Liquid AI releases the LFM3 model family, with the first batch of multiple models based on LFM3-1.7B-Base, among which LFM-1.7B has a 256K context window, outperforming the previous generation LFM2.5-2.6B while reducing parameters by 35%
Ollama releasesLiquid AI releases the LFM3 small model family, including the 1.7B parameter lfm3 text model (256K token context), lfm3-vl supporting image understanding, lfm3-audio with 18 language speech processing capabilities, and the general model lfm3-omni for local agents and structured output; the d1 decision model is upgraded to 2B parameters, supporting text, image, and audio decision-making, with some models supporting Apple hardware.
Hugging Face BlogFeatured7676Liquid AI releases the new generation Liquid Foundation Models, LFM3, deriving six 2B-level models from the shared backbone LFM3-1.7B-Base, covering text, vision, audio, multimodal, and decision, all open weights on Hugging Face.
Why it matters: Liquid AI derives six modality models from the same 2B-level backbone and provides single-card fine-tuning and edge deployment paths, which can be used to judge the specialization route for small models.
Ollama releasesLiquid AI has released the LFM3 small model family, including the 1.7B parameter text model lfm3 and variants supporting image and audio functions, with a context window of up to 256K tokens; the d1 decision model has been upgraded to 2B parameters, supporting text, image, and audio inference, with Apple hardware already supporting the rc1 version.
Liquid AI BlogSame story · 2 outlets3838How the accounts differ:Liquid AI Blog mentions a compression rate of 1.1 kbps and speed improvements of 3.5x and 8x, while MarkTechPost does not mention these figures.
The Nemotron 3.5 Transcribe open-weight speech model now supports RTX Spark, enabling speech transcription, speaker recognition, and translation, supporting 40 languages. The Liquid AI LFM3 series models have also been supported and can be run locally and fine-tuned. The RTX Spark laptop starts at $2599, with the highest configuration of 128GB unified memory, and FP4 AI computing power up to 1 petaflop.
MarkTechPostSame story · 2 outletsLiquid AI has released the LiquidCodec audio codec, supporting 24 kHz audio, converting speech to tokens predictable by language models, and then converting back to audio, with approximately 74M parameters.
How the accounts differ:Liquid AI Blog mentions a compression rate of 1.1 kbps and speed improvements of 3.5x and 8x, while MarkTechPost does not mention these figures.
Latent SpaceClaude Code can directly read and edit the weights of other models without human intervention or PyTorch/Jax code, achieving weight reading, single-time bug fixes, performance improvements, or writing network weights from scratch. This technology bypasses the traditional process of Autoresearch by running experiments in a cluster to train models, demonstrating the potential of LLMs in weight space programming, currently mainly applicable to simple games and RL policies.
NVIDIA NewsroomSame story · 2 outlets5858NVIDIA RTX Spark laptop with new models, featuring Blackwell RTX GPU and Grace CPU Super Chip, supporting up to 128GB unified memory and 1 petaflop FP4 AI performance.
How the accounts differ:NVIDIA Technical Blog states the platform is for 'AI development, creation, and gaming', while NVIDIA Newsroom specifies 'new models' and 'game support'.
Latent SpaceSame story · 3 outlets6565Anthropic has stopped traditional coding interviews for managers, believing Claude has performed better in React and assembly language tests, and points out that the most successful builders do not depend on these test performances. It suggests that junior engineers should prioritize business impact rather than backlog tasks, propose radical ideas, and build prototypes over the weekend, focusing on a good idea and leveraging fresh perspectives.
How the accounts differ:Latent Space states Anthropic stopped coding interviews because Claude performed better in React and assembly language tests, while The Decoder focuses on AI's ability to handle programming tasks better than candidates.
