1  # ARGUS-AI Launch Playbook
  2  
  3  ## Phase 1: Repository Setup (Day 0)
  4  
  5  ### Step 1: Push to GitHub
  6  
  7  ```bash
  8  cd argus-ai
  9  git init
 10  git add .
 11  git commit -m "v0.1.0: G-ARVIS scoring engine, agentic metrics, threshold monitoring
 12  
 13  - G-ARVIS composite scorer (6 dimensions: G/A/R/V/I/S)
 14  - Agentic evaluation metrics: ASF, ERR, CPCS
 15  - 3-line SDK: init/evaluate/score
 16  - Threshold monitoring with sliding window breach detection
 17  - Prometheus and OpenTelemetry exporters
 18  - Drop-in Anthropic and OpenAI provider wrappers
 19  - 84 unit tests, 93%+ core coverage
 20  - Apache 2.0 license"
 21  
 22  git remote add origin git@github.com:anilatambharii/argus-ai.git
 23  git branch -M main
 24  git push -u origin main
 25  ```
 26  
 27  ### Step 2: GitHub Repository Settings
 28  
 29  1. Add description: "Production-grade LLM observability in 3 lines. G-ARVIS scoring for Groundedness, Accuracy, Reliability, Variance, Inference Cost, and Safety."
 30  2. Add topics: `llm`, `observability`, `ai-safety`, `mlops`, `monitoring`, `evaluation`, `production-ai`, `garvis`, `agentic-ai`, `python`
 31  3. Set homepage URL: `https://argus-ai.ambharii.com`
 32  4. Enable Discussions
 33  5. Enable Sponsors (link to ambharii.com)
 34  
 35  ### Step 3: Create GitHub Release
 36  
 37  ```bash
 38  git tag -a v0.1.0 -m "v0.1.0: Initial open-source release"
 39  git push origin v0.1.0
 40  ```
 41  
 42  Create release on GitHub with CHANGELOG.md content as release notes.
 43  
 44  ### Step 4: Publish to PyPI
 45  
 46  ```bash
 47  pip install twine build
 48  python -m build
 49  twine upload dist/*
 50  ```
 51  
 52  Verify: `pip install argus-ai && python -c "import argus_ai; print(argus_ai.__version__)"`
 53  
 54  ---
 55  
 56  ## Phase 2: Content Launch (Days 0-3)
 57  
 58  ### Day 0: LinkedIn Newsletter
 59  
 60  Publish Edition 4 of "Field Notes: Production AI" (see docs/linkedin-launch-edition4.md).
 61  
 62  Pin the post. Reply to every comment within 2 hours for the first 48 hours.
 63  
 64  ### Day 0: X/Twitter Thread
 65  
 66  Post 1:
 67  "I just open-sourced the G-ARVIS scoring engine.
 68  
 69  pip install argus-ai
 70  
 71  3 lines of code. Every LLM call now has a quality score across 6 dimensions.
 72  
 73  Your LLM app is degrading right now. You just cannot see it. Thread below."
 74  
 75  Post 2:
 76  "G-ARVIS evaluates every LLM response across:
 77  G - Groundedness (hallucination detection)
 78  A - Accuracy (factual correctness)
 79  R - Reliability (format consistency)
 80  V - Variance (output stability)
 81  I - Inference Cost (token efficiency)
 82  S - Safety (PII, toxicity, injection)
 83  
 84  One composite score. Sub-5ms."
 85  
 86  Post 3:
 87  "New in v0.1.0: Agentic evaluation metrics.
 88  
 89  ASF (Agent Stability Factor)
 90  ERR (Error Recovery Rate)
 91  CPCS (Cost Per Completed Step)
 92  
 93  Traditional metrics like BLEU/ROUGE were not designed for 10-step autonomous workflows. These were."
 94  
 95  Post 4:
 96  "Open core strategy:
 97  
 98  Open source: G-ARVIS scorer, SDK, monitoring, exporters
 99  Proprietary: Autonomous correction loop, self-healing pipeline
100  
101  Detection is free. The fix is what you pay for.
102  
103  github.com/anilatambharii/argus-ai"
104  
105  ### Day 1: Hacker News
106  
107  Title: "Show HN: argus-ai – G-ARVIS scoring engine for LLM observability (3 lines of code)"
108  
109  Comment:
110  "Author here. I have been running LLMs in production across Fortune 100s (Duke Energy, UnitedHealth, R1 RCM) for years. The consistent pattern: apps work great at launch, then silently degrade while traditional metrics show green.
111  
112  G-ARVIS scores six dimensions (Groundedness, Accuracy, Reliability, Variance, Inference Cost, Safety) in sub-5ms with zero external dependencies. Threshold monitoring with sliding window breach detection tells you when quality is trending down before it becomes an incident.
113  
114  New in this release: three agentic evaluation metrics (ASF, ERR, CPCS) for autonomous workflow monitoring. Traditional metrics like BLEU/ROUGE were not built for 10-step tool-using agents.
115  
116  Apache 2.0. Open core model: scoring and monitoring are free. The autonomous correction loop stays proprietary.
117  
118  Happy to answer questions about the framework, production LLM observability, or the open-core strategy."
119  
120  ### Day 2: Reddit
121  
122  Post to r/MachineLearning (D), r/LangChain, r/LocalLLaMA.
123  
124  Title: "[P] argus-ai: G-ARVIS scoring engine for production LLM observability"
125  
126  ### Day 3: Medium Cross-Post
127  
128  Adapt the LinkedIn newsletter into a Medium article on @anilAmbharii. Add code examples and architecture diagrams.
129  
130  ---
131  
132  ## Phase 3: Community Growth (Weeks 1-4)
133  
134  ### Week 1: First Contributors
135  
136  1. Create "good first issue" labels on 3-5 issues:
137     - "Add LiteLLM integration"
138     - "Add LangChain callback handler"
139     - "Add Datadog exporter"
140     - "Add CLI tool for batch scoring"
141     - "Improve groundedness scorer with sentence embeddings"
142  
143  2. Respond to every issue and PR within 24 hours.
144  
145  ### Week 2: Ecosystem Integration
146  
147  1. Submit PR to awesome-llm-apps lists
148  2. Submit PR to awesome-mlops lists
149  3. Contact LiteLLM maintainers about native integration
150  4. Contact LangChain about callback handler inclusion
151  
152  ### Week 3: Benchmarks and Content
153  
154  1. Publish benchmark comparing argus-ai scoring speed vs alternatives
155  2. Write "How We Monitor 50M LLM Calls with G-ARVIS" case study
156  3. Create Grafana dashboard screenshot gallery (use docs/grafana-dashboard.json)
157  
158  ### Week 4: CAIO Circle Presentation
159  
160  Present argus-ai at CAIO Circle Tri-State Chapter meeting. Collect feedback from peer CDOs/CTOs. Use as validation signal for LinkedIn content.
161  
162  ---
163  
164  ## Phase 4: Platform Tease (Month 2)
165  
166  ### Actions
167  
168  1. Add "ARGUS Platform" section to README with waitlist link
169  2. Publish Edition 5: "Why Detection Without Correction Is Just a Dashboard"
170  3. Demo the autonomous correction loop (video, not code) on LinkedIn
171  4. Open GitHub Discussion: "What would you want from autonomous LLM correction?"
172  
173  ### Goal
174  
175  Convert argus-ai users into ARGUS Platform waitlist sign-ups. The hook is working when developers say "I can see the degradation but I cannot fix it automatically."
176  
177  ---
178  
179  ## Success Metrics
180  
181  | Metric | 30 Days | 90 Days | 180 Days |
182  |--------|---------|---------|----------|
183  | GitHub Stars | 200 | 1,000 | 5,000 |
184  | PyPI Downloads | 500 | 5,000 | 25,000 |
185  | Contributors | 5 | 15 | 40 |
186  | LinkedIn Newsletter Subs | 800 | 1,500 | 3,000 |
187  | HN Points | 100+ | - | - |
188  | Platform Waitlist | - | 200 | 1,000 |