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Mobile app giving AI-generated football match predictions across 50+ leagues; betting-focused, accuracy self-reported.
AI odds engine that computes its own match probabilities and compares them to bookmaker lines across 11 sports to flag positive-value bets.
Infrastructure company building large-scale GPU data centers and compute for AI, including Anthropic's compute buildout.
Agentic coding platform (Cosmos) that runs software-dev agents at org scale, using a codebase context engine to cut token cost.
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
No public pricing
Free trial available
No public pricing
Free trial available
No public pricing
- ✦ML-based football match predictions
- ✦Confidence ratings per prediction
- ✦Coverage of 50+ leagues
- ✦Real-time match updates and alerts
- ✦Statistical team and player analysis
- ✦Model-derived 'true' odds compared against 40+ bookmakers
- ✦Continuously refreshed positive-edge value-bet stream
- ✦Free live match predictions with confidence percentages
- ✦Coverage across football, basketball, tennis, cricket, and more
- ✦Full audit trail of logged and settled bets
- ✦Chat-based betting bot assistant
- ✦Large-scale GPU and data-center infrastructure for AI
- ✦Power acquisition and data-center design/build
- ✦Fast deployment (gigawatts in ~6 months)
- ✦Operates both hardware and software stack
- ✦Context Engine for codebase understanding
- ✦Agents across the full SDLC
- ✦Model routing / bring-your-own-keys
- ✦Automated code review and test coverage
- ✦CLI, MCP and native tool integrations
- ✦Enterprise security (SOC 2, ISO 42001, SSO)
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
- →Inform soccer betting decisions
- →Track predictions across many leagues
- →Get real-time match alerts
- →Bettors looking for statistically positive-value wagers
- →Users wanting free live win-probability predictions
- →Bettors tracking model performance transparently over time
- →Training and running large AI models at scale
- →Provisioning GPU compute for AI labs
- →Building dedicated AI data-center capacity
- →Automating PR code review
- →Raising test coverage
- →Incident investigation and remediation
- →Large-scale migrations and onboarding
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app