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Once a physical AI product is in the field, the compute problem changes shape. Fleet data starts arriving faster than a team can label it, every retraining cycle has to be validated before it ships to hardware, and the sim-to-real loop stops being a research exercise and becomes a release process. That is the stage […]
Google Cloud AI Research, with UNC-Chapel Hill, Stanford and Washington University in St. Louis, has released RRSI (Regularized Recursive Self-Improvement). It lets an LLM agent rewrite its own harness: prompts, tools, memory, control flow and sub-agents. Model weights never change. RRSI constrains the improvement loop itself, so gains hold on benchmarks the agent never optimized
H Company has released Holo4, a family of generalist computer-use models for AI agents. One set of weights clicks and types on screens. It also writes code and calls MCP or API tools. Holo4 ships in 2 sizes: Holo4 27B (dense) and Holo4 35B-A3B (Mixture of Experts, 3B active). Both serve a 256K context on
Alibaba’s Qwen team has released Qwen-Audio-3.1, a 5-model audio stack spanning ASR, TTS and realtime interaction. The main model is Qwen-Audio-3.1-Realtime, a full-duplex speech model built for voice agents that call tools. Qwen also cut prices: about 85% on Realtime, about 70% on TTS and up to 95% on ASR. Is it deployable? Yes, as
Anthropic just released Claude Sonnet 5.5. It is the second model in the Claude 5.5 family, following Claude Opus 5.5. Anthropic positions it as a faster, lower-cost complement to Opus 5.5. It targets well-scoped everyday tasks, bug fixing, and polished documents, slides, and spreadsheets. Is it deployable? Yes, It is live on the Claude Platform
NVIDIA has launched the NVIDIA Open Agent Safety Platform, an open software platform and reference system design for AI agent security. It pairs the OpenShell secure runtime with NVIDIA Sentry, an out-of-band watchdog on BlueField-4 DPUs. The core idea is simple. Safety controls should not live inside the agent they are meant to control. Today,
Fireworks AI has released Ember-1, a specialized model from Fireworks Research built by post-training Moonshot AI’s open-weight Kimi K3. Ember-1 learns to produce shorter reasoning traces while keeping task accuracy. This is different from lowering the reasoning effort setting at inference time. According to the Fireworks release post, Ember-1 delivers Kimi K3’s quality with about
Fireworks AI Releases Ember-1: A Post-Trained Kimi K3 That Uses About 40% Fewer Tokens Read More »
Last week, TypeSafe AI released Jev, its first System One model. Founder Diogo Almeida previously worked at OpenAI on the instruction-following research behind ChatGPT. Jev does not chat, write code or summarize. It takes unstructured state and returns typed decisions with calibrated probabilities. That makes it a natural fit for the thousands of small judgments
Google Research has introduced an AI video co-director for long-form video generation. The suite of 4 agentic frameworks turns short clips into coherent, minutes-long stories. It targets identity drift and cascading errors, the 2 failures that break most multi-shot AI video pipelines today. Why Long AI Videos Fall Apart Diffusion models render high-fidelity clips in
Sarvam AI has released Saaras V4, the newest generation of its speech recognition model. It covers all 22 scheduled Indian languages plus English, now including global English accents. Sarvam reports state-of-the-art accuracy across all 22 languages. Is it deployable? Yes, through Sarvam’s API today, using model=”saaras:v4″. Weights are not public, and Sarvam’s SageMaker self-hosting docs
