- Core Project Identity
Catchy Title & One-Liner: "OptiCoreSLM: The Privacy-First SLM for High-Performance C++ Optimization."
The Problem (The "Why"): Clearly state that modern C++ developers lack tools for hardware-aware optimization that also respect strict data privacy.
The Solution: Describe how your model solves this as an offline VS Code extension.
🛠️ 2. Technical Architecture
Use a "System Overview" section to explain how the pieces fit together.
Model Specs: Mention the base architecture (e.g., Transformer-based SLM) and why an SLM was chosen (speed, local inference).
Data Pipeline: Explain the Synthesized Dataset—how you created pairs of "Slow vs. Fast" code.
Inference Flow: Describe the local bridge (Ollama/Requestly) that connects the IDE to the model.
-
Evidence of Success (Benchmarking)
Judges love data. Create a "Results" section with a table:
| Task | Original Code | OptiCoreLM Optimization | Latency Reduction |
| :--- | :--- | :--- | :--- |
| Bit Count | Loop-based | __builtin_popcount | ~90% |
| Power of 2 | Modulo | (n & (n-1)) | ~95% |
-
Links & Resources
Make it easy for reviewers to verify your work by grouping your links:
Hugging Face Model: Link to your weights.
Hugging Face Dataset: Link to your training data.
GitHub Repository: Link to the extension source code.
Demo Video: A 2-minute screen recording of the extension in action.
- Getting Started (For Users)
Provide a 3-step installation guide:
Download the model from Hugging Face.
Install the VS Code Extension.
Configure your local inference port
Also,Requestly served as our primary network debugging and mocking layer. It allowed us to build a fully functional VS Code extension for OptiCoreSLM by simulating complex API interactions and ensuring our offline, privacy-first architecture remained robust. Without Requestly, testing our local inference engine against real-world IDE behaviors would have taken twice as long.
