Full archive

Models AI News

Search reporting on AI markets, models, products, companies, policy, and culture.

Showing 1-4 of 4Results matching "Agent Builder"

Last week

1 story

Sep 1, 2026

Phonely Opens Alma, a Voice Model Trained on 10 Million Calls, to Outside Builders

The new external offering turns Phonely’s own call traffic into a model-development pitch, combining voice-specific training with company-reported speed and pricing advantages. Whether those results transfer to other companies’ call flows remains the practical test. Phonely has launched Alma, a language model built for voice agents and trained on more than 10 million phone conversations.

2 min readRead story

August 2026

3 stories

Launch Models

Aug 26, 2026

China Media Group Launches AI Media System Push With Telecom, Cloud and Chip Partners

The effort links a broadcaster’s production workflow to telecom networks, cloud computing, chips and security systems, but leaves the eventual model and deployment timetable undefined. China Media Group has launched two co-building initiatives intended to create a media-specific AI system: one organized around ecosystem infrastructure and another around a large model for audiovisual media.

2 min readRead story

Launch Models

Aug 25, 2026

DeepSeek V4 Pro Lands on Fireworks With $2.50 CyberGym Solves, but Kimi K3 Scores Higher

The provider’s results frame security-agent selection as a three-way choice among raw solve rate, cost per completed task, and whether a model will reliably execute the required tool workflow. Fireworks AI has made DeepSeek-V4-Pro-0813 available on its platform, pairing the release with security-benchmark results that favor the model on cost and tool reliability rather than outright solve rate.

3 min readRead story

Open release Models

Aug 22, 2026

Meta’s 30B Muse Glimmer Tries to Make 131K Context Fit in 24 GB

Quantized weights and a narrow attention cache make Meta’s local-hardware claim plausible. But fitting a 131,072-token prompt is different from processing one quickly, and runtime implementation will decide whether the memory savings appear in practice. The design puts the main constraint of long-running agents in focus: not just model weights, but the memory retained for every prior token.