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How to Connect Cursor to CircleCI: AI-Powered CI/CD Debugging with MCP
Stop context-switching between your IDE and CI dashboard. This video shows you how to connect Cursor to CircleCI using the CircleCI MCP server so your AI agent can read pipeline failures, validate configs, and trigger builds without leaving your editor. In this demo, we introduce a bug, let CI catch it, and watch the agent diagnose and fix it autonomously through a full green pipeline. No manual log hunting required.
DRIVE Deep Dive: Efficiency
This is the fifth and final post in the DRIVE Deep Dive series, following Delivery, Reliability, Initiatives, and Vigilance. For the complete model across all five pillars, download the full DRIVE framework. -- Engineering money and time land in three places a leadership review can actually act on: the cloud bill, the internal spend on AI and LLM tokens, and the split between building new things and keeping old ones running.
Golden paths: how to ship securely without slowing developers
The secure path and the easy path should be the same path. Ralph McTeggart (Principal Engineer), Alex Franzmann (Customer Onboarding Team Lead), and Claire McDyre (Product Manager) at Cloudsmith lay out how platform teams can deliver supply chain security as a capability rather than a checklist. The argument runs from first principles: make a private registry the default, automate policy enforcement at the global level, and extend that same logic to compliance – so SBOM generation happens in the pipeline, not as a developer's manual task.
Updating Reference Data with Rollbacks Using Harness Database DevOps | Harness Blog
Versioning reference data in Git with Liquibase OSS changelogs enables consistent, auditable, and automated deployments across environments. Using loadUpdateData with versioned CSV files provides fast, reliable rollbacks and reduces production risk. Modern applications do not just depend on schema changes. They also depend on data that powers the application itself.
How to prove the business value of AI
Proving AI business value means sorting every AI investment into one of four buckets - revenue growth, cost avoidance, productivity gain, or risk reduction - then tracking spend at the unit level (per feature, customer, or team) so each dollar has a traceable return. Most companies measure one bucket well and leave the rest unattributed. That gap is why the same AI deployment can look like a $40M win and a public reversal at the same time.
The Best Model Is a Routing Decision
I spent the day at NVIDIA thinking I’d be learning about all the newest models and RAM throughput achievements. Instead, a version of the future started to crystallize where AI really is good enough, pervasive and cheap(er). Let me share some of my observations and conclusions.
Claude Opus 5 pricing: same sticker, different bill
Claude Opus 5 launched July 24, 2026 at $5 per million input tokens and $25 per million output tokens, identical to Opus 4.8. It delivers near Claude Fable 5 performance at half Fable's price and is now the default model on Claude Max. New effort settings let teams trade capability for token savings, which means two teams on identical pricing can now run up very different bills. Finance teams, that last part is your problem. Anthropic has shipped a model that costs exactly what the old one cost.
Shipped: Every cost recommendation now comes with the why and the how
A savings number tells you money is on the table, but it doesn’t tell you whether the finding holds up, what it’s based on, or what to do next. In that gap, recommendations pile up unactioned. When you’re staring at thousands of them, a title and a dollar figure isn’t enough to decide which are safe to act on.
Business continuity starts with portability
Business continuity planning has a quiet assumption built into most of it: that the infrastructure the plan runs on will cooperate. Backup systems will be accessible. Recovery procedures will work as documented. The provider whose services underpin the critical path will be available, or at least recoverable within the window the plan specifies. That assumption is tested every time a major provider has an outage, a regional failure, or a service disruption that cascades across dependent workloads.