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Full-stack platform for web scraping, data extraction, and automation; category leader.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
AI tool that converts natural-language questions into SQL queries, sold via a Lemon Squeezy storefront with tiered pricing.
Free AI helper that turns a plain-English description of a task into the matching Git command to copy and run.
Google-owned hub for data scientists to find datasets, enter ML competitions, run notebooks, and learn.
Free trial available
No public pricing
Free trial available
No public pricing
No public pricing
- ✦Web scraping
- ✦Data extraction
- ✦Browser automation
- ✦AI agents
- ✦Anti-blocking
- ✦Proxy rotation
- ✦Open-source tools (Crawlee)
- ✦Ready-made tools and code templates
- ✦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
- ✦Natural language to SQL query generation
- ✦Standard and Pro subscription tiers
- ✦Checkout and billing via Lemon Squeezy
- ✦Natural-language to Git command suggestions
- ✦AI-driven command matching
- ✦Copy-ready command output
- ✦Git guides and reference
- ✦Public dataset repository
- ✦Machine-learning competitions with prizes
- ✦Browser-based notebooks with free GPU/TPU
- ✦Micro-courses on data science topics
- ✦Community forums and shared code
- →Data for generative AI
- →Lead generation
- →Market research
- →Sentiment analysis
- →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
- →Generating SQL queries without writing raw syntax
- →Helping non-technical users query databases
- →Speeding up ad hoc data lookups for analysts
- →Find the correct Git command quickly
- →Learn Git syntax by describing a goal
- →Avoid memorizing Git flags
- →Practicing and benchmarking ML models
- →Finding datasets for analysis
- →Competing in predictive-modeling contests
- →Learning data science skills
- →Sharing reproducible notebooks