DeepSeek-V4-Pro
DeepSeek-V4-Pro is a large-scale Mixture-of-Experts (MoE) language model designed for advanced reasoning, coding, and long-context understanding. It features 1.6 trillion total parameters with 49 billion activated parameters, enabling high performance while maintaining efficiency. The model supports an exceptionally large context window of up to one million tokens, allowing it to process extensive documents and workflows. It uses a hybrid attention architecture to optimize long-context performance and reduce computational cost. DeepSeek-V4-Pro is trained on over 32 trillion tokens, improving its knowledge and reasoning capabilities. It also includes advanced optimization techniques for stability and faster convergence during training. The model supports multiple reasoning modes, allowing users to balance speed and accuracy based on their needs. Overall, it provides a powerful open-source solution for complex AI tasks and large-scale applications.
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Flowacts
Flowacts is a shared AI canvas where individuals, teams, and AI build together without losing context inside private chat threads. It turns prompts into interactive workspaces where sources, notes, prompts, AI outputs, claims, branches, decisions, and conclusions remain visually connected, making the path from research to final answer clear at a glance. Teams can select any node, branch, group, or generated result as the starting point for the next action instead of rewriting prompts to rebuild context. A source can become a summary, a summary can become a table, and a research branch can become slides or a final brief while every new artifact stays tied to the context that produced it. Flowacts supports documents, tables, slides, mind maps, charts, flowcharts, timelines, images, research maps, strategy plans, and multi-step project canvases. Every output is editable, so users can refine blocks, regenerate sections, reorganize the structure, or continue building.
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MiniMax M3
MiniMax M3 is an open-weight multimodal AI model designed for coding, agentic workflows, long-context reasoning, and complex automation tasks. The model combines frontier-level coding performance, native multimodal understanding, and a context window of up to 1 million tokens. MiniMax M3 uses MiniMax Sparse Attention to improve long-context efficiency while reducing compute requirements for large-scale inputs. It supports text, image, and video understanding, making it useful for workflows that combine code, documents, visual references, and tool-driven tasks. The model is built for repository-scale reasoning, software engineering, autonomous task execution, tool calling, and multi-step agent workflows. MiniMax M3 helps developers, AI teams, and enterprises build capable agents that can reason across large contexts and work with multimodal information.
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Entire
Entire is a developer platform that integrates deeply with your Git workflow to capture and preserve AI agent sessions alongside your code, making the context of AI-assisted development transparent, searchable, and shareable. Every time you commit, Entire’s CLI hooks into Git to automatically record comprehensive session data, including transcripts, prompts, files changed, token usage, and tool calls, as versioned checkpoints that link directly to Git commits, helping developers understand how and why AI-generated code was produced. These checkpoints become first-class, permanent data stored in special Git branches so team members can review AI interactions during code reviews, recall decision context, trace history, and collaborate more effectively. Entire’s model ensures AI sessions aren’t ephemeral but become part of a project’s source context, searchable and explainable through tooling that helps teams rewind, analyze, and share workflows the same way they manage code.
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