The "What vs. How" split often builds silos instead of bridges. By reframing our work as advocacies for the Why, the What, and the How, we can move beyond the "feature factory" and build products that deliver meaningful value to our customers and purpose to our teams.
Struggling with WIFIonICE on your Linux distro? Your secure DNS configuration might be blocking the login page. This guide provides a quick fix to get you online on Deutsche Bahn trains without compromising your privacy on other networks.
Indecisive leadership creates "Leadership Debt," a hidden liability that stalls projects, demotivates teams, and cripples innovation. Building psychological safety and contextual decision-making systems is key to high-performing engineering teams.
Radical Candor alone can backfire if trust and safety are missing. Build resilient engineering teams by focusing first on psychological safety and the TAPPs pillars: Trust, Autonomy, Purpose, and Psychological Safety. Move beyond buzzwords—become a cultural architect.
The painful choice between proprietary performance and open economics is over. The fabric schism has ended. As performance equalizes, the overwhelming TCO and strategic advantages of open Ethernet make it the only logical path forward for AI at scale.
How do you actually build a 100,000-GPU cluster? This blueprint outlines a practical reference architecture: a hybrid model using proprietary links for scale-up and open UEC Ethernet for the massive scale-out, all tied together by the DPU.
Bitnami is moving older container images from docker.io/bitnami to docker.io/bitnamilegacy in August 2025. Use Kubernetes containerd registry remapping via a DaemonSet to transparently redirect image pulls, ensuring smooth migration without immediate manifest changes.
The most expensive network isn't always the best. As open Ethernet closes the performance gap, the real math comes down to Performance-Adjusted TCO. When speed is equal, the lower cost of hardware and people becomes the deciding factor.
Enterprise AI is shifting from giant, costly models to efficient, specialized Small Language Models (SLMs) working in teams. This composable approach boosts accuracy, cuts costs, enhances privacy, and enables on-premise deployment—transforming AI into a competitive advantage for 2026 and beyond.
The battle for AI networking is a clash of philosophies. NVIDIA's integrated fortress offers turnkey performance at a proprietary cost. The Open Alliance bets on collaboration and open standards. Your choice is more than tech—it's a strategic vote.
Your multi-million dollar GPUs are wasting time on chores. The DPU is the third pillar of compute, a specialized processor that offloads infrastructure tasks, freeing up your CPU/GPU and enabling the entire business model of the secure AI cloud.
Your app looks fine on the surface, but users say it's failing. Traditional logs can't explain why. Enter OpenLLMetry and OpenInference—tools that bring clarity to AI observability by capturing rich, standardized telemetry from your LLM stack.