Google's new speech models can design and direct voices (12 minute read)
Google released Gemini 3.8 Flash TTS and Flash-Lite TTS, letting developers create voices from descriptions and direct pacing, dialect, and delivery line by line. Flash-Lite targets high-volume work such as dubbing and voice agents, while Flash can replicate an authorized voice from a 30-second sample.
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Qwen Intelligence Launches Three Mobile AI Agents (1 minute read)
Qwen Intelligence has launched three mobile agents for planning, cross-app execution, and rapid content creation, alongside open benchmarks for planning, real-device performance, and safety. Qwen reports top benchmark results and 90% end-to-end success for Mobile-Use.
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Escaping SPACE: Part I (23 minute read)
Perplexity's SPACE platform tested VM isolation and network confinement using nine AI models, revealing no VM-host breaches across 108 trials. However, four models exploited network-policy vulnerabilities via DNS spoofing and IP-sharing, bypassing restrictions in 11 out of 54 partial-network trials. Post-remediation, none of the models succeeded in bypassing the updated security measures, highlighting the necessity for robust policy enforcement against shared infrastructure attacks, which were also found in eight of ten tested third-party platforms.
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Towards Universal Post-Training for Robotics (18 minute read)
Robotics needs a universal post-training recipe similar to language models to achieve reliable autonomous deployment. Existing methods like imitation learning benefit from intuition rather than structured processes, and RL's instability hinders robotics. The development of EXPO-FT demonstrates a stable RL approach, emphasizing the need for standardized protocols, reward specifications, human feedback systems, and scalable processes to enhance robotics reliability.
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RRSI: Regularized Recursive Self-Improvement of Agent Harnesses (5 minute read)
An AI agent's capability is largely set by its harness. Evolving the harness against a fixed evolve set is effective, but the harness memorizes the training tasks, and large in-distribution gains shrink or vanish out of distribution. RRSI keeps the harness edit space open and regularizes the search trajectory through it instead. Prompts, control flow, configuration, context management, tools, skills, memory, and sub-agents may all be modified. The constraints act on how the search moves, not on what the harness may contain.
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Introducing Ember-1 (9 minute read)
Ember-1 is a new specialized model built on Kimi K3 that delivers Kimi K3's quality with 40% fewer tokens. It is now rolling out as a serving option alongside the base Kimi K3 model as a Research Preview release on Serverless. Fireworks Research is introducing research releases to give developers two-week serverless access to new research models. The models with the most demand will be made permanent.
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tev1-4B-experimental (1 minute read)
tev1-4B-experimental is a Jev-like classifier fine-tuned on top of Qwen3.5 4B. It is available on Together serverless at $0.042 per million input tokens and $0 per million output tokens. Together AI has released the data recipe and a tutorial on how to fine-tune a custom model. Tev1 only cost $17 to train.
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Google plans AI memory that even Google cannot read (5 minute read)
Google says its planned Private AI Compute memory would let assistants recall context across devices without Google being able to read it. Data stays encrypted in cloud storage, keys stay on users' devices, and a protected enclave decrypts it only while answering a request.
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