Most AI video tools hand you a black box: closed weights, a subscription, and no way to see what is happening under the hood. LTX takes the opposite approach. Built by Lightricks, LTX is an open foundation model that generates and simulates across video, audio, and the physical world, and it puts the weights, the code, and the control in your hands.
At the center of the model is LTX-2.5, a 22B-parameter dual-stream diffusion transformer that produces native 4K video at up to 50 frames per second, with audio and video generated together in a single pass rather than stitched together afterward. Artificial Analysis, an independent benchmarking group, currently ranks LTX among the top three AI video models in the world.
You choose how you want to use it. Download the open weights and run LTX-2.5 on your own hardware. License the model for on-premise deployment backed by enterprise support. Or build directly on LTX Studio, the production suite that turns the model into a full creative workflow. Companies like ElevenLabs, Asteria Film Co., Magnopus, and NVIDIA already rely on LTX for their own work.
LTX is not built for one-off social clips. It is infrastructure for teams that generate motion, audio, and physical environments as part of their own products and pipelines.
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JetBrains Junie is an innovative AI coding assistant that works inside many JetBrains IDEs to streamline programming efforts and boost efficiency. This agent leverages advanced AI to help developers write, test, and inspect code without leaving their familiar development environment. Junie offers both code execution and interactive collaboration, allowing programmers to switch between automated code writing and brainstorming sessions for features and improvements. By deeply understanding the codebase, Junie identifies the best ways to tackle tasks and ensures all changes meet quality standards through syntax and semantic checks. It also runs tests to minimize errors and keep the project healthy, freeing developers from routine tasks. Many developers have successfully built complex applications and games using Junie, highlighting its flexibility across different languages and frameworks. The AI adapts to each task’s complexity and workflow, making coding less tedious and more focused on creativity. Whether you are building a simple web app or a complex game, Junie offers smart support throughout the development cycle.
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GLM-5.3-Flash
GLM-5.3-Flash is a multimodal foundation model from Z.ai built for high-efficiency reasoning, coding, agents, and visual understanding. The model contains 320 billion parameters in total but activates only 18 billion parameters during inference, helping reduce compute requirements. Its architecture combines linear attention with sparse attention so it can efficiently handle both local dependencies and relevant information spread across very long contexts. Z.ai also introduced IndexPool to reduce the memory and latency overhead associated with long-context retrieval at context lengths reaching one million tokens. The model was pretrained on a 30-trillion-token multimodal dataset that incorporates both textual and visual information. GLM-5.3-Flash is designed for software engineering tasks, autonomous workflows, frontend development, computer use, document analysis, and other professional workloads that benefit from visual reasoning. Its visual coding capabilities allow it to inspect rendered interfaces, identify layout or interaction problems, and use those observations to revise its work. Benchmark results published by Z.ai show that it improves substantially over GLM-5.2 on multiple coding and agentic tests while remaining competitive with more expensive frontier models. GLM-5.3-Flash can be accessed through Z.ai services and is also available as downloadable model weights for deployment through supported open inference frameworks.
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Inkling
Inkling is Thinking Machines’ open-weights foundation model built for customization, multimodal reasoning, and agentic AI workflows. The model uses a Mixture-of-Experts architecture with 975 billion total parameters and 41 billion active parameters, making it large in capacity while activating only a subset of experts per token. Inkling supports up to a 1 million token context window and was pretrained on 45 trillion tokens spanning text, images, audio, and video. It is designed as a broad generalist model with strengths across coding, reasoning, instruction following, factuality, tool use, vision, audio understanding, forecasting, and safety. Developers can tune its thinking effort to trade off latency, cost, and performance, which is useful for production systems that need efficient reasoning at scale. Inkling can be fine-tuned on Tinker, tested in the Inkling Playground, and deployed through partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, vLLM, SGLang, llama.cpp, and Hugging Face transformers. The model can generate applications, operate tools, create styled artifacts, reason over visual and audio inputs, and support long refinement loops for collaborative work. Thinking Machines also previewed Inkling-Small, a lighter Mixture-of-Experts model with 276 billion total parameters and 12 billion active parameters for lower-cost and lower-latency workloads. By combining open weights, multimodal training, agentic capabilities, efficient reasoning, and fine-tuning support, Inkling gives builders a flexible AI foundation for specialized products and workflows.
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