Sonar today made available an artificial intelligence (AI) agent designed to discover vulnerabilities and business logic flaws that pose the greatest risk to an organization should they be exploited. The SonarQube Hunter Agent first analyzes an entire codebase to find three categories of flaws: broken access control, business-logic vulnerabilities, and authentication or session-management issues. Satinder Khasriya, a technical product marketing manager for Sonar, said that, historically, discovering these issues would have required manual testing or a penetration test. The AI agent developed by Sonar automates those investigations by tracing how code and data move through a system in a way that can now run on demand, he added. Additionally, SonarQube Hunter Agent is able to identify the developer who created any piece of code, with verified issues that are surfaced within a DevSecOps workflow via integrations with continuous integration/continuous delivery (CI/CD) platforms. That’...
Every on-call engineer knows the ritual. A page lands in Slack: the 5xx rate on a service is above the threshold. The message tells you that something is wrong and nothing about what. So you open Kibana or Loki in another tab, set the time window to the last few minutes, filter by the service, and start reading. In my team this took two or three minutes on every alert, and these are the worst minutes of the incident, because nothing is being diagnosed yet. The obvious request is to put the last few error lines into the alert message itself. I tried to do this inside Alertmanager, and it cannot be done there. It helps to understand why before building anything around it. Why Alertmanager Cannot Do It Alertmanager renders notifications through Go templates, and a template can only use what is already attached to the alert: the labels and the annotations which Prometheus put there when the rule fired. There is no mechanism which would call Elasticsearch, Loki or any other HTTP API at t...
For most of Git’s history, a commit marked a decision. A developer finished a change, checked it, and pushed. Hosting platforms were built around that rhythm. AI coding agents don’t work that way. “An agent in a tight loop commits or checkpoints after nearly every action,” GitHub engineer Brian Celenza wrote in a GitHub blog post explaining why the company is rebuilding the Git infrastructure behind its platform. The numbers show how fast the load has shifted. GitHub recorded 7.38 billion commits in September 2026, more than five times the total a year earlier. Monthly Git activity climbed from 218.2 billion events in September 2025 to 473.3 billion in August 2026. Pushes grew 4.9x year over year, from 0.69 billion to 3.35 billion per month. Pull request merges are up nearly 4x. GitHub Actions ran 3.26 billion times in September, also 4x the prior year. The busiest single repository handled roughly 1 billion requests in August. Nobody planned capacity for curv...
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