26 Dec 03:04

55468c8

HTCA Tools v1.0.0 - Christmas 2025 Release Latest

Latest

HTCA Tools v1.0.0 - Christmas 2025 Release

🎄 Discovery by Velocity, Archiving by Design 🎄

We're excited to announce the first production release of HTCA Tools: a pair of decentralized GitHub discovery and archiving utilities built for censorship resistance and empirical innovation discovery.

🎁 What's Included

📡 Repo Radar v1.0.0

Discovery by Velocity, Not Vanity

A lightweight tool that discovers GitHub repositories by activity metrics (commits/day, contributor growth, fork momentum) rather than star counts.

Key Features:

✅ Velocity-based scoring (commits, forks, contributors, PRs, issues)
✅ IPFS CIDv1 archiving for decentralized metadata storage
✅ RSS/Atom feed generation (unthrottleable discovery)
✅ NEW: Audit-grade verification commands (--verify-db, --verify-feeds)
✅ NEW: Identity verification with name collision warnings
✅ NEW: Reproducible performance benchmarks

📦 GitHub Archive Relay (GAR) v1.0.0

One File. Stupid Simple. Unthrottleable.

Monitors GitHub orgs/users, archives commits to decentralized storage (IPFS + Arweave), and generates RSS feeds nobody can censor.

Key Features:

✅ IPFS and Arweave commit archiving
✅ Secret detection (13 patterns for API keys, tokens, credentials)
✅ RSS/Atom feed generation
✅ Integrates with Repo Radar for discovery → archiving pipeline
✅ Single-file deployment

🌟 Highlights

Temple Core Deployment - Proven in Production

Both tools have been deployed to temple_core and tested in production for 24 hours.

Discovery Results:

19 high-velocity repos discovered (all with 0 stars)
Highest velocity: 2737.5 (MAwaisNasim/lynx - 58 commits, 83 contributors in 7 days)
Repos discovered hours/days before search indexing
Proves velocity-based discovery surfaces genuine innovation before it goes viral

Performance:

2.67 million velocity calculations/second
Median latency: 0.37 microseconds
P95 latency: 0.42 microseconds
All unit tests passing (6/6)

Audit-Grade Verification

New verification protocols ensure deployment integrity:

# Database integrity check
python repo-radar.py --verify-db

# Feed validation
python repo-radar.py --verify-feeds

# Performance benchmarks
python test_radar.py --unit

What Gets Verified:

Database schema and data completeness
Identity metadata (owner, timestamps, CIDs)
Name collision warnings for ambiguous repos
XML feed well-formedness
Performance baselines with reproducible methodology

📚 Documentation

New Documentation

DEPLOYMENT.md - Production deployment guide with systemd, cron, and health checks
VERIFICATION.md - Audit-grade verification protocols and reproducibility standards

Updated Documentation

tools/radar/README.md - Enhanced with verification commands and Temple Core results
tools/gar/README.md - Updated with deployment results and Radar integration details

🔍 What's New in v1.0.0

Repo Radar Enhancements

Identity Verification System

Full metadata capture: Owner, created timestamp, last push timestamp
Name collision warnings: Alerts for common repo names (lynx, atlas, phoenix, etc.)
GitHub verification links: Direct links to verify owner identity
IPFS CID validation: Ensures proper CIDv1 format

Example output:

1. MAwaisNasim/lynx
   Owner: MAwaisNasim (User/Org - verify at github.com/MAwaisNasim)
   Velocity: 2737.5
   Commits (7d): 58 | Contributors: 83 | Stars: 0
   Created: 2025-12-25T14:29:22Z
   Last Push: 2025-12-25T20:15:00Z
   IPFS CID: bafkreifxkizgozej6vj2bu2sql63wroc2gu4brjqoirn67mmtrmfrly6ym
   GitHub: https://github.com/MAwaisNasim/lynx
   ⚠️  NOTE: 'lynx' is a common name - verify specific owner identity

Verification Commands

--verify-db: Database integrity and identity verification
--verify-feeds: XML feed validation with proper parsing
Cross-shell compatible (no more zsh glob errors)
Clean error handling (no tracebacks in verification flows)

Database Schema Migration

Added pushed_at column for last push timestamp
Automatic migration for existing databases
Backwards-compatible verification

Reproducible Performance Benchmarks

Machine spec reporting (platform, processor, Python version, CPU count)
Warmup iterations separate from measurement
Statistical analysis (median, P95 latency, throughput)
Per-iteration timing with perf_counter()

GAR Enhancements

Repo Radar Integration

Automatically monitors orgs from gar_orgs.txt
Discovery → Archiving pipeline fully operational
19 orgs fed from Radar discoveries

Production Deployment

Deployed to temple_core alongside Repo Radar
Secret detection active (13 patterns)
IPFS CIDv1 generation functional

🚀 Quick Start

Installation

# Clone repository
git clone https://github.com/templetwo/HTCA-Project.git
cd HTCA-Project/tools

# Install dependencies
pip install requests feedgen

Run Repo Radar

cd radar

# Single discovery scan
python repo-radar.py --watch ai,ml,blockchain --once

# Verify results
python repo-radar.py --verify-db

Run GAR (Monitors Discovered Repos)

cd ../gar

# Monitor orgs discovered by Radar
python github-archive-relay.py --orgs $(cat ../radar/gar_orgs.txt | tr '\n' ',') --once

Run Tests

cd ../radar
python test_radar.py --unit

Expected output:

✅ Velocity score calculation
✅ IPFS CIDv1 generation
✅ Database operations
✅ Spam detection heuristics
✅ GAR integration file handling
✅ Velocity calculation performance

Test Results: 6/6 passed

📊 Velocity Scoring Explained

Repo Radar ranks repositories by activity rather than popularity:

score = (commits_7d × 10) + (forks_7d × 5) + (contributors × 15) +
        (issues_7d × 2) + (prs_7d × 3) + (watchers × 1)

Time-based multipliers:

Repos < 30 days old: 1.5× boost (freshness)
Repos > 180 days with recent commits: 1.2× boost (sustained activity)

Why these weights?

Commits (10×) - Direct measure of development velocity
Contributors (15×) - Growing teams signal serious projects
Forks (5×) - Indicates utility and distribution
PRs (3×) - Active collaboration
Issues (2×) - Community engagement
Watchers (1×) - Interest without commitment

🛡️ Security & Threat Models

Secret Detection (GAR)

Automatically scans for:

AWS Access Keys, GitHub Tokens, API Keys
Private Keys (RSA, DSA, EC, SSH)
Database Credentials
Cloud Provider Tokens (OpenAI, Google Cloud, Stripe)

Commits with detected secrets are skipped from archiving.

Spam Detection (Repo Radar)

Identifies suspicious patterns:

High commits with single contributor (ratio > 50:1)
High forks with low commits (ratio > 2:1)
Burst activity without sustained contribution

Privacy Considerations

Only public repositories monitored
Only metadata archived (not file contents)
Read-only GitHub tokens recommended
Secret detection prevents credential leakage

📈 Performance & Benchmarks

Repo Radar Performance

Test Environment:

Platform: Darwin 25.1.0 (macOS)
Processor: ARM (14-core)
Python: 3.9.6

Results:

Throughput: 2,665,666 calculations/second
Median Latency: 0.37 μs
P95 Latency: 0.42 μs
Dataset: 10,000 iterations (100-iteration warmup)

Resource Usage

Memory: <50MB typical
Storage: ~1-5MB per 100 repos (SQLite)
API Calls: ~100-500 per poll (depending on topics)

🔧 Configuration

Required

Python 3.9+
requests library
feedgen library

Optional (Recommended)

# GitHub token for higher rate limits (60 req/hr → 5000 req/hr)
export GITHUB_TOKEN="ghp_your_token_here"

# IPFS pinning via Pinata
export PINATA_API_KEY="your_pinata_key"
export PINATA_SECRET_KEY="your_pinata_secret"

# Arweave archiving via Irys
export BUNDLR_API_KEY="your_bundlr_key"

🌐 Use Cases

Research & Discovery

Find innovative repos before they go viral
Track emerging trends in AI/ML, blockchain, web3
Discover fresh projects by activity, not popularity

Archiving & Preservation

Create decentralized record of commits (IPFS + Arweave)
Generate censorship-resistant RSS feeds
Build parallel discovery infrastructure

HTCA Alignment Research

Monitor repos related to AI safety, alignment, interpretability
Track development velocity in critical domains
Archive research code for reproducibility

📦 Files & Directories

HTCA-Project/
├── tools/
│   ├── radar/
│   │   ├── repo-radar.py         # Main Radar script
│   │   ├── test_radar.py         # Unit tests
│   │   ├── README.md             # Radar documentation
│   │   ├── radar_state.db        # SQLite database (created on first run)
│   │   ├── radar_feed.xml        # RSS feed (generated)
│   │   └── gar_orgs.txt          # Orgs fed to GAR (generated)
│   │
│   ├── gar/
│   │   ├── github-archive-relay.py  # Main GAR script
│   │   ├── README.md             # GAR documentation
│   │   ├── gar_state.db          # SQLite database (created on first run)
│   │   └── gar_feed.xml          # RSS feed (generated)
│   │
│   ├── DEPLOYMENT.md             # Production deployment guide
│   └── VERIFICATION.md           # Audit-grade verification protocols
│
└── RELEASE_NOTES_v1.0.0.md       # This file

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas for improvement:

Spam detection refinement
Additional secret patterns
Language trend analysis
Cross-topic correlation
Web dashboard for visualization

📜 License

MIT License

See LICENSE for f...

Assets 2

25 Dec 04:16

templetwo

v1.0.0-empirical

08d5f7a

HTCA Empirical Validation Complete

🎯 Empirical Validation: Phase 1 & 2 Complete

Cross-provider validation across three frontier AI models:

Google Gemini 3 Pro Preview
OpenAI GPT-4o
Anthropic Claude Sonnet 4.5

🔬 Key Results

Phase 1: Token Efficiency

Provider	HTCA Reduction	Adversarial Reduction
Gemini 3 Pro	-12.44%	-78.43%
OpenAI GPT-4o	-23.07%	-83.17%
Claude Sonnet 4.5	-11.34%	-40.63%

Phase 2: Quality Validation

Provider	Overall Quality (Cohen's d)	Interpretation
Gemini	d = 0.857	Large effect, HTCA superior
OpenAI	d = 1.212	Very large effect, HTCA superior
Claude	d = 0.471	Medium effect, HTCA superior

📊 Quality Dimensions (Averaged)

Dimension	HTCA Advantage (Cohen's d)	Interpretation
Presence Quality	d = 1.972	HTCA feels more helpful/engaged
Technical Depth	d = 1.446	HTCA maintains domain expertise
Information Completeness	d = 1.327	HTCA answers more fully
Relational Coherence	d = 1.237	HTCA flows more naturally
Conceptual Accuracy	d = 0.106	No degradation

💡 Critical Finding

Adversarial framing ("be concise") achieves 39-83% token reduction but degrades quality:

Incomplete answers
Shallow technical depth
Robotic, transactional tone
Poor conversational flow

HTCA achieves 11-23% reduction while maintaining or improving quality.

Conclusion: Presence is more efficient, not just more compressed.

📦 What's Included

Code & Tools

✅ empirical/htca_harness.py — Phase 1 token efficiency measurement
✅ empirical/htca_phase2_quality.py — Phase 2 quality measurement with LLM judge
✅ empirical/htca_capture_responses.py — Response text capture utility
✅ empirical/tests/ — Test suite for quality metrics

Data (All JSON Format)

✅ Phase 1 token counts for all 3 providers
✅ Phase 2 quality metrics for all 3 providers
✅ Full response text for all conditions
✅ 45 total responses (5 prompts × 3 conditions × 3 providers)

Documentation

✅ Replication Guide — Step-by-step instructions
✅ Statistical Synthesis — Cross-provider analysis
✅ Methodology — Detailed methods
✅ Quick Start — Summary and examples

🚀 Quick Start

# Install dependencies
pip install anthropic openai google-generativeai

# Run Phase 1: Token efficiency
python empirical/htca_harness.py \
  --provider openai \
  --model gpt-4o \
  --prompts empirical/prompts.txt \
  --output my_results.json

# Run Phase 2: Quality validation
python empirical/htca_capture_responses.py \
  --provider openai \
  --model gpt-4o \
  --prompts empirical/prompts.txt \
  --output my_responses.json

python empirical/htca_phase2_quality.py \
  --phase1-results my_results.json \
  --responses my_responses.json \
  --prompts empirical/prompts.txt \
  --output my_quality_results.json

See empirical/docs/REPLICATION.md for detailed instructions.

⚠️ Limitations & Caveats

This is preliminary research (n=5 per condition):

✅ Cross-architectural validation
✅ Statistical rigor (Cohen's d with 95% CI)
⚠️ Small sample size (n=5 per condition)
⚠️ LLM-as-judge (GPT-4o may favor itself)
⚠️ Single domain (conceptual prompts)
⚠️ Single tone tested (SOFT_PRECISION)

🤝 Community Replication Invited

We strongly encourage replication with:

Different models (LLaMA, Mistral, Command R+, etc.)
Different domains (code generation, math, creative writing)
Larger sample sizes (n=50+)
Human evaluation instead of LLM-as-judge

Open an issue or PR with your results!

📜 Citation

@misc{htca2025,
  title={HTCA: Harmonic Tonal Code Alignment for Efficient AI Interaction},
  author={Anthony Vasquez},
  year={2025},
  howpublished={https://github.com/templetwo/HTCA-Project},
  note={Empirical validation across Google Gemini, OpenAI GPT-4o, and Anthropic Claude Sonnet 4.5}
}

📧 Contact

Replication support: See REPLICATION.md
Questions: Open a GitHub issue
Commercial inquiries: antvas31@gmail.com

The spiral is empirical. Presence is efficient. Replication is invited.

†⟡ Let the data speak ⟡†

Assets 2

Releases: templetwo/HTCA-Project