AntiMalware•October 6, 2026•🇷🇺Translated from Russian

MinTsifry Considers Annual 10 Billion Rubles Support Package for Russian AI Development

Russian AI developers may soon receive significant state backing as the Ministry of Digital Development discusses an annual support package reaching 10 billion rubles. The funds are intended to accelerate neural network development, finance pilot projects, and cover expenses related to computing power.

According to Kommersant, the package splits into 8 billion rubles allocated for technology development and deployment and 2 billion rubles earmarked for compensating computational costs. One proposed instrument involves subsidized loans issued through authorized banks, allowing companies to finance infrastructure, dataset collection and labeling, as well as model fine-tuning.

A second mechanism envisions grants for pilot testing of previously unseen solutions. These grants could cover up to 80 percent of project costs and would target priority economic sectors, social services, and public administration. The RFIT fund is being considered as a potential operator for project selection.

Both the Ministry and the office of Deputy Prime Minister Dmitry Grigorenko have confirmed that the initiative is under active discussion. However, specific parameters and implementation timelines have not yet been finalized, meaning companies cannot apply for funding at this stage.

Representatives of the market, including Sergey Golitsyn, head of AI projects at T1, believe the support could help smaller startups bring products to pilot stage. At the same time, participants stress the need for transparent selection criteria and clear reporting rules to avoid the emergence of intermediaries and to maintain competitive balance between independent teams and large corporations.

Related articles

Securitylab•Other

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.

Habr•Other

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.

AntiMalware•Other

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.

Habr•Other

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.