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Jam
✓ verifiedFreemium
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
👁 730K/mo♥ 2.9K
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GitLoop
✓ verifiedFree trial
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
👁 11K/mo♥ 2.7K
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OLMo 2
✓ verifiedFree
Ai2's family of fully open language models with weights, code, and training data released, built for transparent LLM research and building.
Pricing
No public pricing
Free: $0 (30 Jams/mo, 5 recording links)
Team: $14/creator per month billed yearly (unlimited Jams)
Free trial available
No public pricing
Free trial available
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)
- ✦One-click bug capture via browser extension
- ✦Automatic repro steps
- ✦Console, network and device logs
- ✦Instant replay of recent activity
- ✦Backend tracing and an AI debugger
- ✦Integrations with Jira, Linear, GitHub and Slack
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Fully open weights, code, and training data
- ✦Base, Think (reasoning), and Instruct variants
- ✦7B and 32B model sizes
- ✦Open model flow across all training stages
- ✦Open-source training/eval tools (OlmoCore, OLMES)
- ✦OlmoTrace to trace outputs to training data
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Researching language-model training and behavior
- →Building and fine-tuning open models
- →Machine-unlearning and clinical-NLP research
- →Deploying transparent open LLMs
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