Build AI Faster
Our AI Developer Tools provide everything you need to go from idea to production AI. With intuitive APIs, pre-built components, and seamless integration with quantum computing resources, you can build sophisticated AI applications without starting from scratch.
Whether you're developing computer vision systems, NLP applications, or reinforcement learning agents, our platform accelerates development while maintaining production-grade quality.
OptaFly_Zed: AI-Powered Development
OptaFly_Zed is a performance-enhanced distribution of Zed editor with Widget-Log semantic caching natively integrated, delivering 280x faster AI responses out of the box.
Performance Highlights
- 280x faster responses on cache hits (43ms vs 12,201ms)
- 60% cost reduction on Claude API usage
- 95% semantic similarity accuracy - catches rephrased questions
- Zero configuration required - works immediately on first run
- Cross-platform - Linux, macOS, and Windows support
Quick Start Installation
git clone
https://github.com/Optaquan/OptaFly_Zed.git cd
OptaFly_Zed chmod +x install-phase25-parallel.sh
./install-phase25-parallel.sh
This script automatically checks and installs system dependencies, builds OptaFly_Zed in release mode, sets up Widget-Log with Python virtual environment, and launches the editor with semantic caching enabled.
How Widget-Log Semantic Caching Works
OptaFly_Zed Editor ↓ (Claude API Request)
Widget-Log Proxy (127.0.0.1:8443) ↓ [Semantic Cache
Check] ├─→ Cache HIT (43ms) → Return Cached Response
⚡ └─→ Cache MISS (12s) → Claude API → Store in
Cache
Semantic Matching
Detects similar questions even with different wording using 384-dimensional embeddings and FAISS search.
Multi-Project Intelligence
Separate caches per project maintain context boundaries while sharing general programming knowledge.
Secure & Local
Localhost-only HTTPS proxy with token authentication. Your code never leaves your machine.
Real-World Performance
From actual testing with complex architectural queries:
Test | Cache Status | Response Time | Speedup
--------------------- | ------------ | -------------
| -------- Architecture query #1 | MISS | 45,551ms |
baseline Exact repeat | HIT | 30ms | 1518x Semantic
variant | HIT | 45ms | 1012x Different query | MISS
| 21,780ms | baseline Repeat different | HIT | 38ms
| 573x Cache hit rate: 57-60% typical Average
speedup: 1122x faster
Platform Features
Model Studio
Visual interface for designing, training, and evaluating machine learning models. No PhD required.
Pre-Trained Models
Library of state-of-the-art models for vision, language, and structured data ready for fine-tuning.
AutoML
Automated model selection, hyperparameter tuning, and feature engineering to find optimal configurations.
Distributed Training
Scale training across GPUs and TPUs with automatic data parallelism and gradient synchronization.
Experiment Tracking
Version control for ML experiments with metrics logging, artifact storage, and reproducibility.
Model Registry
Central repository for model versioning, staging, and production deployment management.
Deployment & Serving
One-Click Deploy
Deploy models to production with a single click. Automatic scaling, load balancing, and failover.
Edge Deployment
Optimize and deploy models to edge devices for low-latency inference in the field.
A/B Testing
Built-in A/B testing framework for safe model rollouts and performance comparison.
Monitoring
Real-time monitoring for model performance, data drift, and prediction quality.