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honeycomb

Shipping Is Your Company's Heartbeat: A Letter from a CTO

Jul 14, 2026 By Charity Majors In Honeycomb
The world is especially hard right now. The future of the software engineering profession looks more uncertain than ever. Execs are under heavy pressure to turn AI into magic results, and teams are fighting product competition and AI-induced burnout on one side, melting mental models and hellish oncall on the other side. Observability was supposed to be a solved problem by now.
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pulsant

Why colocation is becoming the foundation of sovereign AI

Jul 14, 2026 By Mark Lewis In Pulsant
The last few years have seen AI conversations dominated by the need for investment in hyperscale infrastructure as firms race to build ever larger training models. But as those conversations evolve, the emphasis is shifting to the next phase of AI adoption, focusing on the scaling of use cases and real-world value. In line with this shift, organisations are looking beyond where AI is trained to the specifics of where it is actually used.
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upsun

The rise of dark code and the death of architectural intent

Jul 14, 2026 By Upsun In Upsun
As Staff Engineers and Principal Architects, most of us have spent years thinking about long-term system health. We are considerate of the company’s business objectives and strategy, accumulation of technical debt, and operational risk. For us it is not about whether code works today, but whether the engineer who inherits it in three years will be able to understand what it was trying to do and why. That's what makes a codebase maintainable rather than just functional.
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cloudzero

What is AI cost observability? A guide to tracking LLM and AI spend

Jul 14, 2026 By Keith MacKenzie In CloudZero
AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value.
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cloudzero

Enterprises are making their biggest AI bets blind

Jul 14, 2026 By Scott Castle In CloudZero
AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value. On July 14, IBM had its worst trading day since 1987.
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ScienceLogic

Automation That Protects, Not Replaces: The Human Side of AI-Driven Operations

Jul 14, 2026 By ScienceLogic In ScienceLogic
Automation has a branding problem. For years, it has been associated with cost reduction and workforce replacement. But operators tell a different story. Across eleven interviews, the consistent theme was relief. Relief from manual ticket creation. Relief from repetitive triage. Relief from workflows that once required three days and now take five minutes. These are not stories about eliminating people. They are stories about protecting them. Operators spoke with clear ownership over their environments.
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meshiq

ActiveMQ Log Analysis & Diagnostics: The Expert Guide

Jul 14, 2026 By meshIQ In meshIQ
Senior engineers who are fast at diagnosing ActiveMQ incidents share one trait: they know exactly what they are looking for in the broker log before they open it. They know the PFC signature, the OOM warning pattern, the journal recovery sequence, and the connection drop format. For them, the log is not text to search through, it is a structured operational record that maps each entry to a specific broker state.
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meshiq

ActiveMQ Capacity Planning: The Complete Framework

Jul 14, 2026 By meshIQ In meshIQ
Most ActiveMQ deployments are sized in one of two ways: either under-provisioned from underestimating growth ("we'll upgrade when we need to") or over-provisioned from anxiety ("better give it 32GB just in case"). Both approaches are avoidable with a structured capacity planning framework that translates your messaging workload characteristics into specific hardware and configuration requirements.
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The internal hardware of a quantum computer, representing the technology set to reshape enterprise data security.

Quantum Computing Security: Why Enterprises Need to Prepare Now

Jul 14, 2026 By OpsMatters In OpsMatters
Most security teams assume their encryption will hold. That assumption has an expiry date. A 2025 ISACA poll of more than 2,600 professionals found that 62% expect quantum computers to break current encryption, yet only 5% of their organizations have a defined plan to respond. That distance between worry and action is the real exposure. Machines able to crack widely used encryption are no longer science fiction, and the data you guard today could be unlocked the moment they arrive. Here is what the shift means for your business, how much runway you actually have, and where a sensible response begins.
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