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JamGPT
✓ verifiedPaid
Streamlines bug reporting with automatic capture; strong adoption.
👁 730K/mo♥ 2.9K
Pricing
Free: $0 per month
Pro: $12 / month
Team: $14 / creator / month*
Enterprise: Custom
Jam for Customer Support: Custom / month
No public pricing
Free trial available
No public pricing
No public pricing
Core features
- ✦Automatic capture of device and browser information
- ✦Console and network logs recording
- ✦Repro steps recording
- ✦Backend tracing
- ✦AI debugger
- ✦Integration with popular tools like Notion, GitHub, Jira, and Slack
- ✦Video recording and screenshot annotation
- ✦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)
- ✦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)
- ✦Zero-ETL data integration
- ✦Federated Query
- ✦Streaming Ingestion
- ✦Instant Replication with CDC
- ✦API to SQL conversion
- ✦NoSQL to SQL conversion
- ✦SQL to API conversion
- ✦Self-service Integration
- ✦Generate SQL with AI
Use cases
- →Reporting bugs quickly and efficiently
- →Streamlining communication between QA and development teams
- →Capturing detailed information for debugging
- →Integrating bug reporting into existing workflows
- →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.
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Query data directly from its source in real-time.
- →Process data wherever it is, blending data from different sources.
- →Ingest streaming data from Kafka, Segment, etc., into Peaka BI Table.
- →Replace nightly batch ingestion with real-time data access.
- →Treat every data source like a relational database by converting APIs to tables.
- →Use SQL to query NoSQL databases.
- →Query consolidated data and expose it with APIs.
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