π Deep-diving into Lightricks/LTX-2 β fresh off the trending list.
π https://github.com/Lightricks/LTX-2
π Official Python inference and LoRA trainer package for the LTX-2 audioβvideo generative model.
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The Lightricks/LTX-2 GitHub repository presents a groundbreaking DiT-based audio-video foundation model, LTX-2, which integrates all core capabilities of modern video generation into one model. This includes synchronized audio and video, high fidelity, multiple performance modes, production-ready outputs, API access, and open access.
To get started, users can clone the repository, install dependencies, and download the required models using the
LTX-2 features various models, including the
The repository also offers multiple pipelines, such as
LTX-2 is suitable for a wide range of users, from researchers and developers to content creators and artists.
In a nutshell, LTX-2 revolutionizes video generation, and its capabilities are a game-changer: unleash your creativity with LTX-2, where AI meets art.
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π§ Channel: https://t.iss.one/GithubRe
π https://github.com/Lightricks/LTX-2
π Official Python inference and LoRA trainer package for the LTX-2 audioβvideo generative model.
ββββββββββββββββββββββββββββββ
The Lightricks/LTX-2 GitHub repository presents a groundbreaking DiT-based audio-video foundation model, LTX-2, which integrates all core capabilities of modern video generation into one model. This includes synchronized audio and video, high fidelity, multiple performance modes, production-ready outputs, API access, and open access.
To get started, users can clone the repository, install dependencies, and download the required models using the
Hugging Face CLI. The repository provides a Quick Start guide, which demonstrates how to generate video using the distilled model and pipeline. LTX-2 features various models, including the
transformer, text encoder, video VAE, audio VAE, and spatial upscaler. Each model has different versions, allowing users to choose the best fit for their specific needs. The repository also offers multiple pipelines, such as
DistilledPipeline, DFRPipeline, and TI2VidTwoStagesPipeline, each with its unique features and use cases. LTX-2 is suitable for a wide range of users, from researchers and developers to content creators and artists.
In a nutshell, LTX-2 revolutionizes video generation, and its capabilities are a game-changer: unleash your creativity with LTX-2, where AI meets art.
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π§ Channel: https://t.iss.one/GithubRe
Github Top Repositories
Photo
π₯ embabel/embabel-agent is trending β and it deserves your attention.
π https://github.com/embabel/embabel-agent
π Agent framework for the JVM. Pronounced Em-BAY-bel /ΙmΛbeΙͺbΙl/
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Embabel Agent Framework is a Java-based framework for building intelligent agents that can seamlessly mix large language model (LLM) interactions with code and domain models. It's designed to support sophisticated planning, extensibility, and reuse, making it an ideal choice for developers who want to create complex, dynamic systems.
The framework is built on top of the JVM, leveraging the strengths of Spring and Kotlin, and provides a natural usage model for Java developers. It's highly extensible, allowing developers to add new domain objects, actions, goals, and conditions without modifying existing code.
Key features include:
-
-
-
-
Technical highlights include:
- Sophisticated planning using Goal Oriented Action Planning (GOAP) or Utility AI
- Strong typing and object-oriented benefits
- Platform abstraction for clean separation between programming model and platform internals
- Designed for LLM mixing and cost-effective solution
Audience: Embabel Agent Framework is ideal for developers who want to create complex, dynamic systems that leverage the power of LLMs and agentic programming.
Get started with Embabel in under 5 minutes using the
Embabel Agent Framework: where intelligence meets adaptability.
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π§ Channel: https://t.iss.one/GithubRe
π https://github.com/embabel/embabel-agent
π Agent framework for the JVM. Pronounced Em-BAY-bel /ΙmΛbeΙͺbΙl/
ββββββββββββββββββββββββββββββ
Embabel Agent Framework is a Java-based framework for building intelligent agents that can seamlessly mix large language model (LLM) interactions with code and domain models. It's designed to support sophisticated planning, extensibility, and reuse, making it an ideal choice for developers who want to create complex, dynamic systems.
The framework is built on top of the JVM, leveraging the strengths of Spring and Kotlin, and provides a natural usage model for Java developers. It's highly extensible, allowing developers to add new domain objects, actions, goals, and conditions without modifying existing code.
Key features include:
-
Actions: Steps an agent takes-
Goals: What an agent is trying to achieve-
Conditions: Conditions to assess before executing an action or determining that a goal has been achieved-
Domain model: Objects underpinning the flow and informing actions, goals, and conditionsTechnical highlights include:
- Sophisticated planning using Goal Oriented Action Planning (GOAP) or Utility AI
- Strong typing and object-oriented benefits
- Platform abstraction for clean separation between programming model and platform internals
- Designed for LLM mixing and cost-effective solution
Audience: Embabel Agent Framework is ideal for developers who want to create complex, dynamic systems that leverage the power of LLMs and agentic programming.
Get started with Embabel in under 5 minutes using the
Java or Kotlin template, and explore the Embabel Agent Examples Repository for tutorials and examples.Embabel Agent Framework: where intelligence meets adaptability.
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π§ Channel: https://t.iss.one/GithubRe
β€1
Github Top Repositories
Photo
β‘ cactus-compute/needle is making waves. Here's the full picture.
π https://github.com/cactus-compute/needle
π 14MB foundation model for tiny devices; phones, wearables, smart home, and robots.
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Needle 2 is a compact, open-source model for tool calling, device use, and structured extraction. It's a single 14MB binary that runs in approximately 28MB of RAM. Key features include a
Needle 2 is built on the Simple Attention Network architecture, which includes a Hadamard MLP, GQA attention, and engram key-value memory. The model is
To use Needle 2, you can install the
Audience: This model is suitable for developers and researchers looking for a compact, efficient, and easy-to-use model for tool calling and data extraction tasks.
One-liner takeaway: Needle 2 is a powerful, compact model that makes it easy to build tool calling and data extraction applications with confidence-gated responses.
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π§ Channel: https://t.iss.one/GithubRe
π https://github.com/cactus-compute/needle
π 14MB foundation model for tiny devices; phones, wearables, smart home, and robots.
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Needle 2 is a compact, open-source model for tool calling, device use, and structured extraction. It's a single 14MB binary that runs in approximately 28MB of RAM. Key features include a
self-contained engine, simple contract for tool calls, and confidence-gated responses. Needle 2 is built on the Simple Attention Network architecture, which includes a Hadamard MLP, GQA attention, and engram key-value memory. The model is
compressed to CQ2-bit with Cactus Quants and can be used for a variety of tasks, including tool calling, data extraction, and more.To use Needle 2, you can install the
cactus-needle Python package and describe your tools using a simple contract. The model can then be used to call tools, extract structured data, and more. Audience: This model is suitable for developers and researchers looking for a compact, efficient, and easy-to-use model for tool calling and data extraction tasks.
One-liner takeaway: Needle 2 is a powerful, compact model that makes it easy to build tool calling and data extraction applications with confidence-gated responses.
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π§ Channel: https://t.iss.one/GithubRe
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π€ Peer Group β Share Insights | Exchange Knowledge | Support Each Other
No more studying alone. Join a community of CCNA/CCNP candidates, learn together, and win prizes.
How it works:
β DM admin: "I'M IN + cert name"
β‘ Join the group
β’ Check in 18/21 days β win π
Prizes (first come, first served):
$50 Amazon card Γ1 | SD-Access Training Γ1 | SD-WAN Training Γ1 | CCNA Pro Package Γ10 | Free Learning Pack (all finishers)
Daily check-in:
1οΈβ£ What you learned
2οΈβ£ Explain it in your own words
3οΈβ£ (Optional) Ask the group
Join now: https://chat.whatsapp.com/KZrAj2HZ3Y5K9UhhNhrApf
DM to register: https://wa.me/8619559123054