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Showing 12 open source projects for "ai browser"
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Easy Diffusion
An easy 1-click way to create beautiful artwork on your PC using AI
Easy Diffusion is a widely used community-driven repository offering a simple, one-click way to install and use Stable Diffusion-based generative AI on a personal computer without advanced technical skills or prior setup. It provides a browser-based user interface that runs locally, allowing users to type text prompts and immediately generate images directly within their web browser, democratizing access to powerful text-to-image models for artists and hobbyists alike. The project abstracts away environment setup, dependencies, and model installation — tasks that can be daunting to beginners — and instead lets users focus on creative experimentation with prompt phrasing, model parameters, and image output settings. ... -
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DSH Web UI
Plugin and skin collection for DeepSeek Harness (DSH) Web UI
DSH Web UI is a modular plugin and skin collection that expands the DeepSeek Harness browser interface into a broader development workspace. Its components attach through the official DSH profile mechanism without modifying the Harness source code. Users can add a task board that executes real agent sessions and supports scheduled jobs through cron expressions. A Git graph visualizes branches and commit history, while additional panels provide files, editing, terminals, Git operations, and... -
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SemIf
Semantic ifs from open models, on a 3090 at home
SemIf, formerly OpenJev, is an independent research project for making small semantic decisions with open language models. Instead of generating text and parsing it afterward, it reads probabilities directly from declared answer options. Each request supplies state, a runtime-defined question, and typed choices, allowing the model to behave like a semantic conditional. Shared-state execution can reuse a long context across many related decisions to reduce repeated computation. Backends... -
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Qwen3-Coder
Qwen3-Coder is the code version of Qwen3
Qwen3-Coder is the latest and most powerful agentic code model developed by the Qwen team at Alibaba Cloud. Its flagship version, Qwen3-Coder-480B-A35B-Instruct, features a massive 480 billion-parameter Mixture-of-Experts architecture with 35 billion active parameters, delivering top-tier performance on coding and agentic tasks. This model sets new state-of-the-art benchmarks among open models for agentic coding, browser-use, and tool-use, matching performance comparable to leading models... -
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DSH Better Sidebar
Open sidebar foundation, supports third-party extensions
DSH Better Sidebar is a plugin that turns the DeepSeek Harness interface into a fuller development workspace. It adds a file explorer, code editor, previews, an embedded browser, real terminal sessions, Git tools, and background-task views. Tabs can be moved, split, and shared between the right sidebar and bottom panel. File previews cover code, images, Markdown, HTML, PDF, Word, Excel, and PowerPoint content. Session-specific layouts and tabs persist locally, while mobile layouts collapse... -
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MiniMax-M2
MiniMax-M2, a model built for Max coding & agentic workflows
MiniMax-M2 is an open-weight large language model designed specifically for high-end coding and agentic workflows while staying compact and efficient. It uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only 10 billion activated per token, giving it the behavior of a very large model at a fraction of the runtime cost. The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real... -
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img2obj
Codex plugin that turns attached object images into code-only
img2obj is a Codex plugin that reconstructs an object from a reference image as procedural Three.js code rather than extracting or downloading a mesh. It first checks whether the image is suitable, then creates an ObjectSculptSpec describing geometry, materials, hierarchy, pivots, sockets, and visual priorities. Reconstruction proceeds through blockout, form, look development, and interaction phases so details are added in a controlled order. The plugin supports hard-surface objects, organic... -
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fast-stable-diffusion
Fast-stable-diffusion + DreamBooth
fast-stable-diffusion is a community-curated GitHub repository that provides Colab notebooks and integration examples for running Stable Diffusion and associated UIs like AUTOMATIC1111, ComfyUI, and DreamBooth directly on Google Colab environments. Rather than being a standalone packaged application, this project offers ready-to-use interactive notebooks that install and launch full-feature Stable Diffusion web UIs inside Colab without requiring complex local setups or GPU installations.... -
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LocalMode
Local-First AI for the Web
Run ML models entirely in your browser. Embeddings, vector search, LLM chat, vision, audio, agents, and structured output - all offline, all private. No servers. No API keys. Your data never leaves your device. -
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NVIDIA Nemotron 3.5 Bolt 74B-A9B
Full-precision 74B multimodal MoE for AI agents and model fine-tuning
...It is optimized for coding, browser automation, terminal operations, and multi-step agent workflows. BF16 weights preserve flexibility for supervised fine-tuning, reinforcement learning, distillation, and creating quantized variants. Deployment is supported through vLLM on NVIDIA GPUs, while LiLiCorr speculative decoding improves inference speed. -
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NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB
24GB multimodal MoE model for local coding, reasoning, and AI agents
NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB is an open-weight multimodal AI model optimized for running autonomous agents on systems with 24GB of GPU memory. Derived from Nemotron 3.5 Super VL through NVIDIA's Star Elastic compression method, it features 74B total parameters with approximately 9B activated per token. Its hybrid Mamba-2–Transformer LatentMoE architecture incorporates a C-RADIO v4-H vision encoder for processing text, images, and video. -
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Qwen3.8-27B
Dense 27B multimodal model for coding, agents, and visual reasoning
Qwen3.8-27B is a compact, open-weight dense multimodal model designed for advanced coding, professional work, research, visual understanding, and long-horizon agentic tasks. Built on the Qwen3.5 architecture, it contains 27B parameters and combines Gated DeltaNet with gated attention across 64 layers. The model natively understands text, images, and videos, including documents, STEM diagrams, and hour-scale video content. Agent capabilities emphasize autonomous planning, environment...
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