The Vercel developer account announced that Reflection's Beam model is free for a limited time on Vercel AI Gateway. It previously ran incognito under the codename stealth/cat-jam; it is a low-latency US open-weight model, and its call name is reflection/beam-free. Guillermo Rauch relayed that the model can be connected to deployable eve agents, used to start coding with fx, or used to build your own harness.
Madhu Shashanka, co-founder of Concentric AI, points out that as coding agents rapidly become more reliable, enterprises will deploy large numbers of agents, and the real challenge will shift from the reliability of individual agents to consistency across the swarm. He cites the example of 10 agents, each with 95% accuracy, chained together, where overall accuracy drops to about 60%, to show that reliability does not compound. He proposes that organizations need a continuous self-awareness mechanism akin to proprioception; otherwise, they can only barely keep things going through manual auditing and periodic reviews.
Madhu Shashanka, co-founder of Concentric AI, points out that as coding agents rapidly become more reliable, enterprises will deploy large numbers of agents, and the real challenge will shift from the reliability of individual agents to consistency across the swarm. He cites the example of 10 agents, each with 95% accuracy, chained together, where overall accuracy drops to about 60%, to show that reliability does not compound. He proposes that organizations need a continuous self-awareness mecha
Meta has released the open-weight model Muse Spark Open and worked with Apple's hardware team to enable it to run efficiently on the M5 Mac Studio Ultra. The model has 42 billion active parameters, a 1 million token context window, and multimodal capabilities. Meta says it performs well on agentic and coding tasks, and the weights have been made public on HuggingFace under Meta's Muse Model license. Running the model on an Apple computer requires two Mac Studios equipped with the M5 Ultra, each starting at $5,499.
Meta has released the open-weight model Muse Spark Open and worked with Apple's hardware team to enable it to run efficiently on the M5 Mac Studio Ultra. The model has 42 billion active parameters, a 1 million token context window, and multimodal capabilities. Meta says it performs well on agentic and coding tasks, and the weights have been made public on HuggingFace under Meta's Muse Model license. Running the model on an Apple computer requires two Mac Studios equipped with the M5 Ultra, each
Meta released the open-weight model Muse Spark Open on Thursday, saying its capabilities can match and in some scenarios surpass comparable products from Chinese vendors. Meta AI head Alexandr Wang called it the most capable U.S. open-weight model to date; the model targets coding and agentic tasks, excels at long-horizon tasks, complex software engineering and multi-step reasoning, and has built-in safety safeguards. The model is optimized for the Apple M5 Ultra chip, and users can run it on the desktop with two Mac Studios equipped with that chip.
Meta released the open-weight model Muse Spark Open on Thursday, saying its capabilities can match and in some scenarios surpass comparable products from Chinese vendors. Meta AI head Alexandr Wang called it the most capable U.S. open-weight model to date; the model targets coding and agentic tasks, excels at long-horizon tasks, complex software engineering and multi-step reasoning, and has built-in safety safeguards. The model is optimized for the Apple M5 Ultra chip, and users can run it on th
Reflection's first open-weight model, Beam, is now available on the Baseten platform, with day-0 support through Baseten Model APIs and Dedicated Inference. Beam uses a MoE architecture with 501B total parameters and 23B activated per token, a context window of up to 1 million tokens, and is aimed at coding and agent workflows. During training, Reflection used longer RL rollouts and added a length penalty to encourage the model to complete tasks with fewer tokens; users can also adjust reasoning effort via a parameter to control how much reasoning the model invests.
Reflection's first open-weight model, Beam, is now available on the Baseten platform, with day-0 support through Baseten Model APIs and Dedicated Inference. Beam uses a MoE architecture with 501B total parameters and 23B activated per token, a context window of up to 1 million tokens, and is aimed at coding and agent workflows. During training, Reflection used longer RL rollouts and added a length penalty to encourage the model to complete tasks with fewer tokens; users can also adjust reasoning
