Production observability for AI systems is broken.
We fixed it by moving below the application layer.
Why Traditional Observability Com...
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Good stuff!
Well✌
Appreciate that—this approach is very much aligned with the strengths of Linux as an open, extensible platform, and AI fits naturally into that ecosystem. I’m excited to see how it holds up in real-world usage and feedback from users like you.
Great question — in practice, eBPF programs are usually short-lived and event-driven, often running only as long as needed to capture specific signals, though some can remain loaded safely if they’re minimal and well-scoped. The kernel’s verifier, capability checks, and strict attachment points significantly limit abuse, so with proper design and lifecycle management, the security risk is tightly controlled.
Way over my head, but interesting - bookmarked it "just in case" ...
Totally fair 😄 — it’s definitely one of those “save for later” reads. Love the curiosity though; that mindset usually pays off sooner than expected.
It will only make sense once I'm getting "hands on" with this stuff ...
😎Good Luck!
Interesting read.
Your approach observes systems from below (kernel reality).
Ours explores decision structures before execution (invariants, traceability).
Different layers, same problem: making complex systems auditable.
Great perspective. Looking at the problem from different layers—runtime reality versus pre-execution guarantees—actually strengthens auditability, since invariants and traceability are ultimately validated by what the system does under real conditions.
This is a fantastic breakdown. Observing AI systems from the kernel instead of the app layer just makes sense — especially with GPU-heavy workloads. The eBPF + Rust combo feels like the right level of power and control. Really insightful post.
Thanks for sharing this — it’s a really sharp and thoughtful perspective. I especially like how you connected kernel-level visibility with real-world GPU workloads; the eBPF + Rust approach feels both powerful and well-judged.
Nice title.. but the article itself is just pure AI crap generated content without real use cases, logical reasoning or even properly made paragraphs and sentences. It's almost unreadable.
You didn't even removed AI comment made to you before posting:
Thanks for the honest feedback.
Thanks for your attention.