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Open-source and cloud SQL agent that lets non-technical users query company databases in natural language, with admin controls for teams.
No-code IDE for building custom AI agents in plain English, demoed here via a Twitter personality-analysis agent.
Channel 1 offers AI-powered video production, information structuring, and personalized content delivery platforms for media companies.
Research world-model agent that learns Minecraft and robotic control tasks by training in imagination purely from offline data.
No public pricing
No public pricing
No public pricing
No public pricing
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- ✦Natural-language to SQL query generation
- ✦Support for multiple LLM providers and database backends
- ✦Multi-turn, multi-database conversational querying
- ✦Role-based access control on hosted tiers
- ✦Real-time observability and tracing
- ✦Hosted vector database for agent memory
- ✦Audit logging and long-term data retention
- ✦Natural-language programming environment for AI agents
- ✦Supports loops, conditional logic and structured JSON output
- ✦Custom code execution and API connections
- ✦Example WordApps for legal drafting, marketing and candidate screening
- ✦Twitter personality-analysis demo agent reading profiles and tweets
- ✦Compatibility-check feature between two Twitter profiles
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- ✦Scalable learned world model
- ✦Imagination training via reinforcement learning
- ✦Real-time interactive inference on a single GPU
- ✦Learns from offline data without environment interaction
- ✦Applicable to Minecraft and robotics
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- →Letting non-SQL business users query company data directly
- →Reducing analyst time spent writing routine SQL
- →Deploying a governed, access-controlled chat-to-SQL agent for a team
- →Self-hosting an open-source text-to-SQL agent for full control
- →Non-technical domain experts prototyping LLM applications
- →Teams building legal, marketing or HR AI agents without code
- →Developers wanting fast iteration without heavy LLM abstractions
- →Consumers trying a novelty AI-generated Twitter personality report
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- →Research on world models and agents
- →Training control policies from offline data
- →Simulating environments for robotics research