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ContinueCreated by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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InfoQ Reading List
Grafana Tempo 3.1 Adds Query-Based Trace Redaction and Sampling-Aware TraceQL Metrics
Grafana Labs has released Tempo 3.1, the first minor update to the 3.0 architecture. It adds TLS and additional SASL mechanisms to the Kafka client, lets operators redact traces with a TraceQL query instead of a list of trace IDs, and introduces an experimental query-time hint that corrects TraceQL metrics for sampled data.
By Claudio MasoloArticle: Server-Side DPoP and Token Exchange in Spring Security 7
Spring Authorization Server now lives inside Spring Security 7 under the same Maven coordinates, so the upgrade is a version bump. This server-side guide covers enabling DPoP and RFC 8693 Token Exchange, explains why 7.1 apps can silently stop enforcing DPoP until they call .dPoP(), and shows how to combine both into narrowed, sender-constrained tokens for service-to-service and agentic calls.
By Dhruv AgnihotriMeta Extends Confidential Computing to AI Glasses with Private Processing
Meta has extended Private Processing to AI glasses, using confidential VMs, trusted execution environments, remote attestation, anonymous credentials, OHTTP routing, and TEE-based storage to process AI workloads and persistent user context in the cloud.
By Leela KumiliPresentation: How to Find Resilience Bugs in Systems that Don't Exist
Hillel Wayne discusses how formal methods principles - such as property strength, state-space verification, and environment assumptions - help software engineers uncover critical architectural bugs early. He shares practical mental models like "adversarial non-determinism" to reason about concurrency, edge cases, and distributed system failures without needing complex mathematical tools.
By Hillel WayneGoogle Antigravity SDK Adds Local Model Support for Greater Token Efficiency and Code Privacy
Google has introduced support for local models in the Antigravity SDK, enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based models for higher-level tasks. This approach gives developers greater control over data privacy, inference costs, network dependencies, and computational workload distribution.
By Sergio De Simone
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