Introducing sign-language-to-text (SL2T), our breakthrough model powering new sign language features for Deaf and hard of hearing users.
Google DeepMind has introduced a sign-language-to-text translation model that converts sign language into written text, making it available in consumer products for the first time. The technology enables Deaf and hard of hearing users to sign on their phones in place of typing, similar to how hearing users can dictate spoken words instead of typing. Sign language AI has progressed slowly because it requires true machine translation between independent languages with distinct grammars and lexicons, rather than simple sound-to-text conversion, and because it demands computer vision systems that can accurately track simultaneous movements of hands, arms, torso, head, and face. This advancement matters because an estimated 70 million Deaf and hard of hearing people use over 200 sign languages worldwide, yet they have been excluded from the technological revolution that has enabled automatic translation and conversational interfaces for hearing users.

In this tutorial, we implement an end-to-end supervised fine-tuning pipeline for the XYZ-Aquila-SFT dataset, Hugging Face Transformers, PyTorch, and PEFT. We stream and inspect the dataset, parse multi-turn tool-use trajectories, extract structured tool calls, analyze corpus characteristics, and preserve embedded reasoning and observation patterns. We then convert tool schemas between message-embedded and structured formats, render Qwen-compatible ChatML with assistant-only loss masking, prepar
Meta released Glimmer this week, an open-weight AI model anyone can download and run on their own hardware — a contrast to Muse Spark, the company’s more powerful model that stays locked behind its own APIs. The release landed alongside a letter from Mark Zuckerberg arguing AI should be “for everyone” rather than controlled by a handful of labs, but as Equity’s […]

Z.ai just released GLM-5.3. GLM-5.3 runs on the same 743B base model as GLM-5.2. Every reported gain comes from scaled post-training: more task environments, more environment types, longer training. The results land in two places. Coding jumps most on the longest-horizon benchmarks, with Terminal-Bench 3.0 moving from 4.6 to 28.3. Cybersecurity moved further than Z.ai says it expected, with CyberGym reaching 84.5%. Weights are not public yet. Is It Deployable? Partially, GLM-5.3 is live
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