Distribute and run LLMs with a single file.
- cross-platform
- gguf
- llama-cpp
- local-ai
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Distribute and run LLMs with a single file.
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
Free offline AI voice studio for Windows — text-to-speech with cloned and built-in voices, and speech recognition with speakers. Russian done right. 100% local, one native .exe, zero Python.
Docker image gathering packers and tools for making datasets of packed executables and training machine learning models for packing detection
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
Durable execution framework for AI agents in Go. Keeps agent state, tool calls, and execution loops resilient across process crashes.
A curated dataset of malware and benign Windows executable samples for malware researchers
Beagle helps you identify keywords, phrases, regexes, and complex search queries of interest in streams of text documents.
🌊 Floom, "The K8s for AI", orchestrates & executes Generative AI pipelines, Empowering Developers and DevOps to focus on what matters.
PHP client for the Anthropic API: messages, streaming, tool use, thinking, web search, code execution, batches, and more.
Enterprise-grade platform to generate and execute Cypress, Playwright, WebdriverIO, and Appium end-to-end tests from natural language requirements. (Appium is experimental and requires external mobile infrastructure.)
Anahata ASI - Artificial Super Intelligence - Cross-Platform, Modular, Enterprise Ready, Open Source, Pure Java, AGI and ASI Containers. Context Window Garbage Collector, Stateful Toolkits, Flight Recorder. Autonomous Observable Auditable JVM Agent with JIT java code execution. Anahata ASI SDK, Anahata ASI Desktop, Anahata ASI Studio - NetBeans.
A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.
A Single Library Parser to extract meta information,static analysis and detect macros within the files.