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All-in-one digital-safety subscription protecting families from identity theft, fraud and online threats, with parental controls.
Open-source, Rust-built secure runtime that runs AI agents in encrypted enclaves so credentials never reach the model.
Google's agentic development platform and IDE for building software with autonomous, Gemini-powered coding agents.
Chinese AGI company building multimodal LLMs, Hailuo video, speech and music models, plus AI apps and open APIs.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
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- ✦Identity theft protection with insurance
- ✦3-bureau credit monitoring and lock
- ✦Antivirus, VPN and password manager
- ✦Online data removal from brokers
- ✦Parental controls and safe-gaming alerts
- ✦Dark-web and financial-fraud alerts
- ✦Encrypted credential vault injected only at approved endpoints
- ✦Agents run inside Trusted Execution Environments (encrypted enclaves)
- ✦Sandboxed tools in Wasm containers with capability-based permissions
- ✦Real-time outbound leak detection to block credential exfiltration
- ✦Rust codebase for memory safety
- ✦One-click cloud deploy on NEAR AI Cloud or self-host from source
- ✦Agent-first IDE experience
- ✦Autonomous planning and code execution
- ✦Integrated editor, terminal and browser control
- ✦Powered by Google's Gemini models
- ✦High-level developer supervision
- ✦MiniMax M-series LLMs (M3, 1M context, MSA)
- ✦Hailuo AI video generation
- ✦Speech and music generation models
- ✦MiniMax Code agentic coding tool
- ✦Consumer apps (Hailuo, Xingye)
- ✦Open API and Token Plan for developers
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Protecting against identity theft
- →Monitoring family credit and finances
- →Keeping kids safe online
- →Removing personal data from broker sites
- →Running autonomous AI agents without exposing secrets
- →Self-hosting a secure personal AI assistant
- →Deploying agents in a confidential-compute environment
- →Building apps with AI agents
- →Automating multi-step coding tasks
- →Prototyping and iterating on software
- →Assisting developers on complex work
- →Coding and agentic tasks
- →AI video generation
- →Text-to-speech and music creation
- →Building on MiniMax model APIs
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects