Sparky

AI-Powered Development Activity Monitor & Content Generator for raibid-labs

Sparky is an autonomous system that monitors git activity across all raibid-labs repositories, generates intelligent summaries, and produces engaging content for blogs and social media.

Project Status: Implementation Ready - Rust + k3s + Justfile + Nushell Last Updated: 2025-11-12 Timeline: 60-70 days with parallel workstreams

What is Sparky?

Sparky transforms raw development activity into compelling narratives using 100% open-source tools with zero external costs.

Monitors git activity across 28+ raibid-labs repositories (GitHub CLI)
Analyzes commits, PRs, issues using local LLM (Ollama + Qwen2.5-Coder)
Generates daily digests, weekly reports, and monthly reviews
Publishes content to docs, blogs, and social media
Automates the entire pipeline with zero manual intervention

No API costs. No external dependencies. Runs completely locally.

Quick Start

For Prototyping (15 Minutes)

# Using existing Bash/Python scripts
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5-coder:1.5b
./docs/examples/collect-gh.sh
python3 docs/examples/analyze-ollama.py
cat output/daily/$(date +%Y-%m-% d).md

See QUICKSTART_OSS.md for this approach.

For Production (Rust Implementation)

# Prerequisites: Rust, Just, Nushell, Docker, k3d
just check-requirements
 
# Create local k3s cluster
just k3d-create
 
# Deploy Ollama
just deploy-ollama
 
# Build Sparky (once implemented)
just build
 
# Deploy services
just deploy-local
 
# Run pipeline
just pipeline-daily
 
# Monitor
just status

See IMPLEMENTATION_PROPOSAL.md for full details.

Architecture Overview

GitHub Repos (28+)
    ↓
Data Collectors (6 parallel agents)
    ↓
Analyzers (4 parallel agents)
    ↓
Content Generators (3 parallel agents)
    ↓
Publishers (docs, blog, social media)

Pipeline Duration: ~15-20 minutes end-to-end

Core Features

1. Automated Data Collection

Monitors all raibid-labs repositories
Collects commits, PRs, issues, releases
Concurrent collection (6 repos at a time)
GitHub GraphQL API for efficiency
Rate limit management (5000 req/hr)

2. Intelligent Analysis

AI-powered semantic analysis (Claude API)
Activity metrics (commits/day, PR velocity)
Trend detection (productivity patterns)
Impact scoring (change significance)
Contributor profiling

3. Content Generation

Daily Digest: 200-300 words, key highlights
Weekly Report: 800-1200 words, comprehensive overview
Monthly Review: 2000-3000 words, in-depth analysis
Blog Posts: 1500-2500 words, polished content
Social Media: Twitter/LinkedIn posts

4. Multi-Channel Publishing

raibid-labs/docs repository (internal)
Dev.to (external blog)
Twitter/LinkedIn (social engagement)
GitHub Issues/Comments (team updates)

Documentation

🚀 Start Here

Document	Description	Time
Implementation Proposal	Rust + k3s architecture ⭐	30 min
Parallel Workstreams	18 GitHub issues ready to create	20 min
OSS Quick Start	Prototype with Bash/Python	15 min
Justfile Reference	All available commands	10 min
dgx-pixels Patterns	Orchestration patterns reference	60 min

Core Documentation

Document	Description
Zero-Cost Architecture	OSS design decisions, approaches
Model Research	Best OSS models for summarization
Architecture (Original)	Full system design
Parallel Workstreams	Development organization

Additional Research

These documents informed the design (optional reading):

Resource	Description
Research Report	External market research
Executive Summary	High-level findings
Tools & Libraries	100+ tools catalog

Technology Stack (100% OSS)

Core Technologies

Implementation Language: Rust (all services)
Task Automation: Just + Nushell scripts
Orchestration: k3s (Kubernetes) via k3d (local) or k3sup (production)
Data Collection: GitHub CLI (gh) - free, no API limits
LLM Inference: Ollama + Qwen2.5-Coder-1.5B (local, Apache 2.0)
IPC: ZeroMQ (REQ-REP + PUB-SUB patterns)
Storage: Git repository (JSON + Markdown files)
Containerization: Docker + Docker Compose

Why This Stack?

✅ $0/month operating cost
✅ No API rate limits (GitHub CLI is special)
✅ Full data privacy (everything runs locally)
✅ Fast inference (< 1 second per summary)
✅ High quality (code-specialized models)
✅ Easy deployment (works on existing DGX infrastructure)

Integration with DGX Infrastructure

dgx-spark-playbooks: Deployment patterns (Ollama, vLLM, Docker)
Kubernetes: Optional K8s deployment using existing K3s cluster
Docker: Containerized services for isolation
GPU: Efficient inference using NVIDIA GPUs

Development Roadmap

Phase 0: Bootstrap (Days 1-2)

Research and architecture design
Create documentation
Initialize repository structure
Set up GitHub Actions workflows
Configure secrets and environment

Phase 1: Parallel Workstreams (Days 3-9)

All workstreams execute concurrently:

Workstream 1: Orchestration & Infrastructure
Workstream 2: Data Collection System
Workstream 3: Analysis Engine
Workstream 4: Content Generation Pipeline

Phase 2: Integration & Testing (Days 10-13)

End-to-end pipeline testing
Integration tests across workstreams
Performance optimization
Security audit

Phase 3: Deployment & Documentation (Days 14-15)

Production deployment
Monitoring setup
User guides and runbooks
Launch first daily/weekly/monthly report

Target Timeline: 15 days (3 weeks)

Parallel Workstream Organization

Sparky development is split into 4 independent workstreams that can be executed concurrently:

┌─────────────────────┬─────────────────────┬─────────────────────┬─────────────────────┐
│   Workstream 1      │   Workstream 2      │   Workstream 3      │   Workstream 4      │
│   Orchestration     │   Collection        │   Analysis          │   Generation        │
├─────────────────────┼─────────────────────┼─────────────────────┼─────────────────────┤
│ GitHub Actions      │ GitHub API          │ AI Integration      │ Content Templates   │
│ Monitoring          │ Data Models         │ Metrics Calculation │ Publishing          │
│ Health Checks       │ Rate Limiting       │ Insight Generation  │ Multi-Format Output │
│ Agent Spawning      │ Storage Layer       │ Pattern Detection   │ Social Media        │
├─────────────────────┼─────────────────────┼─────────────────────┼─────────────────────┤
│ Owns:               │ Owns:               │ Owns:               │ Owns:               │
│ .github/workflows/  │ collectors/         │ analyzers/          │ generators/         │
│ scripts/            │ api/                │ ai/                 │ templates/          │
│ monitoring/         │ models/             │ analytics/          │ publishers/         │
│                     │ storage/            │ insights/           │ content/            │
├─────────────────────┼─────────────────────┼─────────────────────┼─────────────────────┤
│ Agent:              │ Agent:              │ Agent:              │ Agent:              │
│ devops-automator    │ backend-architect   │ ai-engineer         │ frontend-developer  │
├─────────────────────┼─────────────────────┼─────────────────────┼─────────────────────┤
│ Duration: 3-5 days  │ Duration: 4-6 days  │ Duration: 4-6 days  │ Duration: 4-6 days  │
└─────────────────────┴─────────────────────┴─────────────────────┴─────────────────────┘

Benefits:

Parallel execution (4x faster than sequential)
Clear ownership (prevents conflicts)
Independent testing (each workstream validated separately)
Flexible staffing (4 agents or developers working simultaneously)

See Parallel Workstreams for detailed breakdown.

Cost Analysis

100% OSS Stack (Current)

Ollama (local LLM):   $0/month (self-hosted)
GitHub CLI:           $0/month (free, no limits)
Storage (git):        $0/month (repository)
Electricity:          ~$0.50/month (minimal GPU usage)
Total:                ~$0.50/month

Comparison to API-Based Approach

Component	API-Based	OSS
LLM Inference	$15-45/mo (Claude API)	$0 (Ollama)
GitHub Data	$0 (rate limited)	$0 (gh CLI, unlimited)
Storage	$0-25/mo (database)	$0 (git)
Infrastructure	$0-20/mo (cloud)	$0 (existing DGX)
Total	$15-90/mo	~$0.50/mo

Savings: $14.50 -$ 89.50/month

Quality Comparison

Model	Quality	Speed	Monthly Cost
GPT-4 API	9/10	5-10s	$30-60
Claude 3.5 API	9.5/10	3-5s	$15-45
Qwen2.5-Coder-1.5B	8.5/10	<1s	$0

For git commit summarization: Code-specialized models like Qwen2.5-Coder are actually better than general-purpose LLMs while being free and 10x faster.

Implementation Approach

Why Rust + k3s + Justfile + Nushell?

Based on dgx-pixels successful patterns:

Rust: Type safety, performance, excellent ecosystem
k3s: Lightweight Kubernetes, perfect for DGX deployment
Justfile: Task automation, better than Make for this use case
Nushell: Modern shell scripting, structured data handling
ZeroMQ: Fast IPC, proven in dgx-pixels (<1ms latency)

Architecture

Meta Orchestrator (Rust)
    ↓ (ZeroMQ)
Collector → Analyzer → Generator → Publisher (all Rust)
    ↓ (calls)
GitHub CLI + Ollama (external)

All services run in k3s pods, communicate via ZeroMQ, and are orchestrated by the meta orchestrator following phase gates.

Available Commands

# See all commands
just --list
 
# Common workflows
just build                # Build all Rust crates
just test                 # Run all tests
just k3d-create          # Create local k3s cluster
just deploy-local        # Deploy to local k3s
just pipeline-daily      # Run daily pipeline
just status              # Check system status
just logs-follow         # Follow service logs
 
# Over 50 commands available!

Acknowledgments

Sparky’s architecture is based on proven patterns from raibid-labs projects:

dgx-pixels: Orchestration patterns, ZeroMQ, Justfile + Nushell automation ⭐
dgx-spark-playbooks: Ollama deployment, Docker, k8s patterns
raibid-cli: Multi-repository management (Rust)
raibid-ci: Event-driven workflows
XPTui: Parallel workstream coordination

Special thanks to all raibid-labs contributors whose work made this possible.

License

MIT License - See LICENSE file

Contact

GitHub Issues: raibid-labs/sparky/issues
Organization: raibid-labs
Documentation: raibid-labs.github.io/docs

Status: Design Phase Complete | Ready for Phase 0 Bootstrap

Last Updated: 2025-11-12

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