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Thin 'Lingbot-map' agent listing on github.com with zero traffic; too thin to tell.

5.2K
Qoder
Freemium
👁 2.7M/mo32K
Code Autopilot
✓ verifiedFreemium

AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.

Pricing

No public pricing

No public pricing

Free trial available

No public pricing

Unlocker API: Starts from $1.05/1k req
Crawl API: Starts from $1.05/1k req
SERP API: Starts from $1.05/1k req
Browser API: Starts from $5.88/GB
Scrapers: Starts from $300/mo
Datasets: Starts from $250/mo
Functions: Starts from $2.7/1k req
RetailInsights: Starts from $1,000/mo
Residential Proxies: Starts from $3.53/GB
Datacenter Proxies: Starts from $0.9/IP
ISP Proxies: Starts from $1.3/IP
Mobile Proxies: Starts from $5.88/GB
Custom Scraper: Starts from $300/mo

No public pricing

Core features
  • Fast tensor operations
  • Differentiable tensors for gradient-based optimization
  • Network connectivity
  • Integration with Bun and Flashlight
  • Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
  • Enhanced Context Engineering for deep codebase analysis and adaptive memory
  • Intelligent Agents for autonomous planning, coding, and testing
  • Spec-Driven Development for clarifying requirements and automating execution
  • Intelligent Codebase Search and Advanced Repository Insight
  • Context-aware code completions and next-edit suggestions
  • Support for leading AI models (Claude, GPT, Gemini)
  • Multi-engine support (Google, Duckduckgo, Bing, Yandex, Baidu)
  • Real-time structured SERP data
  • Pay-per-successful-request pricing
  • Full JavaScript support
  • Precise geo-location targeting
  • Structured data delivery in JSON or HTML
  • Chat inside GitHub issues and PRs
  • Task-to-implementation plans with code
  • Automatic bug-fix suggestions
  • Pull-request summaries for faster review
  • Full-codebase context
  • GitHub-native integration
Use cases
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • Delegating complex software development tasks to AI agents for autonomous completion.
  • Performing multi-file code edits and refactoring through natural language chat.
  • Gaining deep architectural understanding of a codebase to resolve issues with precision.
  • Generating unit tests, code explanations, and uncovering codebase architecture.
  • Systematically tackling software development tasks from planning to testing.
  • Organic keyword tracking
  • Brand protection
  • Price comparison
  • Market research
  • Detect copyright infringements
  • Ad intelligence
  • Speeding up pull-request reviews
  • Implementing features from task descriptions
  • Debugging with AI-proposed solutions
  • Answering questions about a repo
  • Boosting a solo developer's output
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