Yandex Drops Earbuds Under X-Ray Microtomography: Detailed Internal Analysis of First AI-Powered TWS Headphones
Yandex marked its birthday by allowing engineers to run its first AI earbuds, Yandex Drops, through a high-resolution X-ray microtomograph. The non-destructive scan produced two voxel datasets—one of the charging case with an earbud inside and one of a standalone earbud—capturing internal structures at 24.44 µm and 14.12 µm resolution respectively.
The earbuds contain an eight-layer HDI PCB with blind vias, a QFN-packaged central SoC approximately 2.8 × 2.0 mm that is presumed to house the NPU for always-on voice detection, and a smaller companion QFN die likely serving as memory or PMIC. A 4 × 4 BGA flash-memory candidate sits adjacent to the main processor.
Each earbud integrates three microphones: two external feedforward sensors positioned at the stem ports and one internal feedback microphone located roughly 5 mm from the driver diaphragm. Signal routing was traced from the MEMS elements through spring connectors and flex cables to the main board without any visible hardware switch or disconnect in the accessible layers.
The 11 mm dynamic driver and 46 mAh pouch cell were measured directly from the calibrated slices, matching the manufacturer’s specifications. The charging case houses a 520 mAh cell, USB-C receptacle, and a three-pin Hall-effect sensor on a flex tail that detects lid position; no shielded radio module was observed inside the case.
Capacitive touch electrodes are formed by stamped metalization on the inner surface of the stem, while a miniature SMD tactile switch on a daughter board provides physical button input. Charging contacts on the earbuds are spring-loaded claws that mate with corresponding pads inside the case.
All imaging was performed at the PRODIS facility using a 2 µm-resolution microtomograph; reconstruction and multi-volume analysis were carried out in the Russian-developed VolÅn software. The authors note that while the physical microphone traces appear continuous, any determination of what audio is actually transmitted requires separate RF traffic analysis.
Related articles
OpenAI Releases GPT-6.1 Terra: Cost-Effective Model Focused on Computer Control, Image Analysis and Practical Automation
OpenAI has launched GPT-6.1 Terra, a new model positioned between its high-end GPT-6 Astra and more affordable options in the GPT-6 family. The model emphasizes real-world computer interaction, visual understanding of documents and interfaces, and reduced error rates rather than raw reasoning benchmarks. It supports a 1.05 million token context window, image inputs, and tool use through the Responses API while cutting input token prices by half compared with its predecessor. Published benchmarks show substantial gains on Terminal-Bench 4.0, OSWorld 2.1 and Agents' Last Exam, with error rates dropping sharply at both low and maximum reasoning levels. OpenAI stresses that the model still requires human oversight for high-stakes actions and does not directly process audio or video. The release targets developers and enterprises seeking reliable automation of repetitive multi-step tasks at lower cost than flagship models.
Yandex Black October: How Architectural Flaws and Capacity Mismanagement Turned Data Center Losses Into a 11-Day Fintech Coma
In October 2026 three Yandex data centers in Central Russia were physically destroyed within days, triggering the collapse of Yandex Cloud and multiple core services. Between the first and third site failures engineers had 74 hours to restore quorum and rebalance workloads yet failed to do so because Control Plane components remained tied to the lost zone and spare capacity had been eliminated to cut costs. Core banking and BNPL systems entered Read-Only mode after losing quorum, forcing a complete stop-the-world reconciliation that lasted until 22 October. Backups stored inside the same regional S3 buckets proved unusable, resulting in a seven-day RPO and primary-key collisions after restoration. The incident exposed that Metro-Cluster designs had been incorrectly treated as full Disaster Recovery and that aggressive AP microservice patterns created logical split-brain conditions outside the database layer.
Astra Group Releases Low-Code Astra AI Platform for Building Corporate AI Agents in Closed Environments
Astra Group has launched the Astra AI Platform, a low-code constructor designed for creating corporate AI applications that operate entirely within closed circuits without reliance on foreign clouds. The platform targets small and medium businesses as well as large corporations, enabling rapid deployment of AI secretaries, technical support assistants, and other agents in days rather than months. It forms part of the broader Astra AI ecosystem and includes two main constructors: one for machine learning model training and deployment, and another for LLM-based language assistants, document search, and process automation. Users can assemble scenarios from visual blocks, debug them in real time, or extend functionality with custom Python code when needed. The solution supports RAG-based knowledge bases, integration with external tools such as CRM and ERP systems, and the coordination of multiple agents for distributed task handling. One demonstrated use case involves processing ten pages of engineering drawings and specifications in five minutes to prepare data for downstream systems. The product is already listed in the Russian domestic software registry and available to customers.
Multi-Tenancy Isolation in VictoriaMetrics, VictoriaLogs and Grafana: Building Client Separation in a Shared Stack
Voltir runs a single shared monitoring stack consisting of VictoriaMetrics, VictoriaLogs, Grafana and vmalert for all customers instead of deploying separate instances per tenant. Isolation relies on numeric account IDs enforced at every layer: nginx validates tokens against the platform API before proxying writes, Grafana data sources hard-code tenant accounts, and vmalert rules receive automatic account filters. The team discovered that a single malformed rule or template could break alerting for every tenant because vmalert loads the entire rules directory at once. They also found that Grafana automatically created personal organizations granting Org Admin rights, accumulating hundreds of orphaned organizations and users that consumed disk space and resources. A dedicated cleanup module plus hourly reconciliation now removes rules, organizations, series and users when a tenant is deleted, while isolation tests verify both data and API objects across tenants.