Low-code automation has grown up, and the competition is getting spicy. In this episode of Pop Goes the Stack, F5's Lori MacVittie and Joel Moses are joined by Aubrey King as they dig into the heavyweight duel between N8N and FlowFuse—two platforms promising to empower teams to automate anything without waiting for overworked developers. We cut through the marketing fluff and look at the real differences in architecture, deployment models, extensibility, security posture, and operational experience. How do they scale? Who controls your data? And what happens when the automation breaks at 2 a.m.? If you care about automation that doesn’t collapse under real-world pressure, you’ll want to hear this.

Read our F5 research for more on the status of automation in IT: https://www.f5.com/resources/reports/state-of-application-strategy-report

The capability to map brain activity to language isn’t just another UI shift—it’s a paradigm shift in how humans and machines might communicate. If you’re building systems that integrate or rely on neuroscience-adjacent tech (or even simply storing neuro-derived data), you’ll want to treat this as a strategic early warning: new input modalities, new risk surfaces, and new expectations of what “internal” means.

In this episode of Pop Goes the Stack, F5's Lori MacVittie and Joel Moses unpack emerging research on decoding neural activity into language—turning brain signals into natural-language output. They explore the promise for accessibility alongside major concerns: privacy, “intrusive thoughts,” and how systems decide which signals to surface. With a massive potential “blast radius” if connected to agentic systems, the research serves a stark reminder on the importance of evaluating AI breakthroughs for practicality and risk.

Read the original research, Mind captioning: Evolving descriptive text of mental content from human brain activity: https://www.science.org/doi/10.1126/sciadv.adw1464

Read the summary, "Mind-captioning" AI decodes brain activity to turn thoughts into text: https://www.nature.com/articles/d41586-025-03624-1

Traditional reliability meant consistency. Given identical inputs, systems produced identical outputs. Costs were stable and behavior predictable. Inference reliability on the other hand is shaped by nondeterminism. Outputs vary due to stochastic generation, retraining introduces drift, and token-based billing can cause cost fluctuations. The new dimension of reliability is semantic consistency, that is, the ability to deliver outputs of acceptable quality, accuracy, and predictability over time despite probabilistic behavior.

In this episode of Pop Goes the Stack, F5's Lori MacVittie and Joel Moses are joined by guests Ken Arora and Kunal Anand as they dive into the topic of reliability in AI systems. They explore the concept of 'slop' (AI variability) as a potential feature rather than a bug, discuss the importance of contextual semantic consistency, and weigh guardrails and evals tailored to specific inference workloads. Tune in to learn how to navigate the evolving AI landscape and take note of practical tools and strategies like multi-model chaining, distillation, and prompt engineering to ensure reliability.

Find out more in the blog How AI inference changes application delivery: https://www.f5.com/company/blog/how-ai-inference-changes-application-delivery