hotg.ai
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About
hotg.ai, developed by Hammer Of The Gods, Inc., provides a specialized developer platform for building and deploying machine learning models directly to edge environments. By utilizing highly portable container technology based on WebAssembly, the platform allows users to run SQL and predictive analytics on disparate datasets without the need for complex cloud infrastructure or heavy engineering dependencies. This "Edge ML" approach moves the compute closer to the data source, which significantly reduces the latency and high costs often associated with ad-hoc analysis on platforms like Databricks or Snowflake. The core of the ecosystem revolves around "Runes"—self-contained, language-agnostic executables that encompass a compiled ML pipeline, including inputs, models, and processing steps. Developers interact with the platform through Hammer Forge, which consists of a drag-and-drop Studio for creating Runefiles (declarative YAML files) and a robust build system called Foundry. The system supports a variety of deployment targets, including iOS, Android, web browsers, and desktop environments, with future support planned for microcontrollers and single-board computers like Raspberry Pi and Arduino. The tool is primarily designed for data scientists, device engineers, and software developers who need to implement AI capabilities in environments where privacy or connectivity is a concern, such as healthcare or industrial IoT. It offers a free tier for individual developers to find product-market fit, while providing an enterprise-grade solution for organizations requiring private deployments, custom hardware integration for MCUs or FPGAs, and continuous integration (CI/CD) pipelines. What distinguishes hotg.ai from traditional cloud-based ML platforms is its emphasis on the "Tinyverse"—the intersection of hardware and AI. By abstracting the complexities of edge deployment through its open-source Rune project and user-friendly Studio, it enables familiar workflows using SQL and Excel on private data. This eliminates the costs of centralizing data and provides a more secure, decentralized method for real-time data processing across siloed systems.
Pros & Cons
Reduces cloud costs by performing ad-hoc analysis at the edge instead of centralized warehouses.
Supports highly portable deployments across desktops, mobile, and web via WebAssembly.
Simplifies infrastructure by removing the need for complex engineering team dependencies.
Offers a free-forever tier for individual developers to build and test their first live deployment.
Enables secure analytics on private data without requiring it to be moved to external servers.
Currently restricted to TensorFlow Lite and TensorFlow JS models with ONNX support still in progress.
Support for specialized hardware like Arduino and Raspberry Pi is currently in the 'coming soon' phase.
The free tier is limited to a single user seat and one live deployment.
Use Cases
Device engineers can deploy optimized ML models to mobile and web apps using a drag-and-drop interface without managing complex backend infrastructure.
Data scientists can run SQL-based analytics on siloed private data directly on user laptops to maintain privacy and reduce ETL overhead.
Healthcare organizations can implement predictive analytics on-site or on-device to ensure sensitive patient data remains secure and compliant.
IoT developers can create self-contained ML pipelines for edge devices that function across different operating systems using a single Runefile.
Platform
Features
• support for tflite and tfjs models
• monitoring and testing logs
• multi-platform deployment (ios, android, web)
• declarative runefile yaml configuration
• sql and predictive analytics on edge
• hammer forge drag-and-drop studio
• webassembly-based portable containers
• edge ml pipeline compilation
FAQs
What is a Rune?
A Rune is a self-contained, language-agnostic executable containing a compiled machine learning pipeline. It is an open-source project designed to be highly portable across various hardware and software environments.
What machine learning frameworks does hotg.ai support?
Currently, the platform supports TensorFlow Lite and TensorFlow JS models. There is active development to add support for the ONNX framework, which will allow for a wider variety of models.
Which platforms can I deploy to?
Hammer Forge supports edge deployment to iOS, Android, web, mobile web, and desktop environments today. Support for Arduino, Coral, Jetson Nano, and Raspberry PI is listed as coming soon.
What is the difference between Hammer Forge Studio and Foundry?
Studio is a drag-and-drop editor used for creating Runefiles and automating the build and deployment process. Foundry is the underlying build system used to compile those files into functional Runes.
How does hotg.ai handle data security?
The product works across siloed systems using WebAssembly containers, allowing you to run analytics and ML workloads on private data without moving it to the cloud, thereby increasing security controls.
Pricing Plans
ENTERPRISE
Unknown Price• Continuous Integration and Continuous Delivery
• Cloud native deployments (on-prem/cloud)
• API integrations for metrics and logs
• Fully private deployments
• Custom hardware (MCUs, FPGA)
• Concierge support
FREE
Free Plan• Build, test, and deploy
• One live deployment
• Deploy to edge devices
• 1 seat
• Full monitoring and testing logs
• Email and community support
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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