Meet SETA: Open Source Training Reinforcement Learning Environments for Terminal Agents with 400 Tasks and CAMEL ToolkitMarkTechPost What does an end to end stack for terminal agents look like when you combine structured toolkits, synthetic RL environments, and benchmark aligned evaluation? A team of researchers from CAMEL AI, Eigent AI and other collaborators have released SETA, a toolkit and environment stack that focuses on reinforcement learning for terminal agents. The project targets
The post Meet SETA: Open Source Training Reinforcement Learning Environments for Terminal Agents with 400 Tasks and CAMEL Toolkit appeared first on MarkTechPost.
What does an end to end stack for terminal agents look like when you combine structured toolkits, synthetic RL environments, and benchmark aligned evaluation? A team of researchers from CAMEL AI, Eigent AI and other collaborators have released SETA, a toolkit and environment stack that focuses on reinforcement learning for terminal agents. The project targets
The post Meet SETA: Open Source Training Reinforcement Learning Environments for Terminal Agents with 400 Tasks and CAMEL Toolkit appeared first on MarkTechPost. Read More
Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car ExampleTowards Data Science Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous vehicle car safety agent running on OpenAI’s GPT 5.2
The post Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example appeared first on Towards Data Science.
Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous vehicle car safety agent running on OpenAI’s GPT 5.2
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Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI Towards Data Science
Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BITowards Data Science A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessons learned along the way.
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A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessons learned along the way.
The post Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI appeared first on Towards Data Science. Read More
How to Leverage Slash Commands to Code EffectivelyTowards Data Science Learn how I utilize slash commands to be a more efficient engineer
The post How to Leverage Slash Commands to Code Effectively appeared first on Towards Data Science.
Learn how I utilize slash commands to be a more efficient engineer
The post How to Leverage Slash Commands to Code Effectively appeared first on Towards Data Science. Read More
Federated Learning, Part 1: The Basics of Training Models Where the Data LivesTowards Data Science Understanding the foundations of federated learning
The post Federated Learning, Part 1: The Basics of Training Models Where the Data Lives appeared first on Towards Data Science.
Understanding the foundations of federated learning
The post Federated Learning, Part 1: The Basics of Training Models Where the Data Lives appeared first on Towards Data Science. Read More
Crossmodal search with Amazon Nova Multimodal EmbeddingsArtificial Intelligence In this post, we explore how Amazon Nova Multimodal Embeddings addresses the challenges of crossmodal search through a practical ecommerce use case. We examine the technical limitations of traditional approaches and demonstrate how Amazon Nova Multimodal Embeddings enables retrieval across text, images, and other modalities. You learn how to implement a crossmodal search system by generating embeddings, handling queries, and measuring performance. We provide working code examples and share how to add these capabilities to your applications.
In this post, we explore how Amazon Nova Multimodal Embeddings addresses the challenges of crossmodal search through a practical ecommerce use case. We examine the technical limitations of traditional approaches and demonstrate how Amazon Nova Multimodal Embeddings enables retrieval across text, images, and other modalities. You learn how to implement a crossmodal search system by generating embeddings, handling queries, and measuring performance. We provide working code examples and share how to add these capabilities to your applications. Read More
Accelerating LLM inference with post-training weight and activation using AWQ and GPTQ on Amazon SageMaker AIArtificial Intelligence Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code. In this post, we explore why quantization matters—how it enables lower-cost inference, supports deployment on resource-constrained hardware, and reduces both the financial and environmental impact of modern LLMs, while preserving most of their original performance. We also take a deep dive into the principles behind PTQ and demonstrate how to quantize the model of your choice and deploy it on Amazon SageMaker.
Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code. In this post, we explore why quantization matters—how it enables lower-cost inference, supports deployment on resource-constrained hardware, and reduces both the financial and environmental impact of modern LLMs, while preserving most of their original performance. We also take a deep dive into the principles behind PTQ and demonstrate how to quantize the model of your choice and deploy it on Amazon SageMaker. Read More
Datadog: How AI code reviews slash incident riskAI News Integrating AI into code review workflows allows engineering leaders to detect systemic risks that often evade human detection at scale. For engineering leaders managing distributed systems, the trade-off between deployment speed and operational stability often defines the success of their platform. Datadog, a company responsible for the observability of complex infrastructures worldwide, operates under intense
The post Datadog: How AI code reviews slash incident risk appeared first on AI News.
Integrating AI into code review workflows allows engineering leaders to detect systemic risks that often evade human detection at scale. For engineering leaders managing distributed systems, the trade-off between deployment speed and operational stability often defines the success of their platform. Datadog, a company responsible for the observability of complex infrastructures worldwide, operates under intense
The post Datadog: How AI code reviews slash incident risk appeared first on AI News. Read More
How LLMs Handle Infinite Context With Finite MemoryTowards Data Science Achieving infinite context with 114× less memory
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Achieving infinite context with 114× less memory
The post How LLMs Handle Infinite Context With Finite Memory appeared first on Towards Data Science. Read More
Architecting TrueLook’s AI-powered construction safety system on Amazon SageMaker AIArtificial Intelligence This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference.
This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference. Read More