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Software only has value when shipped; faster release cadence matters. Development, deployment, and operations require different thinking, metrics, and investments. There are four impacts in tech: product success, compute capacity, engineering capacity, and strategic innovation. The biggest expense is people. AI shifts engineers toward deeper problem understanding, not fewer engineers.
Brendan O'Leary discusses how real-world AI adoption depends on building trust rather than chasing model benchmarks. Drawing from 40 trillion tokens of usage data, he explains the four rungs of the AI adoption ladder and analyzes key failure points in context construction, multi-model routing, and feedback loops to help engineering leaders successfully scale agentic workflows.
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.
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.
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.
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Optimizing Software Workflow: Exploring Fundamental Impacts in Technology
Software only has value when shipped; faster release cadence matters. Development, deployment, and operations require different thinking, metrics, and investments. There are four impacts in tech: product success, compute capacity, engineering capacity, and strategic innovation. The biggest expense is people. AI shifts engineers toward deeper problem understanding, not fewer engineers.
By Ben LindersPresentation: What 25 Trillion Tokens Reveal About How Developers Actually Adopt AI Agents
Brendan O'Leary discusses how real-world AI adoption depends on building trust rather than chasing model benchmarks. Drawing from 40 trillion tokens of usage data, he explains the four rungs of the AI adoption ladder and analyzes key failure points in context construction, multi-model routing, and feedback loops to help engineering leaders successfully scale agentic workflows.
By Brendan O’LearyGrafana 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 Kumili