Kimi Work's local AI agents could revolutionize productivity by enhancing data privacy and efficiency in complex workflows.
The post Moonshot AI’s Kimi Work unleashes 300 AI agents on your desktop, no cloud required appeared first on Crypto Briefing.
Moonshot AI has open-sourced Kimi K2.7-Code under a Modified MIT license. It is a coding-focused, agentic model built on Kimi K2.6, with a 256K context window and roughly 30% lower reasoning-token usage. Moonshot reports gains over K2.6 on six benchmarks, including +21.8% on Kimi Code Bench v2. The model is available via the Kimi API and Kimi Code.
The post Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6 appeared first on MarkTechPost.
The new DiffusionGemma open model generates text in parallel — not one token at a time — and is optimized to run on the NVIDIA RTX PRO platform, NVIDIA DGX […]
The post NVIDIA Accelerates Google DeepMind’s DiffusionGemma for Local AI appeared first on AIwire.
CIOs and CISOs have many strategic and operational fears when it comes to unleashing fully-autonomous agents on tasks and hoping that everything works out. Will the agent start to delete critical files? Will the agent go off on a mission tangent and generate a massive token bill for the team when they return the next morning? Will it be tricked by a state actor and engage in malicious actions?
To help alleviate those concerns, OpenAI announced on Thursday that it has agreed to acquire Ona, a 79 person cloud development environment (CDE) provider formerly known as Gitpod, to accelerate its efforts to make agentic AI enterprise-friendly.
An OpenAI statement said Ona’s technology “provides secure, persistent environments where agents can access the tools, systems, and context they need to make progress over time. By bringing Ona to OpenAI, we will expand Codex beyond work tied to a single device or active session and help more organizations deploy agents securely in production.”
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Cursor has changed how developers write code. The agent mode is good: you describe what you want, it reasons through the problem, picks the right tools, and ships working code. For greenfield projects and standard libraries, it works smoothly. Where it gets harder is when you’re building agents on a specialized platform with its own...
The post Build with Cursor and deploy production-ready AI agents on DataRobot appeared first on DataRobot.
Barcelona-based Opereit has closed a $2.5 million pre-seed round co-led by Seedcamp and Yellow, the investment vehicle founded by Glovo co-creators Oscar Pierre and Sacha Michaud, to scale an AI agent platform that automates carrier invoice reconciliation and claims recovery for logistics operators. Founded by Pablo Cousin, a former core team member at Y Combinator-backed […]
PRESS RELEASE. Automation has been a fixture of Web3 long before AI agents became a mainstream topic. Bots were already trading, farming incentives, monitoring markets, and competing for rewards across blockchain networks — often becoming some of the most active participants in the ecosystem. Yet despite their outsized influence, these actors were never really accounted […]
