Zero data retention in AI: what it really means and how enterprises can enforce it with Regolo
A direct comparison between true zero-retention inference and softer provider retention policies that still leave teams exposed.
Stories, experiments, research and deep‑dives into the world of artificial intelligence
A direct comparison between true zero-retention inference and softer provider retention policies that still leave teams exposed.
What does “multimodal” really mean? Most AI models are built for a single kind of input. A text model reads words, a vision model…
How to migrate an existing Flowise chatflow to an EU-hosted OpenAI-compatible backend without rebuilding the graph.
For most companies, ZAYA1-8B is the better open-weight choice when coding, reasoning efficiency, and serving cost matter more than raw scale, while DeepSeek-R1-0528 is…
Which open model families still make sense when a deployment really scales to zero and cold starts start hurting product experience.
Why EU data residency solves only part of the enterprise AI review and what teams still need to verify after geography.
Zyphra made ZAYA1-8B strong not by making it huge, but by making it efficient at every layer of the stack. The short version is…
The simplest way to connect Regolo Core Models API to Botpress is to use Execute Code Card and call Regolo’s OpenAI-compatible API endpoints to…
Both MiniMax M2.7 and Kimi K2.5 are open-weight Mixture-of-Experts models released in early 2026 that punch well above their cost class. They are not…
At AI Salon Rome, the conversation was not about AI in the abstract, It was about what it actually takes to build systems that…
A practical guide to choosing between Qwen, Mistral, and Gemma based on workload, cost, serving profile, and model family tradeoffs.
Keep inference inside EU jurisdiction, minimize retention, and design the stack so prompts, outputs, and tool payloads do not persist unless they truly must.…