GoKawiil
Tech News
An IEEE Computer Society post argues that leaders should borrow the observability practices engineers use for software systems—continuous monitoring via traces, metrics, and logs—to track team and organizational health. Rather than waiting for a crisis or 'incident' to assess problems, the piece suggests leaders instrument their teams early and treat issues as learning opportunities.
YC S24 startup Laminar has released flow-1, a small open-source model fine-tuned and reinforcement-learned to analyze AI agent traces for hidden errors that don't surface as crashes or failed calls. The company says flow-1 matches the detection quality of GPT-6-sol on its own benchmark while costing 23 times less to run, making full-trace analysis of production agents economically feasible. The model operates within a harness called Signals, treating each agent trace like a codebase to inspect span by span.
A technical blog post, with input from New Relic's Dan Gomez Blanco, lays out methods for designing self-hosted or SaaS products so users can export logs, traces, and metrics to any OpenTelemetry-compatible observability backend. It covers the three established OTel signal types—logs, traces, and metrics—carried via the OpenTelemetry Protocol (OTLP), and notes that a fourth signal, profiles, entered public alpha on March 26, 2026.
Parseable has announced its open observability data lake capable of handling up to 100 million time-series data points every minute. The platform leverages large language models and traditional machine learning to analyze telemetry data, detect anomalies, and identify root causes in real-time. Recently, it identified a spike in error rates caused by exhausted connection pools in a checkout service, affecting over 6,400 users for more than seven minutes.