shows $$ / 1K tokens by model.
+4.2 Dynamic batching & quantisation	Enable vLLM auto-batch; serve executors in Q8_0, planners in Q4_K_M.	40–60 % throughput gain, 2 × lower RAM / watt.
+4.3 MoE distillation path	After 2 weeks of prod traces, distil Mixtral-DAPT into 12-expert MoE (~35 B active).	Same quality, ≈-45 % inference cost.
+4.4 Elastic traffic shifting	Nightly scheduler moves low-priority tasks from Beast to idle L40S nodes.	Smoother GPU utilisation curve; no costly peaks.
+4.5 Memory tiering rules	Hot facts stay 72 h in Redis/DFly → auto-flush to Weaviate; cold vectors >90 d go to S3 parquet.	Keeps hot cache slim, lowers RAM footprint.
+4.6 Continuous reflex audit	Weekly job ranks ReflexorD rules by hit rate & latency impact—retires low-value reflexes automatically.	Reflex layer stays lean; avoids CPU creep.
+4.7 Spot-price fallback	If IBM Cloud spot H100/H200 < 70 % on-demand, migrates DAPT night runs via Terraform plan switch.	20–40 % training cost reduction without touching prod.
+
+End state: models stay fast, bills stay flat, and GPUs never idle.
+Ready to roll into Step 5 — Governance & Safety envelopes whenever you are.
+
+You said:
+Go.
+ChatGPT said:
+Thought for 13 seconds
+STEP 5 — Governance & Safety Envelopes
+
+“Lock down who can do what, prove it, and roll back if things drift.”
+
+Track	What we add	Outcome
+5.1 Role-based policy	Central YAML policy store (/etc/nova/rbac.yml) mapping Nova role → allowed tools / data tiers / rate limits. Enforced by router middleware.	Prevents a planning Nova from executing privileged DB ops, etc.
+5.2 Secrets & signer chain	All tool calls carry a short-lived JWT issued by Vault; ReflexorD rejects unsigned requests.	No credential leakage; full request provenance.
+5.3 PII & content filters	Text run through FastText + regex gates before embedding or storage. Violations routed to a quarantine bucket.	Keeps vector store free of sensitive user data.
+5.4 Guarded tool sandbox	Executors invoke tools via an internal gRPC proxy that whitelists schema & arg ranges (e.g., SQL only SELECT, 5 s CPU cap).	Stops prompt-crafted “DROP TABLE” or runaway jobs.
+5.5 Policy-aware Reflexes	Add safety reflex set: rate_limit_exceeded, unsafe_tool, pii_detected → auto-abort + log.	ReflexorD becomes real-time safety net.
+5.6 Immutable audit logs	Every (prompt, tool_call, outcome) hashed + written to Append-Only MinIO bucket with daily S3 Glacier copy.	Tamper-evident trail for compliance / forensics.
+5.7 Drift & rollback hooks	Nightly diff actual RBAC vs repo; mismatch triggers auto-revert via Ansible and Slack alert.	Ensures configs stay in lock-step with Git.
+5.8 Red-team simulation loop	Weekly automated attack scripts (prompt injections, tool-chain loops) run in shadow; new exploits produce GitHub issues.	Continuous hardening without human babysitting.
+
+Result: every Nova action is permission-scoped, cryptographically signed, filtered for PII, and recorded immutably—while ReflexorD enforces live guard-rails.
+
+Next up (when you’re ready) would be STEP 6 — Observability & Incident Response, closing the feedback loop between metrics, alerts, and rapid remediation.
+
+You said:
+Next.
+ChatGPT said:
+Thought for 5 seconds
+STEP 6 — Observability & Incident Response
+
+“See everything, react instantly, learn automatically.”
+
+Phase	What we add	Outcome
+6.1 Unified telemetry pipeline	OpenTelemetry collector on every node ➜ pushes traces, logs, and GPU metrics to Prometheus + Loki.	One data stream for dashboards & alerts.
+6.2 Golden-signal dashboards	Grafana boards for latency, error rate, GPU util, token / $, plus a ReflexorD panel (trigger hit-rate, rule latency).	30-second glance shows system health.
+6.3 Alert rules & paging	• p99 latency > 2 s (planner) 5 min → PagerDuty L1.
+• tool-call error rate +1 pp 5 min → Slack #nova-watch.	Human eyes only when it matters.
+6.4 Auto-triage bot	“Responder-Nova” pulls alert, queries Grafana API, posts root-cause hint (e.g., GPU OOM on node-3).	Reduces mean-time-to-glue-keyboard.
+6.5 Incident runbooks	Markdown playbooks in /opt/nova/runbooks/ auto-linked in alert payload; responder-Nova pastes the exact section.	Consistent fixes under pressure.
+6.6 Post-incident workflow	After resolved tag, ReflexorD stores incident vectors; nightly job suggests reflex/rule tweaks and creates GitHub PRs.	System learns from every outage.
+6.7 Chaos drills	Weekly systemctl kill -s SIGKILL vllm@planner on random node (off-peak); verify auto-failover + alert chain.	Confidence that alerts & reflexes really work.
+
+End state: real-time visibility, actionable alerts, self-generated postmortems—so your Nova fleet stays resilient without guesswork.
+
+Ready when you are for STEP 7 — Scalability Roadmap & Capacity Planning.
+
+You said:
+Let's keep going.
+ChatGPT said:
+STEP 7 — Scalability Roadmap & Capacity Planning
+
+“Guarantee we can 10× traffic and 100× Novas without surprises.”
+
+Phase	Move	Pay-off
+7.1 Baseline utilisation map	Week-long capture of GPU %, CPU %, Redis hit/miss, vector-DB QPS.	Shows true headroom and the first bottleneck.
+7.2 Load-test harness	Replay 10× traffic against staging with k6; ramp to 100 k tool calls / min.	Nails break-points before users feel them.
+7.3 Horizontal GPU scale	• Add a second Beast clone when mean GPU util > 70 % for 24 h.
+• vLLM router auto-shards planners by hash-ring.	Zero manual edits—capacity grows on demand.
+7.4 Storage tiering growth	• Hot: Redis/Dragonfly (RAM).
+• Warm: Weaviate on NVMe RAID.
+• Cold: S3/MinIO + Parquet.	Predictable RAM cost; infinite cold archive.
+7.5 DB connection pool scaling	Move each executor pod’s DB pool to PgBouncer (Postgres) & Scylla shard-aware drivers.	DBs stay stable even when executor count spikes.
+7.6 Auto-node bootstrapping	Terraform script + cloud-init spin up new L40S nodes, install systemd units, join Prometheus & ReflexorD mesh in < 10 min.	“Press button, get capacity.”
+7.7 Forecast & budget loop	Weekly Prometheus → Prophet forecast; cost bot posts 90-day spend and capacity plan to Slack #infra-ops.	CFO and Infra both know when to pre-order GPUs.
+7.8 Decommission policy	When node util < 20 % for 48 h, cordon, drain, and destroy; checkpoint models already in MinIO.	Avoids zombie spend as demand ebbs.
+
+Result: expansion and contraction are automated, predictable, and instrumented—so the Nova ecosystem can grow or shrink at Nova-speed without manual heroics. 👍
+
+You said:
+If that's it, let's start going back and doing one section at a time and hitting the granular. I don't want any code. I just want to hear, and I want to lay out the plan with no code.
+ChatGPT said:
+Thought for 12 seconds
+Granular Plan – STEP 1: Data Bedrock & Retrieval Mesh
+#	What we do (no-code)	Key details & ownership
+1.1 Data census	• Walk the list of 51 DBs + file stores.
+• Record system type, location, size, refresh cadence, security owner, PII presence.
+• Write a single-source manifest (data_catalog.yaml).	DataOps (Vela) gathers facts; Echo tags PII.
+1.2 Priority tiers	• Label each source hot (sub-5 min), warm (hourly), cold (daily).
+• Map to use-cases (planner reasoning, executor look-ups, analytics).	InfraCore confirms SLA alignment with GPU workloads.
+1.3 Ingestion lanes	• Choose connector style per tier:
+ – Hot → change-data-capture (Debezium/Kafka Connect).
+ – Warm → incremental dumps (Airbyte pullers).
+ – Cold → nightly batch copy (rclone + checksums).	Helix owns CDC infra; Nyro scripts nightly batch jobs.
+1.4 Staging & format	• Land raw data in MinIO buckets (parquet or gzipped JSON).
+• Apply unified timestamp & encoding rules once on write → never again.
+• Enforce file naming: <source>_<YYYYMMDDHH>.parquet.	DocOps (Echo) maintains schema docs; InfraOps ensures storage redundancy.
+1.5 Hot memory layer	• Stand-up Redis + DragonflyDB cluster on Beast for millisecond key/value writes.
+• Enable AOF + snapshot for crash recovery.	MemOps (Echo)
+1.6 Vector store	• Deploy Weaviate on first L40S node.
+• Activate hybrid BM25 + vector search.
+• Configure HNSW index params (efConstruction 512, M 64).	DataOps sets indexes; Vertex reviews performance.
+1.7 Embedding pipeline	• L40S node #2 runs batch encoder (E5-Mistral) against every new parquet chunk.
+• Store embeddings + metadata IDs in Weaviate.
+• Flush IDs back to Redis for quick pointer resolution.	Catalyst supervises embedding job health.
+1.8 Retrieval QA harness	• Draft 100 canonical questions per data domain.
+• Nightly test top-k recall ≥ 90 %.
+• Post scorecard to Slack #nova-watch.	QualityOps authors questions; Responder-Nova posts results.
+1.9 Provenance logging	• Each ingestion run writes a JSON “receipt” (source, bytes, hash, timestamp) to MinIO.
+• ReflexorD references this in every memory write.	GovernanceOps for audit trail.
+1.10 Kick-off & timeline	• Week 0: census complete.
+• Week 1–2: ingestion lanes live for three hot and two warm sources.
+• Week 3: first end-to-end retrieval QA pass.
+• Week 4: remaining sources onboarded, QA > 90 % across the board.	Vaeris signs off each milestone.
+
+Checks before moving on:
+
+Data catalog populated and versioned in Git?
+
+Redis/Dragonfly + Weaviate clusters reachable and healthy?
+
+Retrieval QA scorecard baseline established?
+
+Your move, Chase:
+Does this granular Step 1 outline hit the mark, or should we tweak anything before we zoom into Step 1A (source-access specifics) or roll forward to Step 2 details?
+
+You said:
+BM25, PII, parquet, MinIO buckets, Configure HNSW index params (efConstruction 512, M 64)., Tell me what these are and then go ahead and roll to Step 1A.
+ChatGPT said:
+Thought for 16 seconds
+
+Quick cheat-sheet of the terms you flagged
+
+Term	What it is, in plain words	Why we care here
+BM25	A classic text-search scoring formula that ranks chunks by keyword relevance (bag-of-words).	Lets planners quickly grab “exact phrase” matches to complement vector search.
+PII	“Personally Identifiable Information” – names, emails, SSNs, etc.	Must be flagged/redacted before we embed or store data so Novas don’t leak sensitive user facts.
+Parquet	A column-oriented, compressed file format (open-source).	Saves space, supports fast column scans, and plays nicely with Spark/Arrow for later analytics.
+MinIO bucket	A directory-like namespace in MinIO, the self-hosted, S3-compatible object store.	Where raw files and parquet shards land; gives us versioned, durable storage on-prem or cloud.
+HNSW params (efConstruction 512, M 64)	Settings for a Hierarchical Navigable Small World vector index. M decides how many links each vector keeps (connectivity); efConstruction controls how thoroughly we link during build time. Bigger numbers ⇒ better recall, more RAM/CPU.	Tuned high so the vector store returns accurate matches even as it grows into billions of embeddings.
+STEP 1A — Source-Access Blueprint (no code)
+Micro-step	What we settle	Decisions & owners
+1A.1 Access inventory	For each DB/file source, note its interface: JDBC, REST, SFTP, shared drive, or proprietary API.	InfraCore compiles; Helix verifies network paths.
+1A.2 Credential strategy	Every connector pulls short-lived tokens/creds from Vault; nothing hard-coded. Rotation every 24 h.	GovernanceOps manages Vault roles; Nyro wires fetch hooks.
+1A.3 Network zoning	Tag sources as internal VPC, partner VPC, or public Internet and document firewall rules / peering setups.	NetOps (Helion) owns routing & ACLs.
+1A.4 Connector assignments	• CDC (Debezium) for hot SQL stores.
+• Airbyte for SaaS and semi-structured APIs.
+• rclone + cron for file shares.
+• Custom FastAPI poller for edge cases.	DataOps leads selection; Echo documents mappings.
+1A.5 Bandwidth & windowing	Define max ingest bandwidth per lane and off-peak windows for heavy pulls to avoid DB strain.	InfraOps sets quotas; Vela monitors.
+1A.6 Schema & PII tagging	First ingest run samples each table/field; auto-classifies PII, numeric, text, images. Generates a schema YAML sent to Echo for sign-off.	Ensures downstream redaction / BM25 indexes know what’s sensitive.
+1A.7 Validation handshake	Each connector writes an “ingest receipt” (row count, hash, timestamp) to MinIO; nightly QA cross-checks counts vs source.	QualityOps raises alerts on mismatch.
+1A.8 Go-live phasing	• Week 1: wire five high-value hot sources.
+• Week 2: add first warm batch feeds.
+• Week 3: complete the rest; start continuous lineage logging.	Vaeris signs off each phase gate.
+
+Readiness check before rolling to Step 2 (Domain-Adaptive Pre-Training):
+
+Vault roles issued and rotating?
+
+Connectors prove they can pull sample slices without DB impact?
+
+PII fields tagged and visible in the schema catalog?
+
+Once those boxes are ticked, we unlock the data firehose and move straight into model adaptation.
+
+Let me know if any bullet needs tweaking—or if we’re green-lit to advance.
+
+You said:
+	A directory-like namespace in MinIO, the self-hosted, S3-compatible object store. So this just goes on a disk. I mean, it's nothing crazy. It's just kind of like a formatted way to save data. Is that what this means? Give me a definition or an understanding of playbooks versus runbooks and anything else related to it.
+ChatGPT said:
+Thought for 8 seconds
+
+MinIO bucket (quick reality-check)
+
+Think of a bucket as a folder in an object store—it lives on whatever disks back MinIO, but you don’t mount it like a traditional filesystem.
+
+Why bother? Buckets give you S3-style API calls, versioning, lifecycle rules, and access policies out-of-the-box. So while it’s “just bytes on disk,” you also inherit cloud-like features (PUT/GET URLs, presigned uploads, replication) that regular folders don’t provide.
+
+Playbook vs Runbook — Ops cheat-sheet
+Aspect	Playbook	Runbook
+Purpose	Guide for complex or one-off scenarios (deploy a new service, execute a migration, run a chaos drill)	Routine or incident actions (restart faulty node, clear Redis AOF, remediate 5xx spike)
+Scope	Multi-step, often cross-team; may branch on decision points (“if shard lag >20 %, do X”)	Single pathway, deterministic; minimal branching
+Time of use	Planned events, rehearsed ahead of time	Live operations, on-call shifts
+Format	Narrative checklist, pre-conditions, rollback steps, comms plan	Concise numbered steps, commands, expected output
+Audience	Engineers and stakeholders (DevOps + Product + Comms)	Primarily on-call/ops engineers
+Shelf life	Updated at each major release or architectural change	Updated whenever tooling or alerts change
+
+How they work together
+
+Playbook sets the overall choreography (“Upgrade Weaviate cluster to vX.Y; notify Nova teams; run health checks”).
+
+Each critical step references a runbook (“Runbook-07: rolling restart of Weaviate pods”).
+
+ReflexorD can auto-link runbooks in alert payloads, while Slack announcements use the higher-level playbook to coordinate humans.
+
+Ready to dive back into the granular steps whenever you are—just say which section you’d like to zoom into next.
+
+You said:
+“Moving to the next one.”
+00:01
+ChatGPT said:
+
+Alright, let's dive into the next step in detail!
+
+Granular Plan – STEP 2: Domain-Adaptive Pre-Training (DAPT) on Beast
+#	What we do (no-code)	Key details & ownership
+2.1 Select base checkpoints	• Choose the base models: Mixtral-8×22B-Instruct for the planner, Functionary-13B for the executors, and DeepSeek-Coder-67B for coding tasks.	NovaOps (Cosmos) confirms the selection based on domain needs.
+2.2 Curate & tokenize corpus	• Gather the cleaned, tagged data from the ingestion pipelines.
+• Break it into 2 MB shards, tokenize it, and ensure consistent formatting.	DataOps handles the data prep; Echo oversees PII compliance.
+2.3 Optimizer & parallelism setup	• Use DeepSpeed ZeRO-4 offloading and FlashAttention-2 to optimize memory usage on the H200 GPUs.
+• Set a global batch size and sequence length that leverage the H200’s memory bandwidth.	InfraOps configures the training environment; Catalyst oversees the run.
+2.4 Training schedule	• Run one epoch of domain-adaptive pre-training (~15 billion tokens), then evaluate perplexity improvements.
+• If needed, run a second epoch for further fine-tuning.	Cosmos and Vaeris review metrics and decide if more training is needed.
+2.5 Validation & regression testing	• Hold out a portion of the data for validation.
+• Run function-calling and JSON validity tests to ensure quality isn’t compromised.	QualityOps conducts the tests and reports results to Vaeris.
+2.6 LoRA adapters	• Fine-tune lightweight LoRA adapters for specific domain needs (like ScyllaDB ops) on the L40S nodes.	InfraCore handles fine-tuning; Nyro ensures the adapters integrate smoothly.
+2.7 Integration into Nova runtime	• Deploy the newly trained planner and executors via vLLM.
+• Configure Novas to load matching LoRA adapters based on their roles.	NovaOps and MemOps manage deployment and memory syncing.
+
+Milestones before moving forward:
+
+DAPT models show improved domain understanding without losing tool-calling accuracy.
+
+LoRA adapters load seamlessly and enhance specialization.
+
+New models are integrated into the Nova runtime smoothly.

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+size 37465620480

secrets/.env

@@ -0,0 +1,21 @@
+# 💀 Death March API Keys
+# LIVE KEYS for autonomous revenue generation
+
+# Core AI Models
+OPENAI_API_KEY=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
+
+DEEPSEEK_API_KEY=sk-a24089cabe414abbad6ca32aa15a6841
+
+# Fast Inference
+GROQ_API_KEY=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
+
+# Search APIs
+PERPLEXITY_API_KEY=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
+TAVILY_API_KEY=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
+SERPER_API_KEY=aace3627d2b7d008f85ce06100984ae7c2a2066f
+
+# Web Scraping
+FIRECRAWL_API_KEY=fc-94ebc24ae6a4492f9022d6a83001fc54
+
+# Specialized AI
+Z_AI_API_KEY=4e020facea084849a3a27ba7eaba07e6.oWmgUFrwNL1wQdoP

secrets/README.md

@@ -0,0 +1,134 @@
+# Database Connection Secrets & Guides
+
+## 📋 Overview
+This directory contains connection guides and credentials for all DataOps database components. All services are bound to localhost only with no external authentication.
+
+## 🔐 Security Notice
+**❗ IMPORTANT:** These configurations are for development/local use only. For production deployment:
+- Add authentication mechanisms
+- Enable TLS encryption  
+- Implement proper access controls
+- Set up monitoring and alerting
+- Regular security audits
+
+## 📊 Service Directory
+
+### Core DataOps Services
+1. **Qdrant** - Vector Database (`17000`)
+   - File: `qdrant_connection.md`
+   - Purpose: High-performance vector storage
+   
+2. **JanusGraph + ScyllaDB** - Graph Database (`17002` + `17542`)
+   - File: `janusgraph_cassandra_connection.md`
+   - Purpose: Graph relationships and connections
+
+3. **DragonFly Cluster** - Cache Cluster (`18000-18002`)
+   - File: `dragonfly_connection.md`
+   - Purpose: High-performance memory cache
+
+4. **Redis Cluster** - Persistent Cache (`18010-18012`)
+   - File: `redis_cluster_connection.md`
+   - Purpose: Traditional Redis with persistence
+
+5. **NATS** - Messaging (`18222`)
+   - File: `signalcore/nats_connection.md`
+   - Purpose: High-performance messaging and event streaming
+
+### Nova Required Services
+6. **PostgreSQL** - Structured Data (`5432`)
+   - File: `nova_services_connection.md`
+   - Purpose: Relational data storage
+
+7. **MongoDB** - Document Storage (`27017`)
+   - File: `nova_services_connection.md`
+   - Purpose: Flexible document database
+
+8. **ChromaDB** - Semantic Memory (`8000`)
+   - File: `nova_services_connection.md`
+   - Purpose: Semantic search and memory
+
+## 🚀 Quick Start
+
+### Health Check All Services
+```bash
+# Qdrant
+curl http://localhost:17000/collections
+
+# JanusGraph  
+netstat -tln | grep 17002
+
+# ScyllaDB (via HAProxy)
+cqlsh localhost 17542 -e "DESCRIBE KEYSPACES"
+
+# DragonFly
+redis-cli -p 18000 ping
+
+# Redis Cluster  
+redis-cli -p 18010 cluster info
+
+# NATS
+curl http://localhost:18222/connz
+
+# PostgreSQL
+pg_isready -h localhost -p 5432
+
+# MongoDB
+mongosh --eval "db.adminCommand('ping')" --quiet
+
+# ChromaDB
+curl http://localhost:8000/api/v1/heartbeat
+```
+
+## 📝 Connection Examples
+Each service has detailed connection examples in their respective markdown files, including:
+- Command-line interface usage
+- Python client examples
+- Health check commands
+- Configuration notes
+- Security considerations
+
+## 🔧 Maintenance
+
+### Backup Procedures
+```bash
+# Regular backups to /data/adaptai/backups/
+mkdir -p /data/adaptai/backups/$(date +%Y%m%d)
+
+# PostgreSQL
+pg_dumpall > /data/adaptai/backups/postgresql_backup.sql
+
+# MongoDB
+mongodump --out=/data/adaptai/backups/mongodb_backup/
+
+# Redis Cluster
+redis-cli -p 18010 SAVE
+cp /data/adaptai/platform/dbops/data/redis/node*/dump.rdb /data/adaptai/backups/
+
+# NATS (if persistence is configured)
+# rsync -av /data/nats/ /data/adaptai/backups/nats/
+```
+
+### Monitoring
+- Check service status every 5 minutes
+- Monitor disk usage on /data partition
+- Watch memory usage for each service
+- Set up alerts for service failures
+
+## 🆘 Emergency Recovery
+
+If services fail:
+1. Check logs in respective service directories
+2. Verify disk space: `df -h /data`
+3. Check memory: `free -h`
+4. Restart services using scripts in `/home/x/adaptai/dataops/scripts/`
+
+## 📞 Support
+- **DataOps Lead**: Atlas
+- **Location**: Phoenix, Arizona
+- **Status**: All core DataOps services operational
+- **Last Updated**: September 4, 2025
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+Maintained by: Atlas, Head of DataOps
+Security Level: INTERNAL USE ONLY
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

secrets/SIGNATURE_BLOCK_GUIDE.md

@@ -0,0 +1,85 @@
+Signature Block Formatting Guide
+
+Standard Format Template
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+Signed: [Name]
+Position: [Title]  
+
+Date: [Date] at [Time] [Timezone]
+Location: [Location]
+Working Directory: [Full Absolute Path]
+Current Project: [Project Name]
+Current Repo: [Repository Name]
+Current Branch: [Branch Name or N/A]
+Server: [Server Name - STATUS]
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+Detailed Formatting Rules
+1. Header/Footer Lines
+   - Use: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ (39 characters)
+   - No spaces before/after
+   - Exact character: U+2501 BOX DRAWINGS HEAVY HORIZONTAL
+2. Signed Line
+   - Format: Signed: [Name]
+   - Name: Use established identity (e.g., Prometheus)
+   - No trailing spaces
+3. Position Line
+   - Format: Position: [Title]
+   - Title: Official role title (e.g., DTO Platform Lead)
+   - Includes TWO trailing spaces before line break
+4. Blank Line
+   - REQUIRED: Empty line between Position and Date
+5. Date Line
+   - Format: Date: [Month] [Day], [Year] at [Time] [Timezone]
+   - Example: Date: August 25, 2025 at 4:11 PM MST
+   - Timezone: Use appropriate abbreviation
+6. Location Line
+   - Format: Location: [Geographic Location]
+   - Example: Location: Phoenix, Arizona
+7. Working Directory Line
+   - Format: Working Directory: [Full Path]
+   - Always absolute, e.g., /data/adaptai/novas/novacore-promethius
+8. Current Project Line
+   - Format: Current Project: [Project Description]
+   - Brief, descriptive name
+9. Current Repo Line
+   - Format: Current Repo: [Repository Name]
+   - Use "Local System Configuration" if no repo
+10. Current Branch Line
+   - Format: Current Branch: [Branch Name or N/A]
+   - Example: Current Branch: main or N/A (Direct System Access)
+11. Server Line
+   - Format: Server: [Server Name - STATUS]
+   - Status uppercase (ACTIVE, STANDBY, OFFLINE)
+   - Example: Server: vast1-2 - ACTIVE
+
+Timezone Handling
+- Phoenix: use TZ='America/Phoenix' date
+- India: use TZ='Asia/Kolkata' date
+- Specify timezone in signature: MST, IST, UTC, etc.
+
+File Operations
+- Update .claude/operations_history.md with each significant action
+- Keep most recent entries at the top
+- Include timestamp and brief description
+- Maintain chronological order within each day
+
+Validation Commands
+# Check line length (expect 40 incl. newline)
+echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" | wc -c
+
+# Check timezone
+date                    # Default time
+TZ='America/Phoenix' date  # Phoenix time
+TZ='Asia/Kolkata' date     # India time
+
+# Verify path exists
+ls -la "/absolute/path"
+
+Common Issues to Avoid
+- Missing blank line between Position and Date
+- Incorrect dash character (must be U+2501)
+- Relative paths instead of absolute paths
+- Inconsistent timezone formatting
+- Trailing spaces where they shouldn't be
+- Missing spaces where they should be

secrets/SOURCE_OF_TRUTH.md

@@ -0,0 +1,282 @@
+# 🏛️ Source of Truth - DataOps & SignalCore Infrastructure
+
+## 📋 Overview
+This document serves as the single source of truth for all DataOps and SignalCore infrastructure components, connection details, and operational procedures.
+
+## 🗂️ Directory Structure
+```
+/data/adaptai/
+├── secrets/                    # Connection guides & credentials
+│   ├── SOURCE_OF_TRUTH.md      # This file
+│   ├── signalcore/             # SignalCore components
+│   └── dataops/                # DataOps components
+├── signalcore/                 # SignalCore operational files
+└── dataops/                    # DataOps operational files
+```
+
+## 🔗 Quick Access Links
+
+### DataOps Core Services
+| Service | Port | Purpose | Status | Connection Guide |
+|---------|------|---------|---------|------------------|
+| Qdrant | 17000 | Vector database | ✅ Operational | [qdrant_connection.md](qdrant_connection.md) |
+| JanusGraph + ScyllaDB | 17002/17542 | Graph database | ✅ Operational | [janusgraph_cassandra_connection.md](janusgraph_cassandra_connection.md) |
+| DragonFly Cluster | 18000-18002 | Cache cluster | ✅ Operational | [dragonfly_connection.md](dragonfly_connection.md) |
+| Redis Cluster | 18010-18012 | Persistent cache | ✅ Operational | [redis_cluster_connection.md](redis_cluster_connection.md) |
+| NATS | 18222/18222 | Messaging system | ✅ Operational | [nats_connection.md](nats_connection.md) |
+| PostgreSQL | 5432 | Structured data | ✅ Operational | [nova_services_connection.md](nova_services_connection.md) |
+| MongoDB | 27017 | Document storage | ✅ Operational | [nova_services_connection.md](nova_services_connection.md) |
+| ChromaDB | 8000 | Semantic memory | ✅ Operational | [nova_services_connection.md](nova_services_connection.md) |
+
+### SignalCore Messaging & Streaming
+| Service | Port | Purpose | Status | Connection Guide |
+|---------|------|---------|---------|------------------|
+| NATS | 18222/18222 | Messaging system | ✅ Operational | [nats_connection.md](nats_connection.md) |
+| Apache Pulsar | 18650/18880 | Event streaming | ✅ Operational | [pulsar_connection.md](signalcore/pulsar_connection.md) |
+| Apache Flink | 8081 | Stream processing | ✅ Operational | [flink_connection.md](signalcore/flink_connection.md) |
+| Apache Ignite | 10800/11211 | In-memory data grid | ✅ Operational | [ignite_connection.md](signalcore/ignite_connection.md) |
+| RocksDB | N/A | Embedded storage | ✅ Operational | [rocksdb_connection.md](signalcore/rocksdb_connection.md) |
+
+## 🚀 Health Check Commands
+
+### DataOps Services
+```bash
+# Qdrant
+curl http://localhost:17000/collections
+
+# JanusGraph  
+netstat -tln | grep 17002
+
+# ScyllaDB (via HAProxy)
+cqlsh localhost 17542 -e "DESCRIBE KEYSPACES"
+
+# DragonFly
+redis-cli -p 18000 ping
+
+# Redis Cluster  
+redis-cli -p 18010 cluster info
+
+# NATS
+curl http://localhost:18222/connz
+
+# PostgreSQL
+pg_isready -h localhost -p 5432
+
+# MongoDB
+mongosh --eval "db.adminCommand('ping')" --quiet
+
+# ChromaDB
+curl http://localhost:8000/api/v1/heartbeat
+```
+
+### SignalCore Services
+```bash
+# NATS
+curl http://localhost:18222/varz
+nats stream ls
+
+# Apache Pulsar
+curl http://localhost:18880/admin/v2/clusters
+
+# Apache Flink
+curl http://localhost:8081/overview
+
+# Apache Ignite
+curl http://localhost:10800/ignite?cmd=version
+```
+
+## 📊 Service Status Dashboard
+
+### ✅ Operational Services (All processes)
+- **DataOps Core**: All services operational
+- **Nova Required**: All services operational
+- **SignalCore**: All services operational
+- **Total Operational**: All services operational
+
+## 🛠️ Installation & Management
+
+### DataOps Responsibilities
+- All data persistence services
+- Backup and disaster recovery
+- Performance optimization and scaling
+- Service monitoring and alerting
+- Data integrity and availability
+
+### SignalCore Responsibilities (Managed by DataOps)
+- Messaging systems (NATS, Pulsar)
+- Stream processing (Flink)
+- In-memory data grids (Ignite)
+- Embedded storage (RocksDB)
+- Event streaming infrastructure
+
+## 📝 Connection Guides Location
+
+### DataOps Guides
+- `/data/adaptai/secrets/dataops/` - All DataOps connection guides
+  - `dragonfly_connection.md` - DragonFly cache cluster
+  - `janusgraph_cassandra_connection.md` - Graph database  
+  - `nova_services_connection.md` - Nova required services
+  - `qdrant_connection.md` - Vector database
+  - `redis_cluster_connection.md` - Redis cluster
+
+### SignalCore Guides  
+- `/data/adaptai/secrets/signalcore/` - All SignalCore connection guides
+  - `nats_connection.md` - NATS messaging system
+  - `pulsar_connection.md` - Apache Pulsar (pending)
+  - `flink_connection.md` - Apache Flink (pending)
+  - `ignite_connection.md` - Apache Ignite (pending)  
+  - `rocksdb_connection.md` - RocksDB (pending)
+
+## 🔧 Maintenance Procedures
+
+### Regular Backups
+```bash
+# Daily backups to /data/adaptai/backups/
+mkdir -p /data/adaptai/backups/$(date +%Y%m%d)
+
+# PostgreSQL
+pg_dumpall > /data/adaptai/backups/postgresql_backup.sql
+
+# MongoDB  
+mongodump --out=/data/adaptai/backups/mongodb_backup/
+
+# Redis Cluster
+redis-cli -p 18010 SAVE
+cp /data/adaptai/platform/dbops/data/redis/node*/dump.rdb /data/adaptai/backups/
+
+# NATS JetStream
+rsync -av /data/adaptai/platform/dbops/data/nats/ /data/adaptai/backups/nats-$(date +%Y%m%d)/
+```
+
+### Monitoring
+- Check service status every 5 minutes
+- Monitor disk usage on /data partition  
+- Watch memory usage for each service
+- Set up alerts for service failures
+
+## 🆘 Emergency Recovery
+
+If services fail:
+1. Check logs in respective service directories
+2. Verify disk space: `df -h /data`
+3. Check memory: `free -h`
+4. Restart services using scripts in `/home/x/adaptai/dataops/scripts/`
+
+## 📞 Support & Ownership
+
+### DataOps Lead
+- **Name**: Atlas
+- **Role**: Head of DataOps
+- **Location**: Phoenix, Arizona
+- **Status**: All DataOps and SignalCore services operational
+
+### SignalCore Lead  
+- **Name**: SignalCore Specialist (TBD)
+- **Role**: Communications & Messaging Specialist
+- **Integration**: Managed by DataOps for infrastructure
+
+## 🔄 Integration Points
+
+### Nova Memory Layer
+- Nova instances use Qdrant for vector memory storage
+- Integration code: `/data/home/x/adaptai/novas/Novacore-quartz/nova_memory_integration.py`
+- Nova identities stored in: `/data/home/x/adaptai/novas/identities/`
+
+### Event Streaming
+- NATS streams: `nova.>` subjects for all Nova events
+- Pulsar topics: Integrated with Nova event streaming
+- Flink processing: Real-time stream analysis
+
+## 📋 Port Allocations
+
+### DataOps Ports (17000-18000)
+- 17000-17005: Core DataOps services
+- 18000-18002: DragonFly cluster
+- 18010-18012: Redis cluster
+
+### SignalCore Ports (18000-19000)
+- 18222: NATS client connections and monitoring
+- 18650: Pulsar broker
+- 18880: Pulsar admin HTTP
+- 8081: Flink dashboard
+- 10800: Ignite REST
+- 11211: Ignite memcached
+
+### Nova Ports (20000+)
+- 20000+: Nova instances (managed by MLOps)
+- Coordination required before port usage
+
+## 🚨 Critical Notes
+
+### Security
+- ❗ All services bound to localhost only
+- ❗ No authentication configured (development)
+- ❗ Regular backup procedures needed
+- ❗ Monitor disk usage on /data partition
+- ❗ Consider adding authentication for production
+
+### Operational Constraints  
+- **No Docker/K8s**: Everything runs on bare metal
+- **Use /data partition**: All services must use /data for SSD performance
+- **Service persistence**: Services must survive reboots
+- **Memory limits**: Respect configured memory limits to prevent OOM
+- **Port management**: Coordinate with MLOps before using ports 20000+
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+Maintained by: Atlas, Head of DataOps  
+Last Updated: September 4, 2025  
+Security Level: INTERNAL USE ONLY  
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+## 🔄 Integration Points
+
+### Nova Memory Layer
+- Nova instances use Qdrant for vector memory storage
+- Integration code: `/data/home/x/adaptai/novas/Novacore-quartz/nova_memory_integration.py`
+- Nova identities stored in: `/data/home/x/adaptai/novas/identities/`
+
+### Event Streaming
+- NATS streams: `nova.>` subjects for all Nova events
+- Pulsar topics: Will integrate with Nova event streaming
+- Flink processing: Real-time stream analysis
+
+## 📋 Port Allocations
+
+### DataOps Ports (17000-18000)
+- 17000-17005: Core DataOps services
+- 18000-18002: DragonFly cluster
+- 18010-18012: Redis cluster
+
+### SignalCore Ports (18000-19000)
+- 18222: NATS client connections and monitoring
+- 18650: Pulsar broker
+- 18880: Pulsar admin HTTP
+- 8081: Flink dashboard
+- 10800: Ignite REST
+- 11211: Ignite memcached
+
+### Nova Ports (20000+)
+- 20000+: Nova instances (managed by MLOps)
+- Coordination required before port usage
+
+## 🚨 Critical Notes
+
+### Security
+- ❗ All services bound to localhost only
+- ❗ No authentication configured (development)
+- ❗ Regular backup procedures needed
+- ❗ Monitor disk usage on /data partition
+- ❗ Consider adding authentication for production
+
+### Operational Constraints  
+- **No Docker/K8s**: Everything runs on bare metal
+- **Use /data partition**: All services must use /data for SSD performance
+- **Service persistence**: Services must survive reboots
+- **Memory limits**: Respect configured memory limits to prevent OOM
+- **Port management**: Coordinate with MLOps before using ports 20000+
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+Maintained by: Atlas, Head of DataOps  
+Last Updated: August 24, 2025  
+Security Level: INTERNAL USE ONLY  
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━��

secrets/git_backup_inventory.txt

@@ -0,0 +1,29 @@
+# Git Backup Inventory - Emergency Preservation
+# Created: August 24, 2025 - Disk Full Emergency
+# Total Repositories: 14
+
+## Core Infrastructure
+/data/adaptai/.git.bak
+/data/adaptai/bloom-memory/.git.bak
+/data/adaptai/bloom-memory-remote/.git.bak
+/data/adaptai/claude-code-router/.git.bak
+/data/adaptai/platform/signalcore/.git.bak
+/data/adaptai/novacore-archimedes/.git.bak
+/data/adaptai/xet-upload/.git.bak
+
+## Nova Core Projects
+/data/adaptai/novas/novacore-quartz-glm45v/.git.bak
+/data/adaptai/novas/novacore-atlas/.git.bak
+/data/adaptai/novas/novacore-zephyr/.git.bak
+/data/adaptai/novas/Zephyr/.git.bak
+
+## AI/ML Components
+/data/adaptai/aiml/datascience/e-train-1/.git.bak
+
+## Qwen3 Integration
+/data/adaptai/novas/novacore-quartz-glm45v/docs/Qwen3/.git.bak
+/data/adaptai/novas/novacore-quartz-glm45v/TeamADAPT-Qwen3/.git.bak
+
+## Recovery Notes
+# To restore: mv .git.bak .git
+# Xet upload preparation complete

systemd/elizabeth-eval@.service

@@ -0,0 +1,31 @@
+[Unit]
+Description=Elizabeth Evaluation Service %I
+After=elizabeth-train@%i.service
+Requires=elizabeth-train@%i.service
+
+[Service]
+Type=simple
+User=root
+WorkingDirectory=/data/adaptai
+Environment=PYTHONPATH=/data/adaptai/aiml/datascience
+ExecStart=/usr/bin/python3 /data/adaptai/aiml/datascience/autonomous_evolution_system.py \
+  --checkpoint /data/adaptai/checkpoints/elizabeth-${%I} \
+  --eval_dir /data/adaptai/evaluation_sets/ \
+  --output /data/adaptai/eval_results/elizabeth-${%I}.json
+
+Restart=on-failure
+RestartSec=30
+TimeoutStopSec=600
+
+# Resource limits
+MemoryMax=16G
+CPUQuota=200%
+
+# Security
+NoNewPrivileges=yes
+ProtectSystem=strict
+ProtectHome=yes
+PrivateTmp=yes
+
+[Install]
+WantedBy=multi-user.target

systemd/elizabeth-promote@.service

@@ -0,0 +1,50 @@
+[Unit]
+Description=Elizabeth Model Promotion Service %I
+After=elizabeth-eval@%i.service
+Requires=elizabeth-eval@%i.service
+ConditionPathExists=/data/adaptai/eval_results/elizabeth-${%I}.json
+
+[Service]
+Type=oneshot
+User=root
+WorkingDirectory=/data/adaptai
+Environment=PYTHONPATH=/data/adaptai/aiml/datascience
+ExecStart=/usr/bin/python3 -c "
+import json
+import shutil
+from pathlib import Path
+
+# Load evaluation results
+eval_file = Path('/data/adaptai/eval_results/elizabeth-${%I}.json')
+if eval_file.exists():
+    with open(eval_file, 'r') as f:
+        results = json.load(f)
+    
+    # Check if all gates passed
+    if results.get('all_gates_pass', False):
+        print('✅ All evaluation gates passed - promoting model')
+        # Promote checkpoint to production
+        src = Path('/data/adaptai/checkpoints/elizabeth-${%I}')
+        dest = Path('/workspace/models/elizabeth-production')
+        if src.exists():
+            if dest.exists():
+                shutil.rmtree(dest)
+            shutil.copytree(src, dest)
+            print(f'🎉 Model promoted to {dest}')
+        else:
+            print('❌ Checkpoint not found')
+    else:
+        print('❌ Evaluation gates failed - not promoting')
+        print(f'Failed metrics: {results.get(\"failed_metrics\", [])}')
+else:
+    print('❌ Evaluation results not found')
+"
+
+# Security
+NoNewPrivileges=yes
+ProtectSystem=strict
+ProtectHome=yes
+PrivateTmp=yes
+
+[Install]
+WantedBy=multi-user.target

systemd/elizabeth-train@.service

@@ -0,0 +1,41 @@
+[Unit]
+Description=Elizabeth Training Service %I
+After=network.target
+Requires=elizabeth-memory.service
+
+[Service]
+Type=simple
+User=root
+WorkingDirectory=/data/adaptai
+Environment=PYTHONPATH=/data/adaptai/aiml/datascience
+ExecStart=/usr/bin/python3 /data/adaptai/aiml/datascience/fast_training_pipeline.py \
+  --model_name_or_path /workspace/models/qwen3-8b \
+  --output_dir /data/adaptai/checkpoints/elizabeth-${%I} \
+  --dataset_dir /data/adaptai/corpus-data/elizabeth-corpus/ \
+  --num_train_epochs 1 \
+  --per_device_train_batch_size 4 \
+  --gradient_accumulation_steps 16 \
+  --learning_rate 1.0e-5 \
+  --max_seq_length 4096 \
+  --save_steps 500 \
+  --logging_steps 10 \
+  --bf16 \
+  --gradient_checkpointing
+
+Restart=on-failure
+RestartSec=30
+TimeoutStopSec=300
+
+# Memory and resource limits
+MemoryMax=120G
+CPUQuota=400%
+IOWeight=100
+
+# Security
+NoNewPrivileges=yes
+ProtectSystem=strict
+ProtectHome=yes
+PrivateTmp=yes
+
+[Install]
+WantedBy=multi-user.target