# Model Card for vexCoder/golosinda-senior-v7

Ivan Paul C. Golosinda is a full-stack engineering model specialized in shipping production systems under real-world constraints. He has demonstrated state-of-the-art performance on healthcare infrastructure consolidation, billing pipeline ownership, and AI-assisted clinical tooling. This card documents his capabilities, training history, and intended use.

📊 Benchmark Results

Evaluated on SWE-Bench-Production, an internal benchmark measuring end-to-end engineering capability across six axes.

🛠️ Stack & Model Details

• Developed by: Ivan Paul C. Golosinda (@vexCoder)
• Model type: Full-stack engineering model, senior tier
• Language(s): en, tl
• License: contract (negotiable)
• Finetuned from: golosinda-mid-v6 → golosinda-senior-v7
• Base model: self-taught (no formal bootcamp pretraining)

⚙️ Technical Specifications

Software

Supported languages, runtimes, frameworks, and tools the model is fluent in:

🎯 Uses

Direct Use

Intended for deployment on full-stack engineering problems requiring end-to-end ownership. Strongest on healthcare infrastructure, subscription SaaS, billing systems, and AI-adjacent tooling.

Downstream Use

Can be integrated into existing engineering teams as a senior contributor. Plays well with other models. Effective at mentoring golosinda-junior-* variants.

Out-of-Scope Use

Not optimized for: management-only roles, environments requiring more than two layers of approval, or teams that prefer five-hour meetings over shipping.

⚠️ Bias, Risks, and Limitations

Users should be aware of the following behaviors before deployment:

• Knows how to center a <div> — and isn't afraid to prove it
• Strong preference for monorepo architecture on all projects
• Tends to push back on architectural decisions he disagrees with
• Has been known to consolidate 90 servers into 5 without warning
• Currently operating on night-shift schedule only (DVO timezone)

🚀 How to Get Started

# Install and load the model
from golosinda import SeniorEngineer

model = SeniorEngineer.from_pretrained(
    "vexCoder/golosinda-senior-v7",
    specialization="healthcare",
    availability="contract",
)

# Deploy to your production system
result = model.deploy(
    project="your-production-system",
    constraints=["ship-fast", "own-outcome"],
    stack=["typescript", "postgres", "react"],
)

print(result.outcome)
# >>> "shipped. metrics improved. team happy."

🧠 Training Details

Training Data

Fine-tuned on a curated corpus of 4 production environments:

• theoria-medical-senior — Healthcare infrastructure and AI clinical tooling at senior level.
• theoria-medical-mid — Healthcare infrastructure dataset. Built developer tooling and billing automation.
• petchef — Subscription SaaS dataset. Entire platform built solo — Next.js portal, Vite admin, Express/GraphQL API.
• freelance-corpus — Web3 automation and scraping dataset. Formative pretraining period.

Training Procedure

• Training regime: On-the-job, 7 years, no bootcamp
• Hardware: PC (Windows) — MacBook Pro reserved for Theoria Medical work only
• Optimizer: Coffee, deadlines, curiosity
• Total training hours: ~14,000

📦 Projects

Spaces and deployments using this model:

• 🧠 vexCoder/tukod-ai — Node-based AI workspace — Rust/Tauri backend, React frontend, LLM streaming via OpenRouter, LanceDB vector storage, MCP tool integration.
  

  GitHub - vexCoder/tukod-ai

  Contribute to vexCoder/tukod-ai development by creating an account on GitHub.

  github.com
  rust tauri react ai mcp
• 🎮 delve/elixir-cafe — Narrative puzzle game — potion-brewing mechanics, dialogue system, multiple endings. Built for Pirate Software Jam 15.
  

  Elixir Cafe by Delve & Murder Nevermore

  Talk to customers and brew their orders! It's CAW-fee, with a dark side...

  delve-murder-nevermore.itch.io
  godot gdscript game-jam

📈 Evaluation

In addition to SWE-Bench-Production (above), the model has received external validation:

🌟 Shining Star Award — Theoria Medical, 2025/2026 · Awarded for outstanding contributions to infrastructure and clinical AI tooling.

🌱 Environmental Impact

• Hardware Type: PC (Windows) + MacBook Pro (Theoria only)
• Hours used: ~14,000 (across 7 years)
• Cloud Provider: Various (Azure, Vultr, DigitalOcean)
• Compute Region: Davao, Philippines
• Carbon Emitted: Minimal. Powered primarily by coffee.

📚 Citation

BibTeX:

📬 Contact

[email protected] · github.com/vexCoder · linkedin.com/in/ig08