
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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The brands seeing the strongest results from creator marketing aren't simply increasing campaign volume—they're building an engine for sustainable creator-led growth.
Superfiliate brings every creator initiative into a single platform, replacing disconnected tools with one centralized system. From affiliates and ambassadors to influencers and paid creators, brands can recruit, manage, and grow every partnership through unified profiles, automated workflows, creator discovery, and performance tracking.
Leading ecommerce brands including Graza, Cymbiotika, Omnilux, and MUD\WTR rely on Superfiliate to scale creator programs that consistently generate high-performing content, strengthen paid media, and tie every partnership back to measurable revenue.
Native integrations with Meta, TikTok Shop, and YouTube connect creator performance across channels, giving brands a complete view of which creators, campaigns, and content are driving results. The outcome is a creator program that's no longer managed as a collection of campaigns, but as a scalable acquisition channel that becomes more valuable over time.
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Fluidity
Fluidity is an innovative AI-driven tool that transforms brand briefs into ready-to-use static advertisements, social media content, and WhatsApp Business messages. After initially configuring your brand with elements like the logo, color scheme, tone, and product details, Fluidity consistently utilizes this context for each creative it produces, ensuring all outputs align with your brand identity without the need for repeated briefings. This platform is particularly crafted for direct-to-consumer brands, marketing agencies, and growth teams engaged in performance marketing across various platforms, including Meta, Instagram, LinkedIn, and WhatsApp. By streamlining the creative process, Fluidity empowers teams to maintain a cohesive brand presence while saving valuable time and resources.
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Muse Spark 1.2
Muse Spark 1.2 is Meta’s newest coding-focused model, released alongside Muse Code as part of Meta’s AI developer platform. The model improves on Muse Spark 1.1 with stronger code generation, complex debugging, codebase understanding, and full developer workflow performance. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan changes, write code, validate results, and coordinate persistent background subagents. The model was co-trained with Muse Code so it performs well inside the agentic coding runtime and tool environment. Its training included scaled coding compute, broader training environment diversity, rejection-sampled harness trajectories, recipe optimizations, and Muse Code toolset integration. Muse Spark 1.2 is designed for long-horizon coding tasks such as whole-repository generation, large end-to-end projects, auto-research, and extended optimization work. It uses planning to sequence work, goal conditioning to stay aligned with the user’s objective, and context compaction to preserve useful knowledge over long sessions. The model also benefits from a self-improvement loop where Muse Spark 1.1 generated challenging coding environments and instruction-following templates for training. By combining coding specialization, agentic workflow support, long-horizon training, subagent compatibility, and Meta Model API availability, Muse Spark 1.2 helps developers build, debug, and optimize software more effectively.
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