agent

5 articles tagged with agent

June 2, 2026
model release

H Company Ships Holo3.1 with Local Inference, Mobile Support, and 79.3% AndroidWorld Score

H Company released Holo3.1, a computer-use agent model family ranging from 0.8B to 35B parameters. The 35B-A3B variant scores 79.3% on AndroidWorld, up from 67% in Holo3. For the first time, H Company ships quantized checkpoints (FP8, Q4 GGUF, NVFP4) enabling local inference with 1.74× throughput gains and sub-4-second agent step times.

May 20, 2026
product update

Google Announces Gemini Spark Agent and Antigravity Platform at I/O, Launch Date Not Disclosed

Google announced Gemini Spark at I/O 2026, positioning it as a competitor to OpenAI's Claude-based agents. The service will integrate with Gmail, Calendar, Drive, and other Google apps, running on Gemini 3.5 Flash and a new platform called Antigravity. No general availability date has been disclosed.

April 15, 2026
product update

HCompany Launches HoloTab Chrome Extension for Browser Automation via Computer-Use AI

HCompany has released HoloTab, a Chrome extension that uses its Holo3 computer-use model to automate browser tasks. The free tool requires zero technical setup and includes a routine recording feature for repetitive multi-step workflows.

April 8, 2026
model releaseArcee Ai

Arcee AI releases Trinity-Large-Thinking: 398B sparse MoE model with chain-of-thought reasoning

Arcee AI released Trinity-Large-Thinking, a 398B-parameter sparse Mixture-of-Experts model with approximately 13B active parameters per token, post-trained with extended chain-of-thought reasoning for agentic workflows. The model achieves 94.7% on τ²-Bench, 91.9% on PinchBench, and 98.2% on LiveCodeBench, generating explicit reasoning traces in <think>...</think> blocks before producing responses.

March 27, 2026
model release

Chroma releases Context-1, a 20B parameter retrieval agent for complex multi-hop search

Chroma has released Context-1, a 20B parameter Mixture of Experts model trained specifically for retrieval tasks that require multi-hop reasoning. The model decomposes complex queries into subqueries, performs parallel tool calls, and actively prunes its own context mid-search—achieving comparable performance to frontier models at a fraction of the cost and up to 10x faster inference speed.