Yandex Cloud has reported a new service disruption at one of its data centers. This marks the third such incident affecting the company's cloud infrastructure. The outage was reported on the official Yandex Cloud status page. Engineers are currently investigating the cause and working to restore full service.
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Go applications often encounter database connection errors under real traffic due to a lack of proper connection pooling. A common mistake is opening and closing a new database connection for each HTTP request, which quickly exhausts database limits. The solution is to create a single, shared database connection pool at application startup and configure it correctly. Key configuration parameters include setting maximum open and idle connections, along with connection lifetimes. Properly tuning these settings prevents the API from overwhelming the database and improves performance under load.
Quiet Hours Valet developed a command center managing eight concurrent AI voice agents, five frontend pages, and eight automation workflows. The system uses Supabase Edge Functions to trigger VAPI webhooks and relies on an eight-table Postgres database schema. The architecture supports a vertically integrated waste services operation running on self-hosted infrastructure. The article serves as a reference for building multi-agent systems using Asterisk PBX, VAPI, or Postgres with Supabase for voice automation.
A guide details how to build a free Python script that generates weekly sales reports automatically. It reads data from a CSV file, compares the current week's performance to the previous week, and formats a summary. The report can be delivered via email, Discord, or a GitHub Actions page, with email being the most complex to configure. The script highlights data integrity by noting when rows are skipped due to unreadable information. This automation targets repetitive, data-driven tasks where human error is a risk.
MongoDB version 8.3 introduced a new feature called arrayIndexAs for its $map aggregation operator. This eliminates a previous workaround where developers had to manually construct index ranges and use $arrayElemAt to access array positions. Performance tests showed the new method reduces execution time by 15-30% for arrays with tens of thousands of elements. The efficiency gain is most pronounced in sequential operations, though it diminishes slightly under concurrent loads. This optimization addresses a long-standing inconvenience in MongoDB's aggregation framework.
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