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Sentient OS launches on-device AI that reads user data at night

On October 28, 2026, Sentient OS released on-device AI that at night uses a local LLM to read the user's messages, files, and notes, and writes what it learns into a pure markdown folder on the computer, which the user can open, edit, or delete. On the same day, Testing Catalog reported that Sentient released the open-source AI model Sentient OS, which can gather context from local files and messages and execute tasks on the user's computer; its Sidekick feature supports handing off tasks such as forms or emails open on the screen for it to complete using its own cursor. The report also said that what it learns is stored locally as plain text files, which users can open and edit; the first 1,000 users who enter FINAL_1000 in the app can use it free for life. Another report from the same day mentioned that during the day, Double Tap and Sidekick read these files to draft replies and complete tasks in apps, and only heavier tasks call the user-selected cloud model. There have been no further developments so far.

Sentient OS launches on-device AI that reads user data at night

On October 28, 2026, Sentient OS released on-device AI that at night uses a local LLM to read the user's messages, files, and notes, and writes what it learns into a pure markdown folder on the computer, which the user can open, edit, or delete. On the same day, Testing Catalog reported that Sentient released the open-source AI model Sentient OS, which can gather context from local files and messages and execute tasks on the user's computer; its Sidekick feature supports handing off tasks such as forms or emails open on the screen for it to complete using its own cursor. The report also said that what it learns is stored locally as plain text files, which users can open and edit; the first 1,000 users who enter FINAL_1000 in the app can use it free for life. Another report from the same day mentioned that during the day, Double Tap and Sidekick read these files to draft replies and complete tasks in apps, and only heavier tasks call the user-selected cloud model. There have been no further developments so far.

View story:Sentient OS launches on-device AI that reads user data at night

NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

NVIDIA releases the on-device small model Nemotron-3.5-Bolt-E4B. A report on October 23, 2026 states that the model can run locally on devices with 6GB of memory, targeting on-device devices such as phones; it is trained based on Gemma 4, supports vision, and Unsloth simultaneously provides the GGUF file and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools. A follow-up report on October 24 states that the model has 8B total parameters, 4B effective parameters, and a 64K context, and is post-trained using only reinforcement learning based on Google Gemma 4 E4B; the Q4_K_M version occupies 3.1 to 4.3 GB of VRAM on RTX and can run on GeForce RTX, Jetson, and DGX Spark. The report also states that in NVIDIA's own testing, it leads Gemma 4 E4B, Qwen3.5-4B, and Nemotron 3 Nano 4B in 15 of 21 benchmarks, but Tau2-Bench is 55.1, trailing Qwen3.5-4B's 75.1.

NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

NVIDIA releases the on-device small model Nemotron-3.5-Bolt-E4B. A report on October 23, 2026 states that the model can run locally on devices with 6GB of memory, targeting on-device devices such as phones; it is trained based on Gemma 4, supports vision, and Unsloth simultaneously provides the GGUF file and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools. A follow-up report on October 24 states that the model has 8B total parameters, 4B effective parameters, and a 64K context, and is post-trained using only reinforcement learning based on Google Gemma 4 E4B; the Q4_K_M version occupies 3.1 to 4.3 GB of VRAM on RTX and can run on GeForce RTX, Jetson, and DGX Spark. The report also states that in NVIDIA's own testing, it leads Gemma 4 E4B, Qwen3.5-4B, and Nemotron 3 Nano 4B in 15 of 21 benchmarks, but Tau2-Bench is 55.1, trailing Qwen3.5-4B's 75.1.

View story:NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

Meta distills a 30B on-device agent model, Muse Glimmer

Meta distilled the dense 30B model Muse Glimmer from the larger Muse Spark, training it on synthetic agent trajectories in simulated applications. Its 4-bit weights are under 20 GB, allowing it to run on a single RTX 5090. According to a report dated October 22, 2026, on the multi-tool benchmark MCPAtlas, Muse Glimmer scored 75.5, higher than Gemma4-31B's 54.2 and Qwen3.6-27B's 62.5; however, on OSWorld-Verified it lagged behind Qwen3.6-27B's 75.6 with a score of 65.9. There are currently no subsequent developments.

CAICT and the AIIA On-Device Intelligence Working Group Jointly Release the On-Device Intelligence Industry Development Report

On October 14, 2026, the Artificial Intelligence Research Institute of the China Academy of Information and Communications Technology (CAICT), together with the On-Device Intelligence Working Group of the Artificial Intelligence Industry Alliance (AIIA), and Huawei, ZTE, vivo, Haier, StepFun, ModelBest, Dahua Technology and other organizations released the "On-Device Intelligence Industry Development Report (2026)." The report defines on-device intelligence as a primarily local, device-cloud collaborative form of integrated intelligence, focusing on key technologies such as model lightweighting, heterogeneous hardware integration, foundational software, agents, device-cloud collaboration, and security and trustworthiness. The report also outlines native product forms such as personal intelligence, spatial intelligence, mobile intelligence, industry intelligence nodes, and embodied intelligence, as well as application scenarios including consumer entertainment, smart homes, smart offices, smart mobility, and smart health. The report has now been publicly released, and no further developments have been disclosed.

Meta distills a 30B on-device agent model, Muse Glimmer

Meta distilled the dense 30B model Muse Glimmer from the larger Muse Spark, training it on synthetic agent trajectories in simulated applications. Its 4-bit weights are under 20 GB, allowing it to run on a single RTX 5090. According to a report dated October 22, 2026, on the multi-tool benchmark MCPAtlas, Muse Glimmer scored 75.5, higher than Gemma4-31B's 54.2 and Qwen3.6-27B's 62.5; however, on OSWorld-Verified it lagged behind Qwen3.6-27B's 75.6 with a score of 65.9. There are currently no subsequent developments.

View story:Meta distills a 30B on-device agent model, Muse Glimmer

CAICT and the AIIA On-Device Intelligence Group jointly release the On-Device Intelligence Industry Development Report

On October 14, 2026, the Artificial Intelligence Research Institute of the China Academy of Information and Communications Technology (CAICT), together with the On-Device Intelligence Working Group of the Artificial Intelligence Industry Alliance (AIIA), and Huawei, ZTE, vivo, Haier, StepFun, ModelBest, Dahua Technology and other organizations released the "On-Device Intelligence Industry Development Report (2026)." The report defines on-device intelligence as a primarily local, device-cloud collaborative form of integrated intelligence, focusing on key technologies such as model lightweighting, heterogeneous hardware integration, foundational software, agents, device-cloud collaboration, and security and trustworthiness. The report also outlines native product forms such as personal intelligence, spatial intelligence, mobile intelligence, industry intelligence nodes, and embodied intelligence, as well as application scenarios including consumer entertainment, smart homes, smart offices, smart mobility, and smart health. The report has now been publicly released, and no further developments have been disclosed.

View story:CAICT and the AIIA On-Device Intelligence Working Group Jointly Release the On-Device Intelligence Industry Development Report