Search Results for "memory usage"

whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples....

Vellum is an open-source personal AI assistant platform designed to function as a persistent, autonomous digital companion across desktop and messaging environments. Unlike traditional chatbot interfaces, the project focuses on long-term memory, identity, proactive behavior, and real-world tool usage, enabling assistants to evolve alongside the user over time. The system integrates with macOS, Telegram, Slack, SMS, and additional communication channels while maintaining shared memory and context across platforms. Its architecture combines local-first storage, tool orchestration, sandboxed execution, and extensible workflow automation to allow assistants to read files, manage schedules, send messages, browse the web, and control applications. ...

...It provides a full-stack architecture composed of an inference engine, a computation graph system, and highly optimized hardware kernels tailored for ARM-based processors. Cactus emphasizes efficient memory usage through techniques such as zero-copy computation graphs and quantized model formats, allowing large models to run within the constraints of mobile hardware. It supports a wide range of AI tasks including text generation, speech-to-text, vision processing, and retrieval-augmented workflows through a unified API interface. A notable feature of Cactus is its hybrid execution model, which can dynamically route tasks between on-device processing and cloud services when additional compute is required.

Library for performing speech recognition, with support for several engines and APIs, online and offline. Recognize speech input from the microphone, transcribe an audio file, save audio data to an audio file. Show extended recognition results, calibrate the recognizer energy threshold for ambient noise levels (see recognizer_instance.energy_threshold for details). Listening to a microphone in the background, various other useful recognizer features. The easiest way to install this is using...

...It supports customizable AI behavior, including choosing supported models, uploading models, and adjusting system prompts. The project also includes built-in benchmarking so users can compare performance, speed, and memory usage on their own hardware. Its main value is making personal AI feel private, portable, free, and usable without accounts, subscriptions, or an internet connection.

The OpenClaw Mobile Dashboard. Monitor agent's real-time thoughts, actions, track token costs, and manage tasks from anywhere using your pocket-sized Mission Control.

...Upon activation, it tracks allocations / memory maps, logs usage, and outputs reports that can be post-processed (e.g. via provided Python scripts) to list potential leak suspects — assisting in identifying leaks that traditional tools might miss. The detector supports stack unwinding, mapping analysis, and reporting, making it suitable for complex applications where native memory misuse can lead to crashes or performance degradation.