Mark Soper

"A good programmer is someone who always looks both ways before crossing a one-way street." — Doug Linder

Have there been studies that evaluate how good prediction markets are at predicting? @cwarzel theatlantic.com/technology/202…

We've been working with IoT systems for over a decade. One thing we've consistently seen is that these workloads generate a large amount of operational artifacts, and that data often dwarfs the system-of-record data. For example, a device might have a few dozen configuration fields. But once it's deployed, it starts producing a constant stream of sensor telemetry, events, and other signals. Over time, that telemetry can become 100x larger than the primary dataset. AI systems seem to be developing a similar shape. A single interaction might generate prompts, retrieved context, embeddings, model outputs, traces, and evaluation results. None of these are the core application data. When we talk about the "system of record," this isn’t it. Instead, this generated data represents operational artifacts of the AI system. Unlike traditional software logs or traces, these artifacts are often stored, analyzed, and fed back into the system. Over time, AI systems accumulate a large, append-heavy dataset describing how the system behaves. In that sense, AI systems start to resemble physical systems more than traditional software workloads: small amounts of primary state surrounded by a much larger stream of observations.

@simardeep.oberoi/prometheus-and-opentelemetry-finally-grew-up-together-fda5ef81d24a" target="_blank" rel="nofollow noopener">medium.com/@simardeep.obe…

A Guide to Which AI to Use in the Agentic Era, by @emollick open.substack.com/pub/oneusefult…

Building observable AI agents with Temporal braintrust.dev/blog/temporal-…

Telemetry log deduplication and using telemetrygen for testing #a-complete-pipeline-example" target="_blank" rel="nofollow noopener">opentelemetry.io/blog/2026/log-… github.com/open-telemetry…

VT monster is back after a year hiatus. Looking forward to it! vtmonster.com

"Siloed monitoring data across different tools or systems leads to fragmented visibility." opentelemetry.io/blog/2026/demy…

Observability Trends to Watch in 2026 - Optimized Onboarding youtube.com/shorts/V-NIrOw… OTel eBPF: github.com/open-telemetry…

"dbt-expectations’ statistical validation checks are great for catching anomalies where key metrics unexpectedly deviate from expected ranges. Distribution drift and outliers in metrics can signal upstream issues." #statistical-validation" target="_blank" rel="nofollow noopener">datadoghq.com/blog/dbt-data-…

Hello @X why does my timeline show "@marksoper hasn't posted"? - I joined Twitter almost 20 years ago - I pay for @X Premium, which is supposed to give me _improved_ exposure

@X why does my timeline show "@marksoper hasn't posted"? - I joined Twitter almost 20 years ago - I pay for Premium, which is supposed to give me _more_ exposure