Building a model that fits on a phone or laptop makes pre-training harder. With fewer parameters, quality has to come from better architecture and better data, not just more compute. Our pre-training team, including @TadimetiAditya and @Khoi_H_Nguyen, explains how they design LFMs with the target device in mind from day one.

I wanted to see how well decision models are at... Finding Waldo. After 66 scenes, here are the results: - @liquidai d1-ultra found the most Waldos - @typesafeai Jev 1.14 was the quickest -@perplexity_ai Decider Flash V1.2 was the cheapest

Liquid's d1-3B picks a skill like walk, turn, pick, sit. A trained policy does the move on Microduck. 157 ms, on an Intel Arc b580 Decision models can also be used for robotics* @liquidai @pollenrobotics

I added @liquidai’s d1-3B decision model to my hacked TV so I can talk to it Inspired by @doooyle, I hacked my TV with Claude Code and cleaned up all the bloatware. But I realized I hate using the TV OS. I wanted my TV to be content-first, not apps-first. So I wired a decision model in between that opens the right app and sends the right ADB commands based on my intent.

Liquid AI just released a model that claims the top spot on a dedicated Decision Index and leads all eight categories in their own 112-evaluation suite. Most AI releases are still judged on general benchmarks (coding, math, knowledge). Decision models are optimized for something narrower: choosing among fixed options under constraints, often with multimodal inputs. That is closer to how a lot of enterprise work actually happens (approvals, prioritization, routing, risk scoring) than open-ended chat. If the benchmark results hold up outside their suite, this is another signal that the market is splitting. General models keep getting more capable. Specialized models are starting to win on the narrower jobs where companies are willing to pay for reliability over flexibility. The companies that figure out which workflows belong in each bucket will get better unit economics than the ones still forcing everything through a single frontier model.

#Liquid# has released d1-ultra, a new multimodal (text, images, or both) decision model with state-of-the-art performance. It can answer questions about a situation and assign a probability to each possible answer for unstructured input data:

I've built an incredible todo system with @liquidai D1-3B for action routing (e.g. create new or edit existing) LFM-2.5-24B-A2B for synthesizing transcription blobs into titles, descriptions, and deadlines LFM-2.5-Embedding-350M for semantic lookups Local AI at its best.