Search Results for "opus-cat"

...It is highly extensible through a plugin system that supports custom tools, event hooks, and workflows, giving developers fine-grained control over agent behavior and interactions. Cheshire Cat also supports multi-user environments with granular permissions and identity provider integration, making it suitable for enterprise use cases.

DiscordGo is a Go package that provides low level bindings to the Discord chat client API. DiscordGo has nearly complete support for all of the Discord API endpoints, websocket interface, and voice interface. If you would like to help the DiscordGo package please use this link to add the official DiscordGo test bot dgo to your server. This provides indispensable help to this project. Construct a new Discord client which can be used to access the variety of Discord API functions and to set...

...The core idea behind the project is to pair Grounding DINO — a zero-shot object detector that can locate objects described by natural language — with Segment Anything Model (SAM), which can produce detailed masks for objects once they are localized. This fusion lets users provide arbitrary text descriptions (e.g., “a cat, a bicycle, or a coffee mug”), have the detection model find relevant bounding boxes, and then use SAM to generate precise segmentation masks that isolate each object in the scene.

OpenAI-Compatible Edge-TTS API is a local, OpenAI-compatible text-to-speech API that uses edge-tts—Microsoft Edge’s online TTS service—as the backend. The project emulates the /v1/audio/speech endpoint used by OpenAI, so any client that can talk to the OpenAI TTS API can be redirected to this service with minimal changes. It exposes parameters for input text, voice selection, audio format, and playback speed, mirroring the OpenAI interface while mapping popular OpenAI voice names to...

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Qwable-v1 is an open-weight agentic coding model created through a chained distillation process based on Qwen3.6-35B-A3B. The model combines two distinct training stages: first, it was fine-tuned on reasoning traces derived from Claude Opus 4.7 to improve structured reasoning, and then further trained on Claude Fable-5 agentic tool-use traces to develop autonomous coding and tool-calling behavior. The result is a 35B Mixture-of-Experts model with only 3B active parameters that can switch between deep reasoning and agent-style execution depending on the system prompt. ...