Adobe launched Firefly Graph in Creative Cloud, a visual tool that links AI models and editing steps into customizable, reusable workflows. It offers over 300 node types spanning Adobe apps and third-party tools, letting teams share and replicate creative processes. Enterprise customers get immediate access, while Creative Cloud for Teams can join a public beta.

• Firefly Graph lets teams turn a node-based workflow (300+ node types across Adobe and third-party AI models) into a shareable, reusable asset instead of a one-off process
• It captures an expert's exact decision path so others can replicate results without knowing the original settings or model choices
• It's bundled directly into Creative Cloud (alongside Photoshop, Premiere Pro, Firefly Boards, Creative Production) rather than requiring separate API integration
• Enterprise plans get immediate access with included Graph credits, while Teams users can only join a public beta

This article rounds up recent announcements in enterprise storage and data management, from Cohesity’s new AI patent and Confluent’s data streaming report to integrations by CTERA and product launches from Datadog, d-Matrix, Graid, Hazelcast, Hitachi Vantara, HPE, Keepit, Kioxia and Lightbits Labs. It covers AI platforms, workflow automation, RAID and cooling hardware, flexible consumption models, and survey findings on data growth and governance.

• Cohesity patented a RAG layer that pulls directly from backup data, signaling AI search/synthesis baked into secondary storage.
• Confluent survey: 59% of execs still trust gut feel and 71% think leadership works off stale data, despite 61% betting real-time streaming becomes critical within a year.
• d-Matrix's Corsair accelerators (now in full production) claim up to 10x faster token generation when paired with GPUs, aimed at cutting hyperscaler latency and energy costs.
• Financial-sector study: 35% flag data growth as their top storage worry, but only 10% are buying AI-ready platforms and 9% are building central data hubs—a big readiness gap.

The author argues that modular “Skills”—reusable markdown workflows loaded on demand—outperform standalone AI agents by cutting token bloat and maintenance overhead. A live GEO audit system built with Skills shows how you can turn domain expertise into scalable, service-ready products without managing dozens of agents.

• Claude's "Skills" load modular markdown playbooks on demand instead of baking everything into prompts, citing 53 tokens for passive reference vs. embedding a full prompt every time
• A live GEO audit system built entirely on Skills scrapes visibility across ChatGPT/Gemini, flags gaps like missing Wikipedia entries, and auto-generates client-ready reports without spinning up separate agents
• The whole GEO pipeline is public and forkable, letting anyone productize it without building custom infrastructure
• Documenting expertise once in a markdown file and iterating on it lets teams ship service-ready AI products in days rather than maintaining fleets of bespoke agents