
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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SciSure is a Scientific Management Platform built to support the full range of laboratory operations for scientific organizations. It combines ELN, LIMS, and Health & Safety functionality, giving teams a single system to document experiments, track sample lineage, manage chemical inventory, and run structured, audit-ready compliance processes.
Instead of relying on disconnected systems, organizations get one governed platform that improves reproducibility, increases visibility into lab operations, and reduces risk as they scale.
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Kombyne
Kombyne™ represents a cutting-edge Software as a Service (SaaS) tool designed for high-performance computing (HPC) workflows, originally tailored for clients in sectors such as defense, automotive, aerospace, and academic research. This platform empowers users to access a diverse array of workflow solutions specifically for HPC computational fluid dynamics (CFD) tasks, encompassing features like on-the-fly extract generation, rendering capabilities, and simulation steering options. Users can benefit from interactive monitoring and control functionalities, all while ensuring minimal disruption to simulations and eliminating reliance on VTK. By employing extract workflows, the necessity for handling large files is significantly reduced, allowing for real-time visualization. The system incorporates an in-transit workflow that utilizes a distinct process to swiftly receive data from the solver code, enabling visualization and analysis without hindering the operation of the running solver. This specialized process, referred to as an endpoint, facilitates the direct output of extracts, cutting planes, or point samples useful for data science, in addition to rendering images. Furthermore, the Endpoint serves as a conduit to widely-used visualization software, enhancing the overall usability and integration of the tool within various workflows. With its versatile features and ease of use, Kombyne™ is set to revolutionize the way HPC tasks are managed and executed across multiple industries.
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Gemini for Science
Gemini for Science enhances the process of scientific discovery by offering AI-driven tools and resources specifically designed to bolster scientific initiatives. By integrating experimental tools found in Google Labs with the science workflows offered through Google Antigravity, it aims to expedite research, improve analytical reasoning, and enable researchers to delve into the future of AI-enhanced scientific exploration. The Literature Insights feature compiles scholarly literature to uncover new research possibilities, produce well-founded research artifacts, and convert paper information into structured tables linked directly to original evidence. Meanwhile, Hypothesis Generation employs a multi-agent approach that emulates the scientific method, allowing it to pinpoint knowledge gaps, suggest viable research avenues, and outline testable research plans that could lead to significant breakthroughs. Additionally, Computational Discovery assists researchers in identifying models and algorithms through an intelligent research engine that creates and evaluates code variations according to user-specified optimization criteria, thereby streamlining the research process even further. Ultimately, these innovative tools collectively aim to revolutionize how scientific research is conducted and understood.
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